Complete Estimation Approach for Characterizing Passenger Travel Time Distributions at Rail Transit Stations

2020 ◽  
Vol 146 (7) ◽  
pp. 04020050
Author(s):  
Wei Zhu ◽  
Weili Fan ◽  
Jin Wei ◽  
Wei “David” Fan
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang

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.


Author(s):  
Malvika Dixit ◽  
Ties Brands ◽  
Niels van Oort ◽  
Oded Cats ◽  
Serge Hoogendoorn

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.


Author(s):  
Lieve Creemers ◽  
Mario Cools ◽  
Hans Tormans ◽  
Pieter-Jan Lateur ◽  
Davy Janssens ◽  
...  

The introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of light rail mode choice for medium- and long-distance trips (10 to 40 km) for a new light rail system in Flanders, Belgium. To investigate these choices, the effects of various transport system–specific factors (i.e., travel cost, in-vehicle travel time, transit punctuality, waiting time, access and egress time, transfers, and availability of seats) as well as the travelers' personal traits were analyzed by using an alternating logistic regression model, which explicitly takes into account the correlated responses for binary data. The data used for the analysis stem from a stated preference survey conducted in Flanders. The modeling results are in line with literature: most transport system–specific factors as well as socioeconomic variables, attitudinal factors, perceptions, and the frequency of using public transport contribute significantly to the preference for light rail transit. In particular, the results indicate that the use of light rail is strongly influenced by travel cost and in-vehicle travel time and to a lesser extent by waiting and access–egress time. Seat availability appeared to play a more important role than did transfers in deciding to choose light rail transit. The findings of this paper can be used by policy makers as a frame of reference to make light rail transit more successful.


2015 ◽  
Vol 744-746 ◽  
pp. 2049-2052
Author(s):  
Yao Wu ◽  
Jian Lu ◽  
Yue Chen

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.


2014 ◽  
Vol 587-589 ◽  
pp. 1958-1962
Author(s):  
Jie Ping Sun

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.


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