Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith

Abstract

This paper focuses on dynamic carpooling services in urban areas to address the needs of mobility in real-time by proposing a two-fold contribution: a solution with novel features with respect to the current state-of-the-art, which is named CLACSOON and is available on the market; the analysis of the carpooling services performance in the urban area of the city of Cagliari through emulations. Two new features characterize the proposed solution: partial ridesharing, according to which the riders can walk to reach the driver along his/her route when driving to the destination; the possibility to share the ride when the driver has already started the ride by modeling the mobility to reach the driver destination. To analyze which features of the population bring better performance to changing the characteristics of the users, we also conducted emulations. When compared with current solutions, CLACSOON allows for achieving a decrease in the waiting time of around 55% and an increase in the driver and passenger success rates of around 4% and 10%, respectively. Additionally, the proposed features allowed for having an increase in the reduction of the CO 2 emission by more than 10% with respect to the traditional carpooling service.

Evaluation on Construction Level of Smart City: An Empirical Study from Twenty Chinese Cities

“…However, with the large-scale promotion of advanced ICT and rapid evolution for smart cities, the indicators that become less smartness is an issue hard to avoid completely. Take smart transportation for instance, the indictors like the average daily volume of public transport (×5) and average travel speed (×7) seems not smart enough, while some new smart indicators are excluded or not considered, such as carpooling in Intelligent Transport Systems (ITS) [42], Electronic Toll Collection (ETC) network [43]. This paper explores the reasons as follows.…”

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

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A Two-Sided Matching Model for Task Distribution in Ridesharing: A Sustainable Operations Perspective

“…to achieve the goals of optimizing pricing, improving mileage, and increasing the menu. on the dynamic carpooling services in urban areas to address the needs of mobility in real-time by proposing a two-fold contribution [19]. As mentioned above, the existing studies concentrate on operational efficiency (e.g., increasing the success rate of matching and maximizing travel mileage), but fail to examine the operation process in a sustainable manner from the perspective of direct stakeholders' (both the drivers and riders) rights.…”