International Journal of Transportation Science and Technology最新文献_第7页

Physics-Informed Deep Learning with Kalman Filter Mixture for Traffic State Prediction 利用卡尔曼滤波混合物进行交通状态预测的物理信息深度学习

International Journal of Transportation Science and Technology Pub Date : 2024-04-01 DOI: 10.1016/j.ijtst.2024.04.002

Niharika Deshpande, Hyoshin Park

引用次数: 0

Optimizing Roads for Sustainability: Inverted Pavement Design with Life Cycle Cost Analysis and Carbon Footprint Estimation 优化道路，实现可持续性：采用生命周期成本分析和碳足迹估算的反向路面设计

International Journal of Transportation Science and Technology Pub Date : 2024-04-01 DOI: 10.1016/j.ijtst.2024.04.008

U. Siva Rama Krishna, Mohan Badiger, Yatin Chaudhary, T. Vijaya Gowri, E. Jahnavi Devi

引用次数: 0

How to Detect Occluded Crosswalks in Overview Images? Comparing Three Methods in a Heavily Occluded Area 如何检测概览图像中被遮挡的人行横道？比较严重遮挡区域中的三种方法

International Journal of Transportation Science and Technology Pub Date : 2024-04-01 DOI: 10.1016/j.ijtst.2024.04.001

Yuanyuan Zhang, Joseph Luttrell, Chaoyang Zhang

引用次数: 0

Application of Conditional Deep Generative Networks (CGAN) in empirical bayes estimation of road crash risk and identifying crash hotspots 条件深度生成网络在道路碰撞风险经验贝叶斯估计和碰撞热点识别中的应用

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2023.02.005

Mohammad Zarei, Bruce Hellinga, Pedram Izadpanah

{"title":"Application of Conditional Deep Generative Networks (CGAN) in empirical bayes estimation of road crash risk and identifying crash hotspots","authors":"Mohammad Zarei, Bruce Hellinga, Pedram Izadpanah","doi":"10.1016/j.ijtst.2023.02.005","DOIUrl":"10.1016/j.ijtst.2023.02.005","url":null,"abstract":"<div><p>The conditional generative adversarial network (CGAN) is used in this paper for empirical Bayes (EB) analysis of road crash hotspots. EB is a well-known method for estimating the expected crash frequency of sites (e.g. road segments, intersections) and then prioritising these sites to identify a subset of high priority sites (e.g. hotspots) for additional safety audits/improvements. In contrast to the conventional EB approach, which employs a statistical model such as the negative binomial model (NB-EB) to model crash frequency data, the recently developed CGAN-EB approach uses a conditional generative adversarial network, a form of deep neural network, that can model any form of distributions of the crash frequency data. Previous research has shown that the CGAN-EB performs as well as or better than NB-EB, however that work considered only a small range of crash data characteristics and did not examine the spatial and temporal transferability. In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB. The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model (i.e. data conform to the assumptions of the NB model) and outperforms NB-EB in experiments reflecting conditions frequently encountered in practice (i.e. low sample mean crash rates, and when crash frequency does not follow a log-linear relationship with covariates). Also, temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000084/pdfft?md5=2465256101f2d75ef4563dbd4d2c3a56&pid=1-s2.0-S2046043023000084-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45975797","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}

引用次数: 0

Contributing factors to right-turn crash severity at signalized intersections: An application of econometric modeling 信号交叉口右转交通事故严重程度的影响因素：经济计量模型的应用

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2023.02.004

Hisham Jashami , Jason C. Anderson , Hameed A. Mohammed , Douglas P. Cobb , David S. Hurwitz

{"title":"Contributing factors to right-turn crash severity at signalized intersections: An application of econometric modeling","authors":"Hisham Jashami , Jason C. Anderson , Hameed A. Mohammed , Douglas P. Cobb , David S. Hurwitz","doi":"10.1016/j.ijtst.2023.02.004","DOIUrl":"10.1016/j.ijtst.2023.02.004","url":null,"abstract":"<div><p>Motorists are required to interact with both roadway infrastructure and various users. The complexity of the driving task in certain scenarios can influence the frequency and severity of crashes. Turning vehicles at intersections, for example, pose a collision risk for both motorized and non-motorized road users. The primary goal of this paper is to investigate the underlying factors which contribute to right-turn crashes at signalized intersections. Five years of crash data across Oregon were collected. A random parameters binary logit model was developed to predict the likelihood of whether a crash resulted in an injury or fatality. It was found that 14 variables were statistically significant in contributing to crash severity. The results obtained show that dry conditions and a posted speed limit of 30 mi/hr or 35 mi/hr contributed to a higher percentage of severe crashes, while fixed-object crashes and snowy weather had a higher likelihood of resulting in no injury crashes. Time-of-day (9:00 p.m. to 6:00 a.m.), lighting conditions (dusk), gender (male driver), crash type (vehicle–pedestrian and rear-end), and driver-level crash cause (driver sped too fast for conditions, driver did not yield right-of-way, and driver disregarded the traffic control device) all led to an increase in probability of a fatal or injury crash. The vehicle–pedestrian conflict variable had the highest impact on increasing the probability of such a crash while turning right at a signalized intersection. This observation is important because right turns are often permitted during the pedestrian walk and clearance indications, and often drivers do not give right-of-way to pedestrians.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000072/pdfft?md5=fd0e83f180d0ceabf126a939db8b49a5&pid=1-s2.0-S2046043023000072-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43694397","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}

引用次数: 0

An approach of localizing MOVES to estimate emission factors of trucks 一种基于局部move的卡车排放系数估算方法

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2023.02.002

Jiashuo Lei , Chao Yang , Qingyan Fu , Yuan Chao , Jie Dai , Quan Yuan

{"title":"An approach of localizing MOVES to estimate emission factors of trucks","authors":"Jiashuo Lei , Chao Yang , Qingyan Fu , Yuan Chao , Jie Dai , Quan Yuan","doi":"10.1016/j.ijtst.2023.02.002","DOIUrl":"10.1016/j.ijtst.2023.02.002","url":null,"abstract":"<div><p>Freight has become one of the major contributors to air pollution. This research proposes a method to systematically estimate truck vehicle emissions at the road segment level through localizing MOVES, a widely-used vehicle emission estimation model. We first design a protocol of converting percentage values of rotating speed and torque of engine to second-by-second vehicle speed to accommodate the differences between driving cycles adopted in local emission standards and those used in MOVES. In order to identify the best model year for estimating emissions under different local emission standards, we propose an approach of comparing emission outcomes rather than emission factors, considering the differences in unit used between MOVES and emission standards. To calculate road segment level emission factors, we weight original factors by integrating vehicle fleet information which contains the shares of vehicles under different emission standards and at different ages. We apply the approach to a major freight corridor area in Shanghai and calculate emission factors by air pollutant, average speed of road sections, and road type. Dynamic emissions of each road section per hour are calculated to reflect the spatial distribution of truck emissions. The research outcomes may help local departments, especially in developing countries, better estimate freight vehicle emissions and make policies correspondingly to control their impacts on public health.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000059/pdfft?md5=78c6c5f96a9a8f7665a855270fca774f&pid=1-s2.0-S2046043023000059-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46656231","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}

引用次数: 0

Design of flexible pavements through fuzzy inference system with genetic algorithm optimized rule base 采用遗传算法优化模糊推理系统设计柔性路面的规则库

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2023.03.001

M.A. Jayaram , M. Chandana

{"title":"Design of flexible pavements through fuzzy inference system with genetic algorithm optimized rule base","authors":"M.A. Jayaram , M. Chandana","doi":"10.1016/j.ijtst.2023.03.001","DOIUrl":"10.1016/j.ijtst.2023.03.001","url":null,"abstract":"<div><p>In this paper, a novel method for the design of flexible pavements is elaborated. The method is based on fuzzy inference system with genetic algorithm (GA) aided optimized rule base. The model is founded on layered fuzzy antecedent and consequent conjunctive rules. The data for the model consists of 300 flexible pavement design instances that breaks up in to 25% of the data drawn from research and real field applications and 75% of data generated in spread sheets compliant with Indian road congress (IRC) code guidelines. In the first step, the inputs and outputs were fuzzified and around 110 rules were generated using training data set. GA was implemented to find optimal and a compact rule set. GA was able to garner 35 rules that are adequate to predict the thickness of base course, sub base and surface course with high accuracy. The model with optimized rules was validated using test data set. The results of the evaluation are encouraging with low values of RMSE ranging between 3.6–11 for GSB, binder course (BC) and surface course (SC). The coefficient of determination is also high and between 0.85–0.90 indicating accuracy in prediction. Correlation coefficient values stood at an average of 0.92 indicating closeness between predicted and actual values of thickness of courses.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000230/pdfft?md5=26c20f68ad7f98b780940466061e3ac2&pid=1-s2.0-S2046043023000230-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45660436","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}

引用次数: 0

The Influence of Roadway Characteristics and Built Environment on the Extent of Over-Speeding: An Exploration Using Mobile Automated Traffic Camera Data 道路特征和建筑环境对超速程度的影响：利用移动自动交通摄像头数据进行的探索

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2024.03.003

Boniphace Kutela, Frank Ngeni, Cuthbert Ruseruka, Tumlumbe Juliana Chengula, Norris Novat, Hellen Shita, Abdallah Kinero

引用次数: 0

A weakly-supervised deep learning model for end-to-end detection of airfield pavement distress 用于端到端检测机场路面状况的弱监督深度学习模型

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2024.02.010

Zefeng Tao, Hongren Gong, Liming Liu, Lin Cong, Hai-chiung. Liang

引用次数: 0

A Fuzzy Track-to-Track Association Algorithm with Dynamic Time Warping for Trajectory-Level Vehicle Detection 用于轨迹级车辆检测的动态时间扭曲模糊轨迹关联算法

International Journal of Transportation Science and Technology Pub Date : 2024-03-01 DOI: 10.1016/j.ijtst.2024.03.001

Siqi Wan, Huaqiao Mu, Ke Han, Taesu Cheong, Chi Xie

引用次数: 0