|Institution:||Università degli Studi di Milano|
|Keywords:||Taxi Sharing; Rapid transportation network; Sustainable urban mobility; Settore MAT/09 - Ricerca Operativa; Settore ICAR/05 - Trasporti|
|Full text PDF:||http://hdl.handle.net/2434/367673|
We assess a bimodal transportation system based on a massive urban on-demand transportation service, named Taxi Sharing, and a rapid Local Public Transportation optimized for users without movement impairments, according to users' traveling and walking time. The aim is to increase, qualitatively and quantitatively, public mobility services by exploiting available urban transportation resources, in order to reduce private motorized mobility and related externalities in urban context. We developed a new technique to optimize a high quality Taxi Sharing service starting from state-of-the-art DARP optimization algorithms. In Taxi Sharing, time windows on pick-up and delivery times are narrow and the service is provided by many small vehicles, taxis. These features allow an enumeration of all possible subsets of incoming users' requests for each vehicle and to compute in real time an optimal set of routes by solving a large set partitioning problem with state-of-the-art integer linear programming solvers. Owing to this fast global optimization capability, the system allows for a high quality service without any need of booking the ride in advance. We present three development scenarios according to demand level, we discuss the performance of the service in terms of number of requests serviced per hour, average travel time and waiting time, number of taxis simultaneously on duty, ride fare and taxi revenue. We explored the possibility of planning, in presence of Taxi Sharing, a rapid LPT optimized for users without movement impairments according to users' traveling and walking time. We based the optimization process on data collected in the field. We evaluated the effects of optimal stops spacing on commercial speed, in relation also to traffic light priority. Obtained results indicate a huge potential increase in efficiency related both to taxi service and to local public transportation. Advisors/Committee Members: tutor: Giovanni Righini, correlatore: Luca Tosi, coordinatore: Giovanni Naldi, RIGHINI, GIOVANNI, RIGHINI, GIOVANNI, NALDI, GIOVANNI.