Novel dynamic formulations for real-time ride-sharing systems

Najmi, Ali; Rey, David

doi:10.1016/j.tre.2017.10.009

Cited by 88 publications

(53 citation statements)

References 24 publications

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“…Finally, in the light of the discussion in the previous work (Najmi et al 2017), which focused on the peer-topeer ridesharing problem, i.e., the matching between riders and drivers, we can make the following observations. In Najmi et al (2017), for instance M 1, on the global run, a total of 25550 drivers are attempted to be matched with a total of 20250 riders, that is on average 57 vehicles available and 45 requests per batch of 2 min. As the average trip duration is observed to be of 16 min, therefore 8 batch periods, it would take respectively 600{8 " 75 and 800{8 " 100 vehicles per batch of 2 min to satisfy the demand (up to respectively 76% and 96%) on average considering on-demand ridesharing, which is an explanation of the low matching rate presented in Najmi et al (2017).…”

Section: Melbourne Metropolitan Area Simulationsmentioning

confidence: 87%

“…This method handles more than 10k taxis per day and roughly 500k trips. In Najmi et al (2017), a cluster-based matching algorithm, based on trip spatial features, is run prior to running the assignment problem to match drivers and riders. Instances with hundreds of vehicles in the Melbourne Metropolitan Area can be solved in real-time in a rolling horizon framework.…”

Section: Related Workmentioning

confidence: 99%

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Real-time city-scale ridesharing via linear assignment problems

Simonetto

Monteil

Gambella

2019

Transportation Research Part C: Emerging Technologies

155

135

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In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four times faster than the state-of-the-art, even if it is implemented on a less dedicated hardware, and achieves similar service quality. Current literature showcases the ability of state-ofthe-art ridesharing algorithms to tackle very large fleets and customer requests in almost near real-time, but the benefits of ridesharing seem limited to centralized systems. Our algorithm suggests that this does not need to be the case. The algorithm that we propose is fully distributable among multiple ridesharing companies. By leveraging two datasets, the New York city taxi dataset and the Melbourne Metropolitan Area dataset, we show that with our algorithm, real-time ridesharing offers clear benefits with respect to more traditional taxi fleets in terms of level of service, even if one considers partial adoption of the system. In fact, e.g., the quality of the solutions obtained in the state-of-the-art works that tackle the whole customer set of the New York city taxi dataset is achieved, even if one considers only a proportion of the fleet size and customer requests. This could make real-time urban-scale ridesharing very attractive to small enterprises and city authorities alike. However, in some cases, e.g., in multi-company scenarios where companies have predefined market shares, we show that the number of vehicles needed to achieve a comparable performance to the monopolistic setting increases, and this raises concerns on the possible negative effects of multi-company ridesharing.

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Section: Melbourne Metropolitan Area Simulationsmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Real-time city-scale ridesharing via linear assignment problems

Simonetto

Monteil

Gambella

2019

Transportation Research Part C: Emerging Technologies

155

135

View full text Add to dashboard Cite

show abstract

“…Dynamic ride-matching includes many parameters, and this renders the problem to be non-deterministic polynomial-time hard (NP-hard) [15][16][17]. Therefore, many solutions to the ride-matching problem that have been proposed in the literature use either heuristics or metaheuristics [6,[15][16][17][18][19][20][21][22][23]. Although heuristic and meta-heuristic methods offer feasible processing times, they may not find the best possible matches.…”

Section: Related Studiesmentioning

confidence: 99%

Matching algorithm for improving ride-sharing by incorporating route splits and social factors

2020

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Increasing traffic congestion and the advancements in technology have fostered the growth of alternative transportation modes such as dynamic ride-sharing. Smartphone technologies have enabled dynamic ride-sharing to thrive, as this type of transportation aims to establish ride matches between people with similar routes and schedules on short notice. Many automated matching methods are designed to improve system performance; such methods include minimizing process time, minimizing total system cost or maximizing total distance savings. However, the results may not provide the maximum benefits for the participants. This paper intends to develop an algorithm for optimizing matches when considering participants' gender, age, employment status and social tendencies. The proposed matching algorithm also splits unmatched parts of drivers' routes and creates new travel requests to find additional matches using these unmatched parts. Accordingly, this paper performs an extensive simulation study to assess the performance of the proposed algorithm. The simulation results indicate that route splits may increase the number of matches significantly when there is a shortage of drivers. Furthermore, the paper demonstrates the effects and potential benefits of utilizing a social compatibility score in the objective function.

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“…This results in partial spatial coverage, the coexistence of several non-interoperable systems, "trial and error" pilot tests, etc. Policy-makers should encourage city-wide systems, based on an in-depth analysis of the global users' needs and boundary conditions of the service area [75]. In addition, two facts could impede the desired reduction of the vehicle fleet by the introduction of car-sharing initiatives.…”

Section: Sharing Systemsmentioning

confidence: 99%