A dynamic route choice model for public transport networks with boarding queues

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link:http://openaccess.city.ac.uk/6527/ Link to published version: http://dx.doi.org/10. 1080/03081060.2012.745720 Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to. City Research Online: http://openaccess.city.ac.uk/ publications@city.ac.uk City Research OnlineA dynamic route choice model for public transport networks with boarding queuesThe concepts of optimal strategy and hyperpath were born within the framework of static frequency-based public transport assignment, where it is assumed that travel times and frequencies do not change over time and no overcrowding occurs. However, the formation of queues at public transport stops can prevent passengers from boarding the first vehicle approaching and can thus lead to additional delays in their trip. Assuming that passengers know from previous experience that for certain stops/lines they will have to wait for the arrival of the 2nd, 3rd, ..., k -th vehicle, they may alter their route choices, thus resulting in a different assignment of flows across the network. The aim of this paper is to investigate route choice behaviour changes as a result of the formation and dispersion of queues at stops within the framework of optimal travel strategies. A new model is developed, based on modifications of existing algorithms. IntroductionIt has been largely acknowledged in the last decades that urban sustainable development needs to overcome the dependence on the private car (Newman and Kenworthy 1999, European Commission 2009) and requires a modal shift towards public transport, as this leads to better performance over private transport with regards to the six sub-objectives for sustainability (May 2001 cited Black et al. 2002.In the context of promoting a transfer of passenger mobility from individual to collective means, it is important to improve the quality of service provided (i.e. total travel time reduction, increased service regularity, increased comfort on board). When this scope cannot be pursued by building more suitable infrastructure, which can be politically, financially, and environmentally constrained, Advanced Traveller Information Systems (ATIS) could play a major role if they were able to provide travellers with accurate route guidance which can consider updated and useful information about network conditions. At the present time this is not the case, as journey planners do not currently consider bus travel time which may vary with the time of the day due to recurrent road congestion. The current journey planners also do not take into account queuing time at stops and stations due to passenger congestion on the transit network. From an assignment perspective, this may lead to passenge...

Scheduled Hyperpath

“…The important difference is that here recourse actions stem from new information about traffic conditions on immediate successor links rather than about arrival times at intermediate nodes. In fact, this is closer to strategies proposed by other investigators in the context of public transport assignment when uncertainty about the arrival time of services exists (19,20). With rigorous adaptation of concepts from the public transport domain, these methods and approaches are introduced into the goods delivery context.…”

“…That formulation was extended to congested public transport networks in which both service frequencies and link travel times are time dependent (20). Here the authors derived from these approaches to develop a formulation relevant to traffic networks.…”

“…Here, a formulation and solution algorithm for application to traffic networks were developed by adaptation of the approach of Trozzi et al (20). First, the distinction between different types of nodes and the third equation in the set were discarded, and as described above, delay-related terms were incorporated into the second Bellman equation through the term d i .…”

“…The solution algorithm to Proposition 2 is an adaptation of that presented previously that exploits the properties of a network expanded in space-time (20). However, instead of considering network expansion for a given departure time and trying to establish arrival at particular nodes, the proposed approach considers each time interval in isolation.…”

Dynamic user equilibrium in public transport networks with passenger congestion and hyperpaths

“…On the other hand, the APFs also imply equations (29) and (30), while the diversion probabilities calculated by the SM may be expressed in compact form as in (31). Thus, if the generalised arc cost functions (18), (19) are consolidated as in equation (32), then exit times, arc costs and diversion probabilities, which represent the supply, are expressed as in (33)…”