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Traffic congestion is usually caused by the frequent lane changing behaviour of vehicles in the weaving segments, especially in several adjacent weaving segments. In order to solve the problem of weaving congestion, this paper proposes a traffic guidance method based on self-organizing critical state, and designed a Nash equilibrium optimization scheme based on average traffic delay. Then VISSIM software carries on the simulation verification to the actual neighbor weaving segments. First, it validates the induced traffic organization method, keeps each information input consistent, and only changes the induced distance. Through the daily average traffic volume (4092 pcu / h), the traffic volume in the early peak period (5340 pcu / h), the traffic volume in the late peak period (4596 pcu / h) and annual average traffic volume (3276 pcu/h) of the two near neighbor weaving segments in Chongqing. The results show that the optimal lane change constraint distance is 60% of the length of weaving segment, and the corresponding average traffic delays are reduced by 57%, 73%, 63% and 72% respectively. Through the simulation and optimization of the whole day traffic operation, the average delay reduction rate is as high as 84%. The effectiveness of the proposed method is demonstrated by integrating other output file evaluation indexes, which can be used as a reference for future research on self-organizing criticality optimization method of neighbor weaving segments.

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Traffic volume information is widely used in all aspects of Intelligent Transportation Systems (ITS), such as transportation planning, traffic states identification, traffic management, safety analysis, and so on. In recent years, acoustic sensors are gradually applied to the detection of various traffic parameters. In this paper, acoustic data sets acquired from acoustic sensors are utilized to estimate the road traffic volume. The short-term energy (STE) algorithm and the energy to zero crossing rate (EZCR) algorithm are usually applied to acoustic analysis, and they both perform well under some simple circumstances, however, some urgent problems remain unresolved under certain complex conditions. One of such issues occurs when adjacent vehicle-pass signals (VPSs) intersect partially, seriously decreasing the accuracy of endpoint detection of VPSs, hampering the algorithm ability to maintain satisfactory traffic volume estimation. Another difficulty arises while multiple lanes are considered: some special VPSs cannot be detected. To solve these problems, a novel acoustic characteristic is defined, and an acoustic-based characteristic extraction algorithm for traffic volume estimation, entitled triangular wave analysis (TWA), is proposed. Comparing the TWA algorithm with the STE and EZCR algorithms, experimental results demonstrate the viability of the proposed algorithm in the case of intersectant VPSs.

Purpose In an intelligent transportation system (for short, ITS) environment, a vehicle’s motion is affected by the information in a large scale. The purpose of this paper is to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. Design/methodology/approach This paper constructed a new car-following model to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. The new model is analyzed by linear and nonlinear perturbation method theoretically and also verified by simulation. Findings It is found out that the integration of preceding vehicles’ delayed velocities affect the stability of traffic flow importantly, and three preceding vehicles’ delayed velocities information should be considered in real traffic. Originality/value The new car-following model by considering the integration effect of multiple vehicles’ delayed velocities is firstly proposed in this paper. The research result shows that three preceding vehicles’ delayed velocities information is the best choice to stabilizing traffic flow.