scholarly journals Analyses of Bus Travel Time Reliability and Transit Signal Priority at the Stop-To-Stop Segment Level

2000 ◽  
Author(s):  
Wei Feng
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Transit Signal Priority ◽  
Bus Travel Time

Modeling Bus Travel Time Reliability with Supply and Demand Data from Automatic Vehicle Location and Smart Card Systems

2015 ◽  
Vol 2533 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Zhen-Liang Ma ◽  
Luis Ferreira ◽  
Mahmoud Mesbah ◽  
Ahmad Tavassoli Hojati
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Supply And Demand ◽  
Seemingly Unrelated Regression ◽  
Regression Equations ◽  
Travel Time Reliability ◽  
Automatic Vehicle Location ◽  
Different Types ◽  
Vehicle Location ◽  
Bus Travel Time

Travel time reliability is an important aspect of bus service quality. Despite a significant body of research on private vehicle reliability, little attention has been paid to bus travel time reliability at the stop-to-stop link level on different types of roads. This study aims to identify and quantify the underlying determinants of bus travel time reliability on links of different road types with the use of supply and demand data from automatic vehicle location and smart card systems collected in Brisbane, Australia. Three general bus-related models were developed with respect to the main concerns of travelers and planners: average travel time, buffer time, and coefficient of variation of travel time. Five groups of alternative models were developed to account for variations caused by different road types, including arterial road, motorway, busway, and central business district. Seemingly unrelated regression equations estimation were applied to account for cross-equation correlations across regression models in each group. Three main categories of unreliability contributory factors were identified and tested in this study, namely, planning, operational, and environmental. Model results provided insights into these factors that affect bus travel time and its variability. The most important predictors were found to be the recurrent congestion index, traffic signals, and passenger demand at stops. Results could be used to target specific strategies aimed at reducing unreliability on different types of roads.

Stochastic Simulation of Bus Traveling Time

2011 ◽  
Vol 186 ◽  
pp. 556-559
Author(s):  
Lian Xue ◽  
Dan Jie Zhao ◽  
Gui Mei Liu
Keyword(s):  
Travel Time ◽  
Stochastic Simulation ◽  
Public Transportation ◽  
Simulation Method ◽  
Service Level ◽  
Transportation Systems ◽  
Travel Time Reliability ◽  
Public Transport System ◽  
Public Transportation Systems ◽  
Bus Travel Time

The development of the city's public transport system has an indispensable role to alleviate the pressure of urban roads. Bus travel time reliability is an important evaluation index of the bus operation service level. The simulation of bus travel time helps us understand the reliability of bus running time. In this paper, we use Monte Carlo stochastic simulation method to calculate the reliability of bus travel time. On this basis, we establish a model of the reliability of public transportation systems to research the reliability of bus travel time.

scholarly journals Bus Travel Time Reliability Incorporating Stop Waiting Time and In-vehicle Travel Time with AVL Data

2022 ◽  
Author(s):  
Zixu Zhuang ◽  
Zhanhong Cheng ◽  
Jia Yao ◽  
Jian Wang ◽  
Shi An
Keyword(s):  
Travel Time ◽  
Waiting Time ◽  
Traffic Congestion ◽  
Influence Factors ◽  
Future Research ◽  
Travel Time Reliability ◽  
Independent Events ◽  
Harbin City ◽  
Bus Travel Time ◽  
Vehicle Travel Time

Abstract Improving bus operation quality can attract more commuters to use bus transit, and therefore reduces the share of car and alleviates traffic congestion. One important index of bus operation quality is the bus travel time reliability, which in this paper is defined to be the probability when the sum of bus stop waiting time and in-vehicle travel time is less than a certain threshold. We formulate the bus travel time reliability by the convolution of independent events’ probabilities, and elaborate the calculation method using Automatic Vehicle Location (AVL) data. Next, the No.63 Bus Line in Harbin City is used to test the applicability of the proposed method, and analyze the influence factors of the bus travel time reliability. The numerical results show that factors such as weather, workday, departure time, travel distance, and the distance from the boarding stop to the bus departure station will significantly affect the travel time reliability. At last, some general conclusions and future research are summarized.

Field Evaluation of Connected Vehicle-Based Transit Signal Priority Control under Two Different Signal Plans

2020 ◽  
Vol 2674 (7) ◽  
pp. 172-180
Author(s):  
Qinzheng Wang ◽  
Xianfeng (Terry) Yang ◽  
Blaine D. Leonard ◽  
Jamie Mackey
Keyword(s):  
Travel Time ◽  
Salt Lake ◽  
Salt Lake City ◽  
Field Evaluation ◽  
Connected Vehicle ◽  
Transit Signal Priority ◽  
Running Time ◽  
Lake City ◽  
Transit Operation ◽  
Bus Travel Time

In 2017, a connected vehicle (CV) corridor utilizing dedicated short-range communication (DSRC) technology was built along Redwood Road, Salt Lake City, Utah. One main goal of this CV corridor is to implement transit signal priority (TSP) when the bus is behind its published schedule by a certain threshold. With the data generated by the transit vehicles, transmitted through the DSRC system, logged by traffic signal controller, and coupled with the Utah Transit Authority (UTA) data from transit operation system, some performance data of the TSP can be analyzed including TSP requested, TSP served, bus reliability, bus travel time, and bus running time. For providing better signal coordination to buses, the signal plan for this CV corridor underwent retiming in October 2018. This research aims to compare the TSP performance before and after the signal retiming. The field data of August, September, November, and December in 2018 were selected to perform this evaluation. Results show that the TSP served rate after signal retiming is 35.29%, which is higher than that of 33.12% before signal retiming. In addition, compared with the signal plan before October, bus reliability northbound and southbound on the CV corridor was improved by 2.4% and 1.47%, respectively; bus travel time and bus running time were reduced as well.

scholarly journals Causal analysis of bus travel time reliability in Birmingham, UK

2021 ◽  
Vol 12 ◽  
pp. 100280
Author(s):  
Sakdirat Kaewunruen ◽  
Jessada Sresakoolchai ◽  
Haoran Sun
Keyword(s):  
Travel Time ◽  
Causal Analysis ◽  
Travel Time Reliability ◽  
Bus Travel Time

scholarly journals Bus travel time reliability analysis: a case study

2014 ◽  
Vol 167 (3) ◽  
pp. 178-184 ◽  
Author(s):  
Xiaobo Qu ◽  
Erwin Oh ◽  
Jinxian Weng ◽  
Sheng Jin
Keyword(s):  
Reliability Analysis ◽  
Travel Time ◽  
Travel Time Reliability ◽  
Bus Travel Time

scholarly journals Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Song Xianmin ◽  
Yuan Mili ◽  
Liang Di ◽  
Ma Lin
Keyword(s):  
Travel Time ◽  
Optimization Method ◽  
Connected Vehicle ◽  
Transit Signal Priority ◽  
Travel Time Prediction ◽  
Arrival Times ◽  
Vehicle Delay ◽  
Priority Needs ◽  
Bus Travel Time ◽  
Real Time Information

Aiming at reducing per person delay, this paper presents an optimization method for Transit Signal Priority (TSP) considering multirequest under connected vehicle environment, which is based on the travel time prediction model. Conventional arrival time of transit depended on the detection information and the front road state, which restricted the effect of priority seriously. According to the bidirectional and real-time information transmission under connected vehicle environment, this paper establishes a more accurate forecasting model of bus travel time. Based on minimizing the total person delay at the intersection, the decision mechanism of multirequest is devised to meet the priority needs of buses with different arrival times. And the green time compensation algorithm is developed after considering the arrival information of the buses in the next cycle of compensational phase. Finally, the paper combines the COM interface of VISSIM and Matlab to achieve the proposed method under connected vehicle environment. Four control methods were tested when the VCR was 0.5, 0.7, and 0.9. The results illustrated that the proposed method reduced per person delay by 18.57%, 11.88%, and 18.96% and decreased the private vehicle delay by 3.73%, 7.62%, and 13.10%, respectively.

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