A machine-learned ranking algorithm for dynamic and personalised car pooling services

Campana, Mattia Giovanni; Delmastro, Franca; Bruno, Raffaele

doi:10.1109/itsc.2016.7795857

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…Several ridesharing services have been designed to enable drivers to efficiently share their cars with other travelers (Campana, Delmastro, and Bruno 2016). These services encompass a variety of types ranging from slugging, regular and causal carpooling, vanpooling/ microtransit, ridesplitting, as well as dial-a-ride (Li et al 2017;.…”

Section: Shared Rides Service Optionsmentioning

confidence: 99%

Toward using social media to support ridesharing services: challenges and opportunities

Tang

Duan

Zhao

2019

Transportation Planning and Technology

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Ridesharing has been attracting increasing attention from both academia and industry. One of the challenges posed by the study of ridesharing is to identify the most valuable information for improving the ridesharing decisions taken by participants. Another challenge is to use harvesting techniques to extract specific types of travel-related information. Many methods have been developed by the community in order to solve these issues. However, due to a lack of information sharing between different transit authorities and the difficulty of identifying subjective perceptions of the experience of ridesharing, understanding and evaluating how social media data might support or obstruct goals for mobility, safety and environmental sustainability in ridesharing is a difficult task. In this survey, we first analyze the literature on ridesharing with a focus on the properties and model of service, and introduce a framework to describe the major components required for a ridesharing service. Then, we illustrate the potential value of information extracted from social media and present the rationale for harvesting travel-related data. Finally, we detail some possible directions and different approaches for using social media data, and highlight their assets and drawbacks.

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Section: Shared Rides Service Optionsmentioning

confidence: 99%

Toward using social media to support ridesharing services: challenges and opportunities

Tang

Duan

Zhao

2019

Transportation Planning and Technology

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“…Mattia et.al. [7] in this paper, it uses two types of techniques to propose carpooling system, first is learning-to-rank which is basically used for ranking model of the individual. Secondly they used learning algorithm in their system.…”

Section: Relatedworkmentioning

confidence: 99%

Smart Transportation Based Car Pooling System

et al. 2020

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Sharing of car is Carpooling, so that multiple number of people can travel in single car. the use of car pooling reduces single individuals Travelling cost , reduces the fuel cost and reduces the number of cars. Due to the growth in the population there is inadequate transportation through their own car . Rather than using different mode of Transportation. It results in increasing number of Traffic on roads also increases pollution and increases time to travel to there destination. So by Smart transportation using car pooling system the individual can travel and share there rides with different people of a same destination. In this paper we have carried outs survey. By reviewing various Literature papers on carpooling it aims to reduce the number of cars by sharing the rides.

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“…Social aspects are considered in [30], [31]. The authors of [31] propose a recommender system for carpooling services that leverages on learning-to-rank techniques to automatically derive a personalized ranking model for each user from the history of her choices (i.e., the type of accepted or rejected shared rides). The system builds the list of recommended rides by maximizing the estimated success rate of the offered matches extracted from Foursquare check-in information.…”

Section: Related Workmentioning

confidence: 99%

“…The ABRM algorithm largely increases the number of candidate rides and it is not clear, in this new context, how to address ride allocation without making very strong and unrealistic assumptions on users' flexibility. Similar to [31] we preferred, instead, to adopt a ranking-based solution which orders the potential ride-offers on the basis of a weighted combination of a set of features modeling the different aspects of user flexibility. An advantage of this solution is that by sweeping these weights we can study and better understand the effect of user flexibility in accepting changes involving the three dimensions considered: the desired departure time, the distance of the pick up point and, more importantly, the destination proposed.…”

Section: Related Workmentioning

confidence: 99%

“…For example, we can reward rides with the shortest distance to reduce the emission of pollutants, or favor the passengers by ranking higher rides with the lowest walks and/or duration. Of course we are aware that more sophisticated ranking models considering different and complex aspects could be adopted (see for example [31]). However, the goal of this work is investigating the impact on carpooling of user flexibility and not studying application-specific rankers.…”

Section: A Ranking Modelmentioning

confidence: 99%

See 1 more Smart Citation

Boosting Ride Sharing With Alternative Destinations

Lira

Perego

Renso

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose ACTIVITY-BASED RIDE MATCHING (ABRM), an algorithm aimed at matching ride requests with ride offers possibly reaching alternative destinations where the intended activity can be performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM we introduce and assess a subsequent ranking step to provide the user with the top-k most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can be saved and how the ranking function can be tuned to enforce pro-environment behaviors.

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A machine-learned ranking algorithm for dynamic and personalised car pooling services

Cited by 6 publications

References 28 publications

Toward using social media to support ridesharing services: challenges and opportunities

Toward using social media to support ridesharing services: challenges and opportunities

Smart Transportation Based Car Pooling System

Boosting Ride Sharing With Alternative Destinations

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