International Journal of Transportation Science and Technology最新文献

{"title":"Investigation of Factors Affecting Crash Severity of Rear-End Crashes with High Collision Speeds in Work Zones: A South Carolina Case Study","authors":"Mahyar Madarshahian, Jason Hawkins, Nathan Huynh, C. Siddiqui","doi":"10.1016/j.ijtst.2024.07.003","DOIUrl":"https://doi.org/10.1016/j.ijtst.2024.07.003","url":null,"abstract":"","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph Convolutional LSTM Algorithm for Real-time Crash Prediction on Mountainous Freeways","authors":"Yesihati Azati, Xuesong Wang, Mohammed Quddus, Xuefang Zhang","doi":"10.1016/j.ijtst.2024.07.002","DOIUrl":"https://doi.org/10.1016/j.ijtst.2024.07.002","url":null,"abstract":"","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Hazard degree levels of Metro Operation Accidents based on Ordered Constraint Apriori-RF Method","authors":"Xiaobing Ding, Huilin Wan, Gan Shi, Chen Hong, Zhigang Liu","doi":"10.1016/j.ijtst.2024.06.008","DOIUrl":"https://doi.org/10.1016/j.ijtst.2024.06.008","url":null,"abstract":"","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Flooding Impact on Thin Pavement Structure in Texas Coastal Region","authors":"Feng Hong, J. Prozzi","doi":"10.1016/j.ijtst.2024.07.001","DOIUrl":"https://doi.org/10.1016/j.ijtst.2024.07.001","url":null,"abstract":"","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Literature Review of Defect Detection in Railways Using Machine Vision-Based Inspection Methods","authors":"Ankit Kumar, SP Harsha","doi":"10.1016/j.ijtst.2024.06.006","DOIUrl":"https://doi.org/10.1016/j.ijtst.2024.06.006","url":null,"abstract":"","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-analysis of driving behavior studies and assessment of factors using structural equation modeling","authors":"","doi":"10.1016/j.ijtst.2023.05.002","DOIUrl":"10.1016/j.ijtst.2023.05.002","url":null,"abstract":"<div><p>The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the theory of planned behavior (TPB) to predict driving behavior. To this end, 42 studies published up to the end of 2021 are reviewed to evaluate the predictive utility of TPB by employing a meta-analysis and structural equation model. The results indicate that these studies sought to predict 20 distinct driving behaviors (e.g., drink-driving, use of cellphone while driving, aggressive driving) using the original TPB constructs and 43 additional variables. The TPB model with the three original constructs is found to account for 32% intentional variance and 34% behavioral variance. Among the 43 variables researchers have examined in TPB studies related to driving behavior, this study identified the six that are commonly used to enhance the TPB model’s predictive power. These variables are past behavior, self-identity, descriptive norm, anticipated regret, risk perception, and moral norm. When past behavior is added to the original TPB model, it increases the explained variance in intention to 52%. When all six factors are added to the original TPB model, the best model has only four variables (perceived risk, self-identity, descriptive norm, and moral norm); and increases the explained variance to 48%. The influence of the TPB constructs on intention is modified by behavior category and traffic category. The findings of this paper validate the application of TPB to predicting driving behavior. It is the first study to do this through the use of meta-analysis and structural equation modeling.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000382/pdfft?md5=ccbb2909db79056082a96852fad3d28e&pid=1-s2.0-S2046043023000382-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49356997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling edge computing ability in view-independent vehicle model recognition","authors":"","doi":"10.1016/j.ijtst.2023.03.007","DOIUrl":"10.1016/j.ijtst.2023.03.007","url":null,"abstract":"<div><p>Vehicle model recognition (VMR) benefits the parking, surveillance, and tolling system by automatically identifying the exact make and model of the passing vehicles. Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in real-time. Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network. This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques, which empowers us the ability of recognizing the vehicle model from an arbitrary view. The first-stage model estimates the vehicle posture using object detection and similarity matching, which is cost-efficient and suitable to be programmed in the edge computing devices; the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree (GBDT) algorithm and VGGNet, which is flexible to the changing dataset. More than 8 000 vehicle images are labeled with their components’ information, such as headlights, windows, wheels, and logos. The YOLO network is employed to detect and localize the typical components of a vehicle. The vehicle postures are estimated by the spatial relationship between different segmented components. Due to the variety of the perspectives, a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective. Two algorithms are used to extract the features from each image patch: (1) the scale invariant feature transform (SIFT) combined with the bag-of-features (BoF) and (2) pre-trained deep neural network. The GBDT is applied to evaluate the weight of each component regarding its impact on VMR. The descriptors of each component are then aggregated to retrieve the best matching image from the database. The results showed its advantages in terms of accuracy (89.2%) and efficiency, demonstrating the vast potential of applying this method to large-scale vehicle model recognition.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S204604302300028X/pdfft?md5=ce6f5579d9069f7f5e9ff520676a8fd5&pid=1-s2.0-S204604302300028X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47810036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geotechnical properties of cohesive soils used in the construction of subgrade for the development of the railways in the Azov-Black Sea region","authors":"","doi":"10.1016/j.ijtst.2023.05.003","DOIUrl":"10.1016/j.ijtst.2023.05.003","url":null,"abstract":"<div><p>This work is devoted to the determination and systematization of the properties of clay soils used in the construction of new railway tracks in order to develop the railway network in the Azov-Black Sea region of Russia. To this end, classification characteristics are determined by traditional laboratory methods, and the possibility of soil swelling under excessive moisture is estimated. In addition, the compressibility of soils is studied as the main factor ensuring the trouble-free operation of the subgrade of railways during their long-term operation. Soil samples for measurements were taken from open pits located near construction sites at an extended length of construction of 530 km. The new regression relations proposed in the work provide in some cases the accuracy of determining the soil characteristics close to the accuracy of laboratory tests. They may be in demand when monitoring the accuracy of laboratory tests of soil properties of other open pits and increasing the speed of pre-design surveys during further development of the railroad network in this region.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000485/pdfft?md5=7c46e6e1d1411da71c0b462757a1c7e5&pid=1-s2.0-S2046043023000485-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46685176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tire-pavement friction modeling considering pavement texture and water film","authors":"","doi":"10.1016/j.ijtst.2023.04.001","DOIUrl":"10.1016/j.ijtst.2023.04.001","url":null,"abstract":"<div><p>The accurate estimation of tire-pavement friction, especially under wet conditions, is critical to ensure pavement safety. For this purpose, this paper develops a modified tire-pavement friction model which takes the effect of pavement texture and water film into consideration. The influence of pavement texture is quantified by a newly proposed parameter called texture influence coefficient, which is related to the real contact patch of tire-pavement. The water effect is calculated from two parts, namely lubrication effect and hydrodynamic effect. Based on these two steps, a modified average lumped LuGre (ALL) model is developed. The proposed model is calibrated and verified by GripTester data collected under different vehicle velocities and water film thicknesses. The root mean square error between the calculated value of the model and the measured value is 0.023. In addition, the effects of vehicle velocity, slip rate, water film thickness, and pavement type on the friction coefficient are analyzed by numerical calculation. The results show that the friction coefficient reaches the maximum when the slip rate is in the range of [0.15, 0.20]. The increases in the vehicle speed and water film thickness will lead to the decrease in the friction coefficient. Besides, in thin water film (&lt;1 millimeter) conditions, the deterioration effect of water film thickness on the friction coefficient is more remarkable. The results prove that the modified tire-pavement friction model provides a precise and reliable way to estimate the friction coefficient of pavement, which can assist the pavement management systems in risk warning and safety guarantee.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S204604302300031X/pdfft?md5=7af3bbd8e7f3c373c7350a55fd211356&pid=1-s2.0-S204604302300031X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45188226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic flow modelling of long and short trucks using a hybrid artificial neural network optimized by particle swarm optimization","authors":"","doi":"10.1016/j.ijtst.2023.04.004","DOIUrl":"10.1016/j.ijtst.2023.04.004","url":null,"abstract":"<div><p>The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers, urban planners, and researchers in the last decade. However, limited research has been done on traffic flow modelling of long and short trucks considering that they are among the major causes of traffic congestions and traffic-related accidents on freeways, especially freeway collisions between them and passengers’ vehicles. This study focused on the traffic flow of long and short trucks on the <span><math><mrow><mi>N</mi><mn>1</mn><mspace></mspace><mi>freeway</mi></mrow></math></span> in South Africa due to its high traffic volume and persistent traffic congestions caused by trucks. We obtained traffic data from this freeway using inductive loop detectors and video cameras. Traffic flow variables such as speed, time, traffic density, and traffic volume were identified, and the traffic datasets comprising 920 datasets were divided into 70% for training and 30% for testing. A hybrid <span><math><mrow><mi>ANN</mi><mo>-</mo><mi>PSO</mi></mrow></math></span> model was used in modelling the truck traffic flow due to its ability to converge to optimization quickly. The PSO's features (accelerating factors and number of neurons) assist in evaluating traffic flow conditions (traffic flow, traffic density, and vehicular speed). Also,<!--> <!-->PSO algorithms are simple and require few adjustment parameters. The results suggest that the ANN-PSO model can model long and short trucks traffic flow with a <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></mrow></math></span> training and testing of <span><math><mrow><mn>0.999 0</mn><mspace></mspace><mi>and</mi><mspace></mspace><mn>0.993 0</mn></mrow></math></span>. This is the first study to undertake a longitudinal analysis of traffic flow modelling of long and short trucks on a freeway using a metaheuristic algorithm (ANN-PSO). The results of this study will provide knowledgeable insights (division of traffic flow variables and analysing of traffic flow data) to transportation planners and researchers when it comes to minimizing truck-related accidents and traffic congestions on freeways.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000333/pdfft?md5=6e62e57cd7e4fcf0c803c216bf7ac91e&pid=1-s2.0-S2046043023000333-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43285859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}