A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Abstract

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.

Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities

“…The vehicle ride-sharing topic was addressed in the article written by Anagnostopoulos [67]. The author came up with a new recommender system based on an artificial intelligence (AI)-enabled weighted pattern-matching model, which offer individuals personalized car-sharing options according to their preferences and needs, having major contributions in decreasing the level of congestion in urban areas, minimization of the time spent in traffic, increasing the quality of life for citizens, and sustaining the green ecosystem.…”

Meta-Learning over Time for Destination Prediction Tasks

“…The task of destination and/or trajectory prediction is well-known in the domains of urban planning and management, intelligent transportation systems, "smart cities, " and ride-sharing applications, leading to a proliferation of research in the past few years [2,3,60,61,66,67]. Recent events have also energized a sub-field of mobility modeling and destination prediction for the purposes of pandemic analysis [23].…”

Personalized Recommendation of Location-Based Services Using Spatio-Temporal-Aware Long and Short Term Neural Network

“…Didi [9] proposed a deconfounded multi-agent environment reconstruction (DEMER) approach in order to learn the environment together with the hidden confounder. DigiT.DSS.Lab [10] proposed a predictive vehicle ride sharing system for commuting, which has impact on the smart cities' green ecosystem. Yang et al [11] proposed that feature engineering focuses on traffic recommendations for application scenarios of multi-modal models and designs from multiple perspectives of users, travel, and services.…”