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.
Among the topics investigated in this field, most studies capture passengers time preference by denoting an accepted time window. Agatz et. al @cite_23 considers an acceptable period between earliest departure time and latest arrival time to the destination defined by every passenger. Some papers limit the actual travel time by defining a fixed threshold deviation in traveling time @cite_14 and a maximum ride time @cite_24 . In contrast, we propose a strategy where the central coordinator computes and declares an optimal waiting time by the advent of a passenger leading to optimal overall cost. A passenger's of tendency to move along longer paths to get to his desired destination is the first element to compute his optimal waiting time.
@cite_24 @cite_14 @cite_23
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An anonymous Bluetooth tracking system was deployed at the new Indianapolis International Airport in Indiana from May 8 to June 2, 2009, to measure the time for passengers (a) to move from the nonsterile side of the airport (presecurity), (b) to clear the security screening checkpoint, and (c) to enter the walkway to Concourse B on the sterile side. The maximum passenger transit time between these checkpoints was observed on Monday mornings at approximately 0600, when it could take passengers up to 20 min to transit the security queue and screening and to walk to Concourse B. Depending on the day of the week, this approach was demonstrated to sample between 5 and 6.8 of passengers. This modest sample size provides a more robust measurement of screening times than the current system of manually distributing time-stamped cards as passengers enter the queue and collecting them where passengers pass through the magnetometer. Furthermore, because the final passenger reference point used in this study is on the sterile concourse, it captures the time associated with passengers repackaging their belongings and redonning their shoes. The data from this pilot study suggest the feasibility of using an automated system to provide quantitative information to managers for more effective allocation of scarce resources, as well as providing the traveling public with necessary information about the amount of time they should allocate for transiting the security screening process. The paper concludes by suggesting that additional pilot studies should be performed at several airports with alternative checkpoint configurations to develop a consensus on best practices for locating sensors to measure passage times at airport security screening. We consider the application of route planning in large public-transportation networks (buses, trains, subways, etc). Many connections in such networks are operated at periodic time intervals. When a set of connections has sufficient periodicity, it becomes more efficient to store the time range and frequency (e.g., every 15 minutes from 8:00am-6:00pm) instead of storing each of the time events separately. Identifying an optimal frequency-compression is NP-hard, so we present a time- and space-efficient heuristic. We show how we can use this compression to not only save space but also query time. We particularly consider profile queries, which ask for all optimal routes with departure times in a given interval (e.g., a whole day). In particular, we design a new version of Dijkstra's algorithm that works with frequency-based labels and is suitable for profile queries. We evaluate the savings of our approach on two metropolitan and three country-wide public-transportation networks. On our largest network, we simultaneously achieve a better space consumption than all previous methods as well as profile query times that are about 5 times faster than the best previous method. We also improve Transfer Patterns, a state-of-the-art technique for fully realistic route planning in large public-transportation networks. In particular, we accelerate the expensive preprocessing by a factor of 60 compared to the original publication. This paper investigates the effect of travel time variability on drivers' route choice behavior in the context of Shanghai, China. A stated preference survey is conducted to collect drivers' hypothetical choice between two alternative routes with designated unequal travel time and travel time variability. A binary choice model is developed to quantify trade-offs between travel time and travel time variability across various types of drivers. In the model, travel time and travel time variability are, respectively, measured by expectation and standard deviation of random travel time. The model shows that travel time and travel time variability on a route exert similarly negative effects on drivers' route choice behavior. In particular, it is found that middle-age drivers are more sensitive to travel time variability and less likely to choose a route with travel time uncertainty than younger and elder drivers. In addition, it is shown that taxi drivers are more sensitive to travel time and more inclined to choose a route with less travel time. Drivers with rich driving experience are less likely to choose a route with travel time uncertainty. In 1991, D. J. Bertsimas and G. van Ryzin introduced and analyzed a model for stochastic and dynamic vehicle routing in which a single, uncapacitated vehicle traveling at a constant velocity in a Euclidean region must service demands whose time of arrival, location and on-site service are stochastic. The objective is to find a policy to service demands over an infinite horizon that minimizes the expected system time (wait plus service) of the demands. This paper extends our analysis in several directions. First, we analyze the problem of m identical vehicles with unlimited capacity and show that in heavy traffic the system time is reduced by a factor of 1 m2 over the single-server case. One of these policies improves by a factor of two on the best known system time for the single-server case. We then consider the case in which each vehicle can serve at most q customers before returning to a depot. We show that the stability condition in this case depends strongly on the geometry of the region. Several pol...