The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.
Car pooling is a transportation service organized by a large company which encourages its employees to pick up colleagues while driving to from work to minimize the number of private cars travelling to from the company site. The car pooling problem consists of defining the subsets of employees that will share each car and the paths the drivers should follow, so that sharing is maximized and the sum of the path costs is minimized. The special case of the car pooling problem where all cars are identical can be modeled as a Dial-a-Ride Problem. In this paper, we propose both an exact and a heuristic method for the car pooling problem, based on two integer programming formulations of the problem. The exact method is based on a bounding procedure that combines three lower bounds derived from different relaxations of the problem. A valid upper bound is obtained by the heuristic method, which transforms the solution of a Lagrangean lower bound into a feasible solution. The computational results show the effectiveness of the proposed methods. Ridesharing as a mode of travel is a potential solution to a variety of the transportation sectors toughest challenges including congestion relief, increased energy security, reduced GHG emissions and improved travel options. However, the transportation literature provides little quantified assessment of ridesharing’s overall potential. This paper proposes a data driven methodology for estimating the viability of ridesharing at an organizational scale, and seeks to demonstrate its applicability using the Massachusetts Institute of Technology commuting population as a case. The methodology seeks to improve upon previous research by differentiating between modeled rideshare potential based on known trip characteristics, and observed rideshare behavior, within the same commuting population. By comparing rideshare potential to observed behavior,inferences can be made about the relative importance of trip characteristics vs. the importance of human attitudes in rideshare arrangements. MIT-specific results suggest that between 50 and 77 of the commuting population could rideshare on a maximum-effort day. These are values significantly higher than the 8 of the MIT community that currently choose to rideshare. Maximum achievable VMT reductions from daily ridesharing are between 9 and 27 . The disparity between the modeled potential and observed behavior suggests that human attitudes are a much larger barrier to increased rideshare participation than incompatible trip characteristics. The results suggest that policy makers seeking to increase rideshare participation may want to target large organizations and focus their efforts on personalized travel planning in an effort to improve attitudes towards ridesharing. Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.