Data-driven intelligent transportation systems (D²ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

The detection algorithm commonly misses obscured pedestrians in traffic scenes with a high pedestrian density because mutual occlusion among pedestrians reduces the prediction box score of the concealed pedestrians. The paper uses the YOLOv7 algorithm as the baseline and makes the following three improvements by investigating the variables influencing the detection method’s performance: First, the backbone network of the YOLOv7 algorithm is replaced with the lightweight feature extraction network Mobilenetv3 since the pedestrian detection algorithm frequently needs to be deployed in driverless mobile, which requires a fast operating speed of the algorithm; second, a high-resolution feature pyramid structure is suggested for the issue of missed detection of hidden pedestrians, which upscales the feature maps generated from the feature pyramid to increase the resolution of the output feature maps and introduces shallow feature maps to strengthen the distinctions between adjacent sub-features to enhance the network’s ability to extract features for the visible area of hidden pedestrians and small-sized pedestrians in order to produce deeper features with greater differentiation for pedestrians; and the third is to suggest a detection head based on an attention mechanism that is employed to lower the confidence level of target neighboring sub-features, lower the quantity of redundant detection boxes, and lower the following NMS computation. The mAP of the suggested approach in this work achieves 89.75%, which is 9.5 percentage points better than the YOLOv7 detection algorithm, according to experiments on the CrowdHuman pedestrian-intensive dataset. The algorithm proposed in this paper can considerably increase the detection performance of the detection algorithm, particularly for obscured pedestrians and small-sized pedestrians in the dataset, according to the experimental effect plots.

Aiming at the problems of frequent identity switches (IDs) and trajectory interruption of multi-pedestrian tracking algorithms in dense scenes, this paper proposes a multi-pedestrian tracking algorithm based on an attention mechanism and dual data association. First, the FairMOT algorithm is used as a baseline to introduce the feature pyramid network in the CenterNet detection network and up-sampling the output multi-scale fused feature maps, effectively reducing the rate of missed detection of small-sized and obscured pedestrians. The improved channel attention mechanism module is embedded in the CenterNet’s backbone network to improve detection accuracy. Then, a re-identification (ReID) branch is embedded in the head of the detection network, and the two sub-tasks of pedestrian detection and pedestrian apparent feature extraction are combined in a multi-task joint learning approach to output the pedestrian apparent feature vectors while detecting pedestrians, which improves the computational efficiency and localization accuracy of the algorithm. Finally, we propose a dual data association tracking model that tracks by associating almost every detection box instead of only the high-scoring ones. For low-scoring detection boxes, we utilize their similarities with trajectories to recover obscured pedestrians. The experiment using the MOT17 dataset shows that the tracking accuracy is improved by 0.6% compared with the baseline FairMOT algorithm, and the number of switches decreases from 3303 to 2056, which indicates that the proposed algorithm can effectively reduce the number of trajectory interruptions and identity switching.

Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model.

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.