Smart Transportation needs

Computational Transportation Science

Stephan Winterwinter@unimelb.edu.au

Computational Transportation Science

• an emerging discipline that combines computer science and engineering with the modeling, planning, and economic aspects of transport.

• studies how to improve the safety, mobility, and sustainability of the transport system by taking advantage of IT and ubiquitous computing.

Wikipedia (accessed 11/2015)

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Computational transportation science

• coll.: “The science behind intelligent transportation systems”

• but then: what are “intelligent transportation systems”?

Winter, S.; Sester, M.; Wolfson, O.; Geers, G. (2011): Towards a Computational Transportation Science. Journal of Spatial Information Science, 1 (2): 119-126.

Turing, A. M. (1950): Computing Machinery and Intelligence. Mind, 59 (236): 433-460.

Intelligent transportation systems

• “advanced applications which, without embodying intelligence as such, aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks.”

Wikipedia (accessed 11/2015)

© HERE

Intelligent transportation systems

• “advanced applications which, without embodying intelligence as such, aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks.”

Wikipedia (accessed 11/2015)

© HERE

autonomous vehicles

connected vehicles

sharing, platooning, progressive signal systems, dynamic pricing, mode integration, etc.

But then:Vehicles and people interact

Computational Transportation Science

Transportation Science• Travel demand modeling• Traffic control• Transportation safety• Traffic flow and capacity• Automated vehicle control• Routing and network models • Scheduling and optimization

Computer Science• Theoretical computer science

o …

• Applied computer scienceo Artificial intelligenceo Computer architecture and engineeringo Computer performance analysiso Computer graphics / visualizationo Computer security and cryptographyo Computational scienceo Computer networkso Concurrent, parallel and distributed systemso Databaseso Software engineering

Computational transportation science

• Core elements– goal-directed, time-constrained

movements of independent agents• people, goods

– instrumented by vehicles of various modes and physical constraints

– bound to transport infrastructure of other constraints • guideways, terminals, control policies

• Derived elements– flocks, crowds, queues, platoons– events and processes– flow / capacity

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agents & vehicles are sensor-rich and connected

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• embedded sensor platforms – vehicles, travellers, infrastructure

• black box, smartphone, CCTV, SCATS, e-toll, smartcards, …

– grounding transport simulations

• connectivity– distributed, mobile systems of

unprecedented scale• streams (“big” data), integration

(semantics) analytics (data mining)

– local or central coordination, collaboration

• interaction– informed decisions– cognitive engineering

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Computational transportation science

Computational transportation science

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• After all: a science?– science: yes – a science: no.

• At the intersection of disciplines – transport/mobility: complex

• travel: derived, not for its own sake

– domain of computational challenges• data (availability, accuracy, timeliness,

suitedness)• correlation with structure / design /

behaviour of world• use (economic, environmental, and

social impact)

Examples from Melbourne

Ad-hoc demand-responsive transport• shared• point-to-point• ad-hoc

Let’s share!

1. Some results

2. Exploring susceptibility of shared mobility

3. Ridesharing with social contacts

4. Modelling mobility by DRT vs PT

5. Exploring on-demand co-modality

Some results

• Simulation platforms need extensions for ad-hoc DRT

• Scenario testing, e.g., feeder services

• User interaction: launch pads for service areas

Nicole Ronald

Richard Kelly

Michael Rigby

Objectives – find an estimation of demand patterns

for DRT based on demography– avoid service-specific survey

Methods– review of usage patterns of some of

the existing DRT services in different regions of the world

– analysis of socio-economic demography

– analysis of current trip characteristics from household travel surveys (VISTA)

– predict usage patterns

Exploring susceptibility of shared mobility

Susceptibility of shared mobility in Greater Melbourne

Shubham Jain

Ridesharing with social contacts

Objective– Can uptake be increased

by matching with friends

Why– High overlapping rate of

individual trajectories• 40% trip length saved in NYC1

• 70% ~ 88% of surveyed MIT students and staff can share rides2

– Socio-psychological barriers3,4

• 7% detour tolerance for strangers vs. 30% for friends

• 10% willingness to ridesharing with strangers vs. almost 100% for friends

Key findings– Ridesharing only with

social contacts does not necessarily increase detour cost

– An algorithm giving priority to friends significantly increases the matching rate between friends

• 14% ~ 15% of the total population

(increased from 3%)• average uptake rate in simulation

is 38% (increase from 24%)

1. Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S.H., Ratti, C., 2014. Proc. Natl. Acad. Sci. 111(37), 13290–13294.

2. Amey, A.M., 2010. Thesis: Massachusetts Institute of Technology. 3. Chaube, V., Kavanaugh, A.L., Pérez-Quiñones, M.A., 2010 In: Proceedings of the 43rd Hawaii

International Conference on System Sciences (HICSS). Honolulu, HI.

4. Wessels, R., 2009. Combining ridesharing & social networks.

Yaoli Wang

Modelling mobility by DRT vs PT

Objective: Could DRT be the solution for unprofitable conventional public transport (PT) in low-demand areas?

Replacing PT with DRT

Reduction in perceived travel time

Increase in mobility

Demand level Network shape

Grid network provides a better environment for DRT operation.

DRT costTested:

Method: Using an ad-hoc dynamic routing algorithm embedded in the Multi Agent Transport Simulation (MATSim) software package

Zahra Navidikashani

Exploring on-demand co-modality

Demand-responsive transportationand delivery of on-demand food

Co-modality 1: same scheme, different vehicles

Co-modality 2: shared vehicles

Bus and van icons made by Freepik from www.flaticon.com (CC BY 3.0)

• Findings:– Combining schemes leads

to improved performance for both passengers and deliveries

– more resilient to uneven or unexpected demands

• Currently developing optimisation methods for improved performance

Nicole Ronald

SMART TRANSPORTATION NEEDS CTS

Discussion – Conclusions

Unintended consequences of CTS

• information overflow → interference– stress– errors

• denying choice → surrender – serendipity– engagement– learning– responsibility

• sedentary locomotion → health– obesity– diabetes

Norbert Wiener (1948): Cybernetics

Conclusions

• ‘smart’, ‘intelligent’ are vague terms here

• transport is a complex systems (within the complex system city)

• computational aspects can never be one-dimensional

• evaluations must consider the full system impact

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© Copyright The University of Melbourne 2015

Sensors:• vehicles• travelers• infrastructure

Connected devices:• Transportation systems, due to their distributed/mobile nature, can become the ultimate

test-bed for this ubiquitous (i.e., embedded, highly-distributed, and sensor-laden) computing environment of unprecedented scale.

• if they are to be made available in real-time to wireless devices such as cell phones and PDAs

How people will engage:• numerous novel applications• order of magnitude improvement in the performance• cross-modal real-time information available to travelers

How systems will evolve:• A related development is the emergence of increasingly more sophisticated geospatial and

spatio-temporal information management capabilities. These factors have the potential to revolutionize traveler services, and the provision and analysis of related information. In this revolution, travelers and sensors in the infrastructure and in vehicles will all produce a vast amount of data that could be interpreted and acted upon to produce a sea change in transportation.