scholarly journals Effects of rural bus stops on travel time and reliability

2021 ◽  
Author(s):  
Joel Hansson ◽  
Fredrik Pettersson-Löfstedt ◽  
Helena Svensson ◽  
Anders Wretstrand
Keyword(s):  
Travel Time ◽  
Urban Areas ◽  
Time Variability ◽  
Service Reliability ◽  
Travel Times ◽  
Time Performance ◽  
Bus Stops ◽  
Bus Service ◽  
Bus Services ◽  
Travel Time Variability

AbstractDue to relatively low patronage levels, rural bus stops are sometimes questioned in order to improve travel time and reliability on regional bus services. Previous research into stop spacing has focused on urban areas, which means that there is a lack of knowledge regarding the effects of bus stops in regional networks, with longer distances, higher speeds, and lower passenger volumes, in general. The present study addresses this knowledge gap by analysing the effects of bus stops on a regional bus service regarding average travel times, travel time variability, and on-time performance. This is done by statistical analysis of automatic vehicle location (AVL) data, using a combination of methods previously used for analysis of rail traffic and urban bus operations. The results reveal that bus stops that are only used sporadically have a limited impact on average travel times, in general. In contrast, they are all the more influential on travel time variability, and, in turn, on on-time performance. On the studied bus service, the number of stops made have a far greater impact on travel time variability than any of the other included variables, such as the weather or traffic conditions during peak hours. However, the results suggest that rural bus stops have a much lower impact than what we define as secondary bus stops in urban areas. Consequently, by primarily focusing on bus stop consolidation in urban areas, it is possible to significantly improve service reliability without impairing rural coverage.

scholarly journals Analysis of Component Errors in the Highway Capacity Manual Travel Time Reliability Estimations for Urban Streets

2020 ◽  
Vol 2674 (6) ◽  
pp. 85-97 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Estimate Travel Time ◽  
The Difference ◽  
Travel Time Variability

The Highway Capacity Manual 6th edition (HCM6) includes a new methodology to estimate and predict the distribution of average travel times (TTD) for urban streets. The TTD can then be used to estimate travel time reliability (TTR) metrics. Previous research on a 0.5-mi testbed showed statistically significant differences between the HCM6 estimated TTD and the corresponding empirical TTD. The difference in average travel time was 4 s that, while statistically significant, is not important from a practical perspective. More importantly, the TTD variance was underestimated by 70%. In other words, the HCM6 results reflected a more reliable testbed than field measurement. This paper expands the analysis on a longer testbed. It identifies the sources and magnitude of travel time variability that contribute to the HCM6 error. Understanding the potential sources of error, and their quantitative values, are the first steps in improving the HCM6 model to better reflect actual conditions. Empirical Bluetooth travel times were collected on a 1.16-mi testbed in Lincoln, Nebraska. The HCM6 methodology was used to model the testbed, and the estimated TTD by source of travel time variability was compared statistically to the corresponding empirical TTD. It was found that the HCM6 underestimated the TTD variability on the longer testbed by 67%. The demand component, missing variable(s), or both, which were not explicitly considered in the HCM6, were found to be the main source of the error in the HCM6 TTD. A focus on the demand estimators as the first step in improving the HCM6 TTR model was recommended.

scholarly journals Analysis and Application of Log-Linear and Quantile Regression Models to Predict Bus Dwell Times

2019 ◽  
Vol 2673 (10) ◽  
pp. 118-128 ◽  
Author(s):  
Travis B. Glick ◽  
Miguel A. Figliozzi
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Regression Models ◽  
Dwell Time ◽  
Time Variability ◽  
Linear Regression Models ◽  
Ongoing Research ◽  
Bus Stops ◽  
Log Linear ◽  
Travel Time Variability

Understanding the key factors that contribute to transit travel times and travel-time variability is an essential part of transit planning and research. Delay that occurs when buses service bus stops, dwell time, is one of the main sources of travel-time variability and has therefore been the subject of ongoing research to identify and quantify its determinants. Previous research has focused on testing new variables using linear regressions that may be added to models to improve predictions. An important assumption of linear regression models used in past research efforts is homoscedasticity or the equal distribution of the residuals across all values of the predicted dwell times. The homoscedasticity assumption is usually violated in linear regression models of dwell time and this can lead to inconsistent and inefficient estimations of the independent variable coefficients. Log-linear models can sometimes correct for the lack of homoscedasticity, that is, for heteroscedasticity in the residual distribution. Quantile regressions, which predict the conditional quantiles, rather than the conditional mean, are non-parametric and therefore more robust estimators in the presence of heteroscedasticity. This research furthers the understanding of established dwell determinants using these novel approaches to estimate dwell and provides a relatively simple approach to improve existing models at bus stops with low average dwell times.

Road Pricing under Travel Time Variability

2019 ◽  
Author(s):  
Gege Jiang ◽  
Hong Kam LO ◽  
Zheng LIANG
Keyword(s):  
Travel Time ◽  
Road Pricing ◽  
Time Variability ◽  
Travel Time Variability

scholarly journals Measuring Recurrent and Nonrecurrent Traffic Congestion

2003 ◽  
Vol 1856 (1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexander Skabardonis ◽  
Pravin Varaiya ◽  
Karl F. Petty
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
San Francisco ◽  
Traffic Congestion ◽  
Real Life ◽  
Time Variability ◽  
Loop Detectors ◽  
Bay Area ◽  
Freeway Corridors ◽  
Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

scholarly journals Modeling distributions of travel time variability for bus operations

2015 ◽  
Vol 50 (1) ◽  
pp. 6-24 ◽  
Author(s):  
Zhenliang Ma ◽  
Luis Ferreira ◽  
Mahmoud Mesbah ◽  
Sicong Zhu
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Variability
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