Determinants of Bus Dwell Time

Dueker, Kenneth J.; Kimpel, Thomas J.; Strathman, James G.; Callas, Steve

doi:10.5038/2375-0901.7.1.2

Cited by 136 publications

(134 citation statements)

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“…Examples of measures in the different studies include the gross number of passengers onboard (Zografos and Levinson 1986), the gross number of standing passengers (Lin and Wilson 1992), a "friction" factor based on the number of standing passengers (Dueker et al 2004), a categorical measure of the volume (Fritz 1983) and load factors (Aashtiani and Iravani 2002;Rajbhandari et al 2003). Load factor, however, which is often tracked by transit agencies, is not always effective in capturing what is occurring on-board because it relates to the number of seats and, thus, a large load factor could indicate a very crowded bus or a bus with few seats (Seattle DOT 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Bus Door Crowding on Operations and Safety

Katz¹,

Garrow²

2012

JPT

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Section: Literature Reviewmentioning

confidence: 99%

The Impact of Bus Door Crowding on Operations and Safety

Katz¹,

Garrow²

2012

JPT

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“…Every element D i,j in the matrix D represents the scheduled time of arrival of bus trip i at stop j in minutes. In order to simplify the notation, the arrival time and the departure time differ by k = 12 s following the work of Dueker et al [30] which observed that a typical dwell time is in the range of 12.29 ± 13.47 s. For improving the efficiency of bus services, the dispatching times of buses are modified to create an optimized schedule. Table 1 presents the common notation used in this work for modelling the multi-constrained bus scheduling problem.…”

Section: Modelling the Multi-constrained Scheduling Problemmentioning

confidence: 99%

Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

Gkiotsalitis¹,

Kumar²

2018

Informatics

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Abstract:In public transport operations, vehicles tend to bunch together due to the instability of passenger demand and traffic conditions. Fluctuation of the expected waiting times of passengers at bus stops due to bus bunching is perceived as service unreliability and degrades the overall quality of service. For assessing the performance of high-frequency bus services, transportation authorities monitor the daily operations via Transit Management Systems (TMS) that collect vehicle positioning information in near real-time. This work explores the potential of using Automated Vehicle Location (AVL) data from the running vehicles for generating bus schedules that improve the service reliability and conform to various regulatory constraints. The computer-aided generation of optimal bus schedules is a tedious task due to the nonlinear and multi-variable nature of the bus scheduling problem. For this reason, this work develops a two-level approach where (i) the regulatory constraints are satisfied and (ii) the waiting times of passengers are optimized with the introduction of an evolutionary algorithm. This work also discusses the experimental results from the implementation of such an approach in a bi-directional bus line operated by a major bus operator in northern Europe.

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“…For instance, with the widespread application of automatic data collecting systems including automatic passenger counting (APC) and automatic vehicle location (AVL) systems, transit agencies and researchers are able to analyze a plethora of data by using an archived database (Tirachini 2013;Dueker et al 2004). In addition, several computer simulation models have been applied in bus operation analysis at stops.…”

Section: Literature Reviewmentioning

confidence: 99%