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.

Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

2017 ◽  
Vol 2652 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Zhenliang Ma ◽  
Sicong Zhu ◽  
Haris N. Koutsopoulos ◽  
Luis Ferreira
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Central Tendency ◽  
Automatic Vehicle Location ◽  
Conditional Mean ◽  
Vehicle Location ◽  
Travel Time Variability

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

Valuation of Different Types of Travel Time Reliability in Route Choice

2006 ◽  
Vol 1985 (1) ◽  
pp. 162-170 ◽  
Author(s):  
Enide A. I. Bogers ◽  
Francesco Viti ◽  
Serge P. Hoogendoorn ◽  
Henk J. Van Zuylen
Keyword(s):  
Travel Time ◽  
Route Choice ◽  
Travel Time Reliability ◽  
Different Types

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.

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