Data fusion for agent-based freight demand and traffic simulation

基于代理的货运需求和交通模拟的数据融合

• 批准号: 327567-2011
• 负责人: Roorda, Matthew
• 金额: $ 2.48万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572058
• 关键词: Data; fusion; agent; based; freight

The proposed research will develop an agent-based model of freight demand that is integrated with a traffic simulation model. The agent-based demand model will predict behavioural elements of urban goods movement, including truck tour formation, shipment location, stop sequence, stop duration, and route choice. Such models have extensive data requirements, which will be addressed through the development of data fusion methods that will integrate private sector truck GPS data, survey data from manufacturers and retail establishments, truck parking inventory and usage data, and information from a variety of other sources. The proposed research will integrate the agent-based freight demand model with a vehicle-by-vehicle simulation of combined truck and passenger vehicle traffic on the urban road network. The study area will initially be a small test study area in Toronto. This study area will be expanded to represent regional goods movement within the Greater Toronto and Hamilton Area, making use of parallel computing resources. The integrated agent-based modelling approach will be valuable for evaluating a variety of transportation policies and infrastructure improvements that are under consideration by various levels of government in Canada. Such policies include truck parking policy, truck route plans, road pricing, truck restrictions, and freight related bylaws related to industrial land-uses in urban areas. Impacts that can be assessed include congestion, emissions, noise, reliability and economic effects.

拟议的研究将开发一个基于代理的货运需求模型，与交通仿真模型相结合。 基于代理人的需求模型将预测城市货物流动的行为要素，包括卡车旅行的形成，装运位置，停靠顺序，停靠时间和路线选择。 这些模型有广泛的数据要求，将通过开发数据融合方法来解决，这种方法将把私营部门卡车全球定位系统数据、制造商和零售机构的调查数据、卡车停车场库存和使用数据以及各种其他来源的信息结合起来。 该研究将整合基于代理的货运需求模型与城市道路网络上的卡车和客运车辆联合交通的逐车模拟。 研究区域最初将是多伦多的一个小型测试研究区域。 这个研究领域将扩大到代表大多伦多和汉密尔顿地区内的区域货物流动，利用并行计算资源。 综合代理为基础的建模方法将是有价值的评估各种运输政策和基础设施的改善，正在考虑由各级政府在加拿大。 这些政策包括卡车停车政策、卡车路线计划、道路定价、卡车限制以及与城市地区工业用地有关的货运相关细则。 可以评估的影响包括拥堵、排放、噪音、可靠性和经济影响。