Identifying Causes of Performance Issues in Bus Schedule Adherence with Automatic Vehicle Location and Passenger Count Data

2010 ◽  
Vol 2143 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Michael Mandelzys ◽  
Bruce Hellinga
Keyword(s):  
Count Data ◽  
Automatic Vehicle Location ◽  
Vehicle Location ◽  
Performance Issues ◽  
Schedule Adherence ◽  
Bus Schedule

scholarly journals Bus Travel Time Deviation Analysis Using Automatic Vehicle Location Data and Structural Equation Modeling

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaolin Gong ◽  
Xiucheng Guo ◽  
Xueping Dou ◽  
Lili Lu
Keyword(s):  
Structural Equation Modeling ◽  
Travel Time ◽  
Structural Equation ◽  
Service Planning ◽  
Equation Modeling ◽  
Automatic Vehicle Location ◽  
Schedule Recovery ◽  
Vehicle Location ◽  
Bus Service ◽  
Bus Schedule

To investigate the influences of causes of unreliability and bus schedule recovery phenomenon on microscopic segment-level travel time variance, this study adopts Structural Equation Modeling (SEM) to specify, estimate, and measure the theoretical proposed models. The SEM model establishes and verifies hypotheses for interrelationships among travel time deviations, departure delays, segment lengths, dwell times, and number of traffic signals and access connections. The finally accepted model demonstrates excellent fitness. Most of the hypotheses are supported by the sample dataset from bus Automatic Vehicle Location system. The SEM model confirms the bus schedule recovery phenomenon. The departure delays at bus terminals and upstream travel time deviations indeed have negative impacts on travel time fluctuation of buses en route. Meanwhile, the segment length directly and negatively impacts travel time variability and inversely positively contributes to the schedule recovery process; this exogenous variable also indirectly and positively influences travel times through the existence of signalized intersections and access connections. This study offers a rational approach to analyzing travel time deviation feature. The SEM model structure and estimation results facilitate the understanding of bus service performance characteristics and provide several implications for bus service planning, management, and operation.

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.

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