scholarly journals Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Feng Zhou ◽  
Jun-gang Shi ◽  
Rui-hua Xu
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Time Data ◽  
Travel Time Data ◽  
Urban Rail ◽  
Distribution Parameters ◽  
The Cost ◽  
Walking Time

With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT.

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 Preventive Maintenance Strategy for Train Doors Based on the Competitive Weibull Theory

2020 ◽  
Author(s):  
deqiang he ◽  
Xiaozhen Zhang ◽  
Yanjun Chen ◽  
Jian Miao ◽  
Congbo Li ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Preventive Maintenance ◽  
Estimation Method ◽  
Weibull Model ◽  
Urban Rail Transit ◽  
Fuzzy Cluster ◽  
Rail Transit ◽  
Maintenance Strategy ◽  
Actual Situation ◽  
Urban Rail

Abstract In view of the problems of over-maintenance and under-maintenance in the current urban rail transit maintenance strategy and the reliability of single processing of fault data, which is often inconsistent with the actual situation, an incomplete preventive maintenance strategy based on the competitive Weibull model is proposed in this paper. To make the fault mechanism processing method for urban rail vehicles more accurate, fault feature attributes and fault information sequences are introduced to classify fault data. Fuzzy cluster analysis of vehicle fault data can be performed using the formula of the competitive Weibull model, and parameter estimation of the reliability model can be performed by combining it with the graph parameter estimation method. In addition, the fault rate increase factor and service age reduction factor are introduced into the maintenance strategy, and the optimal preventive maintenance cycle and maintenance times are obtained by combining maintenance and replacement according to reliability. A quantum-genetic intelligent algorithm is used to optimize the model-solving process. Finally, the maintenance of urban rail transit train doors is taken as an example. The results of this study show that compared with the traditional maintenance strategy, the reliability of the proposed maintenance strategy is closer to the actual situation. At the same time, the proposed maintenance strategy can effectively reduce the number of parked vehicles, reduce maintenance costs, and ensure the safety of train operation, maintenance economy and performance of tasks.

Study of the Influencing Factors of Urban Rail Transit Travel Behavior: A Case Study of Xi'an

2015 ◽  
Vol 744-746 ◽  
pp. 2049-2052
Author(s):  
Yao Wu ◽  
Jian Lu ◽  
Yue Chen
Keyword(s):  
Travel Time ◽  
Influencing Factors ◽  
Travel Behavior ◽  
Family Income ◽  
Binary Logistic Regression ◽  
Travel Distance ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Binary Logistic Regression Model ◽  
Urban Rail

In order to study the factors influencing urban rail transit travel behavior, a questionnaire was conducted for residents’ selection of rail transit in Xi'an. Based on the collected data from 1105 valid questionnaires, a binary logistic regression model was established to analyze the influencing factors quantitatively. The results showed that seven factors have statistically significant for rail transit travel behavior including age, occupation, family income, average monthly household transportation costs (T-cost), travel purpose, travel distance, and travel time. Odds ratio analysis revealed that young people and staff were more likely to choose rail transit; the probability of selecting rail transit increased with the increase of family income and the T-cost. In addition, more and more people tend to rail travel with the increase of travel distance and travel time.

The Optimization of Arriving and Departing Time of Trains in Urban Rail Transit Based on Passenger Transfer

2014 ◽  
Vol 587-589 ◽  
pp. 1958-1962
Author(s):  
Jie Ping Sun
Keyword(s):  
Travel Time ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Operation Process ◽  
Urban Rail ◽  
Practical Operation ◽  
Time Extension ◽  
Transfer Stations ◽  
Important Significance ◽  
Beijing Subway

In this paper, there is a study on the effect of the lack of coordination between trains in the network, such as waiting aggravation, travel time extension. Through the study of the practical operation process and passenger transfer characteristics of urban rail transit, according to the types of line crossing, taking the minimized waiting time of all transfer passengers in station as the object function, the optimization model was designed for three lines with two intersections, solve the model by using the Lingo, and transfer stations, Xidan and Dongdan station, from lines No.1 No.4 and No.5 of Beijing Subway Network are chosen as an example to validate its rationality and availability. The result shows that the model is of important significance to reduce the travel time for transfer passengers.

Research on Walking Time of Transfer Pedestrians in Urban Rail Transit Station

2014 ◽  
Vol 610 ◽  
pp. 1053-1056 ◽  
Author(s):  
Jin Chu Zheng ◽  
Chang Xu Ji ◽  
Long Gao
Keyword(s):  
Traffic Congestion ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Statistical Point ◽  
Practical Applications ◽  
Urban Rail ◽  
Basic Work ◽  
Transit Station ◽  
Walking Time

The urban rail transport is the backbone of urban public transport. It alleviates urban traffic congestion and plays an important role. Survey and determination of the passenger walking time in urban rail transit station passages is the basic work to realize train coordination between lines and to reduce the passenger waiting time in transferring. The paper focuses on the passenger walking time on transfer pedestrians from the statistical point of view. Finally, Beijing Dongdan transfer station is selected as an example. This study has some practical applications.

scholarly journals Application of Regenerative Braking with Optimized Speed Profiles for Sustainable Train Operation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Leon Allen ◽  
Steven Chien
Keyword(s):  
Energy Saving ◽  
Travel Time ◽  
Transmission Lines ◽  
Urban Rail Transit ◽  
Regenerative Braking ◽  
Rail Transit ◽  
Time Step ◽  
Urban Rail ◽  
Train Operation ◽  
Storage Limit

This paper presents a method for synergizing the energy-saving strategies of integrated coasting and regenerative braking in urban rail transit operations. Coasting saves energy by maintaining motion with propulsion disabled, but it induces longer travel time. Regenerative braking captures and reuses the braking energy of the train and could shorten travel time but reduces the time available for coasting, indicating a tradeoff between the two strategies. A simulation model was developed based on fundamental kinematic equations for assessing sustainable train operation with Wayside Energy-Saving Systems (WESSs). The objective of this study is to optimize speed profiles that minimize energy consumption, considering the train schedule and specifications, track alignment, speed limit, and the WESS parameters such as storage limit and energy losses in the transmission lines. The decision variables are the acceleration at each time step of the respective motion regimes. Since the study optimization problem is combinatorial, a Genetic Algorithm was developed to search for the solution. A case study was conducted which examined various scenarios with and without WESS on a segment of an urban rail transit line to test the applicability of the proposed model and to provide a platform for the application of ideas developed in this study. It was determined that synergizing the energy-saving strategies of coasting and regenerative braking yielded the greatest efficiency of the scenarios examined.

Estimating Distributions of Walking Speed, Walking Distance, and Waiting Time with Automated Fare Collection Data for Rail Transit

2017 ◽  
Vol 2648 (1) ◽  
pp. 134-141 ◽  
Author(s):  
Xiaoyan Xie ◽  
Fabien Leurent
Keyword(s):  
Stochastic Model ◽  
Travel Time ◽  
Waiting Time ◽  
Walking Speed ◽  
Urban Rail Transit ◽  
Walking Distance ◽  
Rail Transit ◽  
Journey Time ◽  
Urban Rail ◽  
Collection Data

Journey time is one of the key factors of public transport quality of service that is of concern to passengers. The variability in passenger journey time stems from the variability of in-vehicle travel time, walking time, and waiting time. A better understanding of passenger walking and waiting behavior along an urban rail transit line, especially in mass transit hubs and stations, is thus of much relevance. However, the estimation of passengers’ walking speed, walking distance, and waiting time is still a complicated and difficult task: individual walking speed and waiting time keep changing throughout the interindividual journey. A novel stochastic model that uses automated fare collection data and automatic vehicle location data is proposed to estimate the distributions of walking speed, walking distance, and waiting time indirectly. The stochastic model relates tap-out time to tap-in time on an individual basis and with respect to train circulation on the basis of statistical distributions for the individual’s cruise walking speed, in-station walking distance, and waiting time. Analytical formulas are provided, first conditional to an individual walking speed and waiting time and then without conditions. The model is applied to the maximum likelihood estimate (MLE) of the parameters with constrained numerical optimization. A case study of the urban rail transit line Réseau Express Régional [Regional Express Network (RER)] A in the Paris region yielded reasonable parameter values and factor mean values. This study paves the way for estimating passenger elementary travel time along a journey.

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