Garage Location for an Urban Mass Transit System

Ball, Michael O.; Assad, Arjang A.; Bodin, Lawrence; Golden, Bruce L.; Spielberg, Frank

doi:10.1287/trsc.18.1.56

Cited by 11 publications

(11 citation statements)

References 5 publications

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“…The natural continuation of this work is directed towards the determination of criteria for location of facilities within urban areas from the point of view of the urban planner and the level of service. It is not enough to consider only minimization of a cost function as described in Maze et al (1981Maze et al ( , 1984 and Ball et al (1984). The relative location of land areas allocated within the urban fabric for each type of facility needed by public transport should be determined.…”

Section: Discussionmentioning

confidence: 99%

“…Three cost components are considered: non-revenue transport costs, operating costs and construction costs. Ball et al, (1984) develop an optimization model to minimize the sum of fixedsite related costs, variable vehicle costs and crew-relief costs, but their paper refers primarily to location rather than quantity of land required. Models dealing with trip distribution (White, 1976;Pushkarev, 1977;NATO/ CCMS, 1979) analyze public transport demand by using different variables-household characteristics: size, number of employed persons, income, vehicle ownership, etc.…”

Section: Introductionmentioning

confidence: 99%

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Land requirements for public transport facilities in urban areas—A statistical approach

Ceder

Shefer

1987

J of Advced Transportation

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The major aim of this work is to develop a primary tool for the quantitative determination of land areas needed to fulfil the diverse functions of public transport. The need for such a tool is obvious; worldwide there is a lack of criteria for determining the types of areas needed, their size, and relative location in the urban system. Moreover, there is a lack of awareness regarding land allocation during the various planning stages, both by urban planners and decision makers.This work attempts to identify various types of facilities to be allocated within the urban framework, and to determine quantitative guidelines for the land needed for them. A statistical analysis is performed in order (i) to identify major transit facilities and operational data functions for which land must be allocated, and (ii) to forecast areas for types of facilities not dealt with by the statistical analysis.The statistical forecast is based on fitting a regression model to the input data. The outcome of this work is presented in terms of three statistical relationships and an indirect relationship: between socio-economic and demographic variables (independent) to the dependent variables, either operating variables or land area variables, and between operating variables (independent) and land area variables (dependent), where for the latter there is also an indirect relationship while using standards.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land requirements for public transport facilities in urban areas—A statistical approach

Ceder

Shefer

1987

J of Advced Transportation

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“…The most similar topic is the "deadhead" problem in transit networks, the essence of which is to assign a set of fixed bus or train routes to storage/maintenance bases (Spielberg and Golenberg, 1980;Ball et al, 1984;Maze et al, 1983;Maze and Khasnabis, 1985;Hall, 1991). The deadhead objective is to minimize the amount of non-productive (i.e., deadhead) travel to/from the bases, taking into account capacity limitations for storing vehicles at each base.…”

Section: Hallmentioning

confidence: 99%

Domicile selection and risk pooling for trucking networks

Hall

2004

IIE Transactions

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Less-than-TruckLoad (LTL) carriers transport small to medium sized shipments through networks of terminals. One of the issues in designing an LTL network is to determine how drivers should be distributed among locations, with the goal of reducing total variability through risk pooling. By concentrating drivers at a limited number of terminals, the carrier has greater flexibility to respond to random variations in demand, because they can be more readily shifted among routes. The problem is challenging because the risk-pooling objective is concave/minimization, leading to multiple locally optimal solutions. Each solution is defined by a rank ordering of terminals according to assigned demand. When a tour visits multiple terminals, it is assigned to the terminal with the highest rank (i.e., the one with the largest total demand). Based on the rank-ordering concept, a heuristic algorithm and an exact (branch-and-bound) algorithm are developed and applied to test problems. The exact algorithm offers reasonable computation times for moderately sized networks, typical of center-to-center routes of LTL carriers. For large networks, incorporating smaller end-of-line terminals in addition to centers, early termination of the algorithm seems to be a reasonable heuristic.

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“…It is also an integral element of any transit system. Methodology for determining their location has attracted increasing attention over the last decade (see Denver Rapid Transit District, 1976; Maze et al 1982;1984).…”

Section: Introductionmentioning

confidence: 99%

“…With this approach potential garage sites must be predetermined. The approach of Ball et al(1984) is similar to the above in that it has the same objective function but differs in the sense that it has disaggregated the deadheading cost into three parts, namely: base non-peak, a.m. and p.m. They also argue that the number of candidate sites can be quite large.…”

Section: Introductionmentioning

confidence: 99%

Location of bus garages

Waters

Wirasinghe

Babalola³

et al. 1986

J of Advced Transportation

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The location of bus garages is a complex issue that has received recent attention in the literature. Given a bus system, the number of bus garages and their Locations depend on garage cost, deadheading cost and environmental impacts. An approximate analytical model is used to determine the number of bus garages that minimizes the above costs. The concept of a slowly varying density of bus-route origins (hence deadheads) per unit area is used to model deadheading costs. The increased deadheading caused by breakdowns and accidents is also considered. The garage cost is modeled as a function of the number of buses stored. A closed-form solution is obtained for the optimal density of garages, when the garage cost function is linear. The actual locations of garages and the allocations of buses to the garages are found using a discrete space location-allocation model formulated so as to consider the environmental impact associated with buses deadheading through populated neighborhoods.

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Garage Location for an Urban Mass Transit System

Cited by 11 publications

References 5 publications

Land requirements for public transport facilities in urban areas—A statistical approach

Land requirements for public transport facilities in urban areas—A statistical approach

Domicile selection and risk pooling for trucking networks

Location of bus garages

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