Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks

Cats, Oded; West, Jens

doi:10.1177/0361198119900138

Cited by 11 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this model, passenger route choices and vehicle travel times are stochastic. The passenger assignment model includes within-day and day-to-day dynamics (Cats and West 2020). A random utility model determines the passenger's path choice decisions.…”

Section: Simulationmentioning

confidence: 99%

Network design for line-based autonomous bus services

2021

Self Cite

View full text Add to dashboard Cite

The maturing of autonomous driving technology in recent years has led to several pilot projects and the initial integration of autonomous pods and buses into the public transport (PT) system. An emerging field of interest is the design of public transport networks operating autonomous buses and the potential to attract higher levels of travel demand. In this work a multi-objective optimization and multi-agent simulation framework is developed to study potential changes in the network design and frequency settings compared to conventional PT systems when autonomous vehicles (AV) systems are deployed on fixed-route networks. During the optimization process multiple deployment scenarios (network configurations and service frequency) are evaluated and optimized considering the operator cost, user cost and infrastructure preparation costs of the system. User-focused network design and operator-focused network design are studied for a real-world urban area in Sweden. The results provide insights into the network design and level of service implications brought about by the deployment of autonomous bus (AB) when those are integrated in route-based PT systems. We show that the deployment of autonomous buses result with a network design that increases service ridership. In the context of our case study this increase is likely to primarily substitute walking.

show abstract

Section: Simulationmentioning

confidence: 99%

Network design for line-based autonomous bus services

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Fourthly, the reliability-based transit assignment model is adopted as the lower level model. It would be interesting to examine the effect of using a different assignment model such as a schedule-based model (Tong and Wong 1999, Nuzzolo et al 2001, 2012, Poon et al 2004, Wilson and Nuzzolo 2013, Gentile et al 2016, Cats and West 2020, Xie et al 2021 or a personalised assignment model (Ceder andJiang 2019, 2020;Jiang and Ceder, 2021). More importantly, when a schedule-based transit assignment model is adopted.…”

Section: Sioux Falls Networkmentioning

confidence: 99%

Reliability-based equitable transit frequency design

Jiang

2021

Transportmetrica A: Transport Science

View full text Add to dashboard Cite

Fairness is an important criterion for achieving sustainable urban development, and thus, is of major concern in providing public transport services. While most existing studies focus on accessing or evaluating the fairness condition of a given transit network, this study explicitly incorporates fairness as an objective in the planning step to allocate a fleet to a set of bus lines. A multi-objective bilevel programming model is developed for the transit frequency setting problem. The lower level problem is the reliability-based transit assignment problem that captures the effects of supply-side uncertainty on passengers' route choice behaviour via the concept of travel time budget referred to as effective travel cost. The upper level problem is to determine optimal frequency settings to simultaneously minimise the total effective travel cost and maximise the network fairness condition, measured by minimum reduction in effective travel cost of all Origin-Destination (OD) pairs. A multi-objective Artificial Bee Colony algorithm (MOABC) is developed to solve the bilevel model, where non-dominated solutions are maintained by a predefined external archive. Numerical studies find that: 1) increasing the frequency may not improve the fairness condition; 2) there is a tradeoff between the objective of minimising total effective travel cost and that of maximising the fairness measurement; 3) the two objectives can be improved simultaneously using a large fleet; 4) the effect of passengers' risk aversion attitude on the fairness measurement depends on the frequency setting; it could either amplify the fairness measurements or have no impact.

show abstract

“…The path may consist of multiple line segments, transfer connections between lines within stops and/or walking links between stops. The approach in the BusMezzo model is to define path i as a set of all the alternatives that imply the same chain of stops with equivalent link attributes (Cats, West, and Eliasson 2016;Cats and West 2020). Additionally, we relax the default dominancy and filtering rules, in order to increase the choice set size and include additional paths that might only become attractive with access to RTCI.…”

Section: Pt Agent-based Simulation Model Platformmentioning

confidence: 99%

Modelling the effects of real-time crowding information in urban public transport systems

Kucharski

Cats

Szarata

2020

Transportmetrica A: Transport Science

Self Cite

View full text Add to dashboard Cite

Public transport (PT) overcrowding is a notorious problem in urban transport networks. Its negative effects upon travel experience can be potentially addressed by disseminating real-time crowding information (RTCI) to passengers. However, impacts of RTCI provision in urban PT networks remain largely unknown. This study aims to contribute by developing an extended dynamic PT simulation model that enables a thorough analysis of instantaneous RTCI consequences. In the model, RTCI is generated and disseminated across the network, and then utilised in passengers' sequential en-route choices. A case-study demonstration of the RTCI algorithm on urban PT network model of Kraków (Poland) shows that instantaneous RTCI has the potential to improve passengers' travel experience, although it is also susceptible to inaccuracy. RTCI provision can yield total travel utility improvements of 3% in typical PM peak-hour, with reduced impacts of the worst overcrowding effects (in terms of denied-boarding and in-vehicle travel disutility in overcrowded conditions) of 30%. Highlights: • Real-time crowding information (RTCI) is an increasingly feasible solution in public transport. • We introduce a novel framework for modelling the network effects of instantaneous RTCI. • Instantaneous RTCI can result in improved travel experience but also substantial inaccuracy risk. • Reduced impacts of the worst overcrowding experience amount to up to 30%.

show abstract

Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks

Cited by 11 publications

References 34 publications

Network design for line-based autonomous bus services

Network design for line-based autonomous bus services

Reliability-based equitable transit frequency design

Modelling the effects of real-time crowding information in urban public transport systems

Contact Info

Product

Resources

About