Exploring the Performance of Different On-Demand Transit Services Provided by a Fleet of Shared Automated Vehicles: An Agent-Based Model

Abstract

Automated vehicles used as public transport show a great promise of revolutionizing current transportation systems. Still, there are many questions as to how these systems should be organized and operated in cities to bring the best out of future services. In this study, an agent-based model (ABM) is developed to simulate the on-demand operations of shared automated vehicles (SAVs) in a parallel transit service (PTS) and a tailored time-varying transit service (TVTS). The proposed TVTS system can switch service schemes between a door-to-door service (DDS) and a station-to-station service (SSS) according to what is best for the service providers and the travelers. In addition, the proposed PTS system that allows DDS and SSS to operate simultaneously is simulated. To test the conceptual design of the proposed SAV system, simulation experiments are performed in a hypothetical urban area to show the potential of different SAV schemes. Simulation results suggest that SAV systems together with dynamic ridesharing can significantly reduce average waiting time, the vehicle kilometres travelled and empty SAV trips. Moreover, the proposed optimal vehicle assignment algorithm can significantly reduce the empty vehicle kilometres travelled (VKT) for the pickups for all tested SAV systems up to about 40% and improve the system capacity for transporting the passengers. Comparing the TVTS system, which has inconvenient access in peak hours, with the PTS systems, which always makes available door-to-door transport, we conclude that the latter could achieve a similar system performance as the former in terms of average waiting time, service time and system capacity.

Fixed-Route vs. Demand-Responsive Transport Feeder Services: An Exploratory Study Using an Agent-Based Model

“…In contrast, simulation models are ideal for reproducing the complexity of a system; once the input parameters are set, the simulation model runs a series of operations to the data whose number usually grows linearly with the size of the problem and can then generate several outputs, which can be used as key performance indicators of the system. In this respect, simulation models have been used extensively for making tactical and operational decisions regarding transport services in general and in particular for flexible transport services and last-mile connection [22,[33][34][35][36]. Winter et al [11] designed, simulated, and tested an automated DRT for a campus-train station service; the simulation determined the optimal fleet size for the operation of the service and results showed the importance of adequate vehicle sizes and short vehicle dwell times.…”

“…Finally, any analysis of these systems cannot ignore the technological evolution of the vehicles used. ese seem to be oriented towards the conversion to full-electric ones and in the longer run to full automation [22].…”

A Review of Internet of Things Application in Malaysia

“…Malaysia also wants the opportunity to improve trade facilities and strengthen Malaysia's competitive edge in supply chain management methods [59]. E-parking, taxi reservations, bus transportation information, and, most significantly, travel and train information recommendations with real-time information for convenient travelling are all suggested apps [76].…”

Evaluation Methods for the Impacts of Shared Mobility: Classification and Critical Review

“…There are studies that investigate the impacts of SAVs, SEVs, and/or SAEVs on all the categories discussed on this section: the environment [73][74][75][76][77], travel behavior [76,[78][79][80], the built environment [77,81], society [82,83], traffic conditions [84][85][86][87], and the economy [88]. For a comprehensive review of existing up-to-date work regarding the impacts of shared autonomous vehicle services, see Narayanan et al [89].…”