Measuring the Determinants of Bus Dwell Time: New Insights and Potential Biases

2017 ◽  
Vol 2647 (1) ◽  
pp. 109-117 ◽  
Author(s):  
Travis B. Glick ◽  
Miguel A. Figliozzi
Keyword(s):  
Travel Time ◽  
Dwell Time ◽  
Time Variability ◽  
Trajectory Data ◽  
Key Factors ◽  
Time Duration ◽  
Time Points ◽  
Bus Stop ◽  
Level Data ◽  
Bus Stops

Dwell time is a major component of bus travel time and travel time variability. In turn, the distribution of bus travel times affects transit operators’ costs and customer satisfaction. Previous research used dwell time from bus stop–level data to understand the key factors that contribute to dwell time duration. However, bus stop–level data have significant shortcomings when bus stops are located near intersections or at time points. Regression results show that the use of only stop-level data can significantly bias estimation of boarding and alighting coefficients. This research complements bus stop data with bus GPS trajectory data around bus stops to prevent estimation bias and to measure better the key factors that determine dwell time. Regression results from individual and pooled bus stop models are compared to provide new insights into the impacts of traffic conditions, signalized intersections, bus bays, and time points on dwell times. The impacts of nearside, midblock, and farside bus stops are included in the analysis. The number of passengers boarding and alighting has a nonlinear effect with economies or efficiencies of scale.

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.

scholarly journals Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1217
Author(s):  
Teresa Cristóbal ◽  
Gabino Padrón ◽  
Alexis Quesada ◽  
Francisco Alayón ◽  
Gabriel de Blasio ◽  
...  
Keyword(s):  
Data Mining ◽  
Travel Time ◽  
Dwell Time ◽  
Mass Transit ◽  
Transit Systems ◽  
Running Time ◽  
Bus Stop ◽  
Transport Line ◽  
Using Data

Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.

Optimizing School Bus Stop Placement in Howard County, Maryland

2016 ◽  
pp. 1660-1676 ◽  
Author(s):  
Michael Galdi ◽  
Paporn Thebpanya
Keyword(s):  
Travel Time ◽  
Carbon Footprint ◽  
Current System ◽  
School Bus ◽  
School Officials ◽  
Statistical Estimates ◽  
Bus Stop ◽  
Network Analyst ◽  
Bus Stops ◽  
State And Local

In the current system, school bus stops in Howard County, Maryland are manually placed along the school bus routes based on safety, cost-efficiency, and many other variables. With such liberal placement, bus stops are sometimes placed unnecessarily. This issue is prevalent in many school districts and often results in needlessly close bus stop proximity. In this study, the authors implemented a GIS-based heuristic to assist school officials in optimizing their districts bus stop placement. They also estimated the proportion of county-wide bus stops that could be eliminated by this approach. Following the constraints determined by State and local guidelines, the ArcGIS Network Analyst Extension was used to identify unnecessary bus stops across the study area. The initial output was re-evaluated by school officials in order to determine if those bus stops would be eliminated. The results indicate that approximately 30% of the existing bus stops were marked as “candidates for elimination” by the GIS process. After a review of these candidates, it was determined that at least 15% of the total school bus stops could be eliminated. Statistical estimates lent credence to the benefit of a re-evaluation of these bus stops. The method developed in this study can easily be replicated. Hence, it may inspire other school systems to exercise the same approach. Additionally, the results provide a gateway for future studies in examining more efficient school bus routes with less travel time, as well as investigating how much the carbon footprint of school bus fleets can be reduced.

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.

Statistical analysis of the stability of bus vehicles based on GPS trajectory data

2019 ◽  
Vol 33 (03) ◽  
pp. 1950015 ◽  
Author(s):  
Hui Zhang ◽  
Baiying Shi ◽  
Shuguang Song ◽  
Quanman Zhao ◽  
Xiangming Yao ◽  
...  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Dwell Time ◽  
Trajectory Data ◽  
Running Time ◽  
Bus Bunching ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Bus Service ◽  
The Stability

High quality bus service is considered as an efficient way to mitigate traffic congestion in big cities. Global positioning system (GPS) data provide sufficient sources to evaluate the performance of bus vehicles that both passengers and operator concern about. This paper aims to propose a framework to assess the operational performance of bus routes based on the GPS trajectory data collected from Jinan, China. Several important indicators of bus operation including travel time of routes, section running time, dwell time and bus bunching have been studied. The results show that the travel time of routes follow right skewed distributions. Moreover, section running time between two consecutive stations varies in different time period and it is larger in evening peak hours. Additionally, the dwell time has been discussed and the results show that there is no big variation in most stations except some stations, which provides a help to identify the key stations. Furthermore, we propose an approach to detect the bunching points. The results indicate the bunching points are easy to occur in the peak hours and the congested road section.

Improved Transit Signal Priority System for Networks with Nearside Bus Stops

2005 ◽  
Vol 1925 (1) ◽  
pp. 205-214 ◽  
Author(s):  
Wonho Kim ◽  
L. R. Rilett
Keyword(s):  
Dwell Time ◽  
Prediction Interval ◽  
Weighted Least Squares ◽  
Least Squares Regression ◽  
Transit Signal Priority ◽  
Bus Stop ◽  
Bus Stops ◽  
Negative Impacts ◽  
In Transit ◽  
Signal Operations

Transit signal priority (TSP), which has been deployed in many cities in North America and Europe, is a traffic signal enhancement strategy that facilitates efficient movement of transit vehicles through signalized intersections. Most TSP systems, however, do not work well in transit networks with nearside bus stops because of the uncertainty in bus dwell time. Unfortunately, most bus stops on U.S. arterial roadways are nearside ones. In this research, weighted-least-squares regression modeling was used to estimate bus stop dwell time and, more important, the associated prediction interval. An improved TSP algorithm that explicitly considers the prediction interval was developed to reduce the negative impacts of nearside bus stops. The proposed TSP algorithm was tested on a VISSIM model of an urban arterial section of Bellaire Boulevard in Houston, Texas. In general, it was found that the proposed TSP algorithm was more effective than other algorithms because it improved bus operations without statistically significant impacts on signal operations.

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

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

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.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

2021 ◽  
pp. 036119812110302
Author(s):  
Chao Wang ◽  
Weijie Chen ◽  
Yueru Xu ◽  
Zhirui Ye
Keyword(s):  
Approximation Method ◽  
Service Time ◽  
Diffusion Approximation ◽  
Coefficient Of Variation ◽  
Queuing Theory ◽  
Queuing Model ◽  
Bus Stop ◽  
Proposed Model ◽  
Bus Stops ◽  
Bus Service

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

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