A multi-class transit assignment model for estimating transit passenger flows-a case study of Beijing subway network

2015 ◽  
Vol 50 (1) ◽  
pp. 50-68 ◽  
Author(s):  
Bingfeng Si ◽  
Liping Fu ◽  
Jianfeng Liu ◽  
Sajad Shiravi ◽  
Ziyou Gao
Keyword(s):  
Transit Assignment ◽  
Assignment Model ◽  
Passenger Flows ◽  
Beijing Subway

scholarly journals A Capacity-Restraint Transit Assignment Model When a Predetermination Method Indicates the Invalidity of Time Independence

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Haoyang Ding ◽  
Yu Bao ◽  
Sida Luo ◽  
Hanxia Shen ◽  
Wei Wang ◽  
...  
Keyword(s):  
Engineering Practice ◽  
Transit Assignment ◽  
Urban Transit ◽  
Simple Method ◽  
Assignment Method ◽  
Assignment Model ◽  
Traditional Research ◽  
Nanjing City ◽  
Assignment Models

The statistical independence of time of every two adjacent bus links plays a crucial role in deciding the feasibility of using many mathematical models to analyze urban transit networks. Traditional research generally ignores the time independence that acts as the ground of their models. Assumption is usually made that time independence of every two adjacent links is sound. This is, however, actually groundless and probably causes problematic conclusions reached by corresponding models. Many transit assignment models such as multinomial probit-based models lose their effects when the time independence is not valid. In this paper, a simple method to predetermine the time independence is proposed. Based on the predetermination method, a modified capacity-restraint transit assignment method aimed at engineering practice is put forward and tested through a small contrived network and a case study in Nanjing city, China, respectively. It is found that the slope of regression equation between the mean and standard deviation of normal distribution acts as the indicator of time independence at the same time. Besides, our modified assignment method performs better than the traditional one with more reasonable results while keeping the property of simplicity well.

Passenger Route Choice Behavior with Congestion Consideration

2013 ◽  
Vol 368-370 ◽  
pp. 1876-1880 ◽  
Author(s):  
Ying Zeng ◽  
Jun Li ◽  
Hui Zhu
Keyword(s):  
Choice Behavior ◽  
Route Choice ◽  
Transit Assignment ◽  
Transit System ◽  
Transit Network ◽  
Route Choice Behavior ◽  
Assignment Model ◽  
Proposed Model ◽  
Vehicle Capacity

Few studies have adequately focused on passenger route choice behavior with congestion consideration, or provided useful guidance on passenger route choice and hence the transit assignment model, which is the writing motivation of this paper. With congestion consideration, travel cost is assessed and and ways to reduce it also identified. Finally, an actual transit network of Chengdu is used as a case study to demonstrate the benefits of the proposed model. The result indicates that the vehicle capacity is an important factor that cant be ignored and a better understanding of passenger route behavior could significantly benefit public transit system.

scholarly journals Frequency and Vehicle Capacity Determination using a Dynamic Transit Assignment Model

2019 ◽  
Vol 2673 (3) ◽  
pp. 574-585 ◽  
Author(s):  
Oded Cats ◽  
Stefan Glück
Keyword(s):  
Optimization Approach ◽  
Tactical Planning ◽  
Transit Assignment ◽  
Assignment Model ◽  
Planning Phase ◽  
Passenger Flows ◽  
Vehicle Capacity ◽  
Improve Service Quality ◽  
New Generation ◽  
The Impact

We integrate for the first time, to our knowledge, a dynamic transit assignment model into the tactical planning phase. The settings of service frequencies and vehicle capacities determine line capacity and have significant consequences for level-of-service and operational costs. The objective of this study is to determine frequency and vehicle capacity at the network level while accounting for the impact of service variations on users and operator costs. To this end, we propose a simulation-based optimization approach. The proposed model allows accounting for variations in service headways and crowding as well as their consequences for passenger flows distribution, all of which have not been accounted for in the tactical planning so far. Practical benefits of the model are demonstrated by an application to a bus network in the Amsterdam metropolitan area. This study contributes to the development of a new generation of methods that integrate reliability into the tactical planning phase to improve service quality.

Application of smart card data in validating a large-scale multi-modal transit assignment model

2017 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ahmad Tavassoli ◽  
Mahmoud Mesbah ◽  
Mark Hickman
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Transit Assignment ◽  
Assignment Model ◽  
Smart Card Data

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.

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