Leveraging connected vehicle data for user-centred and equitable urban traffic control strategies

For many decades, urban traffic management systems have been vehicle-dominated. That is not only because of a lack of attention to users, but conventional data collection tools are powerless to collect individual vehicle data as well as vehicle users data. Connected vehicles (CV), as an emerging technology, can collect and transmit real-time vehicle and its users data. This ability facilitates the development of user-centred traffic management strategies in urban transport networks. However, there are some challenges yet to be addressed to convert raw CV data to efficient input for traffic controls. Moreover, achieving a fully connected environment is not possible in near future due to various limitations.Accordingly, this dissertation aims at developing a traffic management strategy based on CV data that improves user-related performance measures at signalized intersections. Furthermore, this dissertation assesses the effect of CV data accuracy on traffic controllers and presents a method to compensate lack of CVs in the urban environment to deploy in traffic management strategies. In this dissertation, we research two vital aspects of traffic signal control which are signal timing optimization and data. For signal timing optimization, First, using CV data, We develop a user-based signal timing optimization strategy where the objective of the controller is to maximize the user throughput of a signalized intersection. Second, We present a user-based Transit signal priority strategy where the objective of the controller is to reduce users average delay and bus scheduled delay by providing priority for buses that are behind the schedule and with a higher number of passengers on board. Moreover, secondary effects of the current transit priority systems and the proposed transit signal priority are compared, by considering the concept of total social cost. In the data section, first, the impact of CV data accuracy on the performance of signal controllers is investigated. Second, We develop a data-driven vehicle estimation method to make limited CV data usable for a signal controller. The results of this dissertation show that implementing proper signal timing optimisation-based CVs data improves user and vehicle performance measures at signalized intersections. Moreover, a CV-based transit signal propriety that considers users of buses as well as other motorists can improve current Transit signal priority strategies while the delay of other motorists would not be increased. Moreover, the proposed transit signal priority strategy can reduce other social costs such as emission and fuel consumption. According to this dissertation's findings, data collection tools' accuracy can affect signal timing performance in some circumstances. Furthermore, the potential of a data-driven method to compensate lack of CVs has been presented in this dissertation