Optimum Zone Configuration for Planned Urban Commuter Rail Lines

Abstract

The optimum zone structure for a planned urban commuter rail line with one to many or many to one type demand during peak periods is analyzed. The objective is to minimize the passenger time costs as well as the relevant system operating and capital costs. The analysis is based on the interaction of a range of variables such as the number of zones, zone boundaries, uniform station spacings, fleet size and train headway. The approximately optimum values of the decision variables are determined mostly in closed form. A numerical application and sensitivity analyses are presented.

A model for market share distribution between high-speed and conventional rail services in a transportation corridor

“…Traditional methods used in route choice and zonal design problems for transit or rail corridors usually consider an "average" value of time per person to estimate the generalized travel cost or the generalized travel time (e.g., Wirasinghe and Seneviratne 1986;Furth 1986;Ghoneim and Wirasinghe 1987;Jansson and Ridderstolpe 1992, etc.). Instead of using an average value of time assumption, this paper treats an individual's value of time, c, as a random variable, and derives its relationship with trip length, and the resulting route and mode choice of the individual.…”

“…Passengers' optimal route choices are assumed by minimizing their generalized travel time, which is composed of access/egress time, waiting/transfer time, riding time, and fare conversion time. Instead of assuming an "average" value of time, which is commonly used in route choice or zonal design literature for transit or rail corridors (e.g., Wirasinghe and Seneviratne 1986;Furth 1986;Ghoneim and Wirasinghe 1987;Janjsson and Ridderstople 1992), this paper treats an individual's value of time as a random variable and uses it to convert fare into an individual-dependent fare time. Passengers' route choices are formulated as depending on trip distance, value of time, departure time, and fare and service characteristics of HSR and CR services.…”

A New Computational Model for the Design of an Urban Inter‐modal Public Transit Network

“…The second is the demand pattern. Ghoneim and Wirasinghe (1987) studied the optimum zone configuration for urban commuter rail lines with “one‐to‐many” or “many‐to‐one” demand pattern. Several studies on bus services concerning optimum stop spacing and operating headway assumed the “many‐to‐many” demand patterns (e.g., Wirasinghe and Ghoneim, 1981; Vaughan, 1986).…”

“…Whether demand varies over time and space is a third aspect. Inelastic demand was assumed in most analytical models previously developed to optimize an urban public transit system (e.g., Vuchic and Newell, 1968; Tsao and Schonfeld, 1984; Ghoneim and Wirasinghe, 1987; Kuah and Perl, 1988). However, inelastic demand may not be a reasonable assumption when many riders have alternative choices of mode.…”

Radial bus networks with multi‐period and many‐to‐many demand

“…The analytic approach and the literature on analytic optimization of public transportation systems have been extensively reviewed by Chang (1990) and by Chang and Schonfeld (1990), but a brief review of the previous studies is repeated here for convenience. Basically most of the previous analytic models for public transportation system have assumed a fixed demand that is steady over time (for example, Vuchic and Newell, 1968;Black, 1975;Byme, 1975 and1976;Wirasinghe, 1980;Hurdle and Wirasinghe, 1980;Tsao and Schonfeld, 1984;Ghoneim and Wirasinghe, 1987;Kuah and Perl, 1988). Newell (1971) has optimized the schedule for a transit route with a given fleet size by minimizing user wait time for demand expressed as a smooth function of time.…”