Cost-of-crowding model for light rail train and platform length

Abstract

With light rail transit (LRT) and other similar rail-based commuter transit systems, train and associated station platform length provides an added dimension of flexibility not available to buses. Train and platform lengths are important factors in the planning and expansion phases of a network. Existing cost models that determine optimal headway by combining passenger and operational costs provide headways that are small and close to a logistical minimum (2-3 min); this type of standard waiting cost model is not sensitive to train and platform length. In this paper, on-board crowding is used as a cost factor and a cost-of-crowding model is developed from supporting psychological research. Two models are proposed and optimized with respect to train length to determine the optimal train and platform length for a many-to-one peak period commuter LRT system. Data from the C-Train network in Calgary, Alberta is used for numerical analysis of the model. The model demonstrated that crowding has an effect on optimal train length. The model produced feasible results when applied to a real-world scenario.

Integrating COVID-19 health risks into crowding costs for transit schedule planning

“…The second approach calculates the cost of discomfort due to crowding as a portion of user costs. For example, in Klumpenhouwer and Wirasinghe (2016) and in Lu et al (2008) , a value of discomfort (measured in dollars per unit time per person) is multiplied by a discomfort factor (a function of crowding level/loading) to represent different levels of crowding. The third approach calculates the cost of crowding discomfort as a “per trip” value for a given level of crowding as in Polydoropoulou and Ben-Akiva, 2001 , Hensher et al, 2011 .…”

A technology selection and design model of a semi-rapid transit line

“…Analyzing this behavior the authors find that the standing multiplier can be as much as 1.55 with a density of three standees per square meter. Klumpenhouwer and Wirasinghe (2016) define a single-period model of a LRT line where the frequency, the stop spacing, and the demand are given. The demand is centripetal, the passenger cost is related to the invehicle crowding disutility, and the operator cost is a function of the fleet size and of the platform length.…”

“…Regarding in-vehicle passenger crowding, both the recent survey of Haywood et al (2017) and the revealed preference studies of Tirachini et al (2016) and Hörcher et al (2017) reaffirm the finding of the meta-analysis of Wardman and Whelan (2010) that the crowding multiplier of the base in-vehicle time with respect to the load factor is well approximated by a piecewise linear function which is equal to one up to the load factor corresponding to the seated capacity, and then starts to increase. The optimization model of Klumpenhouwer and Wirasinghe (2016) highlights the relevance of in-vehicle passenger crowding for station design and fleet sizing. Our contribution to this literature thread is that we overcome the inherent underestimation of the crowding disutility when applying directly a piecewise linear function to the average vehicle load along a bidirectional line (we note that this issue does not pertain to the model of Klumpenhouwer and Wirasinghe (2016)).…”

“…The optimization model of Klumpenhouwer and Wirasinghe (2016) highlights the relevance of in-vehicle passenger crowding for station design and fleet sizing. Our contribution to this literature thread is that we overcome the inherent underestimation of the crowding disutility when applying directly a piecewise linear function to the average vehicle load along a bidirectional line (we note that this issue does not pertain to the model of Klumpenhouwer and Wirasinghe (2016)). This is accomplished by a new crowding disutility function presented in the next section and derived in "Appendix 2".…”