Space-time clustering-based method to optimize shareability in real-time ride-sharing

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Dynamic stop pooling for flexible and sustainable ride sharing

Ride sharing -- the bundling of simultaneous trips of several people in one vehicle -- may help to reduce the carbon footprint of human mobility. However, the complex collective dynamics pose a challenge when predicting the efficiency and sustainability of ride-sharing systems. Standard door-to-door ride sharing services trade reduced route length for increased user travel times and come with the burden of many stops and detours to pick up individual users. Requiring some users to walk to nearby shared stops reduces detours, but could become inefficient if spatio-temporal demand patterns do not well fit the stop locations. Here, we present a simple model of dynamic stop pooling with flexible stop positions. We analyze the performance of ride sharing services with and without stop pooling by numerically and analytically evaluating the steady state dynamics of the vehicles and requests of the ride sharing service. Dynamic stop pooling does a-priori not save route length, but occupancy. Intriguingly, it also reduces the travel time, although users walk parts of their trip. Together, these insights explain how dynamic stop pooling may break the trade-off between route lengths and travel time in door-to-door ride sharing, thus enabling higher sustainability and service quality.