Automated Mobility-on-Demand (AMoD) systems are expected to revolutionize urban mobility systems. However, there are uncertainties in the planning and operations of AMoD systems. We deem the agent-based approach as being well suited for modeling new phenomena in future AMoD systems and therefore shed some light on the uncertainties about the operation and the impacts of such systems. Recommendations to various stakeholders are provided through the different contributions.@en

Automated Mobility-on-Demand (AMoD) systems, in which fleets of automated vehicles provide on-demand services, are expected to transform urban mobility systems. Motivated by the rapid development of AMoD services delivered by self-driving car companies, an agent-based model (ABM) has been developed to study the coexistence phenomena of multiple AMoD operators competing for customers. The ABM is used to investigate how changes in pricing strategies, assignment methods, and fleet sizes affect travelers’ choice of different AMoD services and the operating performance of competing operators in the case-study city of The Hague, in the Netherlands. Findings suggest that an optimal assignment algorithm can reduce the average waiting time by up to 24% compared to a simple heuristic algorithm. We also find that a larger fleet could increase demand but lead to higher waiting times for its users and higher travel times for competing operators’ users due to the added congestion. Notably, pricing strategies can significantly affect travelers’ choice of AMoD services, but the effect depends strongly on the time of the day. Low-priced AMoD services can provide high service levels and effectively attract more demand, with up to 64.7% of customers choosing the very early morning service [5:30 AM,7:20 AM]. In the subsequent morning hours, high-priced AMoD services are more competitive in attracting customers as more idle vehicles are available. Based on the quantitative analysis, policies are recommended for the government and service operators.

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This paper addresses the problem of studying the impacts of the strategic formation of platoons in automated mobility-on-demand (AMoD) systems in future cities. Forming platoons has the potential to improve traffic efficiency, resulting in reduced travel times and energy consumption. However, in the platoon formation phase, coordinating the vehicles at formation locations for forming a platoon may delay travelers. In order to assess these effects, an agent-based model has been developed to simulate an urban AMoD system in which vehicles travel between service points transporting passengers either forming or not forming platoons. A simulation study was performed on the road network of the city of The Hague, Netherlands, to assess the impact on traveling and energy usage by the strategic formation of platoons. Results show that forming platoons could save up to 9.6% of the system-wide energy consumption for the most efficient car model. However, this effect can vary significantly with the vehicle types and strategies used to form platoons. Findings suggest that, on average, forming platoons reduces the travel times for travelers even if they experience delays while waiting for a platoon to be formed. However, delays lead to longer travel times for the travelers with the platoon leaders, similar to what people experience while traveling in highly congested networks when platoon formation does not happen. Moreover, the platoon delay increases as the volume of AMoD requests decreases; in the case of an AMoD system serving only 20% of the commuter trips (by private cars in the case-study city), the average platoon delays experienced by these trips increase by 25%. We conclude that it is beneficial to form platoons to achieve energy and travel efficiency goals when the volume of AMoD requests is high.@en

This paper aims to explore the performance of the autonomous mobility-on-demand system (AMoD) with the coordinated formation of vehicle platooning. In this study, an agent-based model (ABM) is developed to explicitly simulate the operations of platooning formation and interactions between shared automated vehicles (SAVs) and real-time travel requests. The objective is to capture the real-time behavior of SAVs as trip makers, and then assess the performance of the AMoD system with the mechanism of coordinated formation of platoons. We conclude that the impact of vehicle assignment strategies in the AMoD system with vehicle platooning formation predominately affects the average waiting time and system capacity to transport travelers as a whole; however, vehicle platooning, to some extent, could lengthen the travel time of platoon vehicles. The hold-on time (imposed delay) of leading vehicles in order to form a platoon could affect the average time delay of vehicles part of those platoons. The developed ABM provides the first insight into the impact of the pervasive formation of vehicle platooning on the performance of the AMoD system.

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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.

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The introduction of the concept of a fleet of shared autonomous vehicle (SAVs) which function as a centralized taxi service system presents an innovative way in transport modes. A fleet of SAVs can provide tailored on-demand services via a centralized operation to serve travel demand over time. In our study, An agent-based model (ABM) is developed to simulate tailored time-varying service (TVS) provided by a fleet of SAVs in a demand-responsive fashion. The proposed system can switch the service scheme between the door-to-door service (DDS) and station-to-station service (SSS) automatically based on the time of day; In peak hours, the SAV system aims to serve as many trips as possible with predesignated stations as a SSS by providing an on-demand service. In o-peak hours, a DDS in a demand-responsive fashion is provided by a fleet of SAVs which can benet the travelers with great convenience. Also, DDS and SSS provided by SAVs in a demand responsive fashion are simulated separately. The potential benets of TVS provided by SAVs are investigated and then compare it with DDS and SSS. The simulation results indicate that the tailored TVS can increase the utilization of SAVs by 2.5% and number of passengers transported per days by 2.9%. Compared with DDS in peak hours, there are reductions of averaging waiting time and energy consumption up to 25.5 % and 3.7% respectively. In o-peak hour, the TVS can be easily employed to eliminate the avearge 9-minute walking time of the SSS. In addition, we nd out that there is a signicant increase of trips by empty SAVs at around 82% for all service schemes. It is an important issue for further investigation.@en