Heuristic Algorithms for the Dynamic Taxipooling Problem Based on Intelligent Transportation System Technologies

[[abstract]]The convergence of the intelligent transportation system (ITS) technologies has given rise to new opportunities for creative and incentive taxi services such as taxipooling. Taxipooling is similar to carpooling which is based on the idea that sets of users having the same travel destination and sharing vehicles. This paper presents two heuristic algorithms based on greedy method and the time-space network for the case of one origin to many destinations ("one- to-many") and many origins to one destination ("many-to-one"). These algorithms are used to support a field trial at Taipei Nei-Hu Science and Technology Park in Taiwan. The results of numerical tests have demonstrated that the outcomes of these heuristic algorithms are fairly plausible.[[conferencetype]]國際[[conferencedate]]20070824~20070827[[conferencelocation]]海南省海口市, 中

Mobility-On-Demand Service In Mass Transit: Hypercommute options

Digitization, increasing automation and new business models like shared mobility have revolutionized transportation and mobility. Ridesharing companies like Uber and Lyft provide technological platforms and support to connect drivers and riders on the basis of demand-response services. Although the most improvements in on-demand applications have been experimented in private transit services, there is no any implementation in public transportation to connect public transit services and passengers each other. Ondemand is still vague. However, providing on-demand services in public transportation is complicated because of the big capacity problem in mass transit, its application in public transit services can enable flexible mobility for riders and provide personalized mobility experience. This paper presents the concept of mobility-on-demand service and its application in public transit services with an technological innovation of FM/LM pilot project represented by HyperCommute. The paper starts with introduction, then the business model of mobility-on-demand service is described and the most used algorithms are explained, then an illustrative example of HyperCommute mobility-on-demand service is given. Also, the applicability of mobility-on-demand service in Istanbul is discussed. The paper ends up with conclusion and future directions

A two-stage approach to ridesharing assignment and auction in a crowdsourcing collaborative transportation platform.

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open requests at any given time can be large. On the other hand, service providers or drivers often evaluate the first couple of pending requests in deciding which request to participate in. This kind of behavior made by the driver may have potential detrimental implications for all parties involved. First, the drivers typically end up participating in those requests that require longer driving distance for higher profit. Second, the passengers tend to overpay under a competition free environment compared to the situation where the drivers are competing with each other. Lastly, when the drivers and passengers are not satisfied with their outcomes, they may leave the platforms. Therefore the platform could lose revenues in the short term and market share in the long term. In order to address these concerns, a decision-making support procedure is needed to: (i) provide recommendations for drivers to identify the most preferable requests, (ii) offer reasonable rates to passengers without hurting driver’s profit. This dissertation proposes a mathematical modeling approach to address two aspects of the crowdsourcing ridesharing platform. One is of interest to the centralized platform management on the assignment of requests to drivers; and this is done through a multi-criterion many to many assignment optimization. The other is of interest to the decentralized individual drivers on making optimal bid for multiple assigned requests; and this is done through the use of prospect theory. To further validate our proposed collaborative transportation framework, we analyze the taxi yellow cab data collected from New York city in 2017 in both demand and supply perspective. We attempt to examine and understand the collected data to predict Uber-like ridesharing trip demands and driver supplies in order to use these information to the subsequent multi-criterion driver-to-passenger assignment model and driver\u27s prospect maximization model. Particularly regression and time series techniques are used to develop the forecasting models so that centralized module in the platform can predict the ridesharing demands and supply within certain census tracts at a given hour. There are several future research directions along the research stream in this dissertation. First, one could investigate to extend the models to the emerging concept of Physical Internet on commodity and goods transportation under the interconnected crowdsourcing platform. In other words, integrate crowdsourcing in prevalent supply chain logistics and transportation. Second, it\u27s interesting to study the effect of Uber-like crowdsourcing transportation platforms on existing traffic flows at the various levels (e.g., urban and regional)

EXPLOITING AVAILABLE URBAN TRANSPORTATION RESOURCES WITH TAXI SHARING AND RAPID TRANSPORTATION NETWORKS: A CASE STUDY FOR MILAN

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