Best Practices In Transit Service Planning

Mistretta, Mark; Goodwill, Jay A; Gregg, Rob; DeAnnuntis, Chirstopher

doi:10.5038/cutr-nctr-rr-2007-01

Cited by 26 publications

(15 citation statements)

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“…A similar effect occurs when we consider a lower number of passengers (N = 500), more in line with the expected boardings per hour stated in [17] (Table 3).…”

Section: Sharing By Demand Typesupporting

confidence: 72%

“…In terms of parameter values, Ashley et al [2] identify the ideal trips per vehicle kilometre for regular buses in the range of 2 to 4; in reality it is closer to 1. Mistretta et al [17] set performance targets based on boardings per hour as 5 (minimum) to 10 (average) for peak hour and 3 (minimum) to 8 (average) at other times for demand-responsive transport.…”

Section: Viability Of Drtsmentioning

confidence: 99%

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Determining the Viability of a Demand-Responsive Transport System under Varying Demand Scenarios

Ronald

Thompson

Haasz³

et al. 2013

Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science

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Collaborative transportation has been proposed as a potential solution to decrease congestion, reduce environmental effects of transport, and provide transportation options to those with no or restricted travel options. One such system is demand-responsive transport, in which passengers share a vehicle, usually a bus, but can be picked up or dropped off at a passenger-specified location and time. However, as these systems are expensive to implement and require long trials in order to gain traction, effective simulation is required in order to explore their viability before implementation. Although previous work has concentrated on the number of trip requests, the spatial distribution of these requests has not been considered. This paper explores four spatiallyvarying demand patterns -random, a many-to-one scenario, all short distance trips, all long distance trips -using a simulation of an ad-hoc demand-responsive bus system. It is shown that along with the number of trip requests and the requested trip distances, the spatial distribution of passengers does indeed have an effect on the level of service.

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“…A similar effect occurs when we consider a lower number of passengers (N = 500), more in line with the expected boardings per hour stated in [17] (Table 3).…”

Section: Sharing By Demand Typesupporting

confidence: 72%

Section: Viability Of Drtsmentioning

confidence: 99%

Determining the Viability of a Demand-Responsive Transport System under Varying Demand Scenarios

Ronald

Thompson

Haasz³

et al. 2013

Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science

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“… Proximity to residences is to get as close as possible to a rider's residence without unnecessarily delaying or detouring other riders and selected as primary parameter by 19 percent.  Proximity to non-residential trip generators is to get as close as possible to a rider's non-home destination without unnecessarily delaying or detouring other riders within major commercial areas (Mistretta, 2009) and selected as primary parameter by 18 percent.  Bus stop spacing is the bus stops with easy walking distances and it follows that the fewer the stops, the greater the number of people who will walk.…”

Section: Preliminary Studymentioning

confidence: 99%

Integrating Network Concept Into Multi Criteria Analysis for Suggesting Bus Rapid Transit Routes

Shamsudin

Khanan

Umar

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Population explosion in the city of Johor Bahru has led to traffic congestions, which hitherto to that there were better movements and flow of vehicles in and around the city. Road transportation in particular has played a gargantuan role in creating and maintaining easy and quick access to various destinations. Increases in the number of vehicle indicate a higher economic prosperity that contributes to traffic congestion within the city. This can be seen in a number of developments such as increased population density and creation of alternative routes over several years, which has still not totally eradicated the traffic congestion problems of the city. Ideally, a good public transport service would carry the passenger directly from their origin to their destination without having to face traffic congestion. The buses may not be available and accessible from certain origins or destinations of residents or they may need to walk some distances from their residents to the bus stops resulting into discouragement for most of the people using public transport services. This paper aims to utilise geospatial analysis approach to suggest effective bus routing that would be able to increase connectivity to rural areas and boost commercial activities through better transportation. Route selection is the process of finding locations that meet the selection criteria for each parameter using Geographic Information System (GIS). The process employs applied multi-criteria decision analysis and network analysis to generate thpsye highest score areas for bus rapid transit (BRT) route by maximising network and mobility and ensuring the newly suggested routes are connected to the existing networks of BRT to serve the community.

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“…In most cities, the common feeder bus systems are mainly operated in a fixed‐route transit (FRT) mode with pre‐determined schedules and stop . However, because of the lack of accessibility and flexibility, such FRT mode is inconvenient for some passengers, such as children, disabled, or senior passengers, to reach the bus stops . Recognizing the aforementioned limitations of FRT, transit agencies have also promoted the demand responsive transit (DRT) mode , such as dial‐a‐ride services , to improve the accessibility of transit systems.…”

Section: Introductionmentioning

confidence: 99%

Flexible feeder transit route design to enhance service accessibility in urban area

Yang

et al. 2015

J of Advced Transportation

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Summary To improve the accessibility of transit system in urban areas, this paper presents a flexible feeder transit routing model that can serve irregular‐shaped networks. By integrating the cost efficiency of fixed‐route transit system and the flexibility of demand responsive transit system, the proposed model is capable of letting operating feeder busses temporarily deviate from their current route so as to serve the reported demand locations. With an objective of minimizing total bus travel time, a new operational mode is then proposed to allow busses to serve passengers on both street sides. In addition, when multiple feeder busses are operating in the target service area, the proposed model can provide an optimal plan to locate the nearest one to response to the demands. A three‐stage solution algorithm is also developed to yield meta‐optimal solutions to the problem in a reasonable amount of time by transforming the problem into a traveling salesman problem. Numerical studies have demonstrated the effectiveness of the proposed model as well as the heuristic solution approach. Copyright © 2015 John Wiley & Sons, Ltd.

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Best Practices In Transit Service Planning

Cited by 26 publications

References 0 publications

Determining the Viability of a Demand-Responsive Transport System under Varying Demand Scenarios

Determining the Viability of a Demand-Responsive Transport System under Varying Demand Scenarios

Integrating Network Concept Into Multi Criteria Analysis for Suggesting Bus Rapid Transit Routes

Flexible feeder transit route design to enhance service accessibility in urban area

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