scholarly journals Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Xing Chen ◽  
Leishan Zhou ◽  
Zixi Bai ◽  
Yixiang Yue ◽  
Bin Guo ◽  
...  
Keyword(s):  
Travel Demand ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Travel Patterns ◽  
Train Control ◽  
Left Behind ◽  
Passenger Travel ◽  
Urban Rail ◽  
Proposed Model ◽  
Time Parameters

The “left-behind” phenomenon occurs frequently in Urban Rail Transit (URT) networks with booming travel demand, especially during peak hours in a complex URT network, which makes passenger travel patterns more complicated. This paper proposes a methodology to mine passenger travel patterns based on fare transaction records from automatic fare collection (AFC) systems and Automatic Vehicle Location (AVL) data from Communication Based Train Control (CBTC) Systems or tracking systems. By introducing the concept of a sequence, a space-time-sequence trajectory model is proposed to simulate a passenger’s travel activities, including when they are left-behind. The paper analyzes passenger travel trajectory links and estimates the weight of each feasible trajectory under tap-in/tap-out constraints. The station time parameters, including access/egress and transfer walking-time parameters, are important inputs for the model. The paper also presents a maximum-likelihood approach to estimate these parameters from AFC transaction data and AVL data. The methodology is applied to a case study using AFC and AVL data from the Beijing URT network during peak hours to test the proposed model and algorithm. The estimation results are consistent with the results obtained from the authorities, and this finding verifies the feasibility of our approach.

scholarly journals Stop Plan of Express and Local Train for Regional Rail Transit Line

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Programming Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Long Distance ◽  
Passenger Travel ◽  
Urban Rail ◽  
Proposed Model ◽  
Rail Line

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.

scholarly journals Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yuedi Yang ◽  
Jun Liu ◽  
Pan Shang ◽  
Xinyue Xu ◽  
Xuchao Chen
Keyword(s):  
Travel Demand ◽  
Urban Rail Transit ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Rail Transit ◽  
Travel Demand Management ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail ◽  
Proposed Model

At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

Scenario-Based Integrated Assignment Model for Bi-Class User Rail Transit Networks

2013 ◽  
Vol 361-363 ◽  
pp. 1963-1966
Author(s):  
Wei Zhu
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Networks ◽  
Assignment Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Scale Network ◽  
Algorithm Framework ◽  
Real Scale

An integrated assignment model for urban rail transit (URT) networks was proposed and discussed in four typical scenarios with the consideration of passenger difference between native and non-native. An overall algorithm framework for the model was also developed, which introduced three critical route choice models and combined them appropriately to different scenarios. A case study was performed on a real-scale network of Shanghai during the Expo 2010. The results revealed that the proposed model can deliver more appropriate solution to the assignment problem compared to the existing practice in the real world.

Disruption Management in Urban Rail Transit System

2018 ◽  
pp. 18-48
Author(s):  
Erfan Hassannayebi ◽  
Arman Sajedinejad ◽  
Soheil Mardani
Keyword(s):  
Waiting Time ◽  
Recovery Time ◽  
Control Strategies ◽  
Discrete Event ◽  
Urban Rail Transit ◽  
Disruption Management ◽  
Rail Transit ◽  
Transit Systems ◽  
Urban Rail ◽  
Proposed Model

The process of disruption management in rail transit systems faces challenging issues such as the unpredictable occurrence time, the consequences and the uncertain duration of disturbance or recovery time. The objective of this chapter is to adopt a discrete-event object-oriented simulation system, which applies the optimization algorithms in order to compensate the system performance after disruption. A line blockage disruption is investigated. The uncertainty associated with blockage recovery time is considered with several probabilistic scenarios. The disruption management model presented here combines short-turning and station-skipping control strategies with the objective to decrease the average passengers' waiting time. A variable neighborhood search (VNS) algorithm is proposed to minimize the average waiting time. The computational experiments on real instances derived from Tehran Metropolitan Railway are applied in the proposed model and the advantages of the implementing the optimized single and combined short-turning and stop-skipping strategies are listed.

Computation Model of Network Scale for Urban Rail Transit Based on Mutualism Model

2014 ◽  
Vol 631-632 ◽  
pp. 1329-1333 ◽  
Author(s):  
Wei Zhao ◽  
Yang Ming Gao ◽  
Man Sheng Dong
Keyword(s):  
Travel Demand ◽  
Nonlinear Differential Equations ◽  
Influencing Factor ◽  
Theory Model ◽  
Urban Rail Transit ◽  
Practical Significance ◽  
Rail Transit ◽  
Demand Model ◽  
Urban Rail ◽  
Network Scale

In order to compute the network scale of urban rail transit, the paper proposed a mutualist model describing the network scale, through investigating the main influencing factor of network scale-the population and the relationship between population and network. The equilibrium point of different equations was given practical meaning.calibrating the key physical quantities, the nonlinear differential equations have been solved by using Runge–Kutta. Applying the model to predict the population and network scale in main city in Hefei in 2020 and long future, compared with the traditional travel demand model and network service coverage model. The result are basically accordant, so the new model is feasible.The theory model provides a new means of quantitative analysis method for urban rail transit network planning, So it has important theoretical and practical significance.

scholarly journals Forecasting Passenger Flow Distribution on Holidays for Urban Rail Transit Based on Destination Choice Behavior Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Enjian Yao ◽  
Junyi Hong ◽  
Long Pan ◽  
Binbin Li ◽  
Yang Yang ◽  
...  
Keyword(s):  
Public Transportation ◽  
Choice Model ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Destination Choice ◽  
Rail Transit ◽  
Efficient Operation ◽  
Passenger Flow ◽  
Urban Rail ◽  
Proposed Model

Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers’ destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers’ travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes.

scholarly journals A Timetable Optimization Model for Urban Rail Transit with Express/Local Mode

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Renjie Zhang ◽  
Shisong Yin ◽  
Mao Ye ◽  
Zhiqiang Yang ◽  
Shanglu He
Keyword(s):  
Waiting Time ◽  
Optimization Model ◽  
Urban Rail Transit ◽  
Local Mode ◽  
Rail Transit ◽  
Long Distance ◽  
Urban Rail ◽  
Proposed Model ◽  
Distance Travel ◽  
Timetable Optimization

Nowadays, an express/local mode has be studied and applied in the operation of urban rail transit, and it has been proved to be beneficial for the long-distance travel. The optimization of train patterns and timetables is vital in the application of the express/local mode. The former one has been widely discussed in the various existing works, while the study on the timetable optimization is limited. In this study, a timetable optimization model is proposed by minimizing the total passenger waiting time at platforms. Further, a genetic algorithm is used to solve the minimization problems in the model. This study uses the data collected from Guangzhou Metro Line 14 and finds that the total passenger waiting time at platforms is reduced by 9.3%. The results indicate that the proposed model can reduce the passenger waiting time and improve passenger service compared with the traditional timetable.

scholarly journals Urban Rail Transit Scheduling under Time-Varying Passenger Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Xing Zhao ◽  
Zhongyan Hou ◽  
Jihuai Chen ◽  
Yin Zhang ◽  
Junying Sun
Keyword(s):  
Urban Rail Transit ◽  
Load Factor ◽  
Rail Transit ◽  
Time Interval ◽  
Train Timetable ◽  
Passenger Demand ◽  
Scheduling Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Transit Scheduling

In view of the conflict between the time-variation of urban rail transit passenger demand and the homogeneity of the train timetable, this paper takes into account the interests of both passengers and operators to build an urban rail transit scheduling model to acquire an optimized time-dependent train timetable. Based on the dynamic passenger volumes of origin-destination pairs from the automatic fare collection system, the model focuses on minimizing the total passenger waiting time with constraints on time interval between two consecutive trains, number and capacity of trains available, and load factor of trains. A hybrid algorithm which consists of the main algorithm based on genetic algorithm and the nested algorithm based on train traction calculation and safety distance requirement is designed to solve the model. To justify the effectiveness and the practical value of the proposed model and algorithm, a case of Nanjing Metro Line S1 is illustrated in this paper. The result shows that the optimized train timetable has advantage compared to the original one.

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