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.

Item Type:	Refereed Article
Keywords:	recommendation system, agent-based modelling, social influence, reward, public transport
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Intelligent robotics
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Application software packages
UTAS Author:	Bai, Q (Dr Quan Bai)
ID Code:	140661
Year Published:	2018
Web of Science® Times Cited:	6
Deposited By:	Information and Communication Technology
Deposited On:	2020-09-01
Last Modified:	2020-09-07
Downloads:	0