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

Abstract

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.

Demonstration of Participation Networks in Urban Transport Policy of Public and Private Sectors through Social Media: The Case of Bike-Sharing Pricing Strategy in China

“…For example, transport policymakers and providers in the USA, like MTA Transit and Metro-North Railroad in New York City, and DOTs in Alabama, California, West Virginia, etc., use Facebook and Twitter to inform travel information or specific events as well as to connect the feedback from the residents, so that to facilitate project improvements and misinformation corrections [8,11]. Furthermore, with the support of web-based tools/toolkits and text mining techniques [12], ridesharing agencies can optimize ridership, timeliness, efficiency, and safety and also reveal the level of satisfaction with ridesharing services by monitoring social media [13,14]. Another example is the adjustment of the No.…”

An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering

“…Jiang et al [6] proposed to introduce the trust model into the collaborative filtering algorithm to increase the accuracy of recommendation. Since then, more and more scholars have proposed various personalized recommendation algorithms based on collaborative filtering, such as personalized recommendation based on adaptive collaborative filtering [17], collaborative filtering based on social psychology [18], collaborative filtering personalized recommendation based on trust awareness [19], and so on.…”

“…e recommendation list is compared with the user's actual browsing records in the test data, and the precision and recall of the fusion method are obtained. Figure 8 shows the precision and recall of the recommended results by comparing the fusion method proposed in this paper with literature [10], literature [12], literature [19], and literature [21] when the number of recommended products is different.…”

The Effects of On- and Before- Journey Advantages Using Ride-Sourcing in Indonesia

“…It is hypothesised that the before-journey advantages are the reason why this mode is perceived to be useful and why travellers use this mode more often. Circella and Alemi [28], Rayle et al [1], Tang et al [29], and Tirachini et al [2] have suggested some on-journey and before-journey advantages of using ride-sourcing, but without detailed classifications of whether these are before-or after-journey advantages and without advanced statistical tests. This study tries to adjust the various on-journey and before-journey advantages of using ride-sourcing to be more relevant in the Indonesian context, as modified from Tirachini et al [2], and thus to apply it by using multivariate analysis.…”