The shared-taxi problem: Formulation and solution methods

Hosni, Hadi El; Naoum-Sawaya, Joe; Artail, Hassan

doi:10.1016/j.trb.2014.09.011

Cited by 180 publications

(79 citation statements)

References 23 publications

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“…We observe that ridesharing can be seen as crowdsourcing in the area of transportation (Alt et al, 2010); for a review of the crowdsourcing literature, see Pedersen et al (2013). It is also somewhat related to the problem of sharing taxis between riders; for a recent review of this, see Hosni et al (2014).…”

Section: Introductionmentioning

confidence: 78%

Dynamic ridesharing: Is there a role for dedicated drivers?

Lee

Savelsbergh

2015

Transportation Research Part B: Methodological

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Section: Introductionmentioning

confidence: 78%

Dynamic ridesharing: Is there a role for dedicated drivers?

Lee

Savelsbergh

2015

Transportation Research Part B: Methodological

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“…The origin and destination of each request ∈ are denoted by and , respectively. For ride-sharing problems, departure time and arrival time are key factors in improving matching rate [24]; therefore, it was assumed that each request has an earliest departure time denoted by , a latest arrival time denoted by , and travel-time flexibility denoted by [25]. Request should be served in the time window ( , ) [26].…”

Section: Demand Generationmentioning

confidence: 99%

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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“…The shared taxi routing problem belongs to the category of the multi-vehicle DARP [2]. To our knowledge, as a generalization of vehicle routing problem, the DARP has been studied extensively in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the work closely related to taxi sharing problem is scarce. Hosni et al [2]argued that taxi sharing plays a prominent role in improving taxi service standard and mitigating traffic congestion as well as reducing the impact on environment and then formulated the taxi sharing problem as a mixed integer program utilizing a Lagrangian decomposition approach.…”

Section: Literature Reviewmentioning

confidence: 99%

The Simulation of Dynamic Shared Taxi Routing Problem

Wang¹,

Wei²,

Chen³

2017

dtcse

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In recent years, taxi sharing is becoming a popular transportation mode. Compared with the traditional operation mode, taxi sharing possesses some advantages. For example, it allows several passengers to share one taxi in the same route thus improving the utilization rate of taxi seats and reduces the taxi traveled miles. Besides, taxi sharing decreases the number of taxis needed, which efficiently relieve the traffic congestion and reduces the emission of carbon dioxide. In this paper, we consider the dynamic shared taxi routing problem (D-STRP) and develop a simulator to mimic the process of taxi sharing. To maximize the objective of the proposed problem, we define a parameterized taxi allocation policy to make a trade-off between the passenger wait and detour time and taxi route length. Furthermore, we propose the insertion algorithm to assign the passenger request to the best available at the lowest expense. The simulation results indicate that the parameterized taxi allocation policy has obvious impact on the objective of D-STRP. Through simulations, we can find the optimal taxi allocation policy in each trip scenario fast, which is of great significance for the taxi service provider.

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The shared-taxi problem: Formulation and solution methods

Cited by 180 publications

References 23 publications

Dynamic ridesharing: Is there a role for dedicated drivers?

Dynamic ridesharing: Is there a role for dedicated drivers?

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

The Simulation of Dynamic Shared Taxi Routing Problem

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