Journal of Intelligent Transportation Systems最新文献_第5页

Fusion attention mechanism bidirectional LSTM for short-term traffic flow prediction 用于短期交通流预测的融合注意力机制双向 LSTM

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2142049

{"title":"Fusion attention mechanism bidirectional LSTM for short-term traffic flow prediction","authors":"","doi":"10.1080/15472450.2022.2142049","DOIUrl":"10.1080/15472450.2022.2142049","url":null,"abstract":"<div><p>Short term forecasting is essential and challenging in time series data analysis for traffic flow research. A novel deep learning architecture on short-term traffic flow prediction was presented in this work. In conventional model-driven prediction method, a critical deviation in prediction accuracy was occurred in face of large fluctuations in traffic flow, while machine and deep learning-based approaches performed well in accuracy study than conventional regression-based models. Moreover, a fusion attention mechanism bidirectional long short-term memory model (ATT-BiLSTM) was proposed due to its bidirectional LSTM (BiLSTM) and attention mechanism units. The model not only dealt with forward and backward dependencies in time series data, but also integrated the attention mechanism to improve the ability on key information representation. The BiLSTM layer was exploited to capture bidirectional temporal and spatial features dependencies from historical data. The proposed model was also trained and validated using freeway toll datasets from Humen Bridge. The results showed that compared with ARIMA and SVR models, the indicators of the proposed model have been significantly improved. The ablation experiments were conducted to evaluate the role of the attention mechanism module. Compared with BiLSTM, CNN and 1DCNN-ATT-BiLSTM models, the MAE, RMSE and MAPE indexes of proposed model were reduced by 0.6–5.9%, 1.6–4.7% and 0.6–22.8%, respectively. More accurate predictions were obtained by the proposed model. The research results are of great significance to improve the level of traffic management.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 511-524"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84892290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah 模拟 COVID-19 在大流行后期对运输的影响：犹他州案例研究

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2157212

{"title":"Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah","authors":"","doi":"10.1080/15472450.2022.2157212","DOIUrl":"10.1080/15472450.2022.2157212","url":null,"abstract":"<div><p>The global COVID-19 pandemic has had a great impact on transportation across the United States. However, there is a lack of studies investigating the pandemic’s impact on vehicular traffic at the later stage of the pandemic. Therefore, this paper studies the change of freeway traffic patterns in two metropolitan counties in the State of Utah at the latter stage of the pandemic. We found that with the relaxation of travel restriction and the COVID vaccine, vehicular traffic has recovered to equaling, if not exceeding, pre-pandemic levels. Truck traffic is higher than the pre-pandemic level due to the growth of online shopping and on-demand delivery. To help responsive agencies to prepare for the near-future traffic pattern, a traffic prediction model based on an innovative approach integrating machine learning with graph theory is proposed. The evaluation shows that the proposed prediction model has a desirable performance. The mean absolute percentage prediction error is between 0.38% and 1.74% for different jurisdictions. On average, the modal outperforms the traditional long short-term memory model by 31.20% in terms of root mean squared prediction error.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 544-554"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75545118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lane change for self-driving in highly dense traffic using motion based uncertainty propagation 利用基于运动的不确定性传播，在高密度交通中实现自动驾驶车道变更

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2137795

引用次数: 0

Driver stress levels detection system using hyperparameter optimization 利用超参数优化的驾驶员压力水平检测系统

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2140046

{"title":"Driver stress levels detection system using hyperparameter optimization","authors":"","doi":"10.1080/15472450.2022.2140046","DOIUrl":"10.1080/15472450.2022.2140046","url":null,"abstract":"<div><p>Stress and driving are a dangerous combination which can lead to crashes, as evidenced by the large number of road traffic crashes that involve stress. Therefore, it is essential to build a practical system that can classify driver stress level with high accuracy. However, the performance of such a system depends on hyperparameter optimization choices such as data segmentation (windowing hyperparameters). The configuration setting of hyperparameters, which has an enormous impact on the system performance, are typically hand-tuned while evaluating the algorithm. This tuning process is time consuming and there are also no generic optimal values for hyperparameters values. In this work, we propose a meta-heuristic approach to support automated hyperparameter optimization and provide a real-time driver stress detection system. This is the first systematic study of optimizing windowing hyperparameters based on Electrocardiogram (ECG) signal in the domain of driving safety. Our approach is to propose a framework based on Particle Swarm Optimization algorithm (PSO) to select an optimal/near optimal windowing hyperparameters values. The performance of the proposed framework is evaluated on two datasets: a public dataset (DRIVEDB dataset) and our collected dataset using an advanced simulator. DRIVEDB dataset was collected in a real-time driving scenario and our dataset was collected using an advanced driving simulator in the control environment. We demonstrate that optimizing the windowing hyperparameters yields significant improvement in terms of accuracy. The most accurate built model applied to the public dataset and our dataset, based on the selected windowing hyperparameters, achieved 92.12% and 77.78% accuracy, respectively.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 443-458"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80512517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empirical study of a cooperative longitudinal control for merging maneuvers considering courtesy and mixed autonomy 考虑礼让和混合自主的并线机动合作纵向控制实证研究

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2023.2174802

{"title":"Empirical study of a cooperative longitudinal control for merging maneuvers considering courtesy and mixed autonomy","authors":"","doi":"10.1080/15472450.2023.2174802","DOIUrl":"10.1080/15472450.2023.2174802","url":null,"abstract":"<div><p>This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative longitudinal Control for Merging maneuvers (CCM) strategy based on Automated Vehicles (AV) is proposed considering cooperation among vehicles, courtesy, and the coexistence of AV and Human-Driven Vehicles (HDV). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AV behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. CCM mainly requires vehicles to have automated longitudinal (such as Adaptive Cruise Control (ACC)) and lane-changing control, which are already commercially available on some vehicles. Also, it does not need 100% AV penetration, presenting itself as a promising solution for improving traffic operations in lane reduction transition areas such as highway work zones.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 573-586"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83839102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of spatial interactions among shared e-scooters, shared bikes, and public transit 共享电动滑板车、共享自行车和公共交通之间的空间互动分析

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2023.2174803

引用次数: 0

Multi-lane’s control performance differentiation on traffic efficiency under the lane-level dynamic coordination strategy 车道级动态协调策略下的多车道控制性能差异对交通效率的影响

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2157213

{"title":"Multi-lane’s control performance differentiation on traffic efficiency under the lane-level dynamic coordination strategy","authors":"","doi":"10.1080/15472450.2022.2157213","DOIUrl":"10.1080/15472450.2022.2157213","url":null,"abstract":"<div><p>Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 555-572"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75498969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy 受交通事故影响的城市路网复原力评估与提升：网络规模优化战略

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2141119

{"title":"Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy","authors":"","doi":"10.1080/15472450.2022.2141119","DOIUrl":"10.1080/15472450.2022.2141119","url":null,"abstract":"<div><p>This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 494-510"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75313323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity analysis of driving event classification using smartphone motion data: case of classifier type, sensor bundling, and data acquisition rate 利用智能手机运动数据进行驾驶事件分类的灵敏度分析：分类器类型、传感器捆绑和数据采集率的情况

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2140048

{"title":"Sensitivity analysis of driving event classification using smartphone motion data: case of classifier type, sensor bundling, and data acquisition rate","authors":"","doi":"10.1080/15472450.2022.2140048","DOIUrl":"10.1080/15472450.2022.2140048","url":null,"abstract":"<div><p>Classification of driving events is a crucial stage in driving behavior monitoring using smartphone sensory data. It has not been previously explored that to what extent classification performance depends on the classifier type and input data characteristics. To fill this gap, a real-world experiment is designed for supervised data collection. Then the effects of different machine learning (ML) classifiers, data sampling rates, and sensor combinations on the final classification accuracy are demonstrated. A considerable number of labeled events (4114) containing 11 types of driving maneuvers are collected using base sensors (accelerometer and gyroscope) and composite sensors (linear accelerometer and rotation vector) available in smartphones. Several models using 23 ML algorithms are trained. The sensitivity of these models is analyzed by changing the characteristics of the input data concerning the type of ML classifier, data sampling rate, and the bundle of mobile sensors. It is demonstrated that: (1) F1 scores vary from 70 to 96% for different ML classifiers, (2) F1 scores drop 30–40% depending on the classifier type when reducing the data sampling rate, and (3) using all four sensors as a bundle for classifying driving events is not reasonable since an approximate equal F1 score is achievable by a three-sensor bundle which includes an accelerometer and a linear accelerometer.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 476-493"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81252404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer learning for cross-modal demand prediction of bike-share and public transit 共享单车和公共交通跨模式需求预测的迁移学习

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-06-30 DOI: 10.1080/15472450.2024.2371913

Mingzhuang Hua, Francisco Camara Pereira, Yu Jiang, Xuewu Chen, Junyi Chen

引用次数: 0