CoLight: Learning Network-level Cooperation for Traffic Signal Control

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on Information and Knowledge Management. ACM, 201

Multi-objective optimization coordination for urban arterial roadway based on operational-features

In this paper, a new coordinated control model is proposed based on the vehicular operational features, and a multi-objective optimization algorithm NSGA-II is employed to the model for the operation of the vehicle traveling on an urban arterial road taking three evaluation indexes into consideration as the average vehicle delay, the queue length, and the vehicle exhaust emission. A numerical experiment was made in an urban arterial road with three intersections on VISSIM for the proposed strategy, and the simulation results were compared with two commonly used pre-timed methods: Webster’s method and MAXBAND coordinated control method to verify the effectiveness of the proposed strategy in dealing with the unbalanced traffic volume condition, and it proved its advantages in designing and managing traffic systems more efficiently

Forecast based traffic signal coordination using congestion modelling and real-time data

This dissertation focusses on the implementation of a Real-Time Simulation-Based Signal Coordination module for arterial traffic, as proof of concept for the potential of integrating a new generation of advanced heuristic optimisation tools into Real-Time Traffic Management Systems. The endeavour represents an attempt to address a number of shortcomings observed in most currently marketed on-line signal setting solutions and provide better adaptive signal timings. It is unprecedented in its use of a Genetic Algorithm coupled with Continuous Dynamic Traffic Assignment as solution evaluation method, only made possible by the recently presented parallelisation strategies for the underlying algorithms. Within a fully functional traffic modelling and management framework, the optimiser is developed independently, leaving ample space for future adaptations and extensions, while relying on the best available technology to provide it fast and realistic solution evaluation based on reliable real-time supply and demand data. The optimiser can in fact operate on high quality network models that are well calibrated and always up-to-date with real-world road conditions; rely on robust, multi-source network wide traffic data, rather than being attached to single detectors; manage area coordination using an external simulation engine, rather than a na¨ıve flow propagation model that overlooks crucial traffic dynamics; and even incorporate real-time traffic forecast to account for transient phenomena in the near future to act as a feedback controller. Results clearly confirm the efficacy of the proposed method, by which it is possible to obtain relevant and consistent corridor performance improvements with respect to widely known arterial bandwidth maximisation techniques under a range of different traffic conditions. The computational efforts involved are already manageable for realistic real-world applications, and future extensions of the presented approach to more complex problems seem within reach thanks to the load distribution strategies already envisioned and prepared for in the context of this work

Strategies for Signal Timing and Coordination for Bicycle Progression

Current signal timing practices in the United States typically give vehicles the highest priority which can make travel by other modes challenging or time consuming. Bicycles are an example of these other modes that are not often prioritized. Due to their generally slow speeds, cyclists typically cannot keep up with timing plans designed for vehicle speeds. This can lead to increased stops and delays, souring the cycling experience.In places that do accommodate cyclists like the Netherlands, standard practice is to coordinate signals by designing for bicycle speeds. In the US, cities like Portland, OR and San Francisco, CA have adopted this practice in places, lowering speed limits and coordinating for bikes, and have become known for their relatively high numbers of cyclists. The other approach to this problem is to keep vehicles at their own speeds, but to also consider bicycle progression. Due to its complexity, this approach is much less popular. At the time of this research, there are few papers or case studies taking this approach. This research looks into the second approach of coordinating with vehicles and bicycles traveling at different speeds. The effort of this research can be divided into a conceptual method and an empirical method. The first method uses the relationship between vehicle and bicycle speeds to determine optimal cycle lengths or split lengths to create bandwidth for both speeds. This conceptual method calculates precise timing parameters that can provide ideal results. However, the calculated parameters may not reasonably serve intersection demand and thus, this method is limited by whether road segment lengths and mode speeds produce useable values. The second method is a brute force approach that takes timing plans and empirically grades them on potential for vehicle and bicycle progression based on timing inputs and expected travel results. This grade is representative of the overall quality of a plan for both vehicle and bicycle progression and can quickly be compared with other plans. The grading was calibrated with simulation done using Vissim for the Center St corridor in Reno, Nevada. Previous signal timing practices typically coordinated for one mode and then adjusted where possible to improve progression for the other, making the second an afterthought in terms of the timing and performance. This research provides a method for designing signal timing while looking at both modes simultaneously for fairer treatment. Provided the calculated timing parameters are sufficient for demand, the first method gives values that can guarantee similar vehicle and bicycle progression, but the second method is more widely applicable and can be used if the requirements for the first method are not met. It is recommended to use the first method, the TTD-Cycle method, if applicable but the second, TSD Performance Estimator, serves as a generally applicable backup

Green Wave Traffic Optimization - A Survey

The objective of this survey is to cover the research in the area of adaptive traffic control with emphasis on the applied optimization methods. The problem of optimizing traffic signals can be viewed in various ways, depending on political, economic and ecological goals. The survey highlights some important conflicts, which support the notion that traffic signal optimization is a multi-objective problem, and relates this to the most common measures of effectiveness. A distinction can be made between classical systems, which operate with a common cycle time, and the more flexible, phase-based, approach, which is shown to be more suitable for adaptive traffic control. To support this claim three adaptive systems, which use alternatives to the classical optimization procedures, are described in detail.