Quantifying the Efficiency of Ride Sharing

Ruch, Claudio; Lu, Chengqi; Sieber, Lukas

doi:10.1109/tits.2020.2990202

Cited by 44 publications

(34 citation statements)

References 29 publications

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“…Another popular approach is heuristic algorithms based on microscopic information such as trip requests [1], [11], [15]. Its advantage is that it is computationally efficient and can be flexibly applied to on-demand services and other complicated or practical situations.…”

Section: A Operational Aspects Of Sav Systemsmentioning

confidence: 99%

“…Apart from the above specific problems, trade-off relation between performance indexes of SAV systems have been noted. As mentioned in Section I, design of SAV systems may vary significantly depending on the aim of the systems, and user-side cost and system-side cost may vary depending on the design [13]- [15]. However, to the authors' knowledge, there are only a few studies that directly investigated this issue in quantitative approaches.…”

Section: B Strategic Aspects Of Sav Systemsmentioning

confidence: 99%

“…The importance of trade-off relations among performance indexes of SAV systems has been noted, especially in strategic levels [13]- [15]. For example, an SAV system could be designed to minimize either user-side cost (e.g., passenger travel time), system-side cost (e.g., operational cost), or socialside cost (e.g., environmental cost); these cases may have completely different system design and cost allocation [13], [16].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Seo

Asakura

2022

IEEE Trans. Intell. Transport. Syst.

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This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

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Section: A Operational Aspects Of Sav Systemsmentioning

confidence: 99%

Section: B Strategic Aspects Of Sav Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Seo

Asakura

2022

IEEE Trans. Intell. Transport. Syst.

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“…Key contributions for scaling optimization-based solution approaches were made by Santi et al and Alonso-Mora et al, and computationally further improved in Liu and Sumaranayake, and Engelhardt et al (24)(25)(26)(27). The developed advanced control policies for ride-pooling can generate substantial benefits over simpler heuristics; numerical experiments conducted in Engelhardt et al and Ruch et al quantify these benefits (28,29). Moreover, the amount of possible matches depends on the offered service parameters, that is, reservation time, waiting time, and detour time (30,31).…”

Section: Literature Reviewmentioning

confidence: 99%

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

Wilkes

Engelhardt

Briem

et al. 2021

Transportation Research Record

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This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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“…Their research focuses on Connected Autonomous Vehicles (CAV) technology exploiting mobility on demand for a data driven model to provide fleet control. Operational policies are examined in [22], where the authors focus on occasional ride sharing with regards to mobility on demand services in SCs. In [23], the authors study the effects of privacy regulations on dynamic car riding sharing systems, where they experiment with spatiotemporal data optimization to provide an optimal privacy-based approach for commute in sustainable cities.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Anagnostopoulos

2021

Smart Cities

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Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

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Quantifying the Efficiency of Ride Sharing

Cited by 44 publications

References 29 publications

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

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