New Results of a Technology Choice Model for a Transit Corridor

New Results of a Technology Choice Model for a Transit Corridor


Luigi Moccia, Consiglio Nazionale Delle Ricerche, Eric Christian Bruun, Consultant, Duncan W. Allen, IBI Group


We compare two semi-rapid technologies: bus rapid transit, and light rail transit.
We assess several techno-economical scenarios by a recently introduced optimization model for technology choice in a transit corridor.


We provide a deep analysis of techno-economical parameters of two semi-rapid technologies, namely bus rapid transit (BRT), and light rail transit (LRT).
Several scenarios are assessed by a recently introduced optimization model for technology choice in a transit corridor (Moccia and Laporte, 2016). The model includes optimal stop spacing and train length, a crowding penalty, and a multi-period generalization (i.e. both peak and off-peak periods are considered). The objective function is the minimization of the sum of passenger and operator costs
under fixed demand. The passenger cost depends on the access, egress, waiting,
and in-vehicle travel times. The operator cost is divided into three components. The first comprises capital costs such as land acquisition and infrastructure. The second depends on the fleet size and reflects crew and transit unit capital costs. The third accounts for running costs such as fuel, tyres, lubricants, etc.

In this paper we expand the numerical experiments of Moccia and Laporte (2016) which were limited to the techno-economical parameters of Tirachini et al. (2010).
We draw additional data from the literature (Vuchic, 2005; Vuchic et al., 2013; Casello et al., 2014; Bruun et al., 2016) and we examine several scenarios depending on the infrastructure effort needed to obtain the right of way (RoW), and ranges of RoW levels. At one end of this spectrum we have a dedicated arterial RoW obtained by a simple change in designation of an existing lane. At the opposite end we have an exclusive at grade RoW when a new separate alignment is developed. These two opposite scenarios differ both in cost and in productive capacity (Vuchic, 2007).

A crucial result is how accounting for crowding moves away the optimal frequency from the minimal value induced by the bottleneck capacity. Moreover, road and rail modes handle crowding in different ways. BRT is limited to offering a higher frequency, whereas LRT leverages on both frequency and train length. Results are illustrated by total cost curves varying on the demand.
The computer code is freely available under the MIT license (Moccia, 2016).


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Fit for purpose or fashion? Choosing the right public transport solution for advancing
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