Vehicles are often caught in dilemma zone when they approach signalized intersections in yellow interval. The existence of dilemma zone which is significantly influenced by driver behavior seriously affects the efficiency and safety of intersections. This paper proposes the driver behavior models in yellow interval by logistic regression and fuzzy decision tree modeling, respectively, based on camera image data. Vehicle’s speed and distance to stop line are considered in logistic regression model, which also brings in a dummy variable to describe installation of countdown timer display. Fuzzy decision tree model is generated by FID3 algorithm whose heuristic information is fuzzy information entropy based on membership functions. This paper concludes that fuzzy decision tree is more accurate to describe driver behavior at signalized intersection than logistic regression model.

This article explores the backbone of urban street networks to reflect the characteristics of the overall network. The model representing the connection of urban road elements based on traffic properties is constructed and then applied to the traffic network in Xiamen, China. The backbone of urban street networks comprises road elements whose hierarchical measure is greater than 1 and named roads in this context. And the difference between the overall road network and the backbone is compared from the perspective of degree distribution and connection characteristics. The results reveal that the backbone (hierarchical measure > 1) of the urban street network is consistent with the overall road network. The skeleton road network in this article saves the amount of work for road elements by 17.75%, and the intersection elements by 4.21%. Our conclusions will be beneficial to assess the current network performance more efficiently and improve existing road network systems as well as discovering the complicated relationship between network structure and dynamic behaviors.

In order to optimize the signal timing for isolated intersection, a new method based on fuzzy programming approach is proposed in this paper. Considering the whole operation efficiency of the intersection comprehensively, traffic capacity, vehicle cycle delay, cycle stops, and exhaust emission are chosen as optimization goals to establish a multiobjective function first. Then fuzzy compromise programming approach is employed to give different weight coefficients to various optimization objectives for different traffic flow ratios states. And then the multiobjective function is converted to a single objective function. By using genetic algorithm, the optimized signal cycle and effective green time can be obtained. Finally, the performance of the traditional method and new method proposed in this paper is compared and analyzed through VISSIM software. It can be concluded that the signal timing optimized in this paper can effectively reduce vehicle delays and stops, which can improve traffic capacity of the intersection as well.

Based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm and kernel online sequential extreme learning machine (KOSELM) algorithm, a new hybrid short-term traffic flow prediction model (ICEEMDAN-KOSELM-ARIMA) for signalized intersections is proposed according to the current and historical traffic flow data. First, traffic flow historical time series are decomposed by ICEEMDAN algorithm for the purpose of improving the prediction accuracy. Several intrinsic mode functions could be obtained by the decomposition process. Then, permutation entropy algorithm is employed to analyze the random properties of intrinsic mode function components. According to the different random properties of intrinsic mode functions, different prediction models can be built. On this basis, KOSELM prediction models are established for the intrinsic mode function components with big randomness. And auto-regressive integrated moving average (ARIMA) prediction models are built for the intrinsic mode function components with small randomness. Finally, an actual signalized intersection is selected to verify the effect and performance of the hybrid prediction model proposed in this article. Results show that compared with other models, the new proposed hybrid prediction model can effectively improve prediction accuracy, of which prediction errors are the lowest and fitting effect with actual values is the best.

In order to optimize the existing signal control period division method and improve signal control effect, a new period division method based on Locally Linear Embedding and Particle Swarm Optimization combined with K-means clustering (LLE-PSO-K) algorithm is proposed in this paper. Firstly, traffic flow characteristics of signal-controlled intersections are fully considered, and a multi-dimensional flow matrix is constructed based on the phase traffic flow. In order to reduce the computational complexity of the model and improve the operating efficiency of the method, manifold learning Locally Linear Embedding (LLE) algorithm is brought in to reduce the dimension of the multidimensional phase flow matrix. Then, the dimensionality reduction matrix is used as input data, and signal control period is divided by using Particle Swarm Optimization combined with K-means clustering (PSO-K) algorithm. Finally, an actual intersection in a city is selected to verify the performance of the proposed method. For comparative analysis, control periods are divided based on the phase traffic flow data with 15min, 30min and 1h interval respectively. Results show that for different time intervals, the division of the proposed method is better than other methods, of which the invalid control periods are less. Besides, the optimal clustering number can be obtained, which proves the effectiveness of the new proposed method.

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on the K‐Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.