Journal of Intelligent Transportation Systems最新文献_第2页

Fuzing multiple erroneous sensors to estimate travel time 引信多个错误传感器来估算旅行时间

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-09-03 DOI: 10.1080/15472450.2024.2315514

Fatemeh Banani Ardecani , Ahmadreza Mahmoudzadeh , Mahmoud Mesbah

Estimating accurate travel time information is one of the fundamental tasks in controlling city traffic. In general, fuzing multiple sensors can generate more accurate information to measure traffic flow characteristics than using each one separately. However, in addition to the cost of installing a new sensor system, the costly steps of data cleaning and preparation are required before using a new sensor; therefore, it is essential to estimate the marginal benefit of adding a new sensor vs its marginal cost. Three datasets are generated and analyzed in this study, namely a Hypothetical Ground Truth (HGT), an Erroneous Ground Truth (EGT), and a set of Erroneous Sensors (E-Sensors). This study also challenges the assumption of an error-free Ground Truth (GT). By computing the optimal number of detections to approximate the GT, the effect of (endogenous) error in the fusion procedure is evaluated. Furthermore, by fuzing the E-Sensors that had different levels of (exogenous) error, it is revealed that the error level has a limited effect on the result of fusion. Multiple sets of E-Sensors are assessed and the RMSE values between using Erroneous Ground Truth and Hypothetical Ground Truth are measured, which shows a significant difference. Additionally, the effect of increasing the number of sensors in estimating the travel time is investigated, which shows that adding a new sensor can improve fusion accuracy if the accuracy of the added sensor is better than a given threshold. Moreover, the optimal number of detections to approximate the ground truth is studied. Real traffic data is also used to validate the results.

估算准确的旅行时间信息是控制城市交通的基本任务之一。一般来说，使用多个传感器可以产生更准确的信息来测量交通流量。

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引用次数: 0

Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles 捕捉真正的边界框：利用无人飞行器提取车辆运动学数据

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-09-03 DOI: 10.1080/15472450.2024.2341395

Tian Mi , Dénes Takács , Henry Liu , Gábor Orosz

This paper presents a methodology by which kinematic variables of road vehicles can be extracted from unmanned aerial vehicle (UAV) footage. The oriented bounding boxes of the vehicles are identified based on the aerial view of the intersection, and the kinematic variables, such as position, longitudinal velocity, lateral velocity, yaw angle and yaw rate, are determined. The bounding boxes are converted to the perspective of a roadside camera using homography, to generate labeled data sets for training the machine learning-based perception systems of smart intersections. Compared to ordinary GPS data-based technology, the proposed method provides smoother data and more information about the dynamics of the vehicles. In the meantime, it does not require any additional instrumentation on the vehicles. The extracted kinematic variables can be used for motion prediction of road traffic participants and for control of connected automated vehicles (CAVs) in intelligent transportation systems.

本文介绍了一种从无人驾驶飞行器（UAV）镜头中提取道路车辆运动学变量的方法。车辆的定向边界框是通过识别车辆的运动变量来确定的。

引用次数: 0

Robust learning control for autonomous vehicle with network delays and disturbances 具有网络延迟和干扰的自主车辆鲁棒学习控制

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-09-03 DOI: 10.1080/15472450.2024.2329912

Jing Wang , Engang Tian , Huaicheng Yan

This paper deals with a robust learning nonlinear model predictive control (RL-NMPC) scheme under time-varying delays and disturbances. It is well known that the in-vehicle network has considerable advantages over the traditional point-to-point communication. However, on the other hand, these technologies would also induce the probability of time-varying delays, which would be a hazard in the active safety of over-actuated autonomous vehicles (AVs). To enjoy the advantages and deal with in-vehicle network delays and external disturbances, a robust learning nonlinear model predictive control (RL-NMPC) scheme is proposed. First, the machine learning (Support Vector Machine called SVM) method is adopted to train delayed measurement signals and disturbances. Then, according to the predictions of the SVM and corrupted sensory signals, the Unscented Kalman filter (UKF) is applied to acquire accurate predictions of the vehicle motion states. Furthermore, the NMPC scheme is used to generate real-time control signals by solving an open-loop optimization problem. The main purpose of the addressed problem is to design a robust learning controller to ensure that the AVs can track the desirable path and run smoothly suffering network delays and disturbances. Finally, simulations with a full-vehicle model are carried out to show the effectiveness of our proposed control scheme.

本文探讨了时变延迟和干扰条件下的鲁棒学习非线性模型预测控制（RL-NMPC）方案。众所周知，车载网络具有相当大的...

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引用次数: 0

Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways 基于多头注意力的高速公路智能车辆变道决策和轨迹预测模型

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-09-03 DOI: 10.1080/15472450.2024.2341392

Junyu Cai , Haobin Jiang , Junyan Wang , Aoxue Li

With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory + Gaussian mixture model) lane change decision and trajectory prediction model, which includes a lane-changing intention decision module. The model comprises a lane change decision module responsible for determining three lane change intentions: left lane change, right lane change, and car-following. Subsequently, a multi-head attention mechanism processes complex vehicle interaction information to enhance modeling accuracy and intelligence. In addition, uncertainty in trajectory prediction is considered by using multimodal trajectory prediction and Gaussian mixture model, and diversity and uncertainty are combined by combining trajectory prediction from several different modalities through probabilistic combinatorial sampling patterns. Test results indicate that the MCLG model, based on the multi-head attention module, outperforms existing methods in trajectory prediction. The decision module, which takes interactive information into account, exhibits higher predictability and accuracy. Furthermore, the MCLG model, considering the lane-changing decision module, significantly enhances trajectory prediction accuracy, providing robust decision-making support for autonomous driving systems.

为了改善智能车辆与人类驾驶员之间的交互，本文提出了 MCLG（多头注意力+卷积社会池+长短期记忆）方法。

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引用次数: 0

Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach 车联网环境下的公交信号优先：深度强化学习方法

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-09-03 DOI: 10.1080/15472450.2024.2324385

Tianjia Yang , Wei (David) Fan

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.

公交信号优先（TSP）是一种交通信号控制策略，可为公交车辆提供优先权，从而改善公交服务并提高交通公平性。传统的 TSP...

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引用次数: 0

Vehicle trajectory reconstruction for freeway traffic considering lane changing behaviors 考虑变道行为的高速公路交通车辆轨迹重构

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2307031

Cong Zhang , Yiheng Feng

Vehicle trajectory data provides critical information for many transportation applications. Due to limitations in the data collection techniques, usually, only partial trajectories can be obtained. As a result, trajectory reconstruction where the missing trajectories are inferenced by the observed data is an essential step for many downstream applications. Existing studies usually consider a connected vehicle (CV) environment for trajectory data collection and ignore the lane-changing (LC) behaviors in the reconstruction process. The deployment of connected and autonomous vehicles (CAVs) makes it possible to collect trajectory data more efficiently with much lower penetrations. This study proposes a vehicle trajectory reconstruction algorithm considering LC maneuvers in the CAV environment. The Pettit test and a rule-based optimization algorithm are designed to predict the possible LC time points. Then two car-following models are applied to reconstruct trajectories. The NGSIM US101 dataset is applied to evaluate the proposed reconstruction algorithm under varying CAV penetration rates (PRs) (e.g., 2%, 3%, 5%). The prediction of LC time points achieves high accuracy with average prediction errors less than 1 s under CAV PRs greater than 2%. Compared to the ground truth trajectories, the reconstructed trajectories have the mean absolute error (MAE) less than one vehicle length under 3% and higher CAV PRs.

车辆轨迹数据为许多交通应用提供了关键信息。由于数据收集技术的限制，通常只能获得部分轨迹。因此，通过观测数据推断缺失轨迹的轨迹重建对于许多下游应用来说是必不可少的一步。现有研究通常考虑网联车辆（CV）环境进行轨迹数据采集，而忽略了重建过程中的变道行为。联网和自动驾驶汽车（cav）的部署使得以更低的穿透率更有效地收集轨迹数据成为可能。在CAV环境下，提出了一种考虑LC机动的车辆轨迹重建算法。设计了Pettit检验和基于规则的优化算法来预测可能的LC时间点。然后应用两个车辆跟随模型重建轨迹。应用NGSIM US101数据集对不同CAV渗透率（例如2%、3%和5%）下提出的重建算法进行了评估。在CAV pr大于2%的情况下，LC时间点的预测精度较高，平均预测误差小于1 s。与地面真实轨迹相比，重建轨迹的平均绝对误差（MAE）在3%以下小于一个飞行器长度，CAV pr更高。

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引用次数: 0

Deep survival analysis model for incident clearance time prediction 用于事故清理时间预测的深度生存分析模型

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2315126

Eui-Jin Kim , Min-Ji Kang , Shin Hyoung Park

Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers “duration dependence” of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.

事故清理时间预测是交通事故管理的一项关键任务。基于危险的持续时间模型是预测和分析事故清理时间的一种常用方法，它可以预测和分析事故清理时间。

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引用次数: 0

An anti-disturbance lane-changing trajectory tracking control method combining extended Kalman filter and robust tube-based model predictive control 结合扩展卡尔曼滤波器和鲁棒管基模型预测控制的抗干扰变道轨迹跟踪控制方法

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2315136

Fangzhi Yin , Changyin Dong , Ye Li , Yujia Chen , Hao Wang

This paper proposes a trajectory tracking control method combining extended Kalman filter (EKF) and robust tube-based model predictive control (RTMPC) methods to improve the anti-disturbance capability during lane-changing maneuver of automated vehicles. A time-based quintic polynomial function is introduced for the implementation of trajectory planning to obtain the desired reference trajectory. The planned trajectory is input to the nominal system-oriented model predictive controller (MPC) in RTMPC for optimization to obtain the optimal control of the nominal system. The EKF collects the state measurements of the current instant and the optimal state estimates of the previous instant, and performs filtering to obtain the optimal state estimates of the current instant. The optimal estimate of the current state and the current state of the nominal system are input into the auxiliary control law of RTMPC to obtain the control of the actual system. Comparative numerical simulation experiments are conducted to analyze robustness and sensitivity of the proposed method. The results show that the control method combining EKF and RTMPC can optimize the trajectory tracking performance of the vehicle system, especially in the lateral displacement and the yaw-rate control. When the amplitude of measurement noise reaches the maximum, the optimization effect of lateral control is most significant in experiments. And the optimization effect in the control of lateral displacement and yaw angle continues to enhance with the increase of measurement disturbance. Therefore, this study can provide a reference for the anti-interference lane change trajectory tracking strategy of automated vehicles in the future.

本文提出了一种结合扩展卡尔曼滤波器（EKF）和鲁棒性管基模型预测控制（RTMPC）方法的轨迹跟踪控制方法，以提高飞机的抗干扰能力。

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引用次数: 0

Reinforcement learning approach to develop variable speed limit strategy using vehicle data and simulations 利用车辆数据和模拟，采用强化学习方法制定变速限制策略

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2312808

Yunjong Kim , Kawon Kang , Nuri Park , Juneyoung Park , Cheol Oh

A variety of studies have been conducted to evaluate real-time crash risk using vehicle trajectory data and to establish active traffic safety management measures. Speed management is an effective way to control traffic flow on freeways and to enhance safety. Currently, the variable speed limit (VSL) system is mainly applied in a limited manner during traffic congestion or bad weather. However, it is necessary to manage traffic safety proactively to prevent crashes by providing an appropriate target safety speed to minimize the real-time crash risk. Herein, a methodology for proactive traffic safety management is developed through speed management based on the estimation of real-time crash risk. The developed methodology evaluates performance through simulations and it consists of two components. First, a crash risk analyzer evaluates freeway crash risk by developing a real-time crash risk model based on real-world vehicle trajectory data matched with crash traffic flow. Then a speed manager implements a reinforcement learning-based VSL system in the simulation environment, which includes the crash risk derived from the crash risk analyzer through VISSIM-COM interfaces. The performance of the developed methodology was evaluated through VISSIM simulation analysis, and the results demonstrated its feasibility. The real-time crash risk was reduced by approximately 55% when the target safety speed information derived from the reinforcement learning model was provided in a scenario where one lane was closed due to a crash. The findings were further applied to establish an operations strategy for VSL systems based on both crash risk and actual traffic conditions.

为了利用车辆轨迹数据评估实时碰撞风险并制定积极的交通安全管理措施，已经开展了多项研究。车速管理是一种有效的交通安全管理措施。

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引用次数: 0

A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data 整合交通视频和收费站数据的桥梁车辆重量时空分布识别方法

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2312810

Jianliang Zhang , Yuyao Cheng , Jian Zhang , Zhishen Wu

Real-time monitoring of the spatiotemporal distribution of vehicle weights on bridge decks is an important component of bridge structural health monitoring systems. However, it is still a challenge to identify the spatiotemporal distribution of vehicle weights on the whole bridge deck because the existing identification techniques are based on the theory of line of influence or need to install a weight-in-motion (WIM) system on the bridge. This paper proposes an information fusion-based identification method for the spatiotemporal distribution of vehicle weights without WIM installation, in which, (1) the traffic videos acquired by multiple cameras arranged along both sides of the bridge are used to detect the spatiotemporal distribution and license plate of the vehicles, and the weights obtained from the toll station are linked to the vehicles by matching the license plates. In addition, (2) a digital image correlation (DIC)-based vehicle tracking method is proposed to solve the problems of frame drop and missing detection and (3) a polynomial fitting-based coordinate transformation method is proposed to avoid the derivation of complicated coordinate conversion formula related to pinhole camera. The efficiency and accuracy of the proposed identification approach are verified by the field data collected from a cable-stayed bridge and nearby toll stations. The results indicate that our proposed method is a feasible and reliable solution for identifying spatiotemporal distribution of vehicle weights on bridges.

实时监测桥面上车辆重量的时空分布是桥梁结构健康监测系统的重要组成部分。然而，这仍然是一项挑战...

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引用次数: 0