Mei Haibo, Poslad Stefan, Du Shuang
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 610051, China.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
Sensors (Basel). 2017 Dec 11;17(12):2874. doi: 10.3390/s17122874.
Intelligent Transportation Systems (ITSs) can be applied to inform and incentivize travellers to help them make cognizant choices concerning their trip routes and transport modality use for their daily travel whilst achieving more sustainable societal and transport authority goals. However, in practice, it is challenging for an ITS to enable incentive generation that is context-driven and personalized, whilst supporting multi-dimensional travel goals. This is because an ITS has to address the situation where different travellers have different travel preferences and constraints for route and modality, in the face of dynamically-varying traffic conditions. Furthermore, personalized incentive generation also needs to dynamically achieve different travel goals from multiple travellers, in the face of their conducts being a mix of both competitive and cooperative behaviours. To address this challenge, a Rule-based Incentive Framework (RIF) is proposed in this paper that utilizes both decision tree and evolutionary game theory to process travel information and intelligently generate personalized incentives for travellers. The travel information processed includes travellers' mobile patterns, travellers' modality preferences and route traffic volume information. A series of MATLAB simulations of RIF was undertaken to validate RIF to show that it is potentially an effective way to incentivize travellers to change travel routes and modalities as an essential smart city service.
智能交通系统（ITSs）可用于为出行者提供信息并激励他们，帮助他们在日常出行中就出行路线和交通方式的选择做出明智决策，同时实现更具可持续性的社会和交通管理目标。然而，在实际应用中，智能交通系统要实现情境驱动和个性化的激励生成，同时支持多维度出行目标，是具有挑战性的。这是因为面对动态变化的交通状况，智能交通系统必须应对不同出行者对路线和交通方式有不同出行偏好和限制的情况。此外，面对出行者的行为既有竞争又有合作的混合情况，个性化激励生成还需要动态地实现多个出行者的不同出行目标。为应对这一挑战，本文提出了一种基于规则的激励框架（RIF），该框架利用决策树和进化博弈论来处理出行信息，并为出行者智能地生成个性化激励。所处理的出行信息包括出行者的移动模式、出行者的交通方式偏好和路线交通流量信息。对基于规则的激励框架进行了一系列MATLAB模拟，以验证该框架，结果表明它可能是激励出行者改变出行路线和交通方式的一种有效方式，是智能城市的一项重要服务。
