Calibrating Rail Transit Assignment Models with Genetic Algorithm and Automated Fare Collection Data

2014 ◽  
Vol 29 (7) ◽  
pp. 518-530 ◽  
Author(s):  
Wei Zhu ◽  
Hao Hu ◽  
Zhaodong Huang
Keyword(s):  
Genetic Algorithm ◽  
Rail Transit ◽  
Transit Assignment ◽  
Collection Data ◽  
Assignment Models

Validating Rail Transit Assignment Models with Cluster Analysis and Automatic Fare Collection Data

2015 ◽  
Vol 2526 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Wei Zhu ◽  
Feng Zhou ◽  
Jiajun Huang ◽  
Ruihua Xu
Keyword(s):  
Cluster Analysis ◽  
Large Scale ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Assignment ◽  
Collection Data ◽  
The Difference ◽  
Cluster Analysis Technique ◽  
Assignment Models ◽  
Automatic Fare Collection Data

Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.

Modelling Urban Public Transit Users' Route Choice Behaviour

2011 ◽  
pp. 134-146
Author(s):  
Yulin Lee ◽  
Jonathan Bunker ◽  
Luis Ferreira
Keyword(s):  
Public Transport ◽  
Public Transit ◽  
Route Choice ◽  
User Equilibrium ◽  
Urban Transport ◽  
Transit Assignment ◽  
The Road ◽  
Transit Route ◽  
The 1960S ◽  
Assignment Models

Public transport is one of the key promoters of sustainable urban transport. To encourage and increase public transport patronage it is important to investigate the route choice behaviours of urban public transit users. This chapter reviews the main developments of modelling urban public transit users’ route choice behaviours in a historical perspective, from the 1960s to the present time. The approaches reviewed for this study include the early heuristic studies on finding the least-cost transit route and all-or-nothing transit assignment, the bus common lines problem, the disaggregate discrete choice models, the deterministic and stochastic user equilibrium transit assignment models, and the recent dynamic transit assignment models. This chapter also provides an outlook for the future directions of modelling transit users’ route choice behaviours. Through the comparison with the development of models for motorists’ route choice and traffic assignment problems, this chapter advocates that transit route choice research should draw inspiration from the research outcomes from the road area, and that the modelling practice of transit users’ route choice should further explore the behavioural complexities.

Integrated simulation-based dynamic traffic and transit assignment model for large-scale network

2020 ◽  
Vol 47 (8) ◽  
pp. 898-907 ◽  
Author(s):  
Islam Kamel ◽  
Amer Shalaby ◽  
Baher Abdulhai
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic ◽  
Transit Assignment ◽  
Large Scale Network ◽  
Assignment Model ◽  
Morning Peak ◽  
Simulation Based ◽  
Scale Network ◽  
Assignment Models

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.

scholarly journals Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks

2020 ◽  
Vol 2674 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Oded Cats ◽  
Jens West
Keyword(s):  
Information Provision ◽  
Agent Based Simulation ◽  
Transit Assignment ◽  
Agent Based ◽  
Transit Network ◽  
Passenger Demand ◽  
Congested Networks ◽  
Learning And Adaptation ◽  
Assignment Models ◽  
Different Levels

The distribution of passenger demand over the transit network is forecasted using transit assignment models which conventionally assume that passenger loads satisfy network equilibrium conditions. The approach taken in this study is to model transit path choice as a within-day dynamic process influenced by network state variation and real-time information. The iterative network loading process leading to steady-state conditions is performed by means of day-to-day learning implemented in an agent-based simulation model. We explicitly account for adaptation and learning in relation to service uncertainty, on-board crowding and information provision in the context of congested transit networks. This study thus combines the underlying assignment principles that govern transit assignment models and the disaggregate demand modeling enabled by agent-based simulation modeling. The model is applied to a toy network for illustration purposes, followed by a demonstration for the rapid transit network of Stockholm, Sweden. A full-scale application of the proposed model shows the day-to-day travel time and crowding development for different levels of network saturation and when deploying different levels of information availability.

Study on the topology and dynamics of the rail transit network based on automatic fare collection data

2020 ◽  
Vol 545 ◽  
pp. 123538 ◽  
Author(s):  
Pengfei Lin ◽  
Jiancheng Weng ◽  
Yu Fu ◽  
Dimitrios Alivanistos ◽  
Baocai Yin
Keyword(s):  
Rail Transit ◽  
Transit Network ◽  
Collection Data ◽  
Automatic Fare Collection Data

scholarly journals A Network-Based Model of Passenger Transfer Flow between Bus and Metro: An Application to the Public Transport System of Beijing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wenjing Wang ◽  
Yihong Wang ◽  
Gonçalo Homem de Almeida Correia ◽  
Yusen Chen
Keyword(s):  
Public Transport ◽  
Transit Assignment ◽  
Public Transport System ◽  
Points Of Interest ◽  
Smart Card Data ◽  
Network Properties ◽  
Computationally Intensive ◽  
Extensive Computation ◽  
Assignment Models ◽  
Transfer Flow

In a multimodal public transport network, transfers are inevitable. Planning and managing an efficient transfer connection is thus important and requires an understanding of the factors that influence those transfers. Existing studies on predicting passenger transfer flows have mainly used transit assignment models based on route choice, which need extensive computation and underlying behavioral assumptions. Inspired by studies that use network properties to estimate public transport (PT) demand, this paper proposes to use the network properties of a multimodal PT system to explain transfer flows. A statistical model is estimated to identify the relationship between transfer flow and the network properties in a joint bus and metro network. Apart from transfer time, the number of stops, and bus lines, the most important network property we propose in this study is transfer accessibility. Transfer accessibility is a newly defined indicator for the geographic factors contributing to the possibility of transferring at a station, given its position in a multimodal PT network, based on an adapted gravity-based measure. It assumes that transfer accessibility at each station is proportional to the number of reachable points of interest within the network and dependent on a cost function describing the effect of distance. The R-squared of the regression model we propose is 0.69, based on the smart card data, PT network data, and Points of Interest (POIs) data from the city of Beijing, China. This suggests that the model could offer some decision support for PT planners especially when complex network assignment models are too computationally intensive to calibrate and use.

scholarly journals Research on the Multiroute Probit-Based Public Transit Assignment Model Based on Bus Stop

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenyu Mei ◽  
Dianhai Wang ◽  
Fujian Wang ◽  
Jun Chen ◽  
Wei Wang
Keyword(s):  
Road Network ◽  
Road Traffic ◽  
Public Transit ◽  
Transit Assignment ◽  
Transit Network ◽  
Assignment Model ◽  
Trip Time ◽  
Resistance Function ◽  
Assignment Models ◽  
The Relationship

A public transit network differs from a general road network. The passenger flow of bus stops and the limited capacity of buses have a greater effect than road traffic flow on the running time of buses. As a result, conventional public transit assignment models that adopt the econometric road network path concept have numerous limitations. Based on the analysis, the generalized bus trip time chain is analyzed, and the concept of a congestion function is proposed to describe the relationship between trip resistance and flow in the current paper. On the premise of this study, the transit network resistance function is formed and the multiroute probit-based loading model is established. With using STOCH or Dial's algorithm, the process of distribution is proposed. Finally, the model is applied to the transit network assignment of Deqing Town in Zhejiang Province. The result indicates that the model can be applied to practical operations with high-precision results.

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