Public Transport Optimisation and Pilot Corridors Study · Project: Green Cities: Integrated Sustainable urban Transport for the City of Batumi and the Achara Region (ISTBAR) Page

მომზადებულია კომპანია A+S Consult GmbH-ის კონსულტანტთა ჯგუფის მიერ

The report has been prepared by a team of experts from A+S Consult GmbH

Public Transport Optimisation and Pilot Corridors Study Technical Report #2

Consultant: A+S Consult GmbH; Germany, 01277 Dresden, Schaufussstraße 19; Tel: +49 351 3121330, E-mail: [email protected] Client: United Nations Development Programme (UNDP) Project: Green Cities: Integrated Sustainable urban Transport for the City of Batumi and the Achara Region (ISTBAR)

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Sub-project: Feasibility Studies for Pilot Low-Carbon Urban Transport Corridor and Integrated Sustainable Urban Mobility Plan for the City of Batumi (ISUMP)

Output 2: Feasibility Study for Low Carbon Sustainable Urban Transport Corridors and Optimisation of Bus Network, Includiing Bus Rapid Transit Line(s) and Bus Terminals

The report has been prepared by A+S Consult GmbH

CEO Dr. Veit Appelt

Team Leader Daniel Wolf

Batumi

2017

mailto:[email protected]


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Published by the United Nations Development Programme (UNDP) UNDP Georgia, 2017

All rights are reserved Published in Georgia

The report has been prepared by the company A+S Consult GmbH in the scope of the project – “Green

Cities: Integrated Sustainable Transport for the City of Batumi and the Achara Region”, funded by the Global

Environmental Facility (GEF) and implemented by the United Nations Development Programme (UNDP), with

support from Batumi City Hall and the Ministry of Environment and Natural Resources Protection of Georgia.

The views expressed in this report are those of the Authors and do not necessarily represent those of GEF

and UNDP.



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TABLE OF CONTENTS

1 Introduction and Overview ................................................................................................. 7

2 Methodology and Resources Used ...................................................................................... 8

2.1 Transport Model Description .................................................................................................. 8

2.1.1 General information .................................................................................................. 8

2.1.2 Data of structure of the transport network ................................................................. 9

2.1.3 Characteristics of traffic organization ....................................................................... 11

2.1.4 Transport Systems and demand segments ............................................................... 13

2.1.5 Data on spatial development of the study area. Transport zoning. ............................. 14

2.2 Scenario Modelling Methodology .......................................................................................... 16

2.2.1 Parking restrictions ................................................................................................. 16

2.2.2 Bus terminals ......................................................................................................... 16

2.2.3 Traffic lights priority for buses ................................................................................. 16

2.2.4 Introduction of EURO-5 diesel and electric buses ...................................................... 16

2.2.5 BRT-like stops ........................................................................................................ 16

2.2.6 Public transport lanes .............................................................................................. 16

2.2.7 Bus route optimization ............................................................................................ 17

2.3 Cost-Benefit Analyses Methodology...................................................................................... 17

2.3.1 The Quantity of Routes ........................................................................................... 18

2.3.2 The Route Frequency .............................................................................................. 18

2.3.3 The Network Patronage .......................................................................................... 18

2.3.4 The Modal Split....................................................................................................... 18

2.3.5 The Service Level ................................................................................................... 18

2.3.6 The Emission Level ................................................................................................. 18

2.3.7 The Bus Fleet Composition ...................................................................................... 19

2.3.8 The Length of separate Bus Lanes to build ............................................................... 19

2.3.9 The Number of Bus Stops to reconstruct .................................................................. 19

2.3.10 The Number of Traffic Lights to Update for Bus Priority ............................................. 19

2.4 Bus Planning Manual ........................................................................................................... 19

3 Measures and Scenarios Definition ................................................................................... 23

3.1 Measures description .......................................................................................................... 23

3.1.1 Bus lanes ............................................................................................................... 23

3.1.2 Parking restrictions ................................................................................................. 23

3.1.3 Bus terminals (optionally with the P&R) ................................................................... 24



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3.1.4 New bicycle rental station ....................................................................................... 24

3.1.5 Traffic lights priority ................................................................................................ 25

3.1.6 Introduction of EURO-5 diesel and electric buses ...................................................... 25

3.1.7 BRT-like stops ........................................................................................................ 25

3.1.8 Bus priority............................................................................................................. 25

3.2 Scenarios Description .......................................................................................................... 25

3.2.1 Existing network ..................................................................................................... 27

3.2.1.1 BASE ......................................................................................................... 27

3.2.1.2 BASE+CA .................................................................................................. 27

3.2.1.3 BASE+CBG ................................................................................................ 27

3.2.1.4 BASE+CACBG ............................................................................................ 28

3.2.2 New bus network by the Saunders Group ................................................................. 28

3.2.2.1 SG ............................................................................................................ 28

3.2.2.2 SG+CA ...................................................................................................... 28

3.2.2.3 SG+CBG .................................................................................................... 28

3.2.2.4 SG+CACBG................................................................................................ 28

3.2.3 New bus network by the Batumi city ........................................................................ 28

3.2.3.1 BCH .......................................................................................................... 28

3.2.3.2 BCH+CA .................................................................................................... 28

3.2.3.3 BCH+CBG ................................................................................................. 29

3.2.3.4 BCH+CACBG ............................................................................................. 29

4 PUBLIC TRANSPORT Network Optimization Scenario Analysis (BASE, SG, BCH) ............ 30

4.1 Route quantity analysis (network density) ............................................................................ 30

4.2 Route headway analysis ...................................................................................................... 33

4.3 Network patronage analysis ................................................................................................ 35

4.4 Modal split ......................................................................................................................... 38

4.5 Service level analysis .......................................................................................................... 39

4.6 Bus fleet composition .......................................................................................................... 44

4.7 Energy consumption and emissions ..................................................................................... 45

4.7.1 The Base network ................................................................................................... 47

4.7.2 Emissions in the SG network ................................................................................... 51

4.7.3 Emissions in the BCH network ................................................................................. 51

4.7.4 Comparison ............................................................................................................ 53

4.8 Summary ........................................................................................................................... 55



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5 Low Carbon Sustainable Urban Transport Corridors Scenario Modelling Analysis

(BASE+CA, BASE+CBG, BASE+CACBG, SG+CA, SG+CBG, SG+CACBG BCH+CA, BCH+CBG, BCH+CACBG) .................................................................................................. 56

5.1 Network patronage analysis ................................................................................................ 56

5.2 Modal split ......................................................................................................................... 65

5.3 Service level analysis .......................................................................................................... 65

5.4 Bus fleet composition .......................................................................................................... 77

5.5 Low Carbon Sustainable Urban Transport Corridors Scenario Summary .................................. 78

6 General Scenario Comparison ........................................................................................... 80

6.1 Scenario summary .............................................................................................................. 80

6.2 Socio-economic impact on marshrutka drivers ...................................................................... 82

6.3 Strategical plan to substitute Marshrutka busses................................................................... 83

6.4 Assessment of impact of possible relocation of congestion/bottlenecks from CBG and CA

corridors to other parallel streets ......................................................................................... 84

7 Conclusion and Discussion ................................................................................................ 86



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Feasibility Studies for Low Carbon Sustainable Urbane Transport Measures along

Demonstration Corridors (CBG, CA) and Optimization of Existing Bus Network

1 INTRODUCTION AND OVERVIEW

The existing public transport of Batumi is redundant: it has too much routes served by too much

buses. Although it is very comfortable for the passengers, that enjoy small headways and doorstep

accessibility, huge quantities of minivans cause traffic jams and air pollution. The aim of this study is the

analysis of different public transport improvement scenarios. These scenarios contain such improvement

measures as network optimization and public transport priority corridors that may help to reduce the quantity

of minivans and improve operation efficiency, as well as accessibility and travel time for the passengers.

The study consists of three feasibility studies; one for Chavchavadze-Baratashvili-Gorgiladze (CBG)

demonstration corridor, one for Chavchavadze-Abuseridze (CA) corridor and one on overall Bus Network

Optimization. Those three feasibility studies were integrated in one report on clients request to show

synergies and complementarities between two demonstration corridors and bus network optimization.

For the purpose of the scenario modelling, transport model of the study area was developed using

PTV Vision® VISUM. It is a very important tool to analyze public transport networks and optimization

scenarios that provides the analyst with the figures, diagrams and other information for the decision-making.

The expected outcomes of this study are:

1. Estimation of the proposed scenarios in terms of:

a. Route quantity and headways

b. Service level

c. Network patronage

d. Modal split

2. Ecologic evaluation of the existing and proposed networks

3. Recommendations on the preferred public transport corridor

Analysis of these scenarios will help the city officials detecting the most appropriate way of city

transport development. Scenario modelling may also show their advantages and disadvantages and reveal

possible ways of improvement.

The improvement of public transport is always an iterative process, when the improvement measures

lead to the result that can be improved more in the future. We hope this study will become a solid ground

for the Batumi city to its livability and attractiveness.



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2 METHODOLOGY AND RESOURCES USED

Transport model of the study area was developed by the modern transport planning software system

PTV Vision® VISUM.

PTV Vision® is an industrial standard of transport planning in 75 countries. Its most popular

applications are: transport planning of cities and regions, optimization of public transport, justification of

investment, forecasting of traffic intensity on toll roads. PTV Vision® VISUM users are more than 2,000

organizations in USA, UK, Germany, Netherlands, Italy, Spain, Poland, Austria, Australia, China, India, the

Middle East, and more than 50 user organizations in CIS. PTV Vision® VISUM is a modern information-

analytical system of decision support, which enables strategic and operational transport planning, forecasting

of traffic intensity, justification of investment in the development of transport infrastructure, optimization of

transport systems of cities and regions, and systematization, storage and visualization of transport data. The

software package PTV Vision® VISUM integrates all road users (cars, passengers, trucks, buses, trams,

pedestrians, cyclists etc.) in a unified mathematical transport model.

PTV Vision® VISUM integrates geo-information systems (GIS) data, transport supply data into a single

database with several levels.

The key feature of PTV Vision® VISUM development is a permanent connection with the fundamental

researches (three centers of product development - USA, Germany and Japan), and consequently the widest

pool of scientific research in transport modeling which allows constantly improve the quality of the algorithms

and capabilities of the system.

2.1 Transport Model Description

2.1.1 General information

Traffic flow modelling consists of two basic models – transportation supply and demand models.

The transport supply model is a transportation network consisting of nodes (crossings and

interchanges), links connecting them (streets, highways) that makes trips available for system users and

describes travel cost. Also, the supply model features public transport routes and stops.

The demand model describes the quantity and quality of trips including reasons of traffic generation,

destination choice, mode choice and route assignment.

The basic concept and the aim of transport model is traffic flow estimation on the network. Transport

model allows creating high quality forecasts of urban and transportation solution subject to different factors

and constraints that influence socio-economic development of the region or its transport situation.



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2.1.2 Data of structure of the transport network

Street network (SN), is based on GIS data and surveys is shown on pic. 2.1. Additional processing

made to bring data into required format for import: merger unbound sectors of the road network, detailed

elaboration of undivided sectors, and assignment of the core network for analysis. Nodes (junctions) were

given allowed turns for the different transport systems.

The links in the model have direction, so, in fact each link consists of two, one for each direction.

Those links are attributed with length in km; maximum allowed speed, km/h; throughput, cars/day; the

number of lanes in each direction; road category.

Network digitizing was performed including such objects of SN:

Link (Link, Strecke) – object of transport supply model which is a sample model of

elementary section of highway, railway, waterway, etc. Each segment is characterized by a number

of geometric parameters (length, number of lanes for transport movement, curvature, etc.),

dynamic parameters (maximum allowed speed, throughput) and list of transport systems that

are allowed to move along this link.

Node (Node, Knoten) - object of transport supply, which is a sample model of

intersection, junction, connection of road, railway docking, waterways, etc. Links always start and

end in nodes. Nodes are characterized by the following parameters - the organization of road

traffic, permitted/banned turns for types of transport, if there is a traffic light regulation - duration

allowed signal, maneuver implementation delay, etc.

To improve the model’s precision of road network model, all roads are divided into several

subcategories.

Transportation supply

model: Consists of network data such

as transportation zones, streets, public transport routes and

stops.

Transportation demand

model: Consists of origin-destination

data, trip purpose, transportation mode choice,

route choice.

Results: Calculated network features;

cost matrices;

O-D matrices; Graphic analysis of the results: node flows, routes of the road users;

Traffic flow forecasts; Ecological assessment of study area;

Feasibility study for infrastructure solutions such as construction of

new roads, interchanges or launching new routes.



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Distinctive features of these subcategories are highway attributes: allowed speed, throughput and

number of lanes in each direction.

Figure 1 – Transport in PTV Vision® VISUM



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SN is represented as a directed graph with the following geometric and technical parameters:

The geometry of the road network (spatial location and configuration of highway are as

close to the real spatial position and parameters of the road plan as possible);

Intersections, junctions, connectors are placed as point objects;

Configuration of interchange ramps;

Length of the road network elements;

Highway category;

Number of lanes in each direction;

Calculated and allowed movement speed of the network section;

Street or road capacity in each direction;

Banned movement on SN elements;

Allowed movement direction at intersections, connectors, junctions;

Highway rank (attractiveness for user).

This set of road parameters is enough to describe all the components that have a significant impact

on traffic flow dynamics, and impose all major restrictions on the traffic flow distribution on SN.

2.1.3 Characteristics of traffic organization

Transport model features detailed description of the road traffic organization on each segment:

availability of one-way traffic, traffic ban for different types of trucks, organization of traffic at junctions.

Following parameters are set for each interchange that is represented as a node in the graph of the transport

network:

Permitted/banned maneuvers;

Capacity in each direction with taking into account number of lanes;

Permitted types of transport.

The following figures show transport graph elements.

The links attributes are represented on Figure 2; in this case link has one-way movements.

The main node attributes (characteristics) are represented on Figure 3. In this case, the maneuver

highlighted in red is allowed for the particular type of transport and maneuver highlighted dotted line -

banned for all types of transport.



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Figure 2 – Links attributes

Figure 3 – Nodes attributes

At each junction/connector transport model takes into account the following characteristics:

Intersection signaling mode (traffic light, unsignalized junction);

Basic delays during crossing the intersection or turn;

Junction or turn capacity.



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2.1.4 Transport Systems and demand segments

To describe the structure of traffic flows that load transport network of the study area, the set of

transport types was introduced. Different types of transport are presented by the transport systems. Each

transport system refers to one or more demand segment. Demand segments describe trips of different

groups of people that use one or several transport systems and are related with demand matrices. Traffic

members of one demand segment of public transport have opportunity to change transport system in one

trip, for example, because of transfer. Each demand segment corresponds to exactly one demand matrix.

Figure 4 shows an example of the representation of the transport systems, modes and demand segments in

the model. List of transport systems and demand segments used in the model is represented in Table 1.

Private transport – Freight transport

Private transport - Motor car

Public Transport - Bus

Public Transport – Train Transport

systems

Modes

Demand segments Freight transport

Motor Car Public transport (bus + train)

Motor Car: private

Motor Car: official

Freight transport

Demand matrices Demand matrix Demand matrix

Demand matrix

Demand matrix

Public transport

Figure 4 – Example of links between transport systems, modes, demand segments and demand matrices.



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Table 1 – Transport systems and demand segments in the model

Code Transport system Correction

factor Demand segment

B Bus - Public transport (PuT)

mB Mini Bus -

L Car 1,0 Car (L)

H Trucks 2,5 HGV

PR Transferring - Transferring (PR)

2.1.5 Data on spatial development of the study area. Transport zoning.

The structure of spatial development of research branch describes by using following data:

transport zoning: boundaries of transport zones;

socioeconomic statistics data on transport zones: urban and rural populations; average

number of employees; number of employed, etc.

Transports zones (Traffic zone, Verkehrsbezirke) - elementary units of spatial structure of study

area. Zoning, based on functional ground is considered the most appropriate (for example, based on

functional zoning of the city Master plan). If it is impossible to obtain statistical information during zoning

based on functional ground, it is permissible to perform zoning basing on the administrative division.

Transport zones act as centers of generation and attraction of traffic and are described in the model with

their centroids.

Transport zones perform two main functions in the model:

1. Reflect structure of the functional and spatial distribution in the modelling area.

2. Form the basis for the aggregated description of transport system status in the

modelling area.

Modelling zone includes a large area. This area was determined in expert way based on the necessity

to take into account the maximum possible types of passenger movements in the modeling area of the

considered study area (business, labor, recreational, cultural and service and transit) with different modes.

Borders of transport zones were agreed with the customer. Several types of transport zones were identified

for the transport model:

Transport zones – transport zones in the modelling area.

Edge zones – transport zones that generate/absorb transit flow concerning the modeling zone. Both

city transport zones and edge zones are shown in Figure 5.

Allocation of edge transport zones was determined basing on the presence of the most high traffic-

intensive highways (concerning the considered modeling area). Edge transport zones generate/absorb traffic

flow that causes an additional traffic load on the city network; they are located outside of the study area.

Data on traffic volumes by transport types is included in the semantics of the edge zones. To model the

amount of flow generation/absorption we take into account the following information:

1. Share of transit traffic in zones - ratio of the number of transit trips (to the

considered modeling area) to the total traffic flow.

2. The volume of outcoming traffic flow

3. The volume of incoming traffic flow



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Transport network used in model of traffic flows attraction field characterized by following parameters:

4794 nodes;

11566 links;

83 transport zones, including 8 edge zones.

Figure 5 – Borders of transport zones



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2.2 Scenario Modelling Methodology

Scenario analysis may vary for each measure, but the general principle is to change the transportation

supply in the model and calculate of the overall effect on the transportation system.

Considering each measure, the actions will be as follows.

2.2.1 Parking restrictions

Today parking occupies right lanes of the main roads, reducing, therefore, capacity. This means that

the streets that have parking actually have less lanes than it is stated officially. For parking restrictions

modelling purpose we are adding lanes where applicable, improving the street capacity.

However, removed parking will increase traffic on the streets, because divers will perform extra travels

to find themselves a place to park. Unfortunately, we cannot use the model to obtain a quantitative

estimation of this situation.

2.2.2 Bus terminals

Optimized network is planned in the way, when the majority of the routes form the city outside does

not enter the central part of the city. Instead, they terminate at the two terminals, where the access for the

BRT-like route to the city center is provided.

2.2.3 Traffic lights priority for buses

Traffic lights are represented in the model as delays on the intersections. To model public transport

priority, we set up intersection delays for buses to zero.

2.2.4 Introduction of EURO-5 diesel and electric buses

PTV Visum has an option to estimate emissions according to the traffic situation. However, this module

is oriented mostly on private transport and is not applicable for public transport emissions estimation. So,

this measure will be evaluated in an alternative way using average emission rates for different type of buses

(Euro-0 to Euro-6 and electric), including the indirect emissions.

2.2.5 BRT-like stops

Each time the bus stops, it spends some time to approach to the curb. We assume that BRT-like stops

will require less time to stop the bus. However, although it is not the only consequence of such design, we,

unfortunately cannot formally estimate the improvement of the accessibility for the disabled people.

2.2.6 Public transport lanes

Restricted parking on the main streets significantly increases their capacity and additional lanes can

be used for exclusive bus movement to improve speed and service quality. However, this measure is likely



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to be ineffective until bus route optimization: excessive quantity of minivans observed today can stuck in a

jam while using a single lane.

Transport model calculates two types of speed on the link: free flow speed and active network speed.

The least is a speed influenced by the traffic. Exclusive public transport lanes are modelled using a filter that

allows not assigning active network speed to the public transport, i.e. public transport moves along such

links with its free flow speed. In the same time, bus lanes do not allow general traffic, so the street is

modelled as if it has 1 lane per direction less.

2.2.7 Bus route optimization

Bus route optimization is a job, already performed by the Saunders Group. It is a new bus network,

mostly oriented on the full-size buses with several minivan routes. To model this action, we replace the

existing route network with a new one, and calculate this update in the Transport model to estimate

network’s operational characteristics.

2.3 Cost-Benefit Analyses Methodology

In the next chapters we will define seven scenarios for the development of public transport network,

which have to be investigated for their effectiveness and which have to be compared to each other.

A good method for comparison is a cost-benefit analysis. If the costs are high and the benefit low it

means, that the scenario in comparison would hardly meet the practical requirements and there have to be

found alternative solutions. The best is to have low costs with the maximum benefit, but the question stays,

which criteria to define for the benefit.

For the following analysis we propose the following operational criteria for the cost-benefit

assessment:

The Quantity of Routes

The Route Frequency

The Network Patronage

The Modal Split

The Service Level

The Emission Level

The Bus Fleet Composition

Each of the criteria defines a certain benefit and the possibility to compare. But it also includes costs.

A higher the quantity of routes could lead to a higher amount of buses needed for operation. The higher the

Service Level, the higher could be the costs. So, the task is always to find the equilibrium and the best

solution to the current case in terms of a well-adjusted cost-benefit for the scenario.

Beside the operational criteria we define also criteria for the case of new investments for certain

scenarios. They are:

The Length of separate Bus Lanes to build

The Number of Bus Stops to reconstruct

The Number of Traffic Lights to Update for Bus Priority



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2.3.1 The Quantity of Routes

The quantity of routes can give an idea of the complexity of the network and the possible service

duplication in certain regions and streets in the network. A balanced public transport network aims to provide

and distribute the routes to all over the city and not only on the busiest and economically beneficial parts.

2.3.2 The Route Frequency

The route frequency gives an indication and is one aspect of the service quality of the public transport

network. A high service frequency means less waiting time for the passengers and therefore a higher service

quality. But it also leads to not necessary supply which can follow into more congestion and to more costs

for the operation.

2.3.3 The Network Patronage

The network patronage gives the number of passenger kilometers per day. The higher the number,

the more people use the public transport.

2.3.4 The Modal Split

The Modal Split gives the share of usage between all means of transport in the network. When public

transport has 30% and motorized individual traffic has 28% it means that 30% of all trips is made by public

transport and 28% by motorized individual transport.

2.3.5 The Service Level

The service level splits into the following three aspects:

The average travel time

The average number of interchanges

The accessibility

The average travel time and the average number of interchanges are parameters, which are calculated

from the transport model.

The accessibility is expressed as the percentage of people of the city, which have access to the city

center (Batumi City Hall) within 15 minutes on public transport.

2.3.6 The Emission Level

Cars and buses release hydrocarbons, nitrogen oxides, carbon monoxide, and carbon dioxide into the

air (EPA, 2006). Implementing of any optimization scenarios for Public Transport could slightly increase air



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emissions by buses. The analysis in this report comprises the calculation of emission level of the bus fleet in

different scenarios and when substituting old EURO-2/3 buses with modern EURO-5 or electric buses. Vehicle

emission levels vary widely depending on make and model. Accurately determining the emissions released

from vehicles in Batumi requires knowledge of car type, year, etc.

2.3.7 The Bus Fleet Composition

The bus fleet composition is the number of buses for each bus category. It is better to have bigger

buses on high demand routes. If the network is designed in such a way, that it considers that fact, so the

number of buses, needed for the operation is decreased, which leads to lower costs in the operation.

2.3.8 The Length of separate Bus Lanes to build

This is an indicator directly related to the investments costs for the introduction of exclusive BRT-like

bus lanes on the CBG and/or CA corridor.

2.3.9 The Number of Bus Stops to reconstruct

The introduction of BRT-like bus lanes on the CBG/CA corridor could also comprise the reconstruction

of current bus stops to allow for a faster boarding to the buses. Each reconstruction has a certain cost.

2.3.10 The Number of Traffic Lights to Update for Bus Priority

To allow buses priority on traffic lights, they need to be updated to support the new functionality. This

is directly related to certain costs.

2.4 Bus Planning Manual

The aim of this Manual is to explain how to improve the existing bus network in Batumi using PTV

Visum the state-of-the-art planning and simultaneously assessment tool developed by PTV AG which is a

comprehensive, flexible software system for transportation planning, travel demand modelling and GIS-based

network data management. Its comprehensive data model and powerful calculation and analysis functions

make PTV Visum the ideal tool for developing advanced transport strategies and solutions. It helps the

transportation planner to develop the methodology to improve and optimize the existing bus network.

PTV Visum provides a wide range of evaluation options and assists the planners in identifying potential

improvements. These tools give the best ways to improve the existing bus network.

First, the project team should define a set of key principles to underpin optimization proposals for

Batumi’s public transport network. These should be complemented by a set of quantitative benchmarks,

which should be used to compare the relative performance of current and optimized network scenarios. The

benchmarks should be derived from comparisons with other cities of similar scale and location to Batumi.



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The key aim for optimizing Batumi’s public transport network is based on improving the liveability of

the city as well as convenience for tourists. This translates into higher quality of life for each citizen; delivered

through fast and convenient public transport, reliable utilities, high quality and affordable health care and

education, and clean air. Set against this context the four key objectives for Batumi’s public transport

network, as defined in partnership with UNDP and colleagues at the City Hall, are:

Ensuring the network meets user needs.

Making the network more cost efficient to provide.

Ensuring the network serves the city in the best possible way.

Considering transit’s contribution to wider policy objectives such as, economic development,

environmental protection, and social well-being.

To address the priority issues, the project team must sequentially apply such optimization principles:

Principle of Patronage: if the public transport route has less than 1,000 pax/day, it

considers to removing or merging. If the public transport route has more than 1,000pax/day it

considers bus, if we have more than 10-15,000 pax/day it considers mass transit.

Principle of Duplication: if the O-D patronage shared between similar routes, we consider

merging to maximize load factor.

Principle of Extension: do lots of people regularly alight in same place? Does route fall short

of desired destinations? If so, we consider extending to better meet demand.

Principle of Shorten: does service run empty for a portion of its route? Would two separate

services be more efficient? If so, consider splitting into two separate routes.

Principle of Directness: is journey time relative to O-D distance acceptable? Can routes be

altered to reduce travel time? If so, consider re-routing to optimize journey times.

Principle of New route: Are there O-Ds that are not being met? If so, consider introducing

a new route.

The corridors for optimization must be identified through analysis of aggregated origin-destination

flows of people in the city. By iteratively working around the city on a corridor-by-corridor basis, and applying

the optimization principles, it possible to propose changes to primary bus routes operated by Municipality;

followed by secondary routes operated by Marshrutkas and smaller buses.

The following workflow must be applied to each area of the city:

Identification of new strategic routes to serve long distance Origin-Destination (O-D) demand

that are not currently served by existing routes. These should be based on an analysis of the O-D

movements and accessibility plots from each area.

Assessment of existing routes following the presented principles: of Patronage, of

Duplication, of Extension, of Shorten, of Directness, of New route.

Realignment of routes to relate to strategic city interchanges, where appropriate.



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Realignment or consolidation of routes, and development of new local routes to serve areas with poor

local accessibility (including locations with concentrations of low income households).

When considering the types of vehicle that would be suitable to apply each route, the project team

should consider the vehicle size, capacities and estimated route carrying capacity thresholds (expressed in

Passengers, Per Hour, Per Direction; or PPHPD) as the basis for allocating vehicles to optimized routes.

Bus service is suited to longer distance routes, as well as hop-on / hop-off operations. A stop spacing

of around 400m would be suitable, while also ensuring major attractors and interchanges are served.

Due to their low capacity marshrutka are not suitable for long-distance routes. They are instead

suitable as feeder services, and hop-on / hop-off services. We recognize this requires a redefinition of

marshrutka operations as they are technically meant to serve point-to-point movements now.

Usually, feeder bus services are designed to pick up passengers in a certain locality, and take them to

a transfer point where they make an onward journey on a main bus service. In case of Batumi, the inner-

city bus routes act as main services and another buses and marshrutkas – as feeders.

Feeder bus network design is the first and most important step in bus transport planning procedure.

The network design problem consists of determining a set of bus routes in a specific area, through the given

travel demand, the area’s topology characteristics, and set of objectives and constraints.

The route structure design is becoming an important input to the subsequent decision making

processes and will affect later planning steps, significantly, which is explained in the following section.

A good design of route network can increase the efficiency of the bus system and decrease the total

cost of supplying the transit service. The users would like to have a bus network with more coverage and

high accessibility to the destinations, just like the existing network in Batumi. However, such systems have

poor efficiency and are not sustainable. Feeder system gives a chance to combine operational efficiency and

good coverage. It will give direct connections to the most demand-generating areas and provide a good

service for the remote areas. On the other hand, the operation costs would be reduced by keeping the total

route length and quantity within a certain bound. Thus, the main challenge of the route network design is

to be able to provide the city with a comfortable transport network at a reasonable operation cost and

emissions.

The global network schedule should consider each transfer area and its associated routes to allow

efficient transfer between lines in distance and time. Transferring between lines can be supported according

to various criteria such as the number of travelers.

The general objective of operators is to minimize the overall route length in view of a reduction in the

number of vehicles and crew resources required to maintain a global transport system. And the number of

lines alternatively can be considered. In addition, routes should not be too short or too long for profit reasons.

Once this process had been completed across all routes, the updated Scenario’s routes and headway

proposals should be transfer to Batumi Transport Model. This process should be repeated several times to

sense-check the combined impact of the proposals which had been developed on an area-by-area basis.

Further changes were made to both routes and headways, with the Scenarios being refined and re-tested

over four iterations.

The next step in the optimization process should be to sense-check the proposed changes and ensure

no significant passenger flows were detrimentally affected by the proposals. This involved sense-checking

to ensure in direct connectivity between the most popular O-D pairs, using accessibility maps.



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Interchange options exist that allow for connectivity between less popular origins and destinations

with minimal transfers between different public transport services.

Finally, team should double-check that proposed revisions to the public transport network were also

consistent with future land use plans for the Batumi metro area. New routes should be developed, or existing

routes modified, to better serve areas go high population growth.

The aim of the optimization process should be to design a public transport network for Batumi that

serves desired movements with the fewest interchanges and fastest journey times possible. Developing the

optimization proposals is an iterative process and the Batumi transport model provided a tool for testing each

set of optimized public transport network proposals (Scenarios), and using the outputs to refine them.

Objective and quantifiable benchmarks should be defined to provide a mechanism for measuring the

difference between the current transit network’s performance and that of optimization scenarios developed

through this study.

Service quality indicators such as safety and security, user satisfaction, and service reliability are not

benchmarked because they rely on public attitudes derived from individual’s tangible experiences. These

tangible user experiences cannot be obtained in respect of the optimization proposals developed through

this study.



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3 MEASURES AND SCENARIOS DEFINITION

3.1 Measures description

This study provides analysis of the following public transport improvement measures:

3.1.1 Bus lanes

There are 2 public transport priority corridors in this study: Chavchavadze-Baratashvili-Goriladze (CBG)

and Chavchavadze-Abuseridze (CA). Their implementation requires exclusive bus lanes creation that will be

about 5 km for the CBG corridor and about 6.5 km long for the CA.

3.1.2 Parking restrictions

Central Batumi heavily struggles from excessive parking in the right lanes of the important streets.

This leads to capacity loss and permanent traffic jams that strongly influence the livability of the city. The

only way that is considered to be effective across the world is the policy that restricts parking in the city

center. This may include parking fare increase as well as institutional framework development to achieve

effective parking regulations enforcement.



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3.1.3 Bus terminals (optionally with the P&R)

Figure 6 – Bus terminals

The key approach to traffic improvement in Batumi lies in the restricted access to the city center for

both public and private transport. Public transport access is limited by the route optimization that will reduce

number of minivan routes and will oblige suburban routes to terminate outside the city center. Private

transport access will be restricted by the parking policies.

Effective restrictions are impossible if no alternative is provided. In this case, bus terminals at each

side of the city are proposed. They will be the final points for the bus and minivan routes from the outside

and provide comfortable interchange to the city transport. Starting from the one terminal and all the way to

the second, public transport priority is planned.

To encourage people to avoid parking in the city center, they will be provided with P+R alternative,

where the parking lots will be located beside the bus terminals for better accessibility.

3.1.4 New bicycle rental station

Public transport network, based on the hub and spoke idea, when the feeder routes go to the bus

transfer terminals, will require people to switch to other routes that enter city center directly. However,

it may be an alternative for people to use bicycle in the city center. For this purpose, it may be effective



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to create bicycle rental stations and the terminals, as well as bicycle lanes that will allow fast,

comfortable and safe approach into the city.

Today’s bicycle rental price is a too high to be competitive with buses, however the city may regard

an option, when there are combined tickets for the feeder bus and bike rental.

3.1.5 Traffic lights priority

Each intersection has a delay for its crossing as an attribute. In case traffic light priority is modelled,

the through movement delay for the public transport is set to 0.

To model this measure, we have to update about 20 exising traffic lights for the CBG corridor and

about 25 for the CA corridor.

3.1.6 Introduction of EURO-5 diesel and electric buses

Buses have their share in an overall pollution level in the city, so cities are looking for the ways to

improve the level of the buses. There are generally three ways:

High-ecology diesel buses (Euro-5)

Gas buses (CNG, LPG)

Electric buses

The city of Batumi considers buying mostly Euro-5 diesel buses and some electric ones.

3.1.7 BRT-like stops

BRT-like stops are the ones with an increased accessibility and reduce stop time: the bus does not

need to perform extra maneuvers to get closer to the sidewalk, and features a doorstep-level platform.

20 existing stops have to be rebuilt for the CBG corridor and 24 stops – for the CA corridor.

3.1.8 Bus priority

Parking restrictions along the main streets will increase their capacity by one lane per direction that

will give an opportunity to provide public transport an exclusive lane. This will be an important incentive for

people to switch to public transport or park&ride model.

3.2 Scenarios Description

In this study, we totally consider thirteen scenarios, including the base model. They regard different

optimization scenarios (3 types of networks), as well as different sustainable transport measures (2 measures

and their combined implementation):

1. Existing network with CBG and CA demonstration corrdiors

Do nothing (BASE)

BRT-like operations in the CA corridor (BASE + CA)



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BRT-like operations in the CBG corridor (BASE + CBG)

BRT-like operations in both CA and CBG corridor (BASE + CACBG)

2. New bus network by the Saunders Group

Do nothing (SG)

BRT-like operations in the CA corridor (SG + CA)

BRT-like operations in the CBG corridor (SG + CBG)


3. New bus network by the Batumi city

Do nothing (BCH)

BRT-like operations in the CA corridor (BCH + CA)

BRT-like operations in the CBG corridor (BCH + CBG)


The purpose of the BRT scenarios on the base network is to identify the pure role of BRT-like

operations in the transportation system. BRT-like operations are studied for Chavchavadze-Baratashvili-

Goriladze (Green Line, only for current network) and Chavchavadze-Abuseridze (Red Line) corridors and

formally modelled as the links, where public transport speed is not influenced by the general traffic. These

corridors are illustrated on Figure 7. Although, exclusive lanes are usually taken from general traffic, so that

the latter has one lane less, this is not applicable to the central Batumi. The right lanes of the streets that

belong to the corridors are used as parking. So, if the city applies parking restrictions in the downtown,

additional street space can be used for the public transport lanes without any harm to the general traffic.

SG network is an optimized network, developed by the Saunders Group. It is modelled alone to catch

its pure features and together with the BRT-like operations along the CA and CBG corridors. It aims to reduce

of the minivan traffic in the city and realign the network to facilitate the demand.

The key features of the SG network are:

Reduced number of routes per street

Increased total headway (number of buses of all routes per unit time)

Altered accessibility time

Advantages of this route layout are:

Reduced load on the street allows more efficient operation

Reasonable general headway allows bus priority operation with exclusive lanes

Reduction of the number of routes will increase patronage

Improved patronage per route will allow switching to the large buses

Reduction of total number of buses will improve energy efficiency and reduce pollution

Potential risks:

Reduced number of routes will inevitably decline accessibility for some people

Minivan drivers will lose their jobs

The new network may possibly make people switch between the routes instead of the direct

access that increases travel time



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BCH is a Saunders Group network, reviewed by the Batumi city officials and has mostly the same idea

as the original optimized network. Three scenarios for this network are calculated: network alone and

together with the BRT-like operations along the CA and CBG corridors.

Figure 7 – CBG (green line) and CA (red line) corridors for the BRT-like operations

3.2.1 Existing network

3.2.1.1 BASE

This modelling scenario is a basic public transport network modelled for the purpose of calibration and

comparison. It is a status-quo scenario that is a base for understanding, what is going to be in the city in

case nothing is changed.

3.2.1.2 BASE+CA

This scenario considers the BRT-like operations in the CA corridor modelled for the existing network.

It helps to estimate the pure effect of the BRT-like operations in this corridor.

3.2.1.3 BASE+CBG

This scenario considers the BRT-like operations in the CBG corridor modelled for the existing network.

It helps to estimate the pure effect of the BRT-like operations in this corridor.



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3.2.1.4 BASE+CACBG

The aim of this scenario is to calculate the overall improvement made by implementing both CA and

CBG corridors within the BASE network.

3.2.2 New bus network by the Saunders Group

3.2.2.1 SG

This scenario is modelled to spot the overall effect of the new network, to evaluate its basic features

without any additional measures. The result of this calculation is also a basis for the comparison.

3.2.2.2 SG+CA

The aim of this scenario is to calculate the overall improvement made by the CA corridor to the

operations of the SG network.

3.2.2.3 SG+CBG

The aim of this scenario is to calculate the overall improvement made by the CBG corridor to the

operations of the SG network.

3.2.2.4 SG+CACBG


CBG corridors within the SG network.

3.2.3 New bus network by the Batumi city

3.2.3.1 BCH

This is the third network modelled in the scope of this project. The Batumi city hall network is modelled

to compare it to the BASE and SG networks and detect its advantages and disadvantages.

3.2.3.2 BCH+CA

As the BCH is originally a review of the SG network, and the SG network was intended to operate

together with the BRT-like operations along the CA corridor, this scenario analyses whether the network will

benefit from the public transport priority.



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3.2.3.3 BCH+CBG

The aim of this scenario is to calculate the overall improvement made by the CBG corridor to the

operations of the BCH network.

3.2.3.4 BCH+CACBG


CBG corridors within the BCH network.



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4 PUBLIC TRANSPORT NETWORK OPTIMIZATION SCENARIO ANALYSIS (BASE, SG, BCH)

4.1 Route quantity analysis (network density)

Figure 8 – Number of routes in the BASE network (buses and minivans)

As it can be seen from Figure 8, Figure 9 and Figure 10, current Batumi PuT network (both, buses and

minivans) is too much concentrated on the Chavachadze avenue, where about 20-25 routes are

concentrated. Moreover, there is a place with over 25 routes, while there are many streets, poorly served

with the public transport, and it is a typical situation for a post-soviet city. This huge number of routes,

concentrated in the same place is a first sign that routes are duplicated; their patronage is split between all

of them that consequently leads to minivan usage.

The SG is much more evenly distributed: it provides services for large number of streets and there are

no streets in the city where the quantity of routes exceeds 10.



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Figure 9 – Number of public transport routes in the city center (BASE)

Figure 10 – Number of public transport routes in the city center (SG)



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Figure 11 – Number of public transport routes in the city center (BCH)



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4.2 Route headway analysis

Figure 12 – Route headways, BASE network

Route headway is one of its major operational characteristics: more services per unit time is more

convenient for the passenger, so up to the certain point, shorter headway can attract more passengers to

the route and improve its patronage. However, headway reduction causes several negative outcomes:

Shorter headway means more cost for the operations

More operations needed means more fuel consumed and more pollution produced

So, case we regard not a single route, but route network instead, we are facing the fact that many

routes with small headways are a heavy load on the road network.

Total headways for the BASE, SG and BCH networks are illustrated on Figure 12, Figure 13 and Figure

14.



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Figure 13 – Route headways, SG network



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Figure 14 – Route headways, BCH network

Existing Batumi network operates with extreme low overall headways (less than 1 minute sometimes)

so that central streets of the city face traffic jams made of busses and together with the strong route

duplication this leads to the low-capacity bus usage. Moreover, it is impossible to introduce exclusive bus

lane in this case: one lane cannot handle this amount of bus and minibus traffic.

Upgraded network offers longer headways on the street network that will allow organizing BRT-like

operations along Chavachadze ave.

4.3 Network patronage analysis

One of the most illustrative indicators of the network is its patronage and patronage structure. It

illustrates the overall demand public transport can attract under the certain conditions.

As it can be seen from the Figure 15, minivans are currently prevailing in the structure of Batumi public

transport, carrying almost 2/3 of all public transport passengers. Together with the small capacity of the



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rolling stock, huge number of routes and small headways, this leads to heavy minivan traffic in the city

center.

Figure 15 – Public transport patronage by systems of transport, BASE



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SG network totally reviews the philosophy of the public transport in Batumi. In this network buses are

prevailing, carrying 62% of passenger and being the only way to access the city center, as it is illustrated on

Figure 16. Minivans, on the contrary, are mostly used to connect the outer parts of the city, as well as

suburbs to the bus terminals on the entrances to the city. Those terminals are very important for the city,

providing interchange between different routes, and especially route N5, that connects the two terminals via

CA corridor.

Figure 16 – Public transport patronage by systems of transport, SG

Batumi city hall officials have reviewed the SG network, and the resulting BCH network is even more

focused on the buses: according to the transport model forecast, 79% of public transport passengers are

carried by the buses, however high-demand Helvachauri remains for minivan traffic (Figure 17).



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Figure 17 – Public transport patronage by systems of transport, BCH network

4.4 Modal split

The key indicator of the transportation system of the city is the modal split. It shows share of each

mode in the transportation structure. Bad public transportation, together with high income and/or cheap

cars leads to the large share of the private transport. In case public transportation is comfortable and

accessible, people are less likely to use private cars and switch to the public transport, especially when cars

are unaffordable (like it was during USSR times). The last case, sometimes seen in the European cities, is

the situation, when both public transport and private transport usage are expensive. Under these conditions,

the shares of bicycle and pedestrian movements grow.



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Table 2 – Modal split for the BASE, SG and BCH networks

Mode BASE SG BCH

Bicycle 0.4% 0.4% 0.4%

Private transport 34.7% 33.5% 33.6%

Pedestrian 31.1% 29.9% 30.2%

Public transport 33.9% 36.3% 35.8%

As it may be seen from the Table 2, public transport optimization causes significant change in the

modal split. The share of the public transport grows, while the shares of the private transport and pedestrians

decline. This means that the new public transport networks become more comfortable, as well they provide

an alternative to walking for some places. For example, if there is a trip that requires 25 minutes walking, or

15 minutes walking to the public transport, then waiting and riding for 10-15 minutes, people are very likely

to choose walking, despite of the travel time. However, if the city provides a route that will be accessible in

5 minutes, people are more likely to switch prom walking to the public transport.

4.5 Service level analysis

There is no general service level parameter, so we consider 3 parameters as service level:

Average number of interchanges in the network

Average travel time with public transport

Accessibility (in the terms of percent population accessible in the specific amount of

time)

Operational optimization of the route network is very likely to cause reduction of accessibility, because

redundant network provides very short headways and doorstep accessibility to the public transport.

Table 3 shows the estimated accessibility parameters. As it was expected, new networks require more

interchanges (because of the bus terminal concept) and have less coverage within 15 minutes.

Table 3 – Public transport accessibility for the BASE, SG and BCH networks

BASE SG BCH

Percent population accessible from the city center in 15 min

49.7% 30.4% 41.5%

Number of interchanges 0.30 0.44 0.49



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Graphic representation of travel time from the selected part of the city is an isochrones diagram.

Isochrones illustrate what parts of the city can be accessed within certain time budget.

In the scope of this study we have analyzed isochrones for the city downtown and 2 remote, but

populated areas, illustrated on Figure 18:

Batumi city hall

Helvachauri

Northern residential district

Those parts of the city are chosen, because city center is important for all the people of Batumi, and

Helvachauri and Northern district have the most population that lives far from the city center, so the new

transportation policy will influence it the most.

Figure 18 – Population of Batumi, by transportation zones



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Figure 19 – Public transport accessibility from the Batumi city hall (top left – BASE; top right – SG; bottom – BCH)



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Figure 20 – Public transport accessibility from Helvachauri (top left – BASE; top right – SG; bottom – BCH)

As we can see from Figure 19, Figure 20 and Figure 21, BASE network provides the best accessibility,

while SG and BCH scenarios have reduced accessibility due to the route optimization. The most significant

reduction of the accessibility is focused on the Northern residential area.



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Figure 21 – Public transport accessibility for the northern residential area (top left – BASE; top right – SG;

bottom – BCH)



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4.6 Bus fleet composition

Bus route composition is an estimated quantity of buses required, by capacity. It is calculated from the

modelled route demand on the most loaded part. Table 4 illustrates that current demand is too much spread

among the routes, so the city network needs 370 buses, and 291 of the are minivans. However, optimized

route networks that have even slighter overall route patronage, need 3 times less buses, and only about 15-

20% of them are minivans.

Table 4 – Bus quantity to fit the demand, by capacity

Bus type and capacity BASE SG BCH

Minivan(15) 291 24 17

Small bus (50) 69 28 44

Medium bus (80) 0 21 5

Large bus (100) 11 54 46

TOTAL 370 127 112



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4.7 Energy consumption and emissions

Heavy marshrutka traffic and overall network inefficiency are a very strong source of air pollution.

For pollution estimation, we use exhaust standards according to the Euro standard for transit

vehicles1 in g/km (Table 5) and kilometers traveled daily in the network by the vehicles (Table 6).

European emission standards regulate the following types of emissions:

Carbon Monoxide (CO): Carbon monoxide results from incomplete combustion of fuel

and is emitted directly from vehicle tailpipes. CO can be a precursor to both CO2 and

ozone, two significant greenhouse gases. Although exposure to CO does not have a

cumulative effect on health, instantaneous effects of high concentrations can be

dangerous (Nylund et al. 2004, Macias, Martinez, and Unal 2010).

Nitrogen oxides (NOx): Nitrogen oxides are an important family of air polluting

chemical compounds. These highly-reactive gases affect health and lead to increases

in global warming. NOx emissions increase as a result of increasing engine

temperature (Macias et al. 2010). Emissions of NOx from combustion are primarily in

the form of nitric oxide (NO) (Nylund et al. 2004). NO can be oxidized into nitrogen

dioxide (NO2) which is a powerful air pollutant by itself and can also react in the

atmosphere to form ozone and acid rain. Some emissions reduction technologies can

increase the portion of NO2 in diesel exhaust. Nitrous oxide (N2O), a significant

greenhouse gas, makes up a very small portion of total NOx emissions for all fuel

types (EPA 2012b).

Total hydrocarbons (THC) refers to nonmethane hydrocarbons plus methane.

Nonmethane hydrocarbons (NMHC): Nonmethane hydrocarbons in exhaust result

from partially burned fuel. There are many potential pollutants resulting from

hydrocarbons with different effects (eye, skin and respiratory tract irritation), including

acetylaldehyde and formaldehyde. Hydrocarbons can have negative health impacts or

contribute to the ground-level ozone or smog (Macias et al. 2010, Nylund et al. 2004).

Methane (CH4): Methane, in the form of unburned fuel, is a tailpipe emission primarily

for natural gas fuels. Although it is not toxic, methane has a global warming potential

1 EMBARQ (2012) Emissions of Transit Buses. Available online at:

http://www.wricities.org/sites/default/files/Exhaust-Emissions-Transit-Buses-EMBARQ.pdf,



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that is 25 times higher than that of CO2 (Nylund et al. 2004, Environment Canada

2011a).

Particulate matter (PM): Particulate matter is a mixture of small particles and droplets,

including acids, such as nitrates and sulfates; organic chemicals; metals; soil; or dust.

Combustion can produce a large quantity of very fine particles 10 nanometers in

diameter or smaller, but it is regulated by measuring the total quantity of all PM

particles sizes. The human body cannot protect against exposure to ultrafine particles,

which can enter the heart and lungs through inhalation and have serious health

effects, including respiratory diseases and heart and lung conditions (EPA 2012a).

Table 5 – Euro Emissions Standards for Transit vehicles, g/km

Emission Standards Date CO THC NOx PM

Euro I 1992 8.10 1.98 14.40 0.65

Euro II 1998 7.20 1.98 12.60 0.27

Euro III 2000 3.78 1.19 9.00 0.18

Euro IV 2005 2.70 0.83 6.30 0.04

Euro V 2008 2.70 0.83 3.60 0.04

EEV 2.70 0.45 3.60 0.04

Euro VI 2013 2.70 0.23 0.72 0.02

Table 6 – Distances traveled by the transit vehicles daily, km

BASE SG BCH

Minivan 45 186 14 205 9 192

Bus 29 044 16 614 21 864

European emission standards do not include CO2, because there is no after-treatment technology

that helps to reduce its emissions. The only things that influences CO2 emissions are amount of fuel used,

as well as fuel efficiency. For these calculations, we assume that vehicles in the Batumi public transport

network consume diesel fuel as in Table 7.

Table 7 – Fuel consumption and CO2 emissions for buses and minivans

Consumption, l/100 km CO2 emission per 1 km, kg

Minivan 18 0.792

Bus 30 0.475



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One liter of diesel fuel produces 2.64 kg2 CO2 that provides us with an estimation, on how much CO2

will be generated by the network.

4.7.1 The Base network

According to the obtained data, there are about 750 minivans in Batumi, and 478 are daily in the

network. As it can be seen at Figure 22, most of them are more than 10 years old, while the median age of

minivan in Batumi is 17 years.

Unfortunately, there is no data on how much minivans of different emission standards are on the

streets, so we assume that all EURO-4 and EURO-5 minivans are on the network, as they are new. Euro-2

and Euro-3 vehicles are rather old, so we assume that only a part of them operates (see Table 8).

Table 8 – Minivans by emission standards

Emission standard Number

EURO-2 202

EURO-3 219

EURO-4 56

EURO-5 1

TOTAL 478

As we see, minibuses of the obsolete Euro-2 and Euro-3 standards prevail in the network, because

the median produce year is 2000. Together with their quantity, this forms a huge challenge for the ecology

of Batumi.

2 http://www.ecoscore.be/en/info/ecoscore/co2



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Figure 22 – Minivan produce year distribution

The same idea is applied to the bus fleet: there are 141 bus in the city, however, only 107 are on

the routes. All the buses in the city were produced several years ago and have Euro-2 and Euro-3 emission

standards. To estimate the emissions we assume that buses are represented on the routes in the proportional

way, as it is in Table 9.

Table 9 – Buses by emission standards

Emission standard Number

Euro-2 33

Euro-3 74

TOTAL 107

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Finally, knowing the structure of the fleet that operates in Batumi and the emissions, according to the

standards and fuel consumption, we estimate daily transit emissions in Batumi (see

Figure 23and Table 10).

Table 10 – Daily emissions in the BASE network, kg

CO THC NOx PM CO2

Minivan 110 078.3 31 961.3 220 172.9 4 338.4 21 472.3

Bus 15 024.9 4 451.0 31 419.5 645.6 23 002.6

0

50000

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Minivan Bus



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Figure 23 – Daily emissions in the BASE network, kg

0

50000

100000

150000

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CO THC NOx PM CO2

Minivan Bus



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4.7.2 Emissions in the SG network

After the emissions of the BASE network have been calculated, we used the same methodology to

estimate the emissions of the SG and BCH networks, assuming that they will use 33 minivans and 124 buses,

as it was planned in the SG network.

Table 11 illustrates the emissions of the SG network.

Table 11 – Daily emissions in the SG network, kg

CO THC NOx PM CO2

Minivan 1 265.7 388.1 2 914.9 16.9 6 750.2

Bus 9 357.5 2 780.0 18 990.2 379.5 13 158.3

Figure 24 – Daily emissions in the SG network, kg

4.7.3 Emissions in the BCH network

The emissions in the BHC network have slightly changed structure, because it has more bus, and

less marshrutka traffic. As buses perform more trips in the network, slightly higher emission estimates (see

Table 12 and Figure 25).

0

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Table 12 – Daily emissions in the BCH network, kg

CO THC NOx PM CO2

Minivan 819.0 251.2 1 886.2 10.9 4 368.0

Bus 12 314.1 3 658.4 24 990.3 499.4 17 316.3

Figure 25 – Daily emissions in the BCH network, kg

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Minivan Bus



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4.7.4 Comparison

Finally, we can compare the total emissions of the BASE, SG and BCH networks. As it can be seen in Table 13 and

Figure 26, the BASE network is very inefficient and significantly pollutes the air of Batumi. The new

scenarios dramatically reduce the amount of emissions, and it is very important for the city that orients itself

on the tourism.

0

50000

100000

150000

200000

250000

300000

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BASE SG BCH



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Table 13 – Total daily emissions in the analyzed networks, kg

CO THC NOx PM CO2

BASE 125 103.2 36 412.3 251 592.4 4 984.0 23 002.6

SG 10 623.2 3 168.2 21 905.1 396.4 13 158.3

BCH 13 133.1 3 909.6 26 876.5 510.3 17 316.3

Figure 26 – Total daily emissions in the analyzed networks, kg

0

50000

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150000

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CO THC NOx PM CO2

BASE SG BCH



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4.8 Summary

Both optimized networks feature significant reduction in route quantity and number of buses required.

However, SG and BCH networks are more attractive to the people: implementation of these networks is

expected to cause significant changes in the model split, and also in route patronage. Overall summary is in

the Table 14.

Table 14 – Bus network scenario summary

BASE SG BCH

Route quantity 44 15 16

Percent population accessed from the downtown in 15 min

79.6% 68.1% 75.7%


Average travel time, min 35.0 35.3 35.7

Service, veh*km 74 229.6 30 819.5 31 055.9

Public transport patronage 155 553 159 006 166 396

Bus 56 177 99 307 131 085

Minibus 99 375 59 698 35 311

Modal split

Bicycle 0.4% 0.4% 0.4%


Pedestrian 31.1% 29.9% 30.2%


Buses required 370 127 112

Minivan(15) 291 24 17

Small bus (50) 69 28 44


Large bus (100) 11 54 46

Daily emissions, kg

CO 125 103.2 10 623.2 13 133.1

THC 36 412.3 3 168.2 3 909.6

NOx 251 592.4 21 905.1 26 876.5

PM 4 984.0 396.4 510.3

CO2 23 002.6 19 908.5 21 684.3



Page 56

5 LOW CARBON SUSTAINABLE URBAN TRANSPORT CORRIDORS SCENARIO MODELLING

ANALYSIS (BASE+CA, BASE+CBG, BASE+CACBG, SG+CA, SG+CBG, SG+CACBG BCH+CA, BCH+CBG, BCH+CACBG)

5.1 Network patronage analysis

When the public transport priority corridor is created, the transportation system is expected to become

more attractive, and so, its patronage is expected to grow. It is not the only one way to measure the

efficiency of some policy, but definitely the important one.

Figure 27 – Public transport patronage by systems of transport, BASE+CA

BASE+CA is an efficient measure for the city: it alters the service level, as well as modal split. However,

as it can be seen in Table 15, it doesn’t change the transportation structure of the city (in the terms of bus

and minibus share).

Table 15 – BASE+CA public transport patronage

BASE BASE+CA ∆

Bus 56 177 58 051 3.3%

Minibus 99 375 104 804 5.5%

TOTAL 155 553 162 855 4.7%



Page 57

Also, it is very important to realize that although this scenario was modelled mathematically, it

is hardly implementable in the real life because of excessive number of routes along Chavachadze

Avenue and extremely small generalized headway (less than 50 sec).

Figure 28 – Public transport patronage by systems of transport, BASE+CBG

This scenario preserves the status-quo situation: despite CBG corridor is implemented, no effect is

observed on the overall city level. Table 16 illustrates the transportation system patronage under this

scenario.

Table 16 – BASE+CBG public transport patronage

BASE BASE+CBG ∆

Bus 56 177 57 075 1.6%

Minibus 99 375 100 437 1.1%

TOTAL 155 553 157 512 1.3%



Page 58

Figure 29 – Public transport patronage by systems of transport, BASE+CACBG

This scenario analyses the case, when both CA and CBG corridors are implemented with the existing

network. It combines the benefits of CA and CBG corridors, so the result is combined. Table 17 illustrates

the transportation system patronage under this scenario.

Table 17 – BASE+CACBG public transport patronage

BASE BASE+CACBG ∆

Bus 56 177 58 565 4.3%

Minibus 99 375 105 384 6.0%

TOTAL 155 553 163 950 5.4%



Page 59

Figure 30 – Public transport patronage by systems of transport, SG+CA

Scenario that features CA corridor together with the SG network shows definitely different situation.

Figure 30 and Table 18 illustrate that buses and minibuses are flipped vice-versa, however the major

influence here is the optimized network, not the priority measures.

Table 18 – SG+CA public transport patronage

SG SG+CA ∆

Bus 99 307 103 850 4.6%

Minibus 59 698 62 282 4.3%

TOTAL 159 006 166 132 4.5%



Page 60

Figure 31 – Public transport patronage by systems of transport, SG+CBG

Despite this scenario provides BRT for the CBG corridor, most of the demand remains along the

Chavchavadze avenue. There is an improvement, however, the differences between SG and SG-CBG

scenarios are very subtle. Figure 31 and Table 19Table 20 illustrate the scenario results.

Table 19 – SG+CBG public transport patronage

SG SG+CBG ∆

Bus 99 307 100 921 1.6%

Minibus 59 698 60 937 2.1%

TOTAL 159 006 161 859 1.8%



Page 61

Figure 32 – Public transport patronage by systems of transport, SG+CACBG

The results of SG+CACBG scenario combine the benefits of SG+CA and SG+CBG, however there is no

synergy in their combination (see Figure 32, Table 20).

Table 20 – SG+CACBG public transport patronage

SA SG+CACBG ∆

Bus 99 307 104 698 5.4%

Minibus 59 698 62 449 4.6%

TOTAL 159 006 167 147 5.1%



Page 62

Figure 33 – Public transport patronage by systems of transport, BCH+CA

The results of BCH+CA scenario are close to the SG+CA, however due to better accessibility of the

BCH network it has larger patronage (see Figure 33, Table 21).

Table 21 – BCH+CA public transport patronage

BCH BCH+CA ∆

Bus 131 085 133 637 1.9%

Minibus 35 311 36 624 3.7%

TOTAL 166 396 170 261 2.3%



Page 63

Figure 34 – Public transport patronage by systems of transport, BCH+CBG

The last scenario that was modelled is BCH+CBG. It acts similar to the SG+CBG, and doesn’t influence

the system in any significant way (see Figure 34 and Table 22).

Table 22 – BCH+CBG public transport patronage

BCH BCH+CBG ∆

Bus 131 085 132 275 0.9%

Minibus 35 311 35 710 1.1%

TOTAL 166 396 167 985 1.0%



Page 64

Figure 35 – Public transport patronage by systems of transport, BCH+CACBG

The results of BCH+CACBG scenario are close to the SG+CACBG, and act like a composition of BCH+CA

and BCH+CBG scenarios (see Figure 35, Table 23).

Table 23 – BCH+CACBG public transport patronage

BCH BCH+CACBG ∆

Bus 131 085 136 275 4.0%

Minibus 35 311 36 781 4.2%

TOTAL 166 396 173 055 4.0%



Page 65

5.2 Modal split

Introduction of bus priority measures affects modal split in the same way as route optimization does.

BRT-like measures along CA corridors add almost 2% percent to the share of public transport, while the

shares of pedestrians and private transport users reduce evenly by about 1% each. Following this logic, only

the scenarios that combine route optimization and the bus priority along the CA corridor show the most

significant effect (see Table 24).

CBG corridor influence modal split as well as route patronage in a subtle way that balances on the

edge of the model’s precision.

Table 24 - Modal split for the public transport priority scenarios

BASE+CA BASE+CBG BASE+CACBG SG+CA SG+CBG SG+CACBG

Bicycle 0.4% 0.4% 0.4% 0.4% 0.4% 0.4%

Private transport 34.0% 34.5% 33.8% 33.2% 33.6% 33.0%

Pedestrian 30.3% 30.8% 30.2% 29.4% 29.8% 29.3%

Public transport 35.3% 34.3% 35.6% 37.0% 36.3% 37.3%

BCH+CA BCH+CBG BCH+CACBG

Bicycle 0.4% 0.4% 0.4%


Pedestrian 29.8% 30.1% 29.6%


5.3 Service level analysis

As it was already mentioned earlier, service level includes three parameters:

Average number of interchanges in the network

Average travel time with public transport

Accessibility (in the terms of percent population accessible in the specific amount of

time)

Table 25 contains the information about the indicators calculated from the model. According to this,

SG+CA scenario demonstrates significant decline in the accessibility.

Table 25 – Service level indicators


Number of interchanges 0.30 0.30 0.30 0.46 0.45 0.46

Average travel time, min 34.3 34.8 34.1 34.8 35.3 34.5



Page 66


62.9% 56.0% 62.9% 43.2% 34.1% 46.9%





54.8% 45.6% 58.4%

It is easier to understand this phenomenon when isochrones are built. As well as for the network

optimizations, we built isochrones for 3 areas in the city: downtown, Helvachauri and Northern residential

district. The differences between the diagrams on Figure 37, Figure 40 are quite subtle, however it is

noticeable that SG+CA usually provides less accessibility, while BASE+CA and BCH+CA provide the best

accessibility.

Figure 43 is different, because SG+CA diagram is significantly different from what we see for the other

scenarios. This means that SG+CA scenario may possibly need some improvement for the routes that connect

the Northern residential district to the rest of the city.

The most possible reason for this effect, is the layout of the route N7. It is the only route that connects

the Northern district to the city center. As it may be seen at Figure 36, route N7 has a loop in the middle

that increases travel time, but serves one extra district. In the same time, the route terminates at the very

edge of the city center, according to the concept of the bus transfer terminals.

This situation may be improved by making N7 a direct route without a loop, providing that district with

an extra feeder bus. This may raise the need in buses as well as emissions, however the level of service may

significantly improve.

Another possible improvement may be extension of the BRT-like route to the most populated areas of

the city, providing them with a direct link to the city center. All the rest routes remain within the original

concept of feeder routes that are connected to the main city BRT-like route.



Page 67

Figure 36 – Route N7 layout



Page 68

Figure 37 – Public transport accessibility from the Batumi city hall (top left – BASE+CA; top right – BASE+CBG; bottom left – SG+CA; bottom right – SG+CBG)



Page 69

Figure 38 – Public transport accessibility from the Batumi city hall (top left – BCH+CA; top right – BCH+CBG; bottom left – SG+CACBG; bottom right – BCH+CACBG)



Page 70

Figure 39– Public transport accessibility from the Batumi city hall (BASE+CACBG)



Page 71

Figure 40 – Public transport accessibility from Helvachauri (top left – BASE+CA; top right – BASE+CBG; bottom left – SG+CA; bottom right – SG+CBG)



Page 72

Figure 41 – Public transport accessibility from Helvachauri (top left – BCH+CA; top right – BCH+CBG; bottom left – SG+CACBG; bottom right – BCH+CACBG)



Page 73

Figure 42 – Public transport accessibility from Helvachauri (BASE+CACBG)



Page 74

Figure 43 – Public transport accessibility from the Northern residential district (top left – BASE+CA; top right – BASE+CBG; bottom left – SG+CA; bottom right – SG+CBG)



Page 75

Figure 44 – Public transport accessibility from the Northern residential district (top left – BCH+CA; top right – BCH+CBG; bottom left – SG+CACBG; bottom right – BCH+CACBG)



Page 76

Figure 45 – Public transport accessibility from the Northern residential district (BASE+CACBG)



Page 77

5.4 Bus fleet composition

Quantity and capacity of buses required to serve the network were calculated from the route demand

of the critical parts of each route. Estimated bus fleet that can fit route demand can be seen in Table 26.

Table 26 – Bus type and capacity

Bus type and

capacity BASE+CA BASE+CBG BASE+CACBG SG+CA SG+CBG SG+CACBG

Minivan(15) 276 296 279 25 25 25

Small bus (50) 67 62 61 20 14 14

Medium bus (80) 7 9 18 16 11 17

Large bus (100) 12 11 12 58 63 62

TOTAL 363 378 370 119 112 118

Bus type and

capacity BCH+CA BCH+CBG BCH+CACBG

Minivan(15) 50 33 17

Small bus (50) 24 25 38


Large bus (100) 54 45 54

TOTAL 133 120 114

As it can be seen, introduction of the BRT-like operation doesn’t produce noticeable effect on the fleet

characteristics for the BASE model. However, when the public transport priority measure is combined with

the route optimization, the difference in bus fleet is significant. According to the calculations, SG+CA/CBG

and BCH+CA/CBG scenarios will require 3 times less busses, than is required by the models with the BASE

network.



Page 78

5.5 Low Carbon Sustainable Urban Transport Corridors Scenario Summary

The modeling of the urban transport corridors reveals that CBG corridor does not influence the

transportation system in any significant way. There are minor changes in the figures, however they are on

the edge of precision. According to the Table 27, CA corridor significantly improves the transportation system

of Batumi.

Although the CBG corridor construction is expected to be cheaper than the construction of the CA

corridor, its effect to the transportation system is very subtle. Scenario modeling summary in figures is in

Table 27.

Table 27 – Public transport corridor scenario modeling results


Route quantity 44 44 44 15 15 15

Number of interchanges 0.30 0.30 0.30 0.46 0.45 0.46

Average travel time,

min 34.3 34.8 34.1 34.8 35.3 34.5

Percent population

accessible in 15 min 62.9% 56.0% 62.9% 43.2% 34.1% 46.9%

Service, veh*km 74 229.6 74 229.6 74 229.6 30 819.5 30 819.5 30 819.5

Daily public transport

patronage, pax 162 855 157 512 163 950 166 132 161 859 167 147

Bus 58 051 57 075 58 565 103 850 100 921 104 698

Minibus 104 804 100 437 105 384 62 282 60 937 62 449

Modal split

Bicycle 0.4% 0.4% 0.4% 0.4% 0.4% 0.4%

Private transport 34.0% 34.5% 33.8% 33.2% 33.6% 33.0%

Pedestrian 30.3% 30.8% 30.2% 29.4% 29.8% 29.3%

Public transport 35.3% 34.3% 35.6% 37.0% 36.3% 37.3%

Buses required 363 378 370 119 112 118

Minivan(15) 276 296 279 25 25 25

Small bus (50) 67 62 61 20 14 14

Medium bus (80) 7 9 18 16 11 17

Large bus (100) 12 11 12 58 63 62

Daily emissions, kg

CO 125 103.2 125 103.2 125 103.2 10 623.2 10 623.2 10 623.2

THC 36 412.3 36 412.3 36 412.3 3 168.2 3 168.2 3 168.2

NOx 251 592.4 251 592.4 251 592.4 21 905.1 21 905.1 21 905.1

PM 4 984.0 4 984.0 4 984.0 396.4 396.4 396.4

CO2 23 002.6 23 002.6 23 002.6 19 908.5 19 908.5 19 908.5

Network requirements

Bus lanes, km 6 5 11 6 5 11

Traffic lights to modify 25 20 45 25 20 45

Bus stops to rebuild 24 20 44 24 20 44



Page 79


Route quantity 16 16 16



Percent population accessible in 15 min 54.8% 45.6% 58.4%

Service, veh*km 31 055.9 31 055.9 31 055.9

Daily public transport patronage, pax 170 261 167 985 173 055

Bus 133 637 132 275 136 275

Minibus 36 624 35 710 36 781

Modal split

Bicycle 0.4% 0.4% 0.4%


Pedestrian 29.8% 30.1% 29.6%


Buses required 133 120 114

Minivan(15) 50 33 17

Small bus (50) 24 25 38


Large bus (100) 54 45 54

Daily emissions, kg

CO 13 133.1 13 133.1 13 133.1

THC 3 909.6 3 909.6 3 909.6

NOx 26 876.5 26 876.5 26 876.5

PM 510.3 510.3 510.3

CO2 21 684.3 21 684.3 21 684.3

Network requirements

Bus lanes, km 6 5 11

Traffic lights to modify 25 20 45

Bus stops to rebuild 24 20 44



Page 80

6 GENERAL SCENARIO COMPARISON

6.1 Scenario summary



Page 81

Table 28 - Scenario modelling summary

BA

SE

BA

SE+

CA

BA

SE+

CB

GB

AS

E+

CA

CB

GS

GS

G+

CA

SG

+C

BG

SG

CA

+C

BG

BC

HB

CH

+C

AB

CH

+C

BG

BC

H C

A+

CB

G

Ro

ute

qu

an

tity

44

44

44

44

15

15

15

15

16

16

16

16

Nu

mb

er

of

inte

rch

an

ge

s0.3

00.3

00.3

00.3

00.4

40.4

60.4

50.4

60.4

90.4

90.5

00.5

1

Av

era

ge

tra

ve

l ti

me

, m

in35.0

34.3

34.8

34.1

35.3

34.8

35.3

34.5

35.7

35.1

35.6

35.1

Pe

rce

nt

po

pu

lati

on

acce

ssib

le in

15

min

49.7

%62

.9%

56.0

%62

.9%

30.4

%43

.2%

34.1

%46

.9%

41.5

%54

.8%

45.6

%58

.4%

Se

rvic

e,

ve

h*

km

74 2

29.6

74 2

29.6

74 2

29.6

74 2

29.6

30 8

19.5

30 8

19.5

30 8

19.5

30 8

19.5

31 0

55.9

31 0

55.9

31 0

55.9

31 0

55.9

Pu

blic

tra

nsp

ort

pa

tro

na

ge

15

5 5

53

16

2 8

55

15

7 5

12

16

3 9

50

15

9 0

06

16

6 1

32

16

1 8

59

16

7 1

47

16

6 3

96

17

0 2

61

16

7 9

85

17

3 0

55

Bus

56 1

77

58 0

51

57 0

75

58 5

65

99 3

07

103 8

50

100 9

21

104 6

98

131 0

85

133 6

37

132 2

75

136 2

75

Min

ibus

99 3

75

104 8

04

100 4

37

105 3

84

59 6

98

62 2

82

60 9

37

62 4

49

35 3

11

36 6

24

35 7

10

36 7

81

Mo

da

l sp

lit

Bic

ycle

0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%0.4

%

Priva

te tra

nsp

ort

34.7

%34.0

%34.5

%33.8

%33.5

%33.2

%33.6

%33.0

%33.6

%33.3

%33.5

%33.1

%

Pedest

rian

31.1

%30.3

%30.8

%30.2

%29.9

%29.4

%29.8

%29.3

%30.2

%29.8

%30.1

%29.6

%

Public

tra

nsp

ort

33.9

%35.3

%34.3

%35.6

%36.3

%37.0

%36.3

%37.3

%35.8

%36.6

%36.0

%36.9

%

Bu

se

s r

eq

uir

ed

37

03

63

37

83

70

12

71

19

11

21

18

11

21

33

12

01

14

Min

ivan(1

5)

291

276

296

279

24

25

25

25

17

50

33

17

Sm

all

bus

(50)

69

67

62

61

28

20

14

14

44

24

25

38

Mediu

m b

us

(80)

07

918

21

16

11

17

56

17

5

Larg

e b

us

(100)

11

12

11

12

54

58

63

62

46

54

45

54

Da

ily e

mis

sio

ns,

kg

CO

125 1

03.2

125 1

03.2

125 1

03.2

125 1

03.2

10 6

23.2

10 6

23.2

10 6

23.2

10 6

23.2

13 1

33.1

13 1

33.1

13 1

33.1

13 1

33.1

THC

36 4

12.3

36 4

12.3

36 4

12.3

36 4

12.3

3 1

68.2

3 1

68.2

3 1

68.2

3 1

68.2

3 9

09.6

3 9

09.6

3 9

09.6

3 9

09.6

NO

x251 5

92.4

251 5

92.4

251 5

92.4

251 5

92.4

21 9

05.1

21 9

05.1

21 9

05.1

21 9

05.1

26 8

76.5

26 8

76.5

26 8

76.5

26 8

76.5

PM

4 9

84.0

4 9

84.0

4 9

84.0

4 9

84.0

396.4

396.4

396.4

396.4

510.3

510.3

510.3

510.3

CO

223 0

02.6

23 0

02.6

23 0

02.6

23 0

02.6

13 1

58.3

19 9

08.5

19 9

08.5

19 9

08.5

21 6

84.3

21 6

84.3

21 6

84.3

21 6

84.3



Page 82

6.2 Socio-economic impact on marshrutka drivers

One of the key objectives of the project to get rid of the marshrutkas in the city center, allowing them to

work only on the routes that connect city terminals to the suburbs. This public transport optimization will

inevitably lead to the reduction of number of marshrutkas, so many marshrutka drivers will lose their jobs.

This may cause resistance on the stage of project implementation as well as quality of life reduction for the

families of drivers in future.

Year Operational Buses Operational Minivans

2014 107 420

2015 107 230

2016-17 107 230

2018 124 185

2019 124 100

2020-21 124 100

2022 124 40

2023-24 124 40

According to the bus route optimization report, daily minivan fleet is 420 items (in general there are about

700 minivan busses registered in drivers’ ownership in Batumi) and then it is reduced to the total fleet of 40

minibuses (Figure 46). In the same time, number of buses faces only a slight change, that means that Batumi

Transportation Company cannot fully provide former minivan drivers with a new job.



Page 83

Figure 46 – Public transport fleet in Batumi

To avoid negative expectation and negative consequences of minivan traffic reduction, we recommend

the City to propose at least some of the minivan drivers, alternative employment options, for example, on

the suburban services and in Taxi companies.

As the minivan busses are in ownership of the drivers, there should be found a solution to mitigate

negative financial consequences and offer the owners a compensation for the loss in their property and

taking the minibuses out of operation. This is especially in the case, if there are open credits on the minivans.

6.3 Strategical plan to substitute Marshrutka busses

First step in a strategy for substituting the minibuses in the public transport network in Batumi is to gain

control over the drivers and get in contact with them.

We propose to issue an electronic license for the minibus drivers. An electronic license has the advantage

of not being easy to copy. The license will be issued by the administration only, so to get detailed data about

the drivers and to have direct contact to them. The driver license is issued for one year and has to be

renewed every year.

0

100

200

300

400

500

600

2014 2015 2016-17 2018 2019 2020-21 2022 2023-24

Operational Buses Operational Minivans



Page 84

The minibus network operators, prior to get the license for the minibus routes (best case is to reissue the

licenses every year), have to provide the administration a list of drivers, which they wish to deploy on that

routes. The administration will only provide a certain number of licenses to the proposed drivers on the list.

So, not every driver will get a license and thereby, the number of drivers in the system can be reduced every

year. With the drivers, which do not get a new license, the administration has to get in contact to find

solution for mitigation of personal and financial consequences of not renewing the license.

Only drivers with a valid license are allowed to use bus lanes and are allowed to use streets on which

minibus routes are located. An efficient enforcement to control the licenses has to be established. If non-

licensed drivers are providing public transport services, penalties have to be issued to the driver itself, as

well as to the operator.

We recommend reducing the number of issued driver licenses every year of about 150. So, in 4-5 years

the number of minibuses can be reduced to around 100 at all (there are less in daily operation).

It is crucial for the success of such an operation to have a good communication to the public – by

newspaper, television and public relations. This is most important for the relationship to the drivers that

could lose their license and have to adapt to a new work. It is also important, as there has to be found public

awareness and support – in the case of public pressure from operators side - for the strategic plan to

substitute the minibuses with a high quality public transport.

We recommend to setup a strategic task force of politicians, transport professionals and public relations

professionals to set the plan in operation and manage the work over the next years.

6.4 Assessment of impact of possible relocation of congestion/bottlenecks from CBG and CA corridors to other parallel streets

The main approach for installing a separate bus lane on the CBG and CA corridors is, to remove the

current parking at the first lane completely and to provide that free lane exclusively to the public transport.

That means, that the capacity for the individual transport doesn’t reduce on the corridors, as the number of

lanes, which is usable at the moment, and which will be usable after bus lane implementation, doesn’t

change.

But the removal of parking space on the corridors will lead to more parking space searching traffic in the

surrounding streets, where parking is allowed. Due to the total reduction in the number of parking spaces in

the region, it means, that there will be the same demand with a less supply in parking space. Parking space

searching traffic will need more time for finding a parking place, which means, that there is more traffic and

probably more congestion on surrounding streets. The congestion is caused by cars, searching for parking

space.



Page 85

In the separate feasibility study in this project about a new parking strategy for Batumi, e.g. a region

wide paid parking, Park&Ride for commuters, hotel tax for provided parking space, etc. is proposed. These

measures are aimed to lead to a less parking space demand in the city and as a consequence in a less

demand in traffic of individual transport in general.

That means, that if removing parking spaces at the CBG and CA corridor to provide space for a separate

bus lane, this has to be seen in a common context with a new parking strategy for the city as a whole. If

introducing effective measures for reorganizing and improving parking management in Batumi together with

the introduction of separate bus lanes on CBG and CA corridor, the negative effects caused by parking space

searching traffic can be reduced and even balanced.



Page 86

7 CONCLUSION AND DISCUSSION

The transport model is a very important tool to analyze public transport networks and optimization

scenarios. However, it provides the analyst with the figures, and it should be his decision, whether some

scenarios are efficient or not. The result of this judgment is mostly depended on the target of the

optimization.

Current public transport system of Batumi is too much passenger-oriented: it provides very short

headways and doorstep access to the most of the city. However, this causes traffic issues and strong pollution

consequences. As a result, we have to admit the tradeoff: every type of the route optimization that improves

economic and ecologic features will inevitably cause some reduction of the service quality in terms of

accessibility and travel times. However, demand estimation shows that some of these issues can be

compensated by the BRT-like operations.

The results of the modelling show the following:

1. Existing network is very comfortable in the terms of travel times and interchanges

2. The implementation of the BRT-line operations along the CBG corridor does create a significant

effect on the overall transportation system, although small improvements are observed

3. The implementation of the BRT-line operations along the CA corridor causes major changes in the

model split, and is expected to make people switch to public transport. Some people are ready to

switch from the private cars, and some people switch from the pedestrian mode. This may be

caused by the improve speed of the public transport that makes it more attractive

4. It is really possible to dramatically reduce the number of the minivans without a significant

increase of bus quantity

5. Scenario SG+CA is the best from the service distance (veh*km) point of view, and so, from the

point of view of ecology

6. Scenario BCH+CA features increased number of services that makes it not so ecology-effective,

but improves its accessibility

Operational optimization of the network is a very important step on the way to the overall service

improvement of the city. Reduction of the redundant services will provide more space and more economic

feasibility to move forward to better accessibility of the network. However, the city will face two important

issues while implementing the optimized network: the socio-economic impact on the marshrutka drivers and

possible reallocation of the bottlenecks to the other streets.


Documents

Public Transport Optimisation and Pilot Corridors Study · Project: Green Cities: Integrated Sustainable urban Transport for the City of Batumi and the Achara Region (ISTBAR) Page