The Impact of Ridesharing in Mobility-on-Demand Systems: Simulation Case Study in Prague

Abstract

In densely populated-cities, the use of private cars for personal transportation is unsustainable, due to high parking and road capacity requirements. The mobility-ondemand systems have been proposed as an alternative to a private car. Such systems consist of a fleet of vehicles that the user of the system can hail for one-way point-to-point trips. These systems employ large-scale vehicle sharing, i.e., one vehicle can be used by several people during one day and consequently the fleet size and the parking space requirements can be reduced, but, at the cost of a non-negligible increase in vehicles miles driven in the system. The miles driven in the system can be reduced by ridesharing, where several people traveling in a similar direction are matched and travel in one vehicle. We quantify the potential of ridesharing in a hypothetical mobility-on-demand system designed to serve all trips that are currently realized by private car in the city of Prague. Our results show that by employing a ridesharing strategy that guarantees travel time prolongation of no more than 10 minutes, the average occupancy of a vehicle will increase to 2.7 passengers. Consequently, the number of vehicle miles traveled will decrease to 35 % of the amount in the MoD system without ridesharing and to 60% of the amount in the present state.

On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms

“…subject to (2), (5b), (7), (13), (14), (15), (16), (17), and (18) Problem (19) has O(|E| + M C + |V R |C + T (|G| + |E p | + |B|))variables: compared to Problem (9), the problem size does not depend on the product of |E| and |V R |, resulting in an order-of-magnitude reduction in the overall number of required variables for prototypical problems. b) Fractional output: In order to adapt Problem (19) for real-time control of AMoD systems, one last difficulty must be overcome.…”

Application of Transportation Big Data to Support Decision-Making for Architecture Teams: Processes and Experiences From Two Case Studies

“…The activity-based approach considers individual trips in context and therefore allows the representation of realistic trip chains. The specific model used in this work (as well as similar studies, such as the one published in Fiedler et al [27]), covers a typical workday in Prague and the surrounding Central Bohemian Region. The population of over 1.3 million is modelled by the same number of autonomous, self-interested agents, whose behaviour is influenced by their sociodemographic attributes, current needs, and situational contexts.…”

Shared Autonomous Mobility on Demand: A Learning-Based Approach and Its Performance in the Presence of Traffic Congestion

“…While the initial work, e.g., [6], [7], [8], [9] focuses only on ride-sharing, approaches in [10], [4], [3], [11], [12] combine rebalancing and ride sharing as their mutual impact has been recognized. Rebalancing in these is, however, done either at fixed intervals rather than dynamically [10], or using a centralized approach [4], [13], [12], [14]. A decentralized rebalancing approach is presented in [11], but in combination with a centralized request and ride-sharing assignment.…”

“…The only approaches that consider congestion with ridesharing and/or rebalancing are [9], [12], [13], [14]. However [15] Hörl et al [13] Zhang et al [6] Simonetto et al [7] Lu et al [8] Levin et al [9] Martinez et al [16] Fagnant and Kockelman [3] Fiedler [10] Alonso-Mora et al [4] Wen et al [11] Vosooghi et al [12] Ruch et al [14] SAMoD Guériau and Dusparic [5] This paper none uses a congestion-aware routing strategy to react to observed congestion level. [9], [12] only account for congestion generated by the fleet of SAVs themselves rather than other vehicles, and in [14], [13] the agent-based simulation used relies on a simplified queue-based model for traffic congestion instead of more realistic car-following behaviours.…”