About: Carpool is a research topic. Over the lifetime, 641 publications have been published within this topic receiving 9925 citations. The topic is also known as: ride sharing & high occupancy vehicle.
TL;DR: In this article, the problem of matching drivers and riders in a dynamic setting is considered, and optimization-based approaches are developed to minimize the total systemwide vehicle miles incurred by system users, and their individual travel costs.
Abstract: 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.
TL;DR: In this article, a carpool matching module is used to determine a match between the first and second travel patterns, and generate a matching carpool proposal directed at the first user and second user.
Abstract: Systems and methods are disclosed including a ride-sharing computer to receive a ride-sharing request from a rider, wherein the computer includes a route analysis module to collect travel data and appointments from a calendar from a first mobile device of a first user and from a second mobile device of a second user, and to determine a first travel pattern associated with the first user and a second travel pattern associated with the second user and a carpool matching module to determine a match between the first and second travel patterns, and to generate a carpool proposal directed at the first and second users, wherein one of the travel patterns is a common portion of the other travel pattern proximally the same time for spatially and temporally common on-demand carpooling; and a ride-sharing vehicle and having a mobile device coupled to the computer, wherein the mobile device picks up the first and second users based on the carpool proposal.
TL;DR: In this paper, the authors studied university students' commute and housing behaviors using samples from Los Angeles, a place notorious for car dependence and dominance, and found that being embedded in this place does not make university students drive alone more than their peers in other places.
Abstract: This paper studies university students’ commute and housing behaviors using samples from Los Angeles, a place notorious for car dependence and dominance. It finds that being embedded in this place does not make university students drive alone more than their peers in other places. Being multimodal and having a discounted transit pass increase the odds of alternative modes while holding a parking permit reduces the odds of these modes. Commute distance is positively related to carpool and telecommuting. Gender, status (undergraduate vs. gradate) and age are significantly correlated to biking, walking or public transit. Students living alone are more likely to commute by driving alone than other students. Having friends and classmates living nearby increases the odds of taking public transit. Due to data constraints, this study cannot prove whether there is any correlation between information contagion and the effects of living alone and having friends and classmates living nearby on alternative mode choice. But it proposes that the issue be worthwhile of further investigations. Base on the above, the paper recommends a comprehensive travel demand management program, utilization of information contagion effects of students and promotion of multimodal commute to better promote alternative mode of commute among university students.
TL;DR: In this paper, the authors proposed a carpooling club model with two main new features: establishing a base trust level for carpoolers to find compatible matches for traditional groups and at the same time allowing to search for a ride in an alternative group when the pool member has a trip schedule different from the usual one.
Abstract: The increase of urban traffic congestion calls for studying alternative measures for mobility management, and one of these measures is carpooling. In theory, these systems could lead to great reductions in the use of private vehicles; however, in practice they have obtained limited success for two main reasons: the psychological barriers associated with riding with strangers and poor schedule flexibility. To overcome some of the limitations of the traditional schemes, we proposed studying a carpooling club model with two main new features: establishing a base trust level for carpoolers to find compatible matches for traditional groups and at the same time allowing to search for a ride in an alternative group when the pool member has a trip schedule different from the usual one. A web-based survey was developed for the Lisbon Metropolitan Region (Portugal), including a Stated Preference experiment, to test the concept and confirm previous knowledge on these systems’ determinants. It was found through a binary logit Discrete Choice Model calibration that carpooling is still attached with lower income strata and that saving money is still an important reason for participating in it. The club itself does not show promise introducing more flexibility in these systems; however, it should provide a way for persons to interact and trust each other at least to the level of working colleagues.
TL;DR: In this article, the characteristics of carpoolers, distinguishes among different types of car poolers, identifies the key differences between carpooling and drive alone and transit commuters, describes how commuters carpool, and offers explanations of why they carpool.