scholarly journals Exploring Influencing Factors of Passenger Satisfaction toward Bus Transit in Small-Medium City in China

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaowei Li ◽  
Jingkun Fan ◽  
Yao Wu ◽  
Jun Chen ◽  
Xuefeng Deng
Keyword(s):  
Waiting Time ◽  
Information Service ◽  
Improvement Strategy ◽  
Travel Information ◽  
Ticket Price ◽  
Charging System ◽  
Bus Transit ◽  
Bus Stop ◽  
Passenger Satisfaction ◽  
Significant Factors

This paper aims to explore the factors influencing passengers’ satisfaction toward bus transit and develop the improvement strategy for the small-medium city. Data including individual attributes, travel activity attributes, and perceived service attributes were collected in the city of Weinan, China. The ordered logit (OL) model and ordered Probit (OP) model were employed to explore the significant factors associated with passengers’ satisfaction toward bus transit. The odds ratio (OR) was applied to quantitatively measure the effects of the significant factors. Improving strategies of bus transit service were proposed based on the model results using the three-factor theory. Results show that the OL model outperforms the OP model. The age, daily average waiting time, perceived waiting time, transferring convenience, the attitude of the driver, intelligent travel information service, hygienic environment inside the bus, ticket price, bus route setting, and bus stop setting significantly affect the passengers’ satisfaction. Among them, the ticket price, perceived waiting time, bus stop setting, intelligent travel information service, transferring convenience, and bus route setting were identified as exciting factors. It is recommended that optimization of bus route and bus stop setting, building bus dedicated lanes, optimizing dynamic charging system, and providing intelligent travel information service could be effective strategies to improve passengers’ satisfaction toward bus transit in Chinese small-medium cities.

Scheduling a Bus Fleet for Evacuation Planning Using Stop-Skipping Method

2021 ◽  
pp. 036119812110200
Author(s):  
Hu Zhao ◽  
Shumin Feng ◽  
Yusheng Ci
Keyword(s):  
Waiting Time ◽  
Ant Colony Algorithm ◽  
Programming Model ◽  
Peer To Peer ◽  
Mathematical Programming Model ◽  
Fleet Size ◽  
Bus Stop ◽  
Passenger Demand ◽  
Service Strategies ◽  
Bus System

Sudden passenger demand at a bus stop can lead to numerous passengers gathering at the stop, which can affect bus system operation. Bus system operators often deal with this problem by adopting peer-to-peer service, where empty buses are added to the fleet and dispatched directly to the stop where passengers are gathered (PG-stop). However, with this strategy, passengers at the PG-stop have a long waiting time to board a bus. Thus, this paper proposes a novel mathematical programming model to reduce the passenger waiting time at a bus stop. A more complete stop-skipping model that including four cases for passengers’ waiting time at bus stops is proposed in this study. The stop-skipping decision and fleet size are modeled as a dynamic program to obtain the optimal strategy that minimizes the passenger waiting time, and the optimization model is solved with an improved ant colony algorithm. The proposed strategy was implemented on a bus line in Harbin, China. The results show that, during the evacuation, using the stop-skipping strategy not only reduced the total waiting time for passengers but also decreased the proportion of passengers with a long waiting time (>6 min) at the stops. Compared with the habitual and peer-to-peer service strategies, the total waiting time for passengers is reduced by 31% and 23%, respectively. Additionally, the proportion of passengers with longer waiting time dropped to 43.19% by adopting the stop-skipping strategy, compared with 72.68% with the habitual strategy and 47.5% with the peer-to-peer service strategy.

scholarly journals First Train Timetabling for Urban Rail Transit Networks with Maximum Passenger Transfer Satisfaction

2020 ◽  
Vol 12 (10) ◽  
pp. 4166 ◽  
Author(s):  
Xuan Li ◽  
Toshiyuki Yamamoto ◽  
Tao Yan ◽  
Lili Lu ◽  
Xiaofei Ye
Keyword(s):  
Waiting Time ◽  
Stated Preference ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Network ◽  
Transit Networks ◽  
Bee Colony ◽  
Passenger Satisfaction ◽  
Urban Rail ◽  
Urban Rail Transit Network

This paper proposes a novel model to optimize the first train timetables for urban rail transit networks, with the goal of maximizing passengers’ transfer waiting time satisfaction. To build up the relationship of transfer waiting time and passenger satisfaction, a reference-based piecewise function is formulated with the consideration of passengers’ expectations, tolerances and dissatisfaction on “just miss”. In order to determine the parameters of zero waiting satisfaction rating, the most comfortable waiting time, and the maximum tolerable waiting time in time satisfaction function, a stated preference survey is conducted in rail transit transfer stations in Shanghai. An artificial bee colony algorithm is developed to solve the timetabling model. Through a real-world case study on Shanghai’s urban rail transit network and comparison with the results of minimizing the total transfer time, we demonstrate that our approach performs better in decreasing extremely long wait and “just miss” events and increasing the number of passengers with a relatively comfortable waiting time in [31s, 5min). Finally, four practical suggestions are proposed for urban rail transit network operations.

scholarly journals Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Vee-Liem Saw ◽  
Luca Vismara ◽  
Lock Yue Chew
Keyword(s):  
Reinforcement Learning ◽  
Phase Difference ◽  
Waiting Time ◽  
Learning Process ◽  
Average Waiting Time ◽  
Bus Stop ◽  
Emergent Behaviour ◽  
Reasonable Amount ◽  
Bus Stops ◽  
Simulation Time

We study how N intelligent buses serving a loop of M bus stops learn a no-boarding strategy and a holding strategy by reinforcement learning. The no-boarding and holding strategies emerge from the actions of stay or leave when a bus is at a bus stop and everyone who wishes to alight has done so. A reward that encourages the buses to strive towards a staggered phase difference amongst them whilst picking up passengers allows the reinforcement learning process to converge to an optimal Q-table within a reasonable amount of simulation time. It is remarkable that this emergent behaviour of intelligent buses turns out to minimise the average waiting time of commuters, in various setups where buses move with the same speed or different speeds, during busy as well as lull periods. Cooperative actions are also observed, e.g., the buses learn to unbunch.

The Analysis of Behavioral Responses to Transit Information

2012 ◽  
Vol 253-255 ◽  
pp. 1431-1437
Author(s):  
Rui Sun ◽  
Hai Tao Yu ◽  
Yong Du ◽  
Song Lin Geng
Keyword(s):  
Waiting Time ◽  
Logit Model ◽  
Service System ◽  
Mode Selection ◽  
Information Service ◽  
Behavioral Responses ◽  
Travel Mode ◽  
Ordered Logit Model ◽  
Information Demand ◽  
Static Information

Road conditions, time for travelling and transfer information can be provided by transit information service system for the person travelling during the trip. The system can help people to select more reasonable routes, which can improve the travelling efficiency and attract more and more people using public transport. Based on the survey of transit information service in Beijing, this article will analysis the demand types of transit information. The effects of transit information-travel mode selection can be researched by ordered logit model. The results indicated that the passengers prefer static information at present. The information contains various elements for people travelling such as gender, main travel mode, average waiting time and the accuracy of transit information, demand types and releasing devices. These factors will impact passengers on selecting and changing their mode choice.

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