A SYSTEMS APPROACH TO INTEGRATED URBAN NETWORK MANAGEMENT STRATEGIES

交通拥堵是城市能源消耗和污染的主要来源之一。智能交通系统(ITS)和出行需求管理(TDM)是实现城市交通拥堵问题可持续解决方案的重要战略。这两种策略可以联合使用来改善和维持长期的运输性能。然而，为了实现最优的交通管理，对各种ITS和TDM策略的集成进行的研究很少。此外，已发表的大部分作品都没有考虑到出行需求的动态化。可变需求的存在的影响可能是显著的，并导致在比较各种交通策略和方案时的临界偏差。这需要了解城市中的网络出行需求如何随着控制、需求管理策略和拥堵程度的变化而变化。 这一研究计划的重点将是整合ITS和TDM战略，以无缝地优化交通设施的效率和减少排放，同时考虑到需求的多变性。我们将研究路线、出发时间、方式和目的地选择的变化对短期到中期交通需求变化的影响。该问题将被表述为一个两层多目标优化问题，以满足需求管理、机动性、安全性和减少能源使用的目标。将能源使用最优化纳入其中将带来进一步的创新：这一点尚未尝试。 HQP的培训是该项目的一个关键组成部分，大部分项目资金(93%)用于此目的。总部基地人员将有机会获得先进的交通流量模型、交通需求模型和先进策略模拟方面的经验。这项建议整合了各种交通控制和需求管理要素，将加强大城市的交通举措，提供经济效益，并有助于规划长期可持续的交通系统，从而推迟网络扩展的需要。因此，这项研究将通过缓解拥堵、提高安全性、降低燃油消耗、促进城市可持续发展和培训HQP，直接和显著地促进加拿大人的安全和福利。

Traffic congestion is one of the major sources of energy consumption and pollution in urban areas. Intelligent Transportation Systems (ITS) and travel demand management (TDM) are instrumental strategies in achieving sustainable solutions to urban traffic congestion problems. These two strategies can be jointly used to improve and sustain long-term transportation performance. However, little research has been conducted into the integration of the various ITS and TDM strategies, in order to achieve optimal traffic management. In addition, most of the published works do not take into consideration the dynamic nature of travel demand. The effect of the presence of variable demand may be significant and lead to critical bias in comparing various traffic strategies and schemes. This requires knowledge on how network travel demand in a city changes as a function of control, demand management strategies and level of congestion. The focus of this research program will be on integrating ITS and TDM strategies to seamlessly optimize the efficiency of transportation facilities and reduce emissions, while taking into consideration the variability of demand. The impact on changes in the short-term to medium-term variations in travel demand as reflected in shifts in route, departure time, mode and destination choice will be examined. The problem will be formulated as a bilevel multi-objective optimization problem to meet the objectives of demand management, mobility, safety and reductions in energy use. Further innovations will result from the inclusion of energy use optimization: this has not yet been attempted. Training of HQPs is a key component of this project, with the majority of the project funds (93%) used for this purpose. The HQPs will have the opportunity to obtain experience in advanced traffic flow modelling, travel demand modelling and simulation of advanced strategies. This proposal, which integrates various traffic control and demand management elements, will enhance mobility initiatives in large cities, provide economic benefits and help plan for long-term sustainable transportation systems that will delay the need for network expansion. Therefore, this research will directly and significantly contribute to the safety and welfare of Canadians by easing congestion, enhancing safety, reducing fuel consumption, promoting urban sustainability and training HQP.