Anticipatory routing methods for an on-demand ridepooling mobility system

Abstract

On-demand mobility systems in which passengers use the same vehicle simultaneously are a promising transport mode, yet difficult to control. One of the most relevant challenges relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We propose, formally discuss and experimentally evaluate several formulations for both approaches. We test these techniques in combination with a state-of-the-art trip-vehicle assignment method, using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users’ traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there.

Joint Optimization of Number of Vehicles, Battery Capacity and Operations of an Electric Autonomous Mobility-on-Demand Fleet

“…Fifth, as ϕ and ψ grow, the number of ride-pooling users is high enough, so matching is possible, even if slightly inconvenient. For larger ϕ and ψ, ride-pooling becomes very efficient-in line the Mohring and Better matching effects [7]-and can substantially decrease the detours and, in consequence, the congestion level and the travel time of all users, as seen in Fig. 6.…”

“…As the demand increases, the percentage of pooled rides increases as well, and as a consequence, the delay follows. However, for larger demands, the delay decreases, as predicted by the Mohring and Better Matching effects [29]: the higher the number of travel requests in the system, the lower the delay experienced. Conversely, the fewer people use a mobility service, the poorer the performance of the system, reflecting in a lower percentage of requests that can be effectively ride-pooled.…”

“…To bridge the gap, Alonso-Mora et al [2] developed an efficient algorithm to compute optimal routing for real-time applications in a ride-pooling environment with high-capacity vehicles. In [6], the authors explored how to split costs between requests pooled together, while in [7] the same authors analyzed the performance of such ridepooling systems as a function of its scale. However, every aforementioned solution is based on a microscopic model, whereby the complexity of the resulting problem grows with the number of travel requests.…”

On-Demand Grocery Delivery From Multiple Local Stores With Autonomous Robots

“…Fleet Sizing Fleet Sizing generally answers the question, "How many vehicles are required to serve some demand?". [5] shows that various effects drive these decisions. The existing literature offers two primary categories of approaches: simulationbased and chaining-based methods.…”

Routing of Heterogeneous Fleets for Flash Deliveries via Vehicle Group Assignment

“…Note that the proposed method does not take any future information into account and thus operates myopic. Doing so could improve obtained results[21],[22], but is regarded as future work.5[19] also provides a more detailed analysis of their proposed method. Interested readers are referred to their work for further details.…”