A review on transit assignment modelling approaches to congested networks: a new perspective

This paper reviews a number of studies on both frequency-and schedule-based transit assignment models that have been proposed by far, wherein various behavioural assumptions on a wide range of aspects are embedded. With a reinvestigation on the relationships and homogeneity between different modelling approaches, it explores the representative veins of the models, and thereby extends a new perspective to the existing reviews under a historical context. Meanwhile, both advantages and disadvantages of these methods are presented. On the strength of the analyses and discussions of the state-of-the-art transit assignment models, further research directions are suggested

Capacity constrained accessibility of high-speed rail

This paper proposes an enhanced measure of accessibility that explicitly considers circumstances in which the capacity of the transport infrastructure is limited. Under these circumstances, passengers may suffer longer waiting times, resulting in the delay or cancellation of trips. Without considering capacity constraints, the standard measure overestimates the accessibility contribution of transport infrastructure. We estimate the expected waiting time and the probability of forgoing trips based on the M/GB/1 type of queuing and discrete-event simulation, and formally incorporate the impacts of capacity constraints into a new measure: capacity constrained accessibility (CCA). To illustrate the differences between CCA and standard measures of accessibility, this paper estimates the accessibility change in the Beijing–Tianjin corridor due to the Beijing–Tianjin intercity high-speed railway (BTIHSR). We simulate and compare the CCA and standard measures in five queuing scenarios with varying demand patterns and service headway assumptions. The results show that (1) under low system loads condition, CCA is compatible with and absorbs the standard measure as a special case; (2) when demand increases and approaches capacity, CCA declines significantly; in two quasi-real scenarios, the standard measure overestimates the accessibility improvement by 14–30 % relative to the CCA; and (3) under the scenario with very high demand and an unreliable timetable, the CCA is almost reduced to the pre-BTIHSR level. Because the new CCA measure effectively incorporates the impact of capacity constraints, it is responsive to different arrival rules, service distributions, and system loads, and therefore provides a more realistic representation of accessibility change than the standard measure

On modal availability, travel strategies and traffic equilibrium on a multimodal network

27 pagesTransportation modes, including Walking and other private modes as well as transit services, provide travel options to the individual trip-maker along a transportation network. On a single basis, any modal option is featured here in terms of travel time to destination conditionally to immediate availability, expected wait time in the adverse case and the probability of availability. The paper is focused on availability in order to state its roles in travel strategy for route choice and traffic assignment onto a multimodal network. It deals with, successively, mode characterization, local travel strategy, network hyperpaths and traffic equilibrium. Assumedly, a private mode is available on a full, continuous basis, while a transit service is available on a partial, discrete basis due to station dwell time and service frequency. However, a capacitated transit service under saturation amounts to a fully available travel option which includes an initial wait time. A local travel strategy at a choice node is made up of either an ordered sequence of discrete options, or a continuous option, or a combination of both so that the discrete options are used opportunistically if available or the continuous one otherwise. This leads us to revisit the common line problem of transit assignment. A framework and an algorithm are provided to search for optimal travel strategies. The sequential treatment along a multimodal network is based on hyperpaths under availability conditions. Traffic equilibrium is addressed in the static setting; the system state includes the vector of trip flows by destination and network link together with vectors of strategy proportions by node and destination

Strategy-based dynamic assignment in transit networks with passenger queues

This thesis develops a mathematical framework to solve the problem of dynamic assignment in densely connected public transport (or transit – the two words are interchangeably used) networks where users do not time their arrival at a stop with the lines’ timetable (if any is published). In the literature there is a fairly broad agreement that, in such transport systems, passengers would not select the single best itinerary available, but would choose a travel strategy, namely a bundle of partially overlapping itineraries diverging at stops along different lines. Then, they would follow a specific path depending on what line arrives first at the stop. From a graph-theory point of view, this route-choice behaviour is modelled as the search for the shortest hyperpath (namely an acyclic sub-graph which includes partially overlapping single paths) to the destination in the hypergraph that describes the transit network. In this thesis, the hyperpath paradigm is extended to model route choice in a dynamic context, where users might be prevented from boarding the lines of their choice because of capacity constraints. More specifically, if the supplied capacity is insufficient to accommodate the travel demand, it is assumed that passenger congestion leads to the formation of passenger First In, First Out (FIFO) queues at stops and that, in the context of commuting trips, passengers have a good estimate of the expected number of vehicle passages of the same line that they must let go before being able to board. By embedding the proposed demand model in a fully dynamic assignment model for transit networks, this thesis also fills in the gap currently existing in the realm of strategy-based transit assignment, where – so far – models that employ the FIFO queuing mechanism have proved to be very complex, and a theoretical framework for reproducing the dynamic build-up and dissipation of queues is still missing.Open Acces

Models and Solution Algorithms for Asymmetric Traffic and Transit Assignment Problems

Modeling the transportation system is important because it provides a “common ground” for discussing policy and examining the future transportation plan required in practices. Generally, modeling is a simplified representation of the real world; however, this research added value to the modeling practice by investigating the asymmetric interactions observed in the real world in order to explore potential improvements of the transportation modeling. The Asymmetric Transportation Equilibrium Problem (ATEP) is designed to precisely model actual transportation systems by considering asymmetric interactions of flows. The enhanced representation of the transportation system by the ATEP is promising because there are various asymmetric interactions in real transportation such as intersections, highway ramps, and toll roads and in the structure of the transit fares. This dissertation characterizes the ATEP with an appropriate solution algorithm and its applications. First, the research investigates the factors affecting the convergence of the ATEP. The double projection method is applied to various asymmetric types and complexities in the different sizes of networks in order to identify the influential factors including demand intensities, network configuration, route composition between modes, and sensitivity of the cost function. Secondly, the research develops an enhancement strategy for improvement in computational speed for the double projection method. The structural characteristics of the ATEP are used to develop the convergence enhancement strategy that significantly reduces the computational burdens. For the application side, instances of asymmetric interactions observed in in-vehicle crowding and the transit fare structure are modeled to provide a suggestion on policy approach for a transit agency. The direct application of the crowding model into the real network indicates that crowd modeling with multi user classes could influence the public transportation system planning and the revenue achievement of transit agencies. Moreover, addition of the disutility factor, crowding, not always causes the increase of disutility from the transit uses. The application of the non-additive fare structure in the Utah Transit Authority (UTA) network addresses the potential of the distance-based fare structure should the UTA make a transition to this fare structure from their current fare model. The analysis finds that the zero base fare has the highest potential for increasing the transit demand. However, collecting less than $0.50 with a certain buffer distance for the first boarding has potential for attracting the users to UTA\u27s transit market upon the fare structure change

The commuter rail circulator network design problem: formulation, solution methods, and applications

Commuter rail is increasingly popular as a means to introduce rail transportation to metropolitan transportation systems. The long-term benefits of commuter rail include the addition of capacity to the transportation system, providing a quality commute alternative, and shifting land use toward transit-oriented development patterns. The success of a commuter rail system depends upon cultivating a ridership base upon which to expand and improve the system. Cultivating this ridership is dependent upon offering a quality transportation option to commuters. Characteristics of commuter rail systems in the United States present challenges to offering quality service that must be overcome. Commuter rail has been implemented only on existing rail right-of-way (ROW) and infrastructure (depending upon condition) in the United States. Existing rail ROW does not often coincide with current commercial and residential demand centers and necessitates the use of a circulator system to expand the service boundary of commuter rail to reach these demand centers. The commuter rail circulator network design problem (CRCNDP) addresses a particular aspect of the commuter rail trip, seeking to improve the performance of the entire system through accurately modeling the portion of the trip from rail station to the final destination. This final leg includes both the trip on the circulator vehicle and the walking trip from the circulator stop to the final destination. This dissertation seeks to provide an innovative mathematical programming formulation and solution methodology for the CRCNDP and apply this method to a case study.Civil, Architectural, and Environmental Engineerin

Modelling route choice behaviour with incomplete data: an application to the London Underground

This thesis develops a modelling framework for learning route choice behaviour of travellers on an underground railway system, with a major emphasis on the use of smart-card data. The motivation for this topic comes from two respects. On the one hand, in a metropolis, particularly those furnished with massive underground services (e.g. London, Beijing and Paris), severe passenger-traffic congestion may often occur, especially during rush hours. In order to support the public transport managers in taking actions that are more effective in smoothening the passenger flows, there is bound to be a need for better understanding of the passengers’ routing behaviour when they are travelling on such public transport networks. On the other hand, a wealth of travel data is nowadays readily obtainable, largely owing to the widespread implementation of automatic fare collection systems (AFC) as well as popularity of smart cards on the public transport. Nevertheless, a core limitation of such data is that the actual route-choice decisions taken by the passengers might not be available, especially when their journeys involve alternative routes and/or within-station interchanges. Mostly, the AFC systems (e.g. the Oyster system in London) record only data of passengers’ entry and exit, rather than their route choices. We are thus interested in whether it is possible to analytically infer the route-choice information based on the ‘incomplete’ data. Within the scope of this thesis, passengers’ single journeys are investigated on a station basis, where sufficiently large samples of the smart-card users’ travel records can be gained. With their journey time data being modelled by simple finite mixture distributions, Bayesian inference is applied to estimate posterior probabilities for each route that a given passenger might have chosen from all possible alternatives. We learn the route-choice probabilities of every individual passenger in any given sample, conditional on an observation of the passenger’s journey time. Further to this, the estimated posterior probabilities are also updated for each passenger, by taking into account additional information including their entry times as well as the timetables. To understand passengers’ actual route choice behaviour, we then make use of adapted discrete choice model, replacing the conventional dependent variable of actual route choices by the posterior choice probabilities for different possible outcomes. This proposed methodology is illustrated with seven case studies based in the area of central zone of the London Underground network, by using the Oyster smart-card data. Two standard mixture models, i.e. the probability distributions of Gaussian and log-normal mixtures, are tested, respectively. The outcome demonstrates a good performance of the mixture models. Moreover, relying on the updated choice probabilities in the estimation of a multinomial logit latent choice model, we show that we could estimate meaningful relative sensitivities to the travel times of different journey segments. This approach thus allows us to gain an insight into passengers’ route choice preferences even in the absence of observations of their actual chosen routes

Integration of passenger satisfaction in railway timetable rescheduling for major disruptions

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancellations, and, eventually, passenger inconvenience. This thesis focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions, such as track unvailabilities due to, e.g., rolling stock malfunction or adverse weather conditions. Its originality is to consider three objectives when designing the so-called disposition timetable: the passenger satisfaction, the operational cost and the deviation from the undisrupted timetable. These goals are usually incompatible: for instance, the best possible service for the passengers may also be the most expensive option for the railway operator. This inadequacy is the key motivation for this thesis. The problem is formally defined as a multi-objective Integer Linear Program and solved to optimality on realistic instances. In order to understand the trade-offs between the objectives, the three-dimensional Pareto frontier is approximated using epsilon-constraints. The results on a Dutch case study indicate that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable. For a more efficient investigation of the multiple dimensions of the problem, a heuristic solution algorithm based on adaptive large neighborhood search is also presented. The timetable is optimized using operators inspired directly from recovery strategies used in practice (such as canceling, delaying or rerouting trains, or scheduling additional trains and buses), and from optimization methods (e.g., feasibility restoration operators). Results on a Swiss case study indicate that the proposed solution approach performs well on large-scale problems, in terms of computational time and solution quality. In addition, a flexible network loading framework, defining priorities among passengers for the capacitated passenger assignment problem, is introduced. Being efficient and producing stable aggregate passenger satisfaction indicators (such as average travel time), it is used in an iterative manner for the evaluation from the passenger perspective of the timetable provided by the rescheduling meta-heuristic. The timetable rescheduling problem is a hard problem and this thesis makes significant methodological and practical contributions to the design of passenger-centric disposition timetables. It is the first attempt to explicitly integrate multiple objectives in a single framework for railway timetable rescheduling, as the state-of-the-art usually neglects passenger considerations, or considers them only implicitly. Further, the use of practice-inspired optimization methods allows railway operators to easily implement the results of the proposed framework