Journal of Intelligent Transportation Systems最新文献

Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR 基于深度学习的行人轨迹预测和风险评估，使用路边激光雷达捕获的轨迹数据

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2209912

Shanglian Zhou , Hao Xu , Guohui Zhang , Tianwei Ma , Yin Yang

In recent years, rapid advancements in the Autonomous Vehicles (AVs) industry have greatly motivated the research and development in pedestrian trajectory prediction and risk assessment. One of the critical requirements for AVs is to predict the future trajectories of pedestrians and provide collision warnings in an accurate and prompt manner. Nevertheless, accurate prediction of pedestrian trajectories remains a technical challenge, mainly caused by the heterogeneity of pedestrian crossing behavior and uncertainties in vehicle-pedestrian interactions. This paper proposes a deep learning-based method for pedestrian trajectory prediction and risk assessment, using trajectory data extracted from roadside LiDAR data and corresponding signal phasing information at MLK and Georgia Avenue in Chattanooga, TN. Meanwhile, a set of criteria referred to as the risk factor is established to quantitatively evaluate the risk of the pedestrian crossing behavior, which also serves as a learnable feature. A Long Short-Term Memory (LSTM) network is proposed, which takes the following data as the input: the pedestrian trajectory data, signal phasing data, and risk factors from the past 10 steps. Meanwhile, the network predicts the pedestrian trajectory and risk factor at the future time step. In the experimental study, the root-mean-square errors between the predicted and ground truth x and y coordinates are 0.225 meters and 0.377 meters, respectively, and the F1 score value for the risk factor is 99.6%, demonstrating the efficacy of the proposed LSTM-based methodology on pedestrian trajectory prediction and risk assessment.

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引用次数: 0

Inferring the number of vehicles between trajectory-observed vehicles 推断轨迹观测车辆之间的车辆数量

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2227940

Zhiyong Wen , Xiaoxiong Weng

Traffic perception is the foundation of intelligent roads, and how to accurately perceive traffic has become a central issue for researchers. With the application of Vehicle-to-Everything communication technology, vehicle IDs, locations, velocities, and accelerations can be obtained by the Roadside Unit (RSU), i.e., trajectory-observed vehicles for the road. Inferring the number of vehicles between trajectory-observed vehicles can make traffic perception more accurate, with which the traffic can be sensed on the whole road. Thus, in the case of mixed traffic flow, a Real-Time Prediction Model was proposed, which is a novel model containing four modules: prior experience of the space headway, linear distribution of velocity and acceleration, identification of traffic shockwave, and filter. The inferred result was calculated in real time. During the test, we used US-101 lane-1 data of the Next Generation Simulation dataset and trajectory-observed vehicles with stochastic distribution for 20% penetration. The length of the study area on the US-101 highway was approximately 2100 feet, which was similar to the communication area of a single RSU. During the evaluation of the model accuracy with the real-world datasets, the error of the inferred vehicle numbers in the study area could be limited to ±5 vehicles almost. Results show that it is feasible to infer the number of vehicles between trajectory-observed vehicles. The model compensates for the shortcomings of traditional models (based on inductive loop, camera, or radar), thus providing a novel method for the traffic perception of intelligent roads.

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引用次数: 0

How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method 空间特征如何影响城市轨道交通预测精度:基于深度学习的客流预测方法

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2279633

Shuang Li , Xiaoxi Liang , Meina Zheng , Junlan Chen , Ting Chen , Xiucheng Guo

Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study’s results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis.

城市轨道交通是公共交通的重要组成部分，在中国得到了广泛的建设。已有研究证明，空间特征与轨道交通客流量密切相关，考虑到短期客流预测在城市轨道交通运营中的基础性作用，探讨这些因素对预测精度的影响具有重要意义。本研究旨在寻找一种基于深度学习的方法，通过考虑台站的空间特征来提高预测精度。因此，采用CNN-LSTM模型捕捉时空特征，并以中国苏州为例，探讨相对位置、地理位置和土地利用三个空间特征对预测精度的影响。所建立的预测模型能够提取时空特征，准确预测全市范围内的站点，结果表明:对于相对位置，中转站的进站流量预测误差最小，中转站的出站流量预测误差最小;在区位特征方面，站点离市中心越远，结果越准确。对于那些土地使用以工作和生活服务为主的车站，预测更为准确。以旅游、交通和休闲服务为主的站点错误率较高。研究结果可以帮助运营商根据不同需求预测目标站点的短期客流，并在此基础上优化服务。关键词:cnn - lstm客流预测精度时空特征城市轨道交通致谢本文作者感谢苏州轨道交通集团有限公司提供的数据集，并感谢朴子源教授的建议。披露声明作者未报告潜在的利益冲突。本研究由江苏省研究生科研与实践创新计划(项目编号:KYCX22_0271)资助。

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引用次数: 0

Adaptive graph convolutional network-based short-term passenger flow prediction for metro 基于自适应图卷积网络的地铁短期客流预测

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2209913

Jianli Zhao , Rumeng Zhang , Qiuxia Sun , Jingshi Shi , Futong Zhuo , Qing Li

With the development and acceleration of urbanization, urban metro traffic is gradually growing up to a large network, and the structure of topology between stations becomes more complex, which makes it increasingly difficult to capture the spatial dependency. The vertical and horizontal interlacing of multiple lines makes the stations distributed topologically, and the traditional graph convolution is implemented on the adjacency matrix generated based on a priori knowledge, which cannot reflect the actual spatial dependence between stations. To address these problems, this paper proposes an adaptive graph convolutional network model (Adapt-GCN), which replaces the fixed adjacency matrix obtained from a priori knowledge in the traditional GCN with a trainable adaptive adjacency matrix. This can not only effectively adjust the weights of correlations between adjacent stations, but also adaptively capture the spatial dependencies between non-adjacent stations. This paper uses the Shanghai Metro dataset to verify the effectiveness of the model in improving prediction accuracy and reducing training time.

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引用次数: 0

Adaptive green split optimization for traffic control with low penetration rate trajectory data 低穿透率轨迹数据下交通控制的自适应绿裂优化

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2227959

Zihao Wang , Roger Lloret-Batlle , Jianfeng Zheng , Henry X. Liu

Adaptive traffic signal control systems often rely on expensive physical detection infrastructure. However, with the advent of widespread trajectory data, it is now possible to implement adaptive control entirely avoiding such costs. We present two simple adaptive control policies which only require sample delay and number of stops, with the goal to mitigate the presence of oversaturation. The simplicity stems from the necessity of controlling under any trajectory penetration rate. The two policies differ on the possibilities of the control infrastructure to be implemented. The first one minimizes oversaturation by deviating from a reference pre-timed signal plan. This signal plan can be an existing one or an estimated one from aggregating trajectory data. The second policy creates first a set of green split plans to be then selected by a control logic. This second policy is intended to be used in SCATS-like systems where signal plans are limited to a pre-defined discrete set. We propose a plan selection logics or alternatively, the original plan selection policy can be used as well. Both policies are tested in the field, achieving a significant reduction in delay, oversaturation and spillover ratios. Lastly, we test an application of this policy as an enhancement of SCATS systems in the presence of malfunctioning physical detectors.

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引用次数: 0

Sensor location models with reliable optimal solution for the observation of origin–destination matrix and route flows 具有可靠最优解的传感器定位模型，用于观察出发地矩阵和路线流

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2247329

Hessam Arefkhani , Yousef Shafahi

Origin–destination matrix (ODM) is a key element in transportation studies. The emergence of new ITS technologies like Automatic Vehicle Identification (AVI) sensors makes the ODM observation problem more interesting in recent decades. However, sensors are subject to failure in reality which highlights the sensor failure phenomenon as a significant issue in real-case problems. This study intends to include the sensor failure phenomenon in AVI Sensor Location Model (SLM) for reliable observation of ODM and route flows. While reliability and cost are usually two conflicting objectives, we try to answer the following question “Is it possible to improve reliability without increasing the cost and only by changing sensor deployment?”. In addressing this study question, first, it is shown that the solution of recent AVI SLMs are not unique. Second, we show that the reliability level of multiple optimal solutions is not the same. Third, two Mixed Integer Linear Programming (MILP) AVI SLMs for reliable observation and parital observation of ODM/route flows are developed considering the sensor failure phenomenon. The models are formulated such that their solutions are selected from the set of multiple optimal solutions. Fourth, a linear surrogate term for reliability is introduced and mathematically proven to be included in the proposed models. Finally, the applicability of the proposed models is examined on several middle-scale networks and a real-size network. Furthermore, a heuristic algorithm is customized to solve the models for the real-size network. The results suggest that there might be alternative sensor deployment strategies with the same number of sensors as in the optimal solution but with higher level of reliability for ODM/route flows observation.

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引用次数: 0

ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage ABAFT:一种基于自适应权重的融合技术，用于基于不同置信度和空间覆盖的多源数据的旅行时间估计

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2228198

Sara Respati , Edward Chung , Zuduo Zheng , Ashish Bhaskar

The evolution of traffic monitoring systems provides rich traffic data from multiple sensors. Fuzing the data has the potential to enhance the quality of travel time estimation. It also provides better spatial-temporal coverage in traffic observations. However, each sensor’s unique data collection process results in fusion challenges with respect to the coverage and data quality differences between various sources. These factors determine the degree of confidence that should be considered when fuzing different types of data. To this end, this paper proposes an adaptive weight-based fusion technique (ABAFT) that considers data spatial coverage and quality or confidence as the factors constructing the weight. The proposed ABAFT was tested using different scenarios on synthetic GPS and Bluetooth MAC Scanners data from an urban arterial corridor. The results show that the ABAFT can increase the travel time estimation accuracy by over 10%, and reliability by over 8% compared to the single sensor estimators. It also outperforms the simple average and standard-error-based fusion by around 4%. ABAFT is easy to be implemented on multiple sources of information available to transport agencies for a single point of truth.

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引用次数: 0

Evaluating the impacts of vehicle-mounted Variable Message Signs on passing vehicles: implications for protecting roadside incident and service personnel 评估车载可变讯息标志对过往车辆的影响:对保护路边事故及服务人员的影响

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2227968

Jun Liu , Xing Fu , Alexander Hainen , Chenxuan Yang , Leon Villavicencio , William J. Horrey

This study aims to evaluate the Variable Message Signs (VMS) mounted over the cab of emergency vehicles as a safety countermeasure to protect roadside incident and service personnel. The research team collaborated with the Alabama Department of Transportation’s Safety Service Patrol (SSP) program, specifically the Alabama Service Assistance Patrol (ASAP) in the West Central Alabama region, to collect video data from their service vehicles. Deep learning techniques were employed to detect vehicles in the recorded videos. A total of 11,338 passing vehicles were detected in 135,946 frames of video footage, and their trajectories were extracted for analysis. The study focused on examining the behaviors and movements of passing vehicles, including their speed and lane change behaviors, and developed statistical models to systematically investigate the impact of VMS on these behaviors. Random intercept models were utilized to account for unobserved factors associated with different stop locations. The modeling results revealed significant relationships between the use of VMS and the behaviors of passing motorists. When the VMS was active, drivers were more likely to change lanes and reduce their speed compared to situations where the VMS was not active. The odds of a vehicle changing lanes were found to be 95% higher when the VMS was in use. These findings suggest that the utilization of VMS can have a positive impact on traffic, particularly for passenger vehicles. The study outcomes imply that the use of VMS could be an effective countermeasure in protecting roadside incidents and ensuring the safety of service personnel.

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引用次数: 0

Traffic-sensitive speed advisory system based on Lagrangian traffic indicators 基于拉格朗日交通指标的交通敏感速度咨询系统

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2236549

Pierre-Antoine Laharotte , Kinjal Bhattacharyya , Jonathan Perun , Nour-Eddin El Faouzi

Can we elaborate a traffic-sensitive eco-driving or GLOSA (Green Light Optimal Speed Advice) strategy with a frugal amount of data when approaching an intersection? Here is the purpose of this work, which aims to adapt a traffic-theory-based estimation of the expected queue-length within mixed traffic (Connected and non-Connected Vehicles) in the vicinity of a signalized intersection. While the expected queue-length methodology was developed recently and fits natively with Eulerian traffic indicators resulting from loop sensors or cameras, this paper adapts such a methodology to Lagrangian indicators as the traces produced by any Connected Vehicle, including Floating Car or Probe Data. The main interest of the methodology lies in the frugal amount of data and expenses required to perform the traffic-sensitive speed-advisory at any connected road intersection. The full methodology is developed to extend the SPAT messages broadcast to end-users and take advantage of the Cooperative Awareness Messages (CAM) acting as GPS traces for Connected Vehicles. Contrary to Eulerian-based indicators, no supplementary and costly investment is required to collect the input data and compute the queue-length estimation. However, applying strategies based on Lagrangian indicators will affect the direct traffic observation through these indicators. Therefore, it requires to develop an assessment and predictive framework to estimate the traffic conditions. The performance of the introduced methodology is compared to alternative methods, among other Eulerian-based methods. It results from the analysis that the introduced approach performs almost as well as the ones based on exhaustive, but costly data collections.

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引用次数: 0

A data-driven traffic shockwave speed detection approach based on vehicle trajectories data 基于车辆轨迹数据的数据驱动交通冲击波速度检测方法

IF 2.8 3区工程技术 Q3 TRANSPORTATION

Journal of Intelligent Transportation Systems

Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2270415

Kaitai Yang , Hanyi Yang , Lili Du

Traffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.

摘要交通冲击波表现了道路上交通波动的形成和传播。现有的方法主要是通过估计交通密度和流量来检测冲击波及其传播，这在仅部分或局部收集交通数据的情况下存在弱点。本文提出了一种四步数据驱动方法，该方法将机器学习与交通特征相结合，仅使用部分车辆轨迹数据来检测冲击波并估计其传播速度。具体来说，我们首先通过快速傅里叶变换(FFT)对从轨迹数据中得到的速度数据进行去噪，以减轻自发随机速度波动的影响。其次，我们确定了轨迹曲线的拐点，在这些拐点中，车辆进入冲击波，其速度在短间隔内呈现高标准偏差。结合交通流特征，采用基于密度的噪声应用空间聚类算法(DBSCAN)将拐点划分为不同的聚类，每个聚类对应一个恒定速度的冲击波。最后，利用一范数距离回归方法对探测冲击波的传播速度进行估计。提出的框架应用于下一代模拟(NGSIM)程序从I-80和US-101高速公路收集的现场数据。结果表明，这种四步数据驱动方法可以在不估计附近交通密度和流量的情况下有效地检测出冲击波及其传播速度。该算法对于从全部或部分交通流中收集轨迹数据的同质和非同质路段都表现良好。本研究得到了国家科学基金CMMI-1901994、CMMI-2213459和CNS-2124858的部分资助。作者在此感谢审稿人和编辑的宝贵意见，提高了本文的质量。作者的贡献作者确认他们对本文的贡献如下:L. Du博士提出了这个想法并监督了整个研究。学生K. Yang和H. Yang博士负责方法的开发、实施和数据收集。三位作者都起草、编辑和审阅了手稿。他们都审查了结果，并批准了手稿的最终版本。披露声明作者未报告潜在的利益冲突。Notes1该阈值是根据我们实验中的流量数据离线设置的。我们的方法对这个阈值不是很敏感。根据您如何定义应用程序中的慢速流量，它可以是10英里/小时左右的值。本研究得到了美国国家科学基金CMMI-1901994、CMMI-2213459和CNS-2124858的部分支持。作者在此感谢审稿人和编辑的宝贵意见，提高了本文的质量。

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引用次数: 0