Challenge the future
Delft University of Technology
BusMezzo
Dynamic Transit Operations and Assignment Model
Modelling approach and applications
Oded Cats; [email protected] / [email protected]
2 CIE4811: PTND 1: Introduction and Urban network design Oded Cats, BusMezzo Intro, July 2018
Key model capabilities (I)
• Traffic dynamics. Each individual vehicle is affected
mesoscopically by traffic conditions on links and at
intersections.
• Transit operations. Sources of uncertainty such as traffic
dynamics, flow-dependent dwell times and propagation of
delays through vehicle scheduling are modeled explicitly. A
library of holding control strategies is available.
3 CIE4811: PTND 1: Introduction and Urban network design Oded Cats, BusMezzo Intro, July 2018
• En-route passenger decisions. Each individual passenger
takes a sequence of travel decisions based on his/her
expectations which are based on service provision, real-time
information (if applicable) and past experience (when using
iterative network loading). A library of real-time information
provision alternatives is available.
Key model capabilities (II)
4 CIE4811: PTND 1: Introduction and Urban network design Oded Cats, BusMezzo Intro, July 2018
• Disruptions. Simulating unplanned partial capacity reductions
on network links.
• Day to day learning. Performing an iterative network loading
where individual travelers gain experience about service
attributes, including waiting times, on-board crowding and
the reliability of real-time information.
Key model capabilities (III)
5 Oded Cats, BusMezzo Intro, July 2018
BusMezzo developers
Erik DAVID
Oded
wilco
Melina jonas
Giorgos
Rafal Arek
Jens
TOMER HEND
Flurin Menno
SANMAY
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Modelling approach
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Modelling philosophy
• Modelling public transport system dynamics
• Multi-modal metropolitan public transport networks
• Represent sources of uncertainty
• Adaptation of both supply and demand agents
• Enable modelling the effects of ITS
Implementation approach • Object-oriented programming
• Integrated with a mesoscopic traffic simulation
• Modular, range of applications
• Research tool
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Model development evolution
Public transport supply
Dynamic route choice
Information, reliability, congestion, disruptions
Day-to-day learning
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Agent-based, bottom-up order
• Emergence of global spontanous order from nomerous inter-
dependent local decisions
• Adaptive behavior
• System dyanmics
• Examples
• Ecology, biology
• Social networks
• Market dynamics
• Crowd management
• Travel patterns
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Dynamic assignment
Network performance
Travel Behavior
Travel time Reliability Crowding
Travel choices
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Positioning Agent-based DTA
Supply
Dem
and
micro
macro
micro macro
Frequency-based
Schedule-based
Agent-based
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Modelling framework
Traffic Dynamics & Transit Operations
Dynamic Loading
Automated Data Collection
Real-Time Prediction
Control Centre
Traveler Decisions
Network
Traveller Population
Fleet
Within-day
Day-to-day
Passenger Assignment
Transit Performance
Traveler Perception
Service Planning
Traveller Strategy
13 Oded Cats, BusMezzo Intro, July 2018
Object-oriented framework
STOPLINETRANSIT VEHICLE
VEHICLE
PRIVATE VEHICLE
ROUTETRANSIT ROUTE
TRIP
PATH
OD
DAY
CONTROL CENTRE
Has
HasFollows Follows
Has
Has
Loads
Loads
STEERS
Has
USER GROUPTRAVELLER
Has
Has
Subclass
One to one relationship between object classes
One to many relationships between object classes
Has
Legend:Loads
Assigns
Has
Arrives
14 Oded Cats, BusMezzo Intro, July 2018
Network representation
•Transit vehicle trajectory • Ride • Queue • Dwell • Recover
•Traveller
trajectory •Walk •Wait • On-board
15 Oded Cats, BusMezzo Intro, July 2018
Demand modelling
Boarding this
vehicle?No
Yes
Start
BOARDING
decision model
Arriving
transit
vehicle
ALIGHTING
decision model
Approaching
transit stop
Alighting at
the next
stop?
No
CONNECTION
decision model
Yes
Arrived at
destination?
End
Yes
No
CONNECTION
decision model
• Poisson arrival process
• Non-compensatory rule-based choice-
set generation process
• En-route decisions
• Assess the attributes of each
avilable path
• Calculate the joint utility (Logsum) of the bundled paths
• Path: outcome of successive decisions
16 Oded Cats, BusMezzo Intro, July 2018
Networks: Stockholm, Amsterdam, BRT Haifa
Strategic
Tactical
Operational
Evaluating network alternatives Network robustness analysis
Reliability of timetable design Transfer synchronization
Real-time control strategies Disruption management
Range of model applications
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Congestion
Appli
cati
on
s
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
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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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Reliability or when the average headway is hardly experienced
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From Research to field implementation
Modeling - Transit operations simulation model, BusMezzo
Validation - Tel-Aviv case study
Evaluation and Optimization - Real-time holding strategies for Stockholm case study
Demonstration– Field trial
Implementation – Full-scale operations
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Example: Preferential measures
• Line 4 in Stockholm, 4-6 minutes headway 65k passengers per day
• Field experiment of bus lanes, stop consolidation, regualrity-based
operations, allowing boarding from the rear-door,...
• Objective: measure impacts on modal share, accessibility, equity and
satisfaction
• Data: AVL and APC
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Simulation results
1min boarding 3min control 2 min physical
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Information or when passengers do not have perfect knowledge
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Impacts of Information
Performance
Prediction
Provision
Perception
Path choice
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Passengers’ Response to Service Reliability and Travel Information
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Day-to-Day Learning of 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.
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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
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Model applications
• 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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Norm
al opera
tions
Disru
ptio
n (D
4)
Passe
nger trip
-loads
Travel time distribution
Impacts of information provision
Change in flow/capacity
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The road ahead or what are we currently working on
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On-going developments (I)
• Real-time control strategies [ADAPT-IT; Giorgos, David, Hend]
• Extending the toolkit
• Scaling-up to corridors and networks
• Real-time transfer management [TRANS-FORM; Menno, Flurin]
• Interfacing with railway scheduling optimization and hub
pedestrian flow models
• Transfer synchronization control
• On-demand services and hybrid PT [SMART, iQMobility; David,
Jonas]
• Combined fixed-flexible operations and passenger assignment
• Optimal allocation of automated buses
36 Oded Cats, BusMezzo Intro, July 2018
On-going developments (II)
• Passenger on-board and station platform crowding [CROWD;
Melina]
• Passenger metro car and platform section choices
• Congestion propagation
• Real-time crowding information [Arek, Rafal]
• Prediction and dissemination alternatives
• Impacts on equilibrium conditions
• User adaptation under uncertainty [MyTRAC; Sanmay]
• Robust route choice making
37 Oded Cats, BusMezzo Intro, July 2018
Relevant references (I)
• Cats O., Burghout W., Toledo T. and Koutsopoulos H.N. (2010). Mesoscopic Modeling of Bus Public Transportation. Transportation Research Record, 2188, 9-18.
• Toledo T., Cats O., Burghout W. and Koutsopoulos H.N. (2010). Mesoscopic Simulation for Transit Operations. Transportation Research Part C, 18(6), 896-908.
• Cats O., Larijani A.N., Burghout W. and Koutsopoulos H.N (2011). Impacts of Holding Control Strategies on Transit Performance: A Bus Simulation Model Analysis. Transportation Research Record, 2216, 51-58.
• Cats O., Koutsopoulos H.N., Burghout W. and Toledo T. (2011). Effect of Real-Time Transit Information on Dynamic Passenger Path Choice. Transportation Research Record, 2217, 46-54.
• Cats O., Larijani A.N., Ólafsdóttir A., Burghout W., Andreasson I. and Koutsopoulos H.N. (2012). Holding Control Strategies: A Simulation-Based Evaluation and Guidelines for Implementation. Transportation Research Record, 2274, 100-108.
• Cats O., Mach Rufi F. and Koutsopoulos H.N. (2014). Optimizing the Number and Location of Time Point Stops. Public Transport, 6 (3), 215-235.
• 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.
38 Oded Cats, BusMezzo Intro, July 2018
Relevant references (II)
• Cats O. and Jenelius E. (2015). Planning for the Unexpected: The Value of Reserve Capacity for Public Transport Network Robustness. Transportation Research Part A, 81, 47-61.
• Jenelius E. and Cats O. (2015). The Value of New Public Transport Links for Network Robustness and Redundancy. Transportmetrica A: Transport Science, 11 (9), 819-835.
• Cats O. and Gkioulou Z. (2015). Modelling the Impacts of Public Transport Reliability and Travel Information on Passengers’ Waiting Time Uncertainty. EURO Journal of Transportation and Logistics. In press, DOI 10.1007/s13676-014-0070-4.
• Cats O., West J. and Eliasson J. (2016). A Dynamic Stochastic Model for Evaluating Congestion and Crowding Effects in Transit Systems. Transportation Research Part B, 89, 43-57.
• Cats O. and Hartl M. (2016). Modelling Public Transport On-board Congestion: Comparing Schedule-based and Agent-based Assignment Approaches and their Implications. Journal of Advanced Transportation. In press.
39 Oded Cats, BusMezzo Intro, July 2018
Relevant references (III)
• Gentile G., Florian M., Hamdouch Y., Cats O. and Nuzzolo A. (2016). The Theory of Transit Assignment: Basic Modelling Frameworks. In: Modeling Public Transport Passenger Flows in the Era of Intelligent Transport Systems, G. Gentile and K. Nökel, pp. 287-386. Springer International Publishing. ISBN 978-3-319-25082-3.
• Chandakas E., Leurent F. and Cats O. (2016). Applications and Future Developments: Modeling Software and Advanced Applications. In: Modeling Public Transport Passenger Flows in the Era of Intelligent Transport Systems, G. Gentile and K. Nökel, pp. 521-560. Springer International Publishing. ISBN 978-3-319-25082-3
• Cats O. and Jenelius E. (2016). Beyond a Complete Failure: The Impact of Partial Capacity Degradation on Public Transport Network Vulnerability. Transportmetrica B: Transport Dynamics. In press.
• Gavriilidou A. and Cats O. (2018). Reconciling Transfer Synchronization and Service Regularity: Real-time Control Strategies using Passenger Data. Transportmetrica A, Accepted.
• Malandri C., Fonzone A. and Cats O. (2018). Recovery Time and Propagation Effects of Passenger Transport Disruption. Physica A, 505, 7-17.
40 Oded Cats, BusMezzo Intro, July 2018
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