Modeling and solving the multimodal car- and ride-sharing problem

Enzi, Miriam; Parragh, Sophie N.; Pisinger, David; Prandtstetter, Matthias

doi:10.1016/j.ejor.2020.11.046

Cited by 37 publications

(17 citation statements)

References 35 publications

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“…is shows that AVPE, which aims to ensure that any extracted PoIs are correct rather than aiming for completeness, gives a significant improvement over the current state-of-the-art clustering algorithms when used alone. is improvement can help assist the development of applications such as destination prediction [55][56][57][58] and identifying ride sharing opportunities [59,60], which benefit from accurate PoI data.…”

Section: Discussionmentioning

confidence: 99%

Classifying Vehicle Activity to Improve Point of Interest Extraction

Hinsbergh

Griffiths

Taylor

et al. 2021

Mobile Information Systems

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Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.

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Section: Discussionmentioning

confidence: 99%

Classifying Vehicle Activity to Improve Point of Interest Extraction

Hinsbergh

Griffiths

Taylor

et al. 2021

Mobile Information Systems

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show abstract

“…Meanwhile, it is possible that the changes in the road pricing will affect the transportation market (Adler et al 2021), which will further trigger the changes in the routes, station, and fare of public transportation, affecting commuter's perception of public transportation service (e.g., Fan et al 2016;Nielsen et al 2021) and their selection on work and house locations. Thus, the road pricing, public transportation network, and even the emerging multi-modal mobility service (e.g., Molenbruch et al 2021;Enzi et al 2021) could be considered simultaneously in future studies.…”

Section: Effects Of Considering Land Usementioning

confidence: 99%

Lexicographic multi-objective road pricing optimization considering land use and transportation effects

Zhong

Jiang

Nielsen

2022

European Journal of Operational Research

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“…In the ride-matching scenario, ride requests can be matched or unmatched depending on the number of vehicles on road. To avoid the unassigned requests, the work in [114] proposed a multi-modal car and ridesharing problem which assigned different modes of transportation to ride requests. In the problem, ride requests are covered by car while the uncovered ride requests can be assigned to other modes of transportation.…”

Section: Exact Algorithms In the Multi-modal Problemmentioning

confidence: 99%

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

et al. 2021

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The Shared Mobility Problems (SMP) with the rideshare concept based on sharing a vehicle are fast becoming a trend in many urban cities around the world. Examples of these problems are like ridesharing, carpooling, taxisharing, buspooling, vanpooling, and multi-modal ridesharing. This is the new way to access transportation services by those who are propelling the sharing economy, where access rather than ownership is the new norm. This paper provides a systematic review of SMP using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. A total of 110 papers from the last decade are selected from 12 abstracts and citation databases to be reviewed and classified. This is done based on the problem types, variants, characteristics, and solution approaches. The current trends and analysis of the survey findings are also summarised. From this systematic review, it is observed that both the time window and multi-objective problems are popular among the researchers, while the minimisation of the total cost is the main concern in the literature of the SMP. Both static and dynamic cases of the SMP are the most researched where heuristic and metaheuristic approaches are widely adopted by the researchers in the literature. Finally, challenges and suggestions for future work are discussed and highlighted.

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Modeling and solving the multimodal car- and ride-sharing problem

Cited by 37 publications

References 35 publications

Classifying Vehicle Activity to Improve Point of Interest Extraction

Classifying Vehicle Activity to Improve Point of Interest Extraction

Lexicographic multi-objective road pricing optimization considering land use and transportation effects

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

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