Can Traffic Simulation Models Contribute on Can Traffic Simulation Models Contribute on Mobility Management Evaluation?Mobility Management Evaluation?

A Conceptual AnalysisA Conceptual Analysis

1313thth European Conference European Conference on Mobility Managementon Mobility Management

Panos PapaioannouPanos PapaioannouProfessorProfessor

Socrates BasbasSocrates BasbasAss. ProfessorAss. Professor

Ioannis PolitisIoannis PolitisPh.D CandidatePh.D Candidate

Kursaal Congress Center Kursaal Congress Center 13-15 May 200913-15 May 2009

DonostiaDonostiaSan Sebastian SpainSan Sebastian Spain

““Cost – Benefit and Evaluation Cost – Benefit and Evaluation of Mobility Management” of Mobility Management”

PRESENTATION OUTLINEPRESENTATION OUTLINE

Objectives and Applications of Transport Planning ToolsObjectives and Applications of Transport Planning Tools

Transportation Models and Benchmarking EvaluationTransportation Models and Benchmarking Evaluation

Introducing TPT into Mobility Management EvaluationIntroducing TPT into Mobility Management Evaluation

Conclusions and DiscussionConclusions and Discussion

Annex: Case Study – Classical ApproachAnnex: Case Study – Classical Approach 2

KEY QUESTIONKEY QUESTION

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Why it is Important to Use Transportation Why it is Important to Use Transportation Planning Software Tools ??Planning Software Tools ??

REASONSREASONS

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Transportation System: Complex multi-dimensional Transportation System: Complex multi-dimensional factors factors not easily determined, measured or estimated directlynot easily determined, measured or estimated directly

Impact Estimations (ex ante!) derived from Impact Estimations (ex ante!) derived from the construction of a new road infrastructurethe construction of a new road infrastructure or operation of a new transport mode, or operation of a new transport mode, or….implementation of a MM plan!or….implementation of a MM plan!

Impact Estimations:Impact Estimations: - The transportation system itself- The transportation system itself - The environmental effects and the potential revenues- The environmental effects and the potential revenues - The redistribution of the land use - The redistribution of the land use

Easier to Introduce Transport Planning Theories Easier to Introduce Transport Planning Theories

OBJECTIVE OF TRANSPORT OBJECTIVE OF TRANSPORT PLANNING & SIMULATION TOOL PLANNING & SIMULATION TOOL

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To represent with accuracy the underlying operation of To represent with accuracy the underlying operation of the transport system the transport system (in terms of traffic conditions and travel patterns)(in terms of traffic conditions and travel patterns)

To create reliable To create reliable mathematicalmathematical models for testing models for testing different / various schemes at the base year (underlying) different / various schemes at the base year (underlying) or at future years (planning horizons )or at future years (planning horizons )

These schemes pertain to be at the supply (new These schemes pertain to be at the supply (new infrastructure, new mode, pedestrialization of roads etc) infrastructure, new mode, pedestrialization of roads etc) or the or the demanddemand (car pooling, flexible working hours etc) (car pooling, flexible working hours etc) sideside

OBJECTIVE OF TRANSPORT OBJECTIVE OF TRANSPORT PLANNING & SIMULATION TOOL PLANNING & SIMULATION TOOL

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A simulation traffic model can estimate the impacts A simulation traffic model can estimate the impacts derived from a Mobility Management Measure, primarily on derived from a Mobility Management Measure, primarily on the demand changes.the demand changes.

In fact, a MMM (such as car pooling, van pooling, flexible In fact, a MMM (such as car pooling, van pooling, flexible or staggered working hours etc.) is translated into changes or staggered working hours etc.) is translated into changes at the Origin – Destination Matrices of each respective at the Origin – Destination Matrices of each respective demand segment and changes in travel chain in general.demand segment and changes in travel chain in general.

An evident disadvantage is that existing simulation tools An evident disadvantage is that existing simulation tools just “simulate” the anticipated improvements of a network. just “simulate” the anticipated improvements of a network. The reality proves that when the traffic conditions are The reality proves that when the traffic conditions are improved new (generated) traffic is added (the vicious circle improved new (generated) traffic is added (the vicious circle of the transportation systems) of the transportation systems)

TRAVEL PATTERNS EXAMPLETRAVEL PATTERNS EXAMPLE

Production tripsAttraction Trips

APPLICATIONS OF TRANSPORTAPPLICATIONS OF TRANSPORT PLANNING SOFTWARE TOOLSPLANNING SOFTWARE TOOLS

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Traffic and Transportation StudiesTraffic and Transportation Studies

Feasibility (Socio – Economic) StudiesFeasibility (Socio – Economic) Studies

Cost – Benefit StudiesCost – Benefit Studies

Urban Planning StudiesUrban Planning Studies

Environmental StudiesEnvironmental Studies

Mode Choice and Travel Behavior Studies!!Mode Choice and Travel Behavior Studies!!

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Transportation Models and Benchmarking Transportation Models and Benchmarking EvaluationEvaluation

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Transportation ModelsTransportation Models and Benchmarking Evaluationand Benchmarking Evaluation

According to the HCM (2000) a transportation model is:According to the HCM (2000) a transportation model is:

“A computer program that uses mathematical models to conduct experiments with traffic events on a transportation facility or system over extended periods of time”

Transportation Models Classification:Transportation Models Classification: * According to their application area* According to their application area * According to the level of presentation of the traffic flows* According to the level of presentation of the traffic flows * According to the time period of the analysis* According to the time period of the analysis

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Transportation ModelsTransportation Models ClassificationClassification

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Macroscopic ModelsMacroscopic Models

Take into account transportation network attributes Take into account transportation network attributes such as capacity, speed limit, flow and densitysuch as capacity, speed limit, flow and density

Simulate large scale facilities (highways, regions etc)Simulate large scale facilities (highways, regions etc)

No need to track individual vehicles (aggregate theory)No need to track individual vehicles (aggregate theory)

No detailed information about road design and signal No detailed information about road design and signal plans is needed plans is needed

CUBE, TRIPS and VISUMCUBE, TRIPS and VISUM

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Mesoscopic ModelsMesoscopic Models

Take into account the actual road geometry and signal Take into account the actual road geometry and signal timing plans timing plans

Simulate intersections in a corridor or citySimulate intersections in a corridor or city

Simulate individual vehicles Simulate individual vehicles

Describe activities based on aggregate or macroscopic Describe activities based on aggregate or macroscopic levellevel

SATURN, CORSIM, TRANSCAD, EMME/3, AIMSUNSATURN, CORSIM, TRANSCAD, EMME/3, AIMSUN

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Microscopic ModelsMicroscopic Models

Simulate characteristics and interactions of individual Simulate characteristics and interactions of individual vehiclesvehicles

Study area: Intersection or a road segment Study area: Intersection or a road segment (e.g. a corridor )(e.g. a corridor )

Enclose theories and rules for vehicle acceleration, Enclose theories and rules for vehicle acceleration, passing manoeuvres and lane-changingpassing manoeuvres and lane-changing PARAMICS, VISSIM, AIMSUNPARAMICS, VISSIM, AIMSUN

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Software Classification

Criteria

User Friendly/ Interface

GIS Compatibility

Microscopic/ Macroscopic

Compatibility

Training and Support

Licence and Maintenance

Cost

EMME/3 Mesoscopic Medium Medium No Yes Low

VISUM Macroscopic High Medium Yes Yes High

TRANSCAD Mesoscopic High High Yes Yes High

SATURN Mesoscopic Low Low No Yes Low

PARAMICS Microscopic Medium Medium No Yes Medium

CUBE Mesoscopic High High Yes Yes High

Comparative Analysis of the most commonly used transportation software Comparative Analysis of the most commonly used transportation software

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The analysis is based only on Quantitative The analysis is based only on Quantitative Data/Results !!Data/Results !!

Existed Transport and Existed Transport and Simulation ModelsSimulation Models

KEY QUESTIONSKEY QUESTIONS

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What are the user needs of the study area?What are the user needs of the study area?

How much dependent the users are to their cars? How much dependent the users are to their cars?

What will be the overall impacts of a “real” Mobility What will be the overall impacts of a “real” Mobility Management Measure (MMM) to the Study areaManagement Measure (MMM) to the Study area

Which MMM is the most promising Which MMM is the most promising to this specific areato this specific area

Which are the potential barriers to implement them?Which are the potential barriers to implement them?

The Qualitative or Quantitative data should be taken into The Qualitative or Quantitative data should be taken into consideration most? The same? consideration most? The same?

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A Conceptual Framework of Introducing A Conceptual Framework of Introducing Transportation Models into Transportation Models into

Mobility Management MeasuresMobility Management Measures Evaluation and ClassificationEvaluation and Classification

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Planning PhasePlanning Phase

The MMM that will be examined should be linked with the The MMM that will be examined should be linked with the trip purposes of the study area (different demand matrices)trip purposes of the study area (different demand matrices)

Why not to follow the categorization of MMM derived from Why not to follow the categorization of MMM derived from MAX project?MAX project?

A well structured questionnaire should A well structured questionnaire should • Estimate the behavioral stage of the targetedEstimate the behavioral stage of the targeted population (why not the diagnostic questions?)population (why not the diagnostic questions?)• Identify the user needs (that wanted or expected) and Identify the user needs (that wanted or expected) and the level of acceptance of the examined MMM through the level of acceptance of the examined MMM through well known–used techniques well known–used techniques

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Planning PhasePlanning Phase

The criteria of evaluation should be clearly determined The criteria of evaluation should be clearly determined • Transportation indices (VKT, Speed, Delays etc.)Transportation indices (VKT, Speed, Delays etc.)• Environmental indices (CO, HC, NOx etc.)Environmental indices (CO, HC, NOx etc.)• Level of maturity (Low, Medium, High)Level of maturity (Low, Medium, High)• Change on Behavioral Stage (0 stage, 1 stage, …3 stages)Change on Behavioral Stage (0 stage, 1 stage, …3 stages)

The selection of the appropriate Transportation Model The selection of the appropriate Transportation Model should be based on:should be based on:

• The criteria of evaluationThe criteria of evaluation• The area under consideration (macro,meso,micro)The area under consideration (macro,meso,micro)

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Analysis PhaseAnalysis Phase

The criteria and sub-criteria (quantitative and qualitative) The criteria and sub-criteria (quantitative and qualitative) should get an evaluation gradeshould get an evaluation grade

All the criteria should also obtain weights (experts survey)All the criteria should also obtain weights (experts survey)

Well know multi criteria decision analysis tools (MCDA) Well know multi criteria decision analysis tools (MCDA) could easily apply the weights to the grades could easily apply the weights to the grades ( software : HIPRE 3+, web-HIPRE, EXPERT Choice model)( software : HIPRE 3+, web-HIPRE, EXPERT Choice model)

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Classification PhaseClassification Phase

The evaluation grade for the qualitative criteria are based The evaluation grade for the qualitative criteria are based on subjective judgmenton subjective judgment

Various techniques can quantify the qualitative criteria Various techniques can quantify the qualitative criteria ( e.g. Evidentional Reasoning Approach)( e.g. Evidentional Reasoning Approach)

If the initial evaluation criteria are properly selected, then If the initial evaluation criteria are properly selected, then the final ranking of the MMM will include qualitative the final ranking of the MMM will include qualitative parameters such as the trip purpose, the behavioral stage parameters such as the trip purpose, the behavioral stage etc. which are etc. which are not not included in conventional evaluations included in conventional evaluations

Alternatively, the proposed methods could be classified Alternatively, the proposed methods could be classified through a cost benefit analysis (all the benefits are through a cost benefit analysis (all the benefits are translated into momentary units – classical approach) translated into momentary units – classical approach)

CONCLUSIONSCONCLUSIONS

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Mobility Management seems to be adopted more and more Mobility Management seems to be adopted more and more by local authorities by local authorities

It is important to have accurate estimations about the most It is important to have accurate estimations about the most promising MMM “before moving out of the office” promising MMM “before moving out of the office”

The classical transportation planning theory cannot include The classical transportation planning theory cannot include qualitative parameters especially from the behavioural – qualitative parameters especially from the behavioural – psychology sidepsychology side

CONCLUSIONSCONCLUSIONS

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These parameters are equal important since can affect the These parameters are equal important since can affect the effectiveness of a measureeffectiveness of a measure

A new framework should be established combining the A new framework should be established combining the knowledge obtained from transportation planning theories knowledge obtained from transportation planning theories and psychology behavioural science and psychology behavioural science

Thank you for your attention!!Thank you for your attention!!

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Ioannis K. PolitisIoannis K. Politis--------------------------------------------------------------------------Ph.D. CandidatePh.D. CandidateLaboratory of Transportation and Construction ManagementLaboratory of Transportation and Construction ManagementDepartment of Civil Engineering Department of Civil Engineering Aristotle University of Thessaloniki, GreeceAristotle University of Thessaloniki, Greecepol@civil.auth.gr

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Case StudyCase StudyThe use of a mesoscopic traffic analysis The use of a mesoscopic traffic analysis

model in order to run alternative road model in order to run alternative road charging schemes at the Outer Ring Road charging schemes at the Outer Ring Road

of Thessalonikiof Thessaloniki

ANNEXANNEX

THE STUDY AREATHE STUDY AREA

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THE STUDY HIGHWAYTHE STUDY HIGHWAY

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35 km length freeway35 km length freeway

Estimated budget of 700 million eurosEstimated budget of 700 million euros

Will offer connections to the Inner Ring RoadWill offer connections to the Inner Ring Road

13 Bridges with a total length of 2 km13 Bridges with a total length of 2 km

20 Tunnels with a total length of 20 km20 Tunnels with a total length of 20 km

9 Interchanges9 Interchanges

Completion date: 2016Completion date: 2016

THE STUDY HIGHWAYTHE STUDY HIGHWAY

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THE EVALUATION MODELTHE EVALUATION MODEL

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Mesoscopic Model SATURN (Mesoscopic Model SATURN (SSimulation and imulation and AAssignment of ssignment of TTraffic to raffic to UUrban rban RRoad Networks)oad Networks) Extended network was coded (base year 2006):Extended network was coded (base year 2006):

*783 simulation nodes including:*783 simulation nodes including:27 external nodes27 external nodes310 priority junctions310 priority junctions292 traffic signals 292 traffic signals 154 dummy nodes154 dummy nodes

*2508 simulation links*2508 simulation links*6350 simulation turns*6350 simulation turns*210 traffic zones*210 traffic zones

Morning Peak Period 08:00-09:00Morning Peak Period 08:00-09:00 Ap. 200 traffic counts were used for calibration purposesAp. 200 traffic counts were used for calibration purposes (180 for new O-D matrix estimation and 20 for validation)(180 for new O-D matrix estimation and 20 for validation)

THE EVALUATION MODELTHE EVALUATION MODEL

32Modeled vs Observed FlowsModeled vs Observed Flows

SCENARIOS TESTEDSCENARIOS TESTED

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Base Year 2006(Do_nothing_06)

Planning Year 2016(Do_minimum_16)

Flat_Toll Charging Scenarios Distance_Based Charging Scenarios

1.0 Euros/Entrance(FT_1.0)

0.087 Euros/Km (DB_0.087))

Low Price Charging

1.5 Euros/Entrance(FT_1.5)

0.132 Euros/Km (DB_0.132)

Central Price Charging

2.0 Euros/Entrance(FT_2.0)

0.175 Euros/Km (DB_0.175)

High Price Charging

2006 BASE YEAR NETWORK2006 BASE YEAR NETWORK

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2016 PLANNING YEAR NETWORK2016 PLANNING YEAR NETWORK

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DETAILED REPRESENTATIONDETAILED REPRESENTATION OF THE INTERSECTIONSOF THE INTERSECTIONS

36IC # 1-2 : Interchange to the Inner Ring RoadIC # 1-2 : Interchange to the Inner Ring Road

37IC # 6 : PanoramaIC # 6 : Panorama

DETAILED REPRESENTATIONDETAILED REPRESENTATION OF THE INTERSECTIONSOF THE INTERSECTIONS

NUMERICAL RESULTSNUMERICAL RESULTS

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Length Distribution per Toll Scheme

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 -

3

3 -

6

6 -

9

9 -

12

12 -

15

15 -

18

18 -

21

21 -

24

24 -

27

27 -

30

30 -

33

33 -

36

Length Distribution

Ve

hic

les

/h

no tolls

mean Flat

mean db

NUMERICAL RESULTSNUMERICAL RESULTS

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Price Elasticity Demand Curves (Flat Tolls)

y = -1,0026Ln(x) + 9,3012

R2 = 0,9807

y = -3,7222Ln(x) + 36,278

R2 = 0,9986

y = -2,1762Ln(x) + 22,264

R2 = 0,9805

0

0,5

1

1,5

2

2,5

1000 6000 11000 16000 21000 26000 31000

Vehicles (Demand)

To

ll L

ev

el

(Pri

ce

)

Flat_Tolls_East_WestFlat_Tolls_West_EastFlat_Tolls_Both_DirectionsLog. (Flat_Tolls_West_East)Log. (Flat_Tolls_East_West)Log. (Flat_Tolls_Both_Directions)

NUMERICAL RESULTSNUMERICAL RESULTS

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Price Elasticity Demand Curves (Distance Based Tolls)

y = -1,8807Ln(x) + 17,487

R2 = 0,9335

y = -10,053Ln(x) + 98,053

R2 = 0,9953

y = -4,3063Ln(x) + 44,105

R2 = 0,9612

0

0,5

1

1,5

2

2,5

1000 6000 11000 16000 21000 26000 31000Vehicles (Demand)

To

ll L

evel

(P

rice

)

DB_Tolls_East_WestDB_Tolls_West_EastDB_Tolls_Both_DirectionsLog. (DB_Tolls_West_East)Log. (DB_Tolls_East_West)Log. (DB_Tolls_Both_Directions)

NUMERICAL RESULTSNUMERICAL RESULTS

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Demand Elasticities(Both Directions)

-0,27

-0,32

-0,44

-0,15-0,12

-0,24

-0,60

-0,40

-0,20

0,00

Low Central High

Toll Level

Ela

sti

cit

y

Flat_Tolls

Distance_Based_Tolls

NUMERICAL RESULTSNUMERICAL RESULTS

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Marginal Revenue Curve

y = -0,0002x2 + 4,4287x + 40

y = -3E-06x2 + 0,5497x + 40

5000

7000

9000

11000

13000

15000

17000

19000

21000

23000

25000

10000 12000 14000 16000 18000 20000 22000

Total Flows (Quantity)

To

tal

Ho

url

y R

ev

en

ue

s (

in e

uro

s)

Flat_Tolls

Distance_Based_Tolls

Poly. (Flat_Tolls)

Poly. (Distance_Based_Tolls)

KEY FINDINGS OF THE STUDY KEY FINDINGS OF THE STUDY

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The distance based tolls frustrate journeys > 20 kmThe distance based tolls frustrate journeys > 20 km

The average journey length varies between 12-15 km for The average journey length varies between 12-15 km for all the methods and toll rate levels examinedall the methods and toll rate levels examined

The demand is inelastic (- 1 < e < 0) for all the examined The demand is inelastic (- 1 < e < 0) for all the examined scenarios, especially for the East – West Directionscenarios, especially for the East – West Direction

Flat tolls schemes lead into more elastic interrelations Flat tolls schemes lead into more elastic interrelations with respect to demand (actual flow) with respect to demand (actual flow)

KEY FINDINGS OF THE STUDYKEY FINDINGS OF THE STUDY

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Flat Tolls : The optimum toll value should be Flat Tolls : The optimum toll value should be greatergreater than than 2 euros 2 euros Higher toll level will lead to lower actual flows Higher toll level will lead to lower actual flows and accordingly to bigger obtained revenues and accordingly to bigger obtained revenues

Distance Based Tolls: The optimum toll value should be Distance Based Tolls: The optimum toll value should be lowerlower than 0.087 euros/km than 0.087 euros/km Lower toll level will lead to higher Lower toll level will lead to higher actual flows and accordingly to actual flows and accordingly to bigger obtained revenuesbigger obtained revenues

OBTAINED REVENUESOBTAINED REVENUES