GreenCommute: An Influence-Aware Persuasive Recommendation Approach for Public-Friendly Commute Options

Authored by Quan Bai, Shiqing Wu, Sotsay Sengvong

Date Published: 2018

DOI: 10.1007/s11518-018-5368-6

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Negative impacts produced by transportation sector have increased in parallel with the increase of urban mobility. In this paper, we introduce GreenCommute, a novel recommendation system which can facilitate commuters to take public friendly commute options, while provide support to alleviate the external cost in society, such as traffic pollution, congestion and accidents. In the meanwhile, a rewarding mechanism for persuading commuters is embedded in the proposed approach for balancing the conflict between personal needs and social aims. The allocation of reward values also takes users' influential degrees in the social network into consideration. Experimental results show that the GreenCommute can promote public friendly commute options more effectively in comparison to the traditional recommendation system.

Tags

Agent-based modelling Social influence Public transport System Social-influence Vehicle Recommendation system Reward