planning and operations of multi-modal public transport systems€¦ · planning and operations of...
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Challenge the future
DelftUniversity ofTechnology
Oded Cats PTV WGM SAB, 10-01-2017
Planning and operations of multi-modal public transport systems
Oded Cats/ [email protected]; [email protected]
2Oded Cats PTV WGM SAB, 10-01-2017
Bio background
• Dual PhD in Transportation Sciences, 2012
• Royal Institute of Technology KTH, Stockholm and Technion - Israel
Institute of Technology
• Transport Engineering, Operations Research, Business Management
• 35 Journal publications and 45 conference proceedings
• Transit assignment and simulation models
• Real-time information and control
• Travel behaviour, route choice
• Network robustness, disruption management
• Leading national and European projects on public transport
planning and operations
3Oded Cats PTV WGM SAB, 10-01-2017
Integrated modelling and simulation
of transport system dynamics
Traffic Dynamics & Transit Operations
Dynamic Loading
Automated Data Collection
Real-Time Prediction
Control Centre
Traveler Decisions
Network
Traveller Population
Fleet
Short-term
Mid/Long-term
Assignment
SystemPerformance
Traveler Perception
Service Planning
Traveller Strategy
Choice modelling
Traffic flow
Traffic prediction
Control theory
Market economics
Optimization
Travel behavior
Network modelling
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Strategic
Tactical
Operational
Evaluating network alternativesNetwork robustness analysis
Reliability of timetable designTransfer synchronization
Real-time control strategiesDisruption management
Range of model applications
• Available with networks on: https://odedcats.weblog.tudelft.nl/busmezzo/
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Congestion
Focus a
reas
Reliability and
Control
Real-time
Information
Network
Resilience
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Congestion
or when VOC=0.8 is not good enough
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How can the value of reduced
congestion be quantified?
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Evaluation of congestion effects
On-board crowding
Denied boarding
Reduced service relaibility
Increased preceived in-vehicle time
Increased waiting time
Increased total travel time
Cats, West and Eliasson (2016). A dynamic stochastic model for evaluating congestion and crowding effects in transit systems.Transportation Research Part B 89, 43-57.
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Appraisal of Increased Capacity
• Crowding factor in static/dynamic model: +3%/+120%
• Value of increased capacity: underestimated in static models
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Modelling congestion in public
transport networks
1
Cats and Hartl (2016). Modelling public transport on-board congestion: Comparing schedule-based and agent-based assignment approaches and their implications. Journal of Advanced Transportation 50 (6), 1209-1224.
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Reliability
or when the average headway is hardly experienced
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A series of field experiments
RETT2
• Experiment – L1
• Focus on regularity, decentalization
RETT3
• Experiment – L1; L3
• Clean-operations, control center actions
• Led to its incorporation in the tendering process
RETT4
• Experiment – L4
• Additional measures – boarding, priority
• Support full-scale implementation
Cats et al. (2012). Holding control strategies: A simulation-based evaluation and guidelines for implementation. Transportation Research Record, 2274, 100-108.
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Results
Fadaei and Cats (2016). Evaluating the impacts and benefits of public transport design and operational measures. Transport Policy, 48, 105-116.
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Information provision
or when passengers do not have perfect knowledge
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Service and Information Reliability
2 4 6 8 10 12 14 16 18 20 22 240
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
day
credibility coefficients - disaggregate analysis
alphaRTI
alphaEXP
alphaPK
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.2
0.4
alphaRTI
fre
qu
en
cy
end of the learning period
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.2
0.4
alphaEXP
fre
qu
en
cy
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.2
0.4
alphaPK
fre
qu
en
cy
Final distribution of credibility coeff. Example: evolution of credbility coeff.
Cats O. and Jenelius E. (2014). Dynamic vulnerability analysis of public transport networks: Mitigation effects of real-time information. Networks and Spatial Economics, 14, 435-463. Cats and Gkioulou (2015). Modelling the impacts of public transport reliability and travel information on passengers’ waiting time uncertainty. EURO Journal of Transportation and Logistics.
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Network vulnerability
or when passengers can not execute their pre-trip plan
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Capturing disruption dynamics
• Static model: underestimation of disruption effects
• En-route decisions, imperfect information
• Both passengers and operators can respond to disruptions
• Graph representation (infra vs. service, multimodality, time-dependency)
• Flow (re-)distribution model (route choice)• Modelling disruptions (capacity, frequency)• Identifying critical links (network indicators)• Modelling adaptation strategies (ex-ante, ex-post)• Measuring the impact (connectivity, robustness)
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Cats O. (2016). The robustness value of public transport development plans. Journal of Transport Geography, 51, 236-246.
Jenelius and Cats (2015). The value of new public transport links for network robustness and redundancy. Transportmetrica A, 11 (9), 819-835.Cats and Jenelius (2015). Planning for the unexpected: The value of reserve capacity for public transport network robustness. Transportation Research Part A 81, 47-61.
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On-going activities
• Real-time control beyond a single line (optimization, simulation)
• Smartcard data analytics (route choice, performance)
• Short-term predictions (travel times, flows)
• Disruption and robustness (criticality, planned works)
• Mitigation measures (from strategic to real-time)
• Demand responsive transit (routing and operations, perceptions)
• PT + bike (usage, determinants)
• Door-to-door mode and route choice models (reliability, crowding)
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Research directions
• Shared mobility
• Demand responsive transport
• Mobility as a Service (MaaS)
• Proactive supply management
• Real-time fleet management strategies
• Anticipatory control
• Disruption management
• Mitigation measures
• Network resilience
• Impacts of information
• Adaptation and learning
• Anticipatory information
