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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
This page was generated automatically upon download from the ETH Zurich Research Collection. For moreinformation please consult the Terms of use.
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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