A heuristic method for a congested capacitated transit assignment model with strategies

Codina, Esteve; Rosell, Francisca

doi:10.1016/j.trb.2017.07.008

Cited by 21 publications

(4 citation statements)

References 33 publications

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“…However, in principle, they can also be used to estimate denied boarding using actual (as opposed to scheduled) train departures and arrivals at stations ( 11 ). Schedule-based assignment ( 12 , 13 ) is appropriate for this purpose. However, such approaches, since they have to be applied at the network level, require assumptions about capacity and route choice fractions.…”

mentioning

confidence: 99%

Estimation of Denied Boarding in Urban Rail Systems: Alternative Formulations and Comparative Analysis

Koutsopoulos

Yun-qing

et al. 2019

Transportation Research Record

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Monitoring rail transit system performance is important for effective operations planning. The number of times passengers are denied boarding is becoming a key measure of the impact of near-capacity operations on customers and is fundamental for calculating other performance metrics, such as expected waiting time for service. This paper reviews existing methods and proposes a denied boarding probability distribution inference method for closed Automated Fare Collection (AFC) urban rail systems. Using AFC (tap-in and tap-out) and Automated Vehicle Location (AVL) data, the method relaxes some of the limitations of existing approaches. The problem is modeled using a mixture distribution framework that incorporates a priori structural information. It is data-driven and requires neither observations of denied boardings, nor assumptions about access/egress time distributions. Also, for comparison purposes, the paper presents an event-based deterministic transit assignment model with explicit capacity constraints. While the network assignment works at the network level and requires train capacity, the mixture model works at the station level, requires no external parameters, and can be easily applied to any station and for any time period. A case study illustrates the application of the proposed methods using actual data and compares the results against existing methods, and also survey data. The results demonstrate the mixture model’s robustness and applicability for monitoring denied boarding.

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confidence: 99%

Estimation of Denied Boarding in Urban Rail Systems: Alternative Formulations and Comparative Analysis

Koutsopoulos

Yun-qing

et al. 2019

Transportation Research Record

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“…Moreover, in general, these approaches do not ensure that the capacity constraints are satisfied (Cominetti and Correa 2001). There are approaches that ensure the satisfaction of the capacity constraints, but they require to solve difficult mathematical models (Codina and Rosell 2017) or rely on heuristics that do not generate user equilibrium solutions (Cheung and Shalaby 2017;Oliker and Bekhor 2020b). Within the TNDFSP, crowding has been included using the concept of effective frequencies and implementing incremental algorithms.…”

Section: Passenger Assignment Problemmentioning

confidence: 99%

A survey on the transit network design and frequency setting problem

Durán-Micco

Vansteenwegen

2021

Public Transp

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Appropriate public transport systems are crucial in modern cities. Given the high costs that they represent and the impact they have on people's lives, effective tools are required to support their design. With this in mind, the Transit Network Design problem (TNDP) and the Transit Network Design and Frequency Setting problem (TNDFSP) have been extensively studied in the domain of Operations Research. However, due to the complexity of these problems, multiple simplifications are typically made when modelling and designing solution algorithms. Therefore, still no optimization techniques are available to address these problems in practice. Moreover, different studies address different versions of the problem, with varying assumptions and constraints, complicating the comparison of results or solution approaches. This paper presents an extensive survey of studies addressing the TNDP and the TNDFSP. It discusses the different assumptions, constraints, objectives, solutions approaches and testing instances that have been considered in the literature. Furthermore, a detailed analysis is done regarding the case studies considered for the TNDFSP. Moreover, the variants of the passenger assignment subproblem that have been applied within the TNDP and the TNDFSP are discussed. The analysis shows that extensive research has been done regarding these problems. However, it also identified the significant gap that still exists between theory and practice, even in the studies addressing case studies.

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“…The traditional research for estimating passenger flow distribution can be divided into two categories: (1) simulation method and (2) mathematical model. The core idea of the former is to depict the passenger flow evolution in a subway network by simulating the passenger’s behavior [ 3 , 4 , 5 ], while the latter is to formulate an equivalent mathematical model by analyzing passenger route choice behavior based on travel cost [ 6 , 7 , 8 ]. Generally, these studies are based on the following assumptions: (1) each train has a fixed capacity [ 3 , 4 , 6 , 8 ]; (2) the passenger boarding process follows the FCFS (First-Come-First-Served) principle [ 6 , 7 ]; (3) ignoring the arrival time of an individual passenger [ 3 , 4 , 6 , 7 , 9 ]; and (4) neglecting the impact of network time-dependent state on passenger choice behavior [ 3 , 4 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…The core idea of the former is to depict the passenger flow evolution in a subway network by simulating the passenger’s behavior [ 3 , 4 , 5 ], while the latter is to formulate an equivalent mathematical model by analyzing passenger route choice behavior based on travel cost [ 6 , 7 , 8 ]. Generally, these studies are based on the following assumptions: (1) each train has a fixed capacity [ 3 , 4 , 6 , 8 ]; (2) the passenger boarding process follows the FCFS (First-Come-First-Served) principle [ 6 , 7 ]; (3) ignoring the arrival time of an individual passenger [ 3 , 4 , 6 , 7 , 9 ]; and (4) neglecting the impact of network time-dependent state on passenger choice behavior [ 3 , 4 , 7 ]. However, the assumptions restrict the traditional methods in accurately depicting the factors influencing the passenger flow distribution [ 10 , 11 , 12 ], such as the in-train congestion [ 13 ], the passengers’ psychology during their travel process [ 14 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability

Zhi

et al. 2022

Entropy

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The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers’ path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger–train matching probability, which is the key problem of passenger flow distribution. Specifically, the existing methods have not been applied to practical large-scale subway networks due to the computational complexity. To fill this research gap, this paper analyzes the relationship between passenger travel behavior and train operation in the space and time dimension and formulates the passenger–train matching probability by using multi-source data including AFC, train timetables, and network topology. Then, a reverse derivation method, which can reduce the scale of possible train combinations for passengers, is proposed to improve the computational efficiency. Simultaneously, an estimation method of passenger flow distribution is presented based on the passenger–train matching probability. Finally, two sets of experiments, including an accuracy verification experiment based on synthetic data and a comparison experiment based on real data from the Beijing subway, are conducted to verify the effectiveness of the proposed method. The calculation results show that the proposed method has a good accuracy and computational efficiency for a large-scale subway network.

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A heuristic method for a congested capacitated transit assignment model with strategies

Cited by 21 publications

References 33 publications

Estimation of Denied Boarding in Urban Rail Systems: Alternative Formulations and Comparative Analysis

Estimation of Denied Boarding in Urban Rail Systems: Alternative Formulations and Comparative Analysis

A survey on the transit network design and frequency setting problem

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability

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