Can dynamic ride-sharing reduce traffic congestion?. (March 2021) - Alisoltani, Negin; Leclercq, Ludovic; Zargayouna, Mahdi
Highlights: An exact method to solve small instances of the optimal ride-sharing problem. A clustering method based on a "shareability index" to solve large-scale problems. A heuristic approach to favor sharing opportunities at the cost of slight increases in the cost function, decreasing passenger waiting time. The experiments performed prove that our heuristic approaches greatly improve computation time, with few compromises on optimality. Two models (plant model and prediction model) to deal with dynamic traffic conditions. Comparing the proposed dynamic ride-sharing system performance with different market-shares and numbers of sharing in terms of reducing traffic congestion. Abstract: Can dynamic ride-sharing reduce traffic congestion? In this paper we show that the answer is yes if the trip density is high, which is usually the case in large-scale networks but not in medium-scale networks where opportunities for sharing in time and space become rather limited. When the demand density is high, the dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Sharing can compensate extra travel distances related to operating a mobility service. The situation is entirely different in small and medium-scale cities when trip shareability is small, even if the ride-sharing system is fully optimized based on the perfect demand prediction in the near future. The reason is simple, mobility services significantly increase the total travel distance, and sharing is simply a means of combating this trend without eliminating it when the trip density is not high enough. This paper proposes a complete framework to represent the functioning of the ride-sharing system and multiple steps to tackle the curse of dimensionality when solving the problem. We address the problem for two city scales in order to compare different trip densities. A city scale of 25 k m 2 with a total market of 11, 235 shareable trips for the medium-scale network and a city scale of 80 k m 2 with 205, 308 demand for service vehicles for the large-scale network over a 4-hour period with a rolling horizon of 20 minutes. The solutions are assessed using a dynamic trip-based macroscopic simulation to account for the congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies on optimal fleet management that usually consider constant and fully deterministic travel time functions.