Agent based model for dynamic ridesharing

Nourinejad, Mehdi; Roorda, Matthew J.

doi:10.1016/j.trc.2015.07.016

Cited by 144 publications

(67 citation statements)

References 14 publications

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“…Ma et al [20] noted that a ride request, made through a smartphone app, should be assigned to whichever taxi that minimizes the increase in the travel distance resulting from the request while meeting the arrival time, capacity, and monetary constraints of both the new passenger and the existing passenger(s). Nourinejad and Roorda [21] verified the performance of ride sharing by maximizing the savings on the total vehicle kilometers traveled and maximizing the matching rate. Najmi et al [22] investigated the effect of different dynamic sharing methods on the performance of the ride-sharing system.…”

Section: Literature Reviewmentioning

confidence: 94%

Shared Autonomous Taxi System and Utilization of Collected Travel-Time Information

Liu

Miwa

Zeng

et al. 2018

Journal of Advanced Transportation

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Shared autonomous taxi systems (SATS) are being regarded as a promising means of improving travel flexibility. Each shared autonomous taxi (SAT) requires very precise traffic information to independently and accurately select its route. In this study, taxis were replaced with ride-sharing autonomous vehicles, and the potential benefits of utilizing collected travel-time information for path finding in the new taxi system examined. Specifically, four categories of available SATs for every taxi request were considered: currently empty, expected-empty, currently sharable, and expected-sharable. Two simulation scenarios-one based on historical traffic information and the other based on real-time traffic information-were developed to examine the performance of information use in a SATS. Interestingly, in the historical traffic information-based scenario, the mean travel time for taxi requests and private vehicle users decreased significantly in the first several simulation days and then remained stable as the number of simulation days increased. Conversely, in the real-time information-based scenario, the mean travel time was constant. As the SAT fleet size increased, the total travel time for taxi requests significantly decreased, and convergence occurred earlier in the historical information-based scenario. The results demonstrate that historical traffic information is better than real-time traffic information for path finding in SATS.

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Section: Literature Reviewmentioning

confidence: 94%

Shared Autonomous Taxi System and Utilization of Collected Travel-Time Information

Liu

Miwa

Zeng

et al. 2018

Journal of Advanced Transportation

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“…Perhaps as a reflection of the growth of the ridesharing industry and of the aforementioned challenges, empirical research in this area is also experiencing a surge, exemplified by a growing number of journal publications [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] that explore the multidimensional challenges and opportunities produced by the widespread adoption of ridesharing services.…”

Section: Introductionmentioning

confidence: 99%

“…There have been a number of studies in the last few years taking aim at understanding ride-sharing services and carpooling schemes -each of which takes a different investigative position on the challenges faced by resource pooling services as a whole [1,22], while some consider some facet of modeling behaviors [3] using agent-based approaches. The majority of papers reviewed were of modeling carpooling decisions as an optimization problem [7,11,12] and finally, some approaches intended to make early-stage predictions about carpooling and ridesharing trends [6,9] were pre-existent in the literature.…”

Section: Introductionmentioning

confidence: 99%

Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM)

Shaheen

2019

Understanding Complex Systems

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The ridesharing economy is experiencing rapid growth and innovation. Companies such as Uber and Lyft are continuing to grow at a considerable pace while providing their platform as an organizing medium for ridesharing services, increasing consumer utility as well as employing thousands in part-time positions. However, many challenges remain in the modeling of ridesharing services, many of which are not currently under wide consideration. In this paper, an agent-based model is developed to simulate a ridesharing service in the Washington D.C. metropolitan region. The model is used to examine levels of utility gained for both riders (customers) and drivers (service providers) of a generic ridesharing service. A description of the Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided, as well as a description of a typical simulation run. We investigate the financial gains of drivers for a 24-hour period under two scenarios and two spatial movement behaviors. The two spatial behaviors were random movement and Voronoi movement, which we describe. Both movement behaviors were tested under a stationary run conditions scenario and a variable run conditions scenario. We find that Voronoi movement increased drivers' utility gained but that emergence of this system property was only viable under variable scenario conditions. This result provides two important insights: The first is that driver movement decisions prior to passenger pickup can impact financial gain for the service and drivers, and consequently, rate of successful pickup for riders. The second is that this phenomenon is only evident under experimentation conditions where variability in passenger and driver arrival rates are administered.

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“…The pursuits of joint travel generally result from the psychological needs for interactions with others or from physical constraints such as vehicle-deficiency or being present at the same destinations for shared activities. In part, joint travel is also stimulated by economic and environmental incentives such as HOV/HOT lanes and carpooling/ridesharing initiatives (Nourinejad and Roorda 2016;Sánchez et al 2016). As evidenced by travel surveys (e.g., Dubernet and Axhausen 2013;Gupta and Vovsha 2013;Ho and Mulley 2015;Liao et al 2017), a significant portion of a region's travel is implemented jointly.…”

Section: Introductionmentioning

confidence: 99%

Joint travel problem in space–time multi-state supernetworks

Liao

2017

Transportation

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Joint travel problem (JTP) is an extension of the classic shortest path problem and relevant to shared mobility. A pioneering endeavor via supernetwork framework has been put forward to model two-person JTP. However, it was only addressed in the static context and with the assumption of zero waiting disutility, which resulted in no or weak synchronization among the travelers. This paper proposes a space-time multi-state supernetwork framework to address JTP for conducting one joint activity in the timedependent context. Space-time synchronization and various choice facets related to joint travel are captured systematically. Two-person JTP is first discussed in a uni-modal transport network, and further extended to incorporate multi-modal and multi-person respectively. Stage-wise recursive formulations are proposed to find the optimal joint paths. It is found that JTP is a variant of Steiner tree problem by reduction and the number of meeting/departing points has no impact on the run-time complexity in space-time multistate supernetworks.

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Agent based model for dynamic ridesharing

Cited by 144 publications

References 14 publications

Shared Autonomous Taxi System and Utilization of Collected Travel-Time Information

Shared Autonomous Taxi System and Utilization of Collected Travel-Time Information

Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM)

Joint travel problem in space–time multi-state supernetworks

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