Abstract
Connected vehicle (CV) technology is expected to bring additional opportunities to share, collect, and exploit various information on vehicles and their occupants. Assuming that CVs are able to transmit on-board users and vehicle data, a user-based signal timing optimisation (UBSTO) strategy is proposed, designed to optimise user throughput for signalised intersections. In the CV environment, the inputs of the proposed algorithm consist of position and speed of CVs, as well as the number of passengers travelling in each vehicle, while the output is the optimum green time duration for each signal phase. In addition, authors' proposed strategy is able to adapt the cycle length to the traffic volume condition. In case of missing users data, the same strategy can also operate in vehicle-based mode, where the objective is vehicle-throughput maximization. The performance of the proposed strategy is compared with a fully actuated controller (FAC) in microscopic simulation, for several scenarios, including different CV penetration rates. Authors' findings show that UBSTO can effectively increase user throughput and decrease average user delay in comparison with FAC, while also prioritising vehicles with higher number of users on-board. These findings have implications for further development of prioritization strategies for public transport and ride-sharing vehicles.
Highlights
Urban transport systems usually depend on traffic signal control for facilitating traffic flow and preventing extensive queuing and delays
Before proceeding with results involving signal timing optimisation, we present here a validation test designed in order to illustrate the accuracy of the proposed prediction model
We can see from our experiments that our models shows an acceptable accuracy in term of prediction of vehicles and users stop-bar passage time, that is, the difference between measured and predicted user throughput never exceeds 5 users in the considered cycle for both traffic condition
Summary
Urban transport systems usually depend on traffic signal control for facilitating traffic flow and preventing extensive queuing and delays. The main detection mechanism in conventional traffic signal control consists dominantly of discrete point or area detection, using technologies such as inductive loops or video image processing [8,9,10]. These detection mechanisms are capable of providing data on various parameters, including vehicular flow and arrival speeds. Despite the technological advancements that have enabled diversification of control strategies, traffic control objectives have dominantly remained vehicle-centered, focusing on maximizing vehicular throughput or minimizing vehicular delay, stops, or queue lengths [16,17,18,19]. There is still a need to further develop and evaluate user-centered signal control strategies
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