Journal of Intelligent Transportation Systems最新文献_第4页

Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception 不利条件下的场景适应：用于路边交通感知的多传感器融合框架

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-08-19 DOI: 10.1080/15472450.2024.2390844

Kong Li, Zhe Dai, Chen Zuo, Xuan Wang, Hua Cui, Huansheng Song, Mengying Cui

引用次数: 0

Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne 基于活动和代理的墨尔本交通模型：大墨尔本地区开放式多模式交通模拟模型

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-11 DOI: 10.1080/15472450.2024.2372894

Afshin Jafari, Dhirendra Singh, Alan Both, Mahsa Abdollahyar, Lucy Gunn, Steve Pemberton, Billie Giles-Corti

引用次数: 0

Optimizing the ground intra-city express delivery network: An integrated multiple centrality assessment, multi-criteria decision-making, and multi-objective integer programming model 优化地面市内快递网络：综合多重中心性评估、多标准决策和多目标整数编程模型

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2157211

{"title":"Optimizing the ground intra-city express delivery network: An integrated multiple centrality assessment, multi-criteria decision-making, and multi-objective integer programming model","authors":"","doi":"10.1080/15472450.2022.2157211","DOIUrl":"10.1080/15472450.2022.2157211","url":null,"abstract":"<div><p>Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 525-543"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85741363","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

Proactive congestion management via data-driven methods and connected vehicle-based microsimulation 通过数据驱动方法和基于互联车辆的微观模拟进行积极的拥堵管理

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2140047

{"title":"Proactive congestion management via data-driven methods and connected vehicle-based microsimulation","authors":"","doi":"10.1080/15472450.2022.2140047","DOIUrl":"10.1080/15472450.2022.2140047","url":null,"abstract":"<div><p>Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm’s methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user’s ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 4","pages":"Pages 459-475"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79341768","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

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