Measuring the Determinants of Bus Dwell Time: New Insights and Potential Biases (original) (raw)
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Bus Dwell-Time Model of Main Urban Route Stops
Transportation Research Record: Journal of the Transportation Research Board, 2012
This paper proposes a bus dwell-time model obtained by means of a robust statistical evaluation of boarding passenger data at stops. This model is a change from the generally accepted linear model (i.e., dwell time increases in a fixed rate of time per passenger). The statistical analysis proves the validity of the potential model. This model was derived from a large number of observations on Line 27 of the Transports Municipal Company in Madrid, Spain, and validated with observations from another line (Line 70). The data were gathered by observers who could attest to the influence of occasional incidents in the boarding process. This model can be used to evaluate line capacity more accurately. The analyzed lines were main urban routes with high passenger demand requiring the use of articulated buses. These lines were selected according to two basic criteria: routes with high demand that guarantee good results and routes with similar vehicle types with an onboard payment method. A l...
Journal of Public Transportation
Busway transit has reemerged as a cost-effective transportation alternative for prodding urban mobility. This article examines the operational characteristics of an exclusive busway svstem witn high passenger ridership and subject to the competitive forces of individual operators. Results of a running time model suggest that the increase In running time associated with an additional passenger movement is low but that the delay imposed by recurrent vehicle deceleration and acceleration related to frequent stops is high. Frequent vehicle stops reduce fuel efficiency, increase pollution, reduce travel time, and decrease productivity. Results of several specifications of dwell-time regression models indicate that established models tend to yield biased coefficients for boarding and alighting passenger movements. These model results also confirm that the dwell-time delay associated with an additional passenger movement is very low in Bogota's busway tl'en though average dwell time per passenger tends to be high. It follows, therefore, that organizing passenger boarding and alighting operations and consolidating passenger activity points promise to be effective strategies for improving operations. More broadly, the findings indicate that under a deregulated operating environment, a regulatory framework that includes monitoring operations and enforcing designated stop locations remains important for efficient bus",a)' operations.
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This study aimed at determining patrons’ acceptable wait times beyond the bus scheduled arrival time at bus stops in Washington, DC and to develop accompanying prediction models to provide decision-makers with additional tools to improve patronage. The research primarily relied on a combination of manual and video-based data collection efforts. Manual field data collection was used for surveying patrons to obtain their suggested acceptable wait times at bus stops, while video-based data collection was used to obtain bus stop characteristics and operations. In all, 3,388 bus patrons at 71 selected bus stops were surveyed. Also, operational data for 2,070 bus arrival events on 226 routes were extracted via video playback. Data were collected for AM peak, PM peak and mid-day periods of nine-month duration from May 2018 through January 2019. The results of the survey showed that the minimum acceptable wait time beyond the scheduled arrival time was reported to be 1 minute, while the max...
Analysis and Application of Log-Linear and Quantile Regression Models to Predict Bus Dwell Times
Transportation Research Record: Journal of the Transportation Research Board, 2019
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 ...
Influence of Platform Height, Door Width, and Fare Collection on Bus Dwell Time
Transportation Research Record: Journal of the Transportation Research Board, 2010
Dwell time is the time that a public transport vehicle remains stopped while transferring passengers. Dwell time depends on the number of boarding and alighting passengers plus other characteristics, such as platform height, door width, fare collection method, internal layout of the vehicle, and occupancy of the vehicle. Traditionally, dwell time has been described as a linear function of the number of passengers boarding and alighting. In this paper, results are presented of dwell time parameters obtained from real-scale experiments made at the Pedestrian Accessibility and Movement Environment Laboratory, University College London. Three variables were controlled: platform height (0, 150, and 300 mm), door width (800 and 1,600 mm), and fare collection method (prepayment outside the vehicle and payment with an electronic card at the entrance of the vehicle). For each value of the variables mentioned above, between 15 and 20 runs were recorded on videotape with four cameras and diffe...
2010
The dwell time is the time that a public transport vehicle remains stopped transferring passengers. It depends on the number of boarding and alighting passengers, plus other characteristics such as platform height, door width, fare collection method, internal layout of vehicles, occupancy of vehicles, etc. Traditionally, the dwell time has been described as a linear function of the number of passengers boarding and alighting. In this paper we present results of dwell time parameters obtained from real-scale experiments made at the Pedestrian Accessibility and Movement Environment LAboratory (PAMELA), University College London. Three variables were controlled: platform height (0, 150 and 300 mm), door width (800 and 1,600 mm), and fare collection method (prepayment outside the vehicle and payment with an electronic card at the entrance of the vehicle). For each value of the abovementioned variables between 15 and 20 runs were recording on video tapes with four cameras and different views. In total 300 records of boarding and alighting processes were obtained. Some of the results of the experiments indicate that door width has more influence than platform height. For example, it was found that a wider door can reduce the average alighting time almost 40%, irrespective of the platform height; for the boarding process, the average boarding time is reduced 20% on average. On the other hand, for the same door width the effect of a lower platform only reduces the average alighting time by 1 to 9%. More results and analysis are reported in this paper.
Bus Transit Operational Efficiency Resulting from Passenger Boardings at Park-and-Ride Facilities
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In order to save time and money by not driving to an ultimate destination, some urban commuters drive themselves a few miles to specially designated parking lots built for transit customers and located where trains or buses stop. The focus of this paper is the effect Park-and-Ride (P&R) lots have on the efficiency of bus transit as measured in five bus transit systems in the western U.S. This study describes a series of probes with models and data to find objective P&R influence measures that, when combined with other readily-available data, permit a quantitative assessment of the significance of P&R on transit efficiency. The authors developed and describe techniques that examine P&R as an influence on transit boardings at bus stops and on bus boardings along an entire route. The regression results reported are based on the two in-depth case studies for which sufficient data were obtained to examine (using econometric techniques) the effects of park-and-ride availability on bus tra...
Influence of Unscheduled Random Public Bus Stops on Transit Travel Time
Journal of Traffic and Logistics Engineering, 2013
Transit Travel time can affect to a large extent the service reliability, operating cost, and system efficiency. This research paper aims to study the negative impact of the unscheduled random public bus stops on travel time for a particular bus route in Cairo, Egypt. These unscheduled stops became a usual behavior for Cairo Public buses, which affects more than four and a half million daily users of this transportation service inside Cairo. In this study, a comprehensive research plan was designed to collect the data concerning the bus behavior along a selected bus route, using GPS data logger. The data collection included time, location, speed, unscheduled stops, and scheduled stops. The collected data was then used to develop a trip time model. The developed model revealed the delay time due to the unscheduled bus stops and the scheduled bus stops. The analysis of the data also showed that passengers rely much more on the unscheduled random bus stops than the scheduled bus stops. The study concluded that minimizing the unscheduled bus stops will decrease the trip time, and so improve the service reliability to a large degree. Index Terms-Travel time, unscheduled random public bus stops, GPS data logger, trip time model, service reliability. 20
The modeling of dwelling time of buses at bus stop
IOP Conference Series: Materials Science and Engineering
This research is aimed to build generic dwelling time modeling for BRT in Indonesia. Dwelling time was counted from the time of the bus entering to leaving the stop, including additional passenger service time. The observations were captured on 172 services period for 6 hours each at 6 heterogeneous bus stop using a video camera. The cameras were mounted inside the bus stop. It was located across stop's gate. This cameras' spot sets to capture the bus and passenger movement time to time. The clustering process used Pearson's correlation to differentiate of several data groups. The groups are global data, passenger by direction and additional data services. The Pearson's correlation value shows best on data that is divided into three categories, which are alighting time, departure time and additional service time. Each category is fitted into several regression models candidates, such as linear, polynomial, power, and logarithmic. A single linear regression model was found valid for representing bus dwelling time for each category. There the dwelling time model is a compound of three categorical model.