Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control

In this paper, a new cyclic structure of a max pressure travel time-based traffic signal control is developed to seek an optimal coordination in large-scale urban networks. The focus of the current paper is on dynamic manipulation of cycle lengths within cyclic structure. Following the application of a decentralized approach, which requires only local information in order to offer proper phase durations, the control strategy aims at maximizing the overall network throughput. Previous works of cyclic max-pressure control have presented a cyclic notion to actuate the controller in a cyclic manner. However, no input has been provided on the optimal cycle length for each intersection to be chosen in a network, and along with the dynamic and stochastic nature of the trips, it is not clear what are the main phases of the intersections and how to coordinate them. The developed cyclic max pressure control schemes are compared with an exiting cyclic scheme in the literature. Simulation results show that the newly proposed cyclic structure of the time-based approach offers better decision-making

Utilization-Aware Adaptive Back-Pressure Traffic Signal Control

Back-pressure control of traffic signal, which computes the control phase to apply based on the real-time queue lengths, has been proposed recently. Features of it include (i) provably maximum stability, (ii) low computational complexity, (iii) no requirement of prior knowledge in traffic demand, and (iv) requirement of only local information at each intersection. The latter three points enable it to be completely distributed over intersections. However, one major issue preventing backpressure control from being used in practice is the utilization of the intersection, especially if the control phase period is fixed, as is considered in existing works. In this paper, we propose a utilization-aware adaptive algorithm of back-pressure traffic signal control, which makes the duration of the control phase adaptively dependent on the real-time queue lengths and strives for high utilization of the intersection. While advantages embedded in the back-pressure control are kept, we prove that this algorithm is work-conserving and achieves the maximum utilization. Simulation results on an isolated intersection show that the proposed adaptive algorithm has better control performance than the fixed-period back-pressure control presented in previous works

Autonomous driving at intersections: combining theoretical analysis with practical considerations

International audienceThe move towards automated driving is gaining impetus recently. This paper follows the approach of combining theoretical analysis with practical issues. It gives an insight of some practical problems that are encountered when running automated vehicles in real environments, using intersection crossing as a major example. The aim is not to try to be exhaustive but to show some criteria (safety, efficiency, reactivity, resilience, scalability…) for decision making in automated driving that have to be balanced before any mass deployment. In a second part we introduce mathematical tools that can help define algorithms and systems that improve current state of the art. We will also show some perspective for accommodating the hypotheses of these mathematical tools with real life constraints

Traffic Congestion Aware Route Assignment

Traffic congestion emerges when traffic load exceeds the available capacity of roads. It is challenging to prevent traffic congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traffic management systems can optimize traffic by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traffic management system can predict the change of traffic flow with a high level of accuracy. Based on the accurate traffic prediction and traffic congestion models, routes can be allocated in such a way that helps mitigating traffic congestions effectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traffic congestion mitigation