Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Passenger flow is the basis for bus operation scheduling. Huge advances are being made to develop smart city traffic using big data. Intelligent bus systems based on bus integrated circuit (IC) card systems are constantly developing and improving. Compared with traditional manual survey data, bus IC data is low-cost, real-time and accurate with a simple acquisition method. Bus IC data is an important basic data resource and data mining of bus IC cards can obtain dynamic information about urban bus passenger flow and help improve urban bus planning and service levels. The crucial factor in determining whether this data can be reasonably applied to the optimization of urban bus systems is whether spatial and temporal characteristics of the passenger bus trip can be obtained through bus IC data mining, and there is much current research interest into this topic. In this paper, the characteristics of one-day passenger flow and time-division passenger flow are analyzed based on data obtained from swiping IC cards for one week on a bus in Qingdao. Then, based on a GA-NARX neural network model, the passenger flow is forecast using the IC card swipe data for five working days of Qingdao No. 1 bus (using ten minutes as the time interval). The forecasting results show that the passenger flow can be successfully predicted using this method and thus this method can be used for short-term passenger flow forecasting using bus IC cards.

At urban signalized intersections, pedestrian twice-crossing is usually viewed as a complex human behavior, since there are many factors influencing it. Mostly, pedestrians engage in a complicated cognitive process of perception, attention and decision-making. Therefore, it is necessary to identify the major factors affecting this behavior, and develop an effective pedestrian dynamic model, in order to increase the safety and efficiency of crossing streets. This study proposes a force-based model of pedestrian dynamics by improving the classic social force model, in order to determine the influencing factors and quantify the forces acting on pedestrians crossing in two stages at signalized intersections. Through analyzing the characteristics of pedestrian twice-crossing behavior, the social force model was enhanced by providing a new component of the green signal countdown. The improved model includes four parts of the self-driving force in the ideal state, the repulsive and attractive forces generated by surrounding pedestrians, the resistance of the crosswalk boundary line, and the force produced by the green signal countdown. Each part was considered with qualitative analysis and quantitative calculation. The results show that the proposed model can achieve high accuracy in measuring the forces acting on pedestrian twice-crossing. The findings of this study have great implications for designing pedestrian facilities and optimizing pedestrian signal timings, helping thus to increase the mobility and safety of pedestrian twice-crossing.

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

Traffic accidents at urban intersections may lead to severe traffic congestion, necessitating effective detection and timely intervention. To achieve real-time traffic accident monitoring at intersections more effectively, this paper proposes an urban road intersection accident detection method based on Generative Adversarial Networks (GANs), Extreme Gradient Boosting (XGBoost), and the SHAP interpretability framework. Data extraction and processing methods are described, and a brief analysis of accident impact features is provided. To address the issue of data imbalance, GAN is used to generate synthetic accident samples. The XGBoost model is then trained on the balanced dataset, and its accident detection performance is validated. In addition, SHAP is employed to interpret the results and analyze the importance of individual features. The results indicate that the accident samples generated by GAN not only retain the characteristics of real data but also enhance sample diversity, improving the AUC value of the XGBoost model by 7.1% to reach 0.844. Compared with the benchmark models mentioned in the study, the AUC value shows an average improvement of 7%. Additionally, the SHAP model confirms that the time–vehicle ratio and average speed are key factors influencing the model’s detection results. These findings provide a reliable method for urban road intersection accident detection, and accurate accident location detection can assist urban planners in formulating comprehensive emergency management strategies for intersections, ensuring the sustainable operation of traffic flow.

Urbanization has intensified traffic loads, posing significant challenges to the efficiency and stability of urban road networks. Overloaded nodes risk congestion, thus making accurate intersection importance classification essential for resource optimization. This study proposes a hybrid clustering method that combines Self-Organizing Maps (SOMs), K-Means, and the Gaussian Mixture Model (GMM), which is supported by the Traffic Flow–Network Topology–Social Economy (TNS) evaluation framework. This framework integrates three dimensions—traffic flow, road network topology, and socio-economic features—capturing six key indicators: intersection saturation, traffic flow balance, mileage coverage, capacity, betweenness efficiency, and node activity. The SOMs method determines the optimal k value and centroids for K-Means, while GMM validates the cluster membership probabilities. The proposed model achieved a silhouette coefficient of 0.737, a Davies–Bouldin index of 1.003, and a Calinski–Harabasz index of 57.688, with the silhouette coefficient improving by 78.1% over SOMs alone, 65.2% over K-Means, and 11.5% over SOM-K-Means, thus demonstrating high robustness. The intersection importance ranking was conducted using the Mahalanobis distance method, and it was validated on 40 intersections within the road network of Zibo City. By comparing the importance rankings across static, off-peak, morning peak, and evening peak periods, a dynamic ranking approach is proposed. This method provides a robust basis for optimizing resource allocation and traffic management at urban intersections.