A signal coordination algorithm for two adjacent intersections based on approximate dynamic programming

To reduce vehicle emissions in road networks, a new signal coordination algorithm based on approximate dynamic programming （ADP） is developed for two intersections. Taking the Jetta car as an experimental vehicle, field tests are conducted in Changchun Street of Changchun city and vehicle emission factors in complete stop and uniform speed states are collected. Queue lengths and signal light colors of approach lanes are selected as state variables, and green switch plans are selected as decision variables of the system. Then the calculation model of the optimization index during the planning horizon is developed based on the basis function method of the ADP. The temporal-difference algorithm is employed to update the weighting factor vector of the approximate function. Simulations are conducted in Matlab and the results show that the established algorithm outperforms the conventional coordination algorithm in reducing vehicle emissions by 8.2%. Sensitive analysis of the planning horizon length on the evaluation index is also conducted and the statistical results show that the optimal length of the planning horizon is directly proportional to the traffic load.

A distributed algorithm for signal coordination of multiple agents with embedded platoon dispersion model

In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.

Connected Vehicle-Based Traffic Signal Coordination

This study presents a connected vehicles(CVs)-based traffic signal optimization framework for a coordinated arterial corridor.The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program(MINLP).The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories.Due to the complexity of the model,we decompose the problem into two levels:an intersection level to optimize phase durations using dynamic programming(DP),and a corridor level to optimize the offsets of all intersections.In order to solve the two-level model,a prediction-based solution technique is developed.The proposed models are tested using traffic simulation under various scenarios.Compared with the traditional actuated signal timing and coordination plan,the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance.When considering varies vehicle types under high demand levels,the proposed two-level model reduced the total system cost by 3.8%comparing to baseline actuated plan.MINLP reduced the system cost by 5.9%.It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels.For intersections with major and minor street,coordination conducted for major street had little impacts on the vehicles at the minor street.

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

Traffic signal coordination control for arterials with dedicated CAV lanes

Purpose–This study aims to make full use of the advantages of connected and autonomous vehicles(CAVs)and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping.Design/methodology/approach–The authors developed a signal coordination model for arteries with dedicated CAV lanes by using mixed integer linear programming.CAV non-stop constraints are proposed to adapt to the characteristics of CAVs.As it is a continuous problem,various situations that CAVs arrive at intersections are analyzed.The rules are discovered to simplify the problem by discretization method.Findings–A case study is conducted via SUMO traffic simulation program.The results show that the efficiency of CAVs can be improved significantly both in high-volume scenario and medium-volume scenario with the plan optimized by the model proposed in this paper.At the same time,the progression efficiency of regular vehicles is not affected significantly.It is indicated that full-scale benefits of dedicated CAV lanes can only be achieved with signal coordination plans considering CAV characteristics.Originality/value–To the best of the authors’knowledge,this is the first research that develops a signal coordination model for arteries with dedicated CAV lanes.

Underdetermined Blind Source Separation of Adjacent Satellite Interference Based on Sparseness

The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform(STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.

Effects of winter weather on traffic operations and optimization of signalized intersections

Adverse winter weather has always been a cause of traffic congestion and road collisions.To mitigate the negative impacts of winter weather, transportation agencies are under increasing pressure to introduce weather responsive traffic management strategies.Currently, most traffic signal control systems are designed for normal weather conditions and are therefore suboptimal regarding efficiency and safety for controlling traffic during winter snow events due to changes in traffic patterns and driver behaviors. The main objective of this research is to explore how to modify pre-timed traffic signal control parameters under adverse weather conditions to increase traffic efficiency and road safety.This research consists of two main components. First, we examine the impacts of winter weather on three key traffic parameters, i.e., saturation flow rate, start-up lost time, and free flow speed. Secondly, we investigate the potential benefits of implementing weatherspecific signal control plans for uncoordinated intersections as well as coordinated corridors. Two case studies are conducted, each with varying levels of traffic demand and winter event severity, to compare the performance of different signal plans. Evaluation results from both Synchro and VISSIM show that implementing such signal plans is most beneficial for intersection with a medium level of traffic demand. It is also found that the benefit of implementing weather-responsive plans was more compelling at a coordinatedcorridor level than at an uncoordinated-intersection level.

Identification and optimization of traffic bottleneck with signal timing

In urban transportation network, traffic congestion is likely to occur at traffic bottlenecks. The signal timing at intersections together with static properties of left-turn and straight-through lanes of roads are two significant factors causing traffic bottlenecks. A discrete-time model of traffic bottleneck is hence developed to analyze these two factors, and a bottleneck indicator is introduced to estimate the comprehensive bottleneck degree of individual road in regional transportation networks universally, the identification approaches are presented to identify traffic bottlenecks, bottleneck-free roads, and bottle- neck-prone roads. Based on above work, the optimization method applies ant colony algorithm with ef- fective green time as decision variables to find out an optimal coordinated signal timing plan for a re- gional network. In addition, a real experimental transportation network is chosen to verify the valida- tion of bottleneck identification. The bottleneck identification approaches can explain the features of oc- currence and dissipation of traffic congestion in a certain extent, and the bottleneck optimization meth- od provides a new way to coordinate signal timing at intersections to mitigate traffic congestion.

Chemical signals coordinate the dispersal of a plant-parasitic nematode with the metamorphosis of its vector beetle

With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,Prof.Sun Jianghua(孙江华),Zou Zhen and Zhao Lilin et al.at the State Key Laboratory of Integrated Management of Pest Insects and Rodents,Institute of Zoology,Chinese Academy of Sciences,uncovered that ascarosides coordinate the dispersal of a plant-parasitic nematode with the metamorphosis