Improving Resilience of Adaptive Capacity in Traffic Networks with Intelligent Systems and Advancd Methods

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This research program is aimed at enhancing the sustainability of urban transportation by improving "resilience of adaptive capacity" in traffic networks in order to prevent extreme congestion under nature-induced or other disruptions. Research products consisting of intelligent technology, predictive models, and traffic management decision aids are needed for this purpose. Resilience is defined as the ability to resist the loss of traffic-serving capability owing to inherent characteristics (i.e. the inherent resilience) and by dynamically activating capacity-enhancing measures (called the dynamic resilience).****The research program consists of three modules. The first module investigates vulnerabilities in traffic networks. These represent susceptibility of links or entire routes to potential disruptions. The second module is devoted to characterizing the inherent resilience of vulnerable parts of the traffic network. In this case, no recovery actions are assumed. In the third module, recovery actions activate adaptive capacity in a dynamic manner. Taking the inherent and dynamic resilience together, the sustainability of the adaptive capacity can be assessed by determining the amount of service that can be maintained with the level of disruption that occurs, as well as how long it takes to return to the pre-event state of service with the help of sustained adaptive capacity.****For testing measures to enhance the resilience of adaptive capacity, corridor level and area-wide traffic networks will be structured as virtual test beds with infrastructure and intelligent system components looped-in. Data required for all parts of this research will be sourced from the Cities of Ottawa and Toronto, and the Ministry of Transportation of Ontario. In addition to using commercially available microsimulation and macroscopic analysis software, there will be the need to develop algorithms for the implementation of new models.****The motivation for carrying out this research program is to advance technology and methods for achieving resilience in urban traffic networks. Results will enhance the scientific basis for (1) development of policies on the supply-side of planning and managing urban traffic networks for enhancing resilience of adaptive capacity so as to prevent extreme congestion under disruptions, (2) overcoming key deficiencies of the current transportation planning and control process, and (3) decision-making on transportation infrastructure that integrates intelligent systems and advanced control methods.****A number of interest groups will benefit from the availability of results of this research. These include: transportation policy analysts and planners, civic leaders, commuters, civic groups, business owners, and environmental protection groups. The objective of training HQP will be advanced due to the challenging and innovative nature of research tasks.******