Modeling and Evaluation of a Ridesharing Matching System from Multi-Stakeholders\u27 Perspective

With increasing travel demand and mobility service quality expectations, demand responsive innovative services continue to emerge. Ridesharing is an established, yet evolving, mobility option that can provide more customized, reliable shared service without any new investment in the transportation infrastructure. To maximize the benefits of ridesharing service, efficient matching and distribution of riders among available drivers can provide a reliable mobility option under most operating conditions. Service efficiency of ridesharing depends on the system performance (e.g., trip travel time, trip delay, trip distance, detour distance, and trip satisfaction) acceptable to diverse mobility stakeholders (e.g., riders, drivers, ridesharing operators, and transportation agencies). This research modeled the performance of a ridesharing service system considering four objectives: (i) minimization of system-wide passengers’ waiting time, (ii) minimization of system-wide vehicle miles travelled (VMT), (iii) minimization of system-wide detour distance, and (iv) maximization of system-wide drivers’ profit. Tradeoff evaluation of objectives revealed that system-wide VMT minimization objective performed best with least sacrifices on the other three objectives from their respective best performance level based on set of routes generated in this study. On the other hand, system-wide drivers’ profit maximization objective provided highest monetary incentives for drivers and riders in terms of maximizing profit and saving travel cost respectively. System-wide minimization of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing system modeling and performance evaluation, and can be used in developing and implementing ridesharing service considering multiple stakeholders’ concerns

An Effective Taxi Recommender System Based on A Spatiotemporal Factor Analysis Model

—For the effective Taxi business we developed the fleet management system based on GPS which is important tool, as well as its useful to provide information to taxi driver for earning profit by mining the historical GPS path of the projectiles. In the literature, distance between current place and recommended place, time for next passengers and exact fare of trip these three factors provide the similar objective have been considered in different work. In this paper, in addition to this factor we added one more factor that is based on driver’s experience which is most likely locations to pick up the passenger given the current passenger drop off location .the one location and another location graph model referred to as OFF-ON model is worked for define the relation between the get off location and next passengers get on location. To estimate the expected fare for a trip started at a recommended location we adopted an ON-OFF model. A real world dataset from CRAWDAD is used to evaluate the proposed system. A simulator has been developed for the simulate journey behavior of taxies in database. Our proposed system still effective on recommending better profitable cruising location

Méthodologie d'analyse et de suivi d'un système de transport par taxi

RÉSUMÉ Le taxi est devenu, au cours des dernières années, un sujet actuel de réflexion, au coeur des enjeux de plusieurs villes. En effet, cette industrie a très peu évolué depuis de nombreuses décennies et se retrouve maintenant confrontée à l’arrivée de modes alternatifs à l’automobile. Elle doit maintenant se questionner sur ses méthodes de gestion de la flotte ainsi que sur sa position dans le système de transport. Ce projet de recherche vise à développer des indicateurs de performance et de suivi sur l’offre et la demande de déplacements par taxi grâce notamment à des données GPS.----------ABSTRACT In the last years, Taxi has become an important topic of reflection, being at the heart of the transportation challenges in several cities. Indeed, this industry has remained unchanged for many decades, and now finds itself confronted with the arrival of innovative transportation alternatives. This industry must question its current management strategies and clearly assessed its role in the transportation system. This research project aims to develop performance and monitoring indicators on taxi supply and demand with GPS data