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What transport policy and design decisions can foster moreliveable and sustainable cities in the future?

Author(s): Fourie, Pieter Jacobus

Publication Date: 2018-11-10

Permanent Link: https://doi.org/10.3929/ethz-b-000299686

Rights / License: In Copyright - Non-Commercial Use Permitted

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What transport policy and design decisions can foster more liveable and sustainable cities in the future?

Singapore’s great strength in urban policy making: Unusual willingness to face up the need for difficult trade-offs.

GoalsMoving people and goods space efficientlyReduce the need for travel, especially car travel through public transport oriented planning

Strategic concept plan, 1971, called for:Urban structure with strong center

Clear corridors of high density new towns served by mass transit

Only 15% of land pass allocated for residential housing -> mass to live in high density dwellings.

BackgroundSingapore

BackgroundVehicle management

BackgroundRoad pricing

Now

Type IExisting cul-de-sac and loop model

2020 onwards

BackgroundMRT / LRT

Now (199.6 km, 2 lines until 2003) 2030 (360km, 3 additional lines)

Cycling in SingaporeCurrent mode share of cycling is low (1%) for trip stages and trips;However, certain residential new towns have mode shares up to 4% (HITS 2012, by authors)Ambitious plans to make cycling a viable mode of transport

Up to 700 kilometres cycling lanes by 2030

Up to 360 km rail by 2030

Challenges for cycling in SingaporeTypical residential developments in Singapore are influenced by 20th century town planning:

Segregated land-uses,

Hierarchical road network

Large distances between crossings

Mainly footways with only pedestrians in mind

Challenges for cycling in SingaporeMain arterials are designed for high speeds and function as barriersLane widths are generous; Long wait times for pedestrians at traffic lightsTropical weather (rain / humidity)

BackgroundCycling

Active mobility

Left: Sembawang Shipyard workers in 1966, image taken from hereMiddle: Bike to the Future

Background

Cycling environment affects your decision to cycle.

Cycling as mode of transport has a low mode share, but high potential.

Can we push the boundaries of classic survey methods?

Aims

Evaluate Virtual Reality (VR) as a tool for surveys and engagement.

Study cyclists’ perceived safety and behaviour

Compare the VR experiment results with ‘classic’ video-based surveys

Active mobilityMethodology

Researchers from different disciplines are collaborating to generate the scenes

Traffic EngineerTraffic simulation

Urban designer and 3D ModellerStreet design

Game developer360 environment

Active mobilityMethodology

EnvironmentAudioImmersive VRCycling simulator

Survey duration is approx. 45 minutes.

Measurements:QuestionnaireCycling simulatorPhysiological sensor

Cycling simulator developed at FCLPedalling, braking, steering

Questionnaire

Physiological sensor

VR Audio

Active mobilitySome results

Active mobilitySome results

Active mobilityCycling simulator

Average speed vs. distance for each infrastructure type

Intersection

Electro-dermal activity is an indicator of the stress level

Raw EDA values for one participant

Active mobilityPhysiological sensor

Start of theexperiment

End of theexperiment

Storyreading

Questionnaire Immersion in VR andanswering to the questions after each scene

Wigginton Conway, M. (2015) Better Measures of Bike Accessibility, Conveyal, Available here

Engaging community and give back

Improving design criteria for bicycle facilities

Neighbourhood-level maps for cyclists

Identifying the best places to build bicycle infrastructure in Singapore and propose bicycle network design scenarios

Estimate the expected demand and the consequent modal shift

Web-based mapping and planningComfort

Bike the the Future III Bike Pulse

Web-based survey inspired by MIT Place Pulse project

Parametrically generating hundreds of relevant street design configurations

Testing it from different view angles (pedestrian, cyclist, bus driver, car)

Local and global sample

Discrete choice modelling

Which road feels safer?

This one That one

Can VR trigger behavioral reaction to comfort oriented attributes?

• Noise• Temperature• Wind• Resistance (eBike) -> Reach

Hardware & software development to gauge physical efforts and mimic cycling in the tropics

Experiment design challenges

New design evidence

Engaging Big DataObjectives & links

Background

Availability of Big Data for urban mobility

Big Data enables new AI models and applications

Big Data raises privacy concerns

Aim

Develop new Big Data-driven and privacy-by-design models for urban mobility and transport planning

Research question

How to use aggregates of Big Data to generate a population for agent based simulations?

Links

Collaboration work with DataSpark – Singtel Group

Joseph Molloy, counterpart in IVT (Zürich)

Within FCL, we share some methods with Aike Steentoftand Prof. Markus Schläpfer(Big Data team)

Engaging Big DataMethodology

Generative model of individual mobility patterns

Estimated with aggregated mobile phone telco data

Engaging Big DataMethodology

Generative model of individual mobility patterns

Estimated with aggregated mobile phone telco data

Why?

1. Able to compensate for market share (50%)

2. Privacy friendly: Artificial population similar to real one

3. Base to extrapolate to different population scenarios

00:00

Bedok

08:45

Raffles Place

09:15

9

18:15

Return

19:00

11

Next day

Bedok

Engaging Big DataMethodology

Generative model for each type of traveller

Archetype #1

Archetype #2

Archetype #n

Engaging Big DataDBSCAN with noise reclassified

16 clusters found!

16 traveller archetypes in Singapore

1

Engaging Big DataResults*

Temporal and Spatial distributions

* Work in progress Results do not show clusters of travellers

Start time of activities

7 am density of telco users 7 am density of agents

2.92% error

4.18% error

HOW WILL ADVENT OF AUTONOMOUS VEHICLES INFLUENCE URBAN FORM

Walking-Horsecar1800-1890

Electric Streetcar1890-1920

Recreational Automobile1920-45

Freeway1945-Presentc Autonomous Vehicles

AVs will set the stage for the next major technology driven urban transformation.

Pictures:http://s.picture-russia.ru/wpic/l/9/b/9bc4c2405b56fcc00df114add273aaa5.jpghttps://www.6sqft.com/worlds-first-streetcar-began-operation-in-lower-manhattan-on-november-14-1832/Diagram adapted from Peter O. Muller’s Transportation and Urban Form

SINGAPORE

Top: The revised Concept Plan in 1971 Source: Chen ZhongRight: From Top to Bottom1. Joan Goodband and Pat Hughes of 81 Mobile Field Hospital, Seletar, Singapore, taking a trishaw ride2. A section of the Ayer Rajah Expressway3. Eunos MRT Station on the Mass Rapid Transit (MRT) system Source: Wikimedia Commons4. Ridesharing apps in Singapore Source: techinasia.com

How will Automation Impact Transport Flows

What are the parameters?

Shared or Private?

Size of Vehicles?

Size of Fleet?

Parking Strategy?

Parking

What Aspects of Urban Form Impact Transport Flows

a. First order impact - Street sections, street capacities, public transit interface, parking,

b. Second order impact - Reimagining the neighbourhood unit – network design, land use distribution

c. Third order impact - Shape of the city -residential/job location choice, density, urban footprint

What are the parameters?

Performance Indicators

Land surface allocated to roadsLand surface allocated to parking

Land surface allocated to green, or public spacePopulation density accommodated

Level of crowding at key destinationsCongestion and delays for commuters

Car ownership ratioVehicle occupancy rates

EmissionsVehicle Kilometres Travelled (VKT)

VKT for Empty tripsWaiting times for transit

Travel TimeMode share by trips

Network Accessibility Walkability scoreBikeability score

Social interaction and Inclusiveness

Space

Comfort and Efficiency

Environmental Sustainability

Accessibility

Active MobilitySocial Cohesion

Typical HDB structure retrofitted to accommodate door-to-door AV service, maximising the use of existing Culs-de-sac and fixed route AV service through loops and X-junctions

Type IExisting cul-de-sac and loop model

Typical HDB cell modified to remove several links and create superblocks, with no door to door service, but high speed fixed route transit and low speed micro transit in shared spaces.

Type IISuperblock Model

Re-imagining HDB cell, as a high density grid model, to facilitate shared rides with dynamic routing.

Type IIPermeable Grid

Urban designNetwork topology

Land use

Parking

What Aspects of Urban Form Impact Transport Flows

a. First order impact - Street sections, street capacities, public transit interface, parking,

b. Second order impact - Reimagining the neighbourhood unit – network design, land use distribution

c. Third order impact - Shape of the city -residential/job location choice, density, urban footprint

What are the parameters?

Progress on AV Implementation in theIterative Design Simulation Framework

Parking

User-vehicle Interface

Energy Infrastructure

Mode choice restrictions

Bays size

Door-to-door

Station-based

Charging stations

Dynamic charging

Available modes

By area or time

Vehicle TypeVehicle Size

Fleet Size

Multiple operators

Urban designNetwork topology

Land use

Always roaming

On the street

In depots

Mixed

Typical HDB structure retrofitted to accommodate door-to-door AV service, maximising the use of existing Culs-de-sac and fixed route AV service through loops and X-junctions

Type IExisting cul-de-sac and loop model

Typical HDB cell modified to remove several links and create superblocks, with no door to door service, but high speed fixed route transit and low speed micro transit in shared spaces.

Type IISuperblock Model

Re-imagining HDB cell, as a high density grid model, to facilitate shared rides with dynamic routing.

Type IIPermeable Grid

Urban designNetwork topology

Land use

Planning for Autonomous VehiclesConstructing Simulations

Urban designNetwork topology

Land use

Engaging MobilityFutureInteractive urban design & transport planning

Team

Dr. Alex ErathProject Advisor

Pieter FourieProject LeaderSimulaiton

Prof. Dr. K. AxhausenCo-PITransport Planning

Prof. Dr. C. HölscherCo-PICognitive Psychology

Michael v. EggermondSenior ResearcherProject CordinatorActive mobility

Tanvi MaheshwariResearcherUrban Design

Dr. Sergio OrdonezSenior ResearcherComputer Science

Shuchen XuResearcherArchitect

Cuauhtémoc AndaPhD ResearcherBig Data Analytics

Mohsen NazemiPhD ResearcherTraffic Simulation

Michael JoosSenior Software EngineerGaming Developer

Prof. Dr. D. SchaffnerPsychologistCognitive experiment