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Abstract
Feeder transport services are fundamental as first and last-mile connectors of mass rapid transit (MRT). They are especially beneficial in low-demand areas where private transport is usually the main transport mode. Besides, the rapid spread of new technologies such as vehicle automation and the shared mobility paradigm gave rise to new mobility-on-demand modes that can dynamically match demand with service supply. In this context, the new generation of real-time demand-responsive transport services can act as on-demand feeders of MRT, but their performance needs to be compared with conventional fixed-route fixed-schedule feeders. This article aims at presenting an agent-based model able to simulate different feeder services and explore the conditions that make a demand-responsive feeder (DRF) service more or less attractive than a fixed-route fixed-schedule feeder (FRF). The parametric simulation environment creates realistic constraints and parameters that are usually not included in analytical models because of high computational complexity. First, we identified the critical demand density representing a switching point between the two services. Once the demand density is fixed, exploratory scenarios are tested by changing the demand spatial distribution and patterns, service area, and service configurations. Main results suggest that the DRF is to be preferred when the demand is spatially concentrated close to the MRT station (e.g., in a TOD-like land-use area) or when station spacing is quite high (e.g., a regional railway service), whereas the FRF performs better when the demand is mainly originated at the MRT station to any other destinations in the service area (e.g., during peak hours). Besides, automated vehicles could play a role in reducing the operator cost if the service is performed with many small vehicles rather than higher-capacity vehicles, even if this would not imply a major benefit gain for the users.
Highlights
Transport systems are experiencing times of unprecedented changes. e push towards sustainable mobility and the advances in technology are changing both transport services and user habits [1]
Our work tries to answer the two following research questions: how can flexible demand-responsive feeder transport services effectively match the demand with the supply in real time, aiming at maximizing shareability, minimizing the operator costs, and limiting passenger travel time? And under which conditions is it more convenient to adopt a fixed-route policy rather than a flexible one in providing feeder services towards mass rapid transit? In the process, we address several open research issues: (i) the transferability to different contexts by introducing a parametric design model; (ii) the booking process, by taking into account user-based time constraints in the dispatching algorithm; and (iii) the possibility to perform the service with automated vehicles, considering the impact they could have especially in terms of the operator cost
Our research focuses on the first- and last-mile leg of public transport (PT) trips, supposing that the PT backbone is a mass rapid transit (MRT) network like rail or bus rapid transit (BRT). e transit agency might choose between the following two operational strategies: (1) A fixed-route feeder (FRF) service carried out by buses that pick-up and drop-off passengers at predetermined stops
Summary
Transport systems are experiencing times of unprecedented changes. e push towards sustainable mobility and the advances in technology are changing both transport services and user habits [1]. Ese seem to be oriented towards the conversion to full-electric ones and in the longer run to full automation [22] Based on these premises, this article aims at proposing the use of an agent-based model (ABM) to explore the performances of DRT in comparison with a fixed-route service as a feeder to mass transit. We address several open research issues: (i) the transferability to different contexts by introducing a parametric design model; (ii) the booking process, by taking into account user-based time constraints in the dispatching algorithm; and (iii) the possibility to perform the service with automated vehicles, considering the impact they could have especially in terms of the operator cost.
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