International Journal of Transportation Science and Technology最新文献

Effective lane width for cars considering lateral oscillation characteristics based on high-precision field data 基于高精度现场数据的考虑横向振动特性的汽车有效车道宽度

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2023.10.007

Jianbei Liu , Zhongyin Guo , Donghui Shan , Xianyong Liu

The cross-sectional width of highways is a major factor that affects the construction cost of engineering projects. With the increasing demand for intensive highway construction, research on dedicated lanes or roadway for cars has attracted research attention. The lateral oscillation value of vehicle’s trajectory is the direct factor that affects the lane width; however, relevant research is relatively limited, and the characteristics are not yet clear. Therefore, this study utilized an integrated radar shorter video system (IRVS) to collect real high-precision trajectory data, obtaining 24 697 datasets. Statistical methods were used to reveal the lateral oscillation value and determine the effective lane width of cars for safe driving. The research results are as follows. 1. The lateral oscillation value of cars varies across different lanes. Vehicles on the inner two lanes tend to drift leftward, whereas those on the outermost lane tend to drift rightward. 2. When the operating speed ranges from 80 km/h to 110 km/h, the lateral oscillation value presents a statistical rule, which obeys a normal distribution for the left and right margins, i.e., D_left ∼ N (0.87, 0.15) and D_right ∼ N (0.72, 0.15). This lateral oscillation value is insensitive to fluctuations in the operating speed (80 km/h ∼ 110 km/h) from the standpoint of field data. 3. The fitted expressions of effective lane width for cars were proposed, and the effective lane width at the 95th, 90th, and 85th percentile is 3.2 m, 3.0 m, and 2.8 m, respectively. These results can provide technical support for dedicated lane width for cars, and have practical significance for the intensive construction of road infrastructure in highly urbanized areas.

公路断面宽度是影响工程造价的主要因素。随着我国高速公路集约化建设需求的不断增加，汽车专用车道的研究日益受到人们的关注。车辆轨迹的横向振荡值是影响车道宽度的直接因素;然而，相关研究相对有限，其特征尚不明确。因此，本研究利用雷达-视频一体化系统采集真实的高精度轨迹数据，获得24,697个数据集。采用统计方法揭示了横向振荡值，确定了车辆安全行驶的有效车道宽度。研究结果如下:1。在不同车道上，汽车的横向振动值存在差异。内两条车道上的车辆倾向于向左漂移，而最外两条车道上的车辆倾向于向右漂移。2. 当运行速度在80 ~ 110 km/h范围内时，横向振荡值呈现统计学规律，左右边缘服从正态分布，即Dleft ~ N(0.87, 0.15)和Dright ~ N(0.72, 0.15)。从现场数据的角度来看，该横向振荡值对运行速度(80km/h ~ 110km/h)的波动不敏感。3.提出了汽车有效车道宽度的拟合表达式，第95、90、85百分位处汽车有效车道宽度分别为3.2m、3.0m、2.8 m。研究结果可为汽车专用道宽度的确定提供技术支持，对高度城市化地区道路基础设施的集约化建设具有现实意义。

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引用次数: 0

Performance evaluation of Bailey method used in asphalt mixtures containing natural river sands 含天然河砂沥青混合料的贝利法性能评价

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2023.10.009

Mohammad Ahmad Alsheyab , Ahmed O. Al-sugaier , Md Lutfor Rahman , Hossein Emami Ahari

This study aims to investigate the efficiency of the Bailey method when natural sand is included in the mix at two coarseness levels of aggregate gradations: fine-graded (FG) and coarse-graided (CG), and three mixes with varing percentages of the natural river sand were prepared at each coarseness level, namely: CG with quarry sand only (CG-QS), CG with natural sands only (CG-NS), CG with quarry and natural sands (CG-QNS), FG with quarry sand only (FG-QS), FG with natural sands only (FG-NS), and FG with quarry and natural sands (CG-QNS). The portions of the natural sand either in CG-QNS and FG-QNS mixes were minimized as possible without violating the Bailey ratios. Four performance tests were utilized to evaluate the mixes including indirect tensile tension test (IDEAL-CT), disk‐shaped compact tension test (DCT), flow number (FN) test, and tensile strength ratio (TSR) test. The sensitivity and the significance of the volumetric measures were investigated including voids of total mix (VTM), voids of mineral aggregate (VMA), dust proportion (DP), voids filled with asphalt (VFA), air voids volume (V_a), and effective binder volume (V_be), and a number of non-linear statistical models were developed. The results of IDEAL-CT, DCT, and FN test were consistent where CG-QNS had the best performance, followed by CG-QS, CG-NS, FG-QS, FG-QNS, and lastly FG-NS. TSR value was always decreasing with the increase in the percentage of the natural river. V_be was the most significant volumetric measure followed and was used to predict the performance test indices. FN test had the highest coefficient of determination (R²) when modeled with any of the other performance tests, while TSR test had the lowest. The Bailey gradation method was successfully proficient to provide a similar gradation coarseness for CG-QNS when compared to CG-QS which indicates a similar aggregate interlock.

本研究旨在考察在细级配(FG)和粗级配(CG)两种骨料级配粗度水平下混合天然砂时Bailey法的效率，并在每个粗度水平下制备三种随天然河砂百分比变化的混合料，即:只含采石场砂的骨灰分级(CG-QS)、只含天然砂的骨灰分级(CG-NS)、只含采石场砂的骨灰分级(CG-QNS)、只含采石场砂的骨灰分级(FG-QS)、只含天然砂的骨灰分级(FG-NS)和只含采石场砂的骨灰分级(CG-QNS)。在不违反贝利比的情况下，CG-QNS和FG-QNS混合物中天然砂的比例都尽可能地最小化。使用四种性能测试来评估混合料，包括间接拉伸张力测试(IDEAL-CT)、圆盘形致密拉伸测试(DCT)、流动数(FN)和拉伸强度比(TSR)。研究了总掺合物空隙率(VTM)、矿物集料空隙率(VMA)、粉尘比(DP)、沥青填充空隙率(VFA)、空气空隙体积(Va)和有效粘结剂体积(Vbe)等体积指标的敏感性和重要性，并建立了非线性统计模型。理想ct、DCT和FN的结果一致，CG-QNS表现最好，其次是CG-QS、CG-NS、FG-QS、FG-QNS，最后是FG-NS。TSR值随河道天然砂的增加而减小。Vbe是最显著的容积度量，用于预测性能测试指标。FN的决定系数(R2)最高，TSR的决定系数最低。当使用任何其他性能测试建模时，FN具有最高的R2，而TSR具有最低的R2。与CG-QS相比，Bailey分级方法成功地为CG-QNS提供了相似的分级粗度，这表明了相似的聚集互锁。

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引用次数: 0

Analysis of traffic conflicts with right-turning vehicles at unsignalized intersections in suburban areas 城郊无信号交叉口右转车辆交通冲突分析

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2023.10.008

Abbas Sheykhfard , Farshidreza Haghighi , Sarah Bakhtiari , Sara Moridpour , Kun Xie , Grigorios Fountas

Right-turn collisions at intersections are one of the most dominant crash types in suburban areas, especially at unsignalized intersections. There is, however, a lack of comprehensive research on the speed patterns of vehicles during right-turn manoeuvres and their impacts on crashes. To provide an in-depth investigation of the factors determining the safety of right-turn manoeuvres, driving behavior data were collected through an instrumented vehicle study. Using this data, binary logistic regression models were developed to identify the factors affecting the probability of vehicle-vehicle (V-V) and vehicle-pedestrian (V-P) conflicts at six suburban intersections in Babol, Iran, during right-turn stage manoeuvres. In total, 1 456 V-V and V-P conflicts were identified from the data analysis. The results from the logistic regression model showed that the vehicle speed, the distance between road users, as well as driver and pedestrian distractions were associated with a higher risk for V-V or V-P conflicts. To estimate the safe right-turn speeds to be selected by drivers at different stages of the right turn, i.e., at the start, during, and end of the movement, linear regression models were developed. The results showed that participants adjust their driving behaviors the same way toward pedestrians as they do toward vehicles. The findings of this study can be leveraged for the development of a robust advanced driving assistance system, the use of which can further improve the safety performance of right-turn manoeuvres.

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引用次数: 0

Characteristics and identification of risky driving behaviors in expressway tunnels based on behavior spectrum 基于行为谱的高速公路隧道危险驾驶行为特征及识别

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2023.10.006

Li Wan , Ying Yan , Chang'an Zhang , Changcheng Liu , Tianyi Mao , Wenxuan Wang

Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments, which can easily lead to drivers developing constrained and irritable psychology. This may result in risky behaviors, e.g., speeding and fatigued driving. Previous research on tunnel driving behaviors mainly focuses on visual factors, neglecting the impacts of nonstationary time-series combined parameters on risky driving. Firstly, 30 drivers were recruited to carry out the real test. Then, based on the evolution of time series, drawing inspiration from the concept of lineage in biology, and considering multiple driving performance indicators, driving behavior chains and the feature spectrum were constructed. The characteristics of the behavior spectrum were divided into six groups: electroencephalogram, heart rate, eye movement, speed, steering, and car-following behaviors. Subsequently, the spectral analysis using the spectral radius property of matrix theory revealed the distinctive characteristics of risky driving behaviors. The study deeply explored the inducing mechanism, hidden patterns, and rules of risky driving behaviors under the coupling effect of tunnel environment and drivers’ attributes. Finally, the significant features that influence driving behaviors were used as the input variables for constructing identification models using the adaptive boosting (AdaBoost) and random forest (RF) algorithms. The synthetic minority over-sampling technique (SMOTE) and adaptive synthetic sampling (ADASYN) were employed for oversampling. The results indicate that the ADASYN-RF algorithm outperformed others, achieving a precise recall rate area under the curve (AUPRC) of 0.978 when using the spectral radius of the speed and steering groups as input variables. These findings offer theoretical guidance for developing tunnel traffic safety strategies.

高速公路隧道为半封闭式结构，线路过渡单调，照明环境独特，容易导致驾驶员产生约束和烦躁心理。这可能会导致超速和疲劳驾驶等危险行为。以往对隧道驾驶行为的研究主要集中在视觉因素上，忽略了组合参数对危险驾驶的非平稳时间序列影响。首先，招募了30名司机进行真实测试。然后，基于时间序列的演化，借鉴生物学中的谱系概念，考虑多个驾驶性能指标，构建了驾驶行为链和特征谱;行为谱特征分为六组:脑电图、心率、眼动、速度、驾驶和汽车跟随行为。随后，利用矩阵理论的谱半径特性进行谱分析，揭示了危险驾驶行为的显著特征。研究深入探讨了隧道环境与驾驶员属性耦合作用下危险驾驶行为的诱发机制、隐藏模式和规律。最后，将影响驾驶行为的重要特征作为输入变量，使用自适应增强(AdaBoost)和随机森林(RF)算法构建识别模型。采用合成少数派过采样技术(SMOTE)和自适应合成采样技术(ADASYN)进行过采样。结果表明，采用速度组和转向组的谱半径作为输入变量时，adasynf - rf算法的召回率曲线下面积(AUPRC)达到0.978，优于其他算法。研究结果为制定隧道交通安全策略提供了理论指导。

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引用次数: 0

Neural network surrogate models for aerodynamic analysis in truck platoons: Implications on autonomous freight delivery 用于卡车编队空气动力学分析的神经网络替代模型：对自主货运的影响

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2024.02.002

Tong Liu, Hadi Meidani

Recent advances in connected vehicles have the potential to revolutionize the efficiency and sustainability of transportation. In particular, truck platooning has emerged as a promising solution for improving freight delivery operations. However, the generalization of truck platoon modeling and the economic implications of truck platoons require further investigation. In this paper, we proposed a data-driven neural network surrogate model to predict the drag force of the truck platoon system. The proposed surrogate model can be generalized to truck platoons of various configurations and allows for the evaluation of fuel consumption reduction of truck platoons. Through a case study on a 100-mile corridor on Illinois I-57 Highway, we demonstrate the substantial fuel savings of up to 10% by truck platooning. Additionally, we conduct a cost-benefit analysis for implementing connected freight delivery systems and highlight the potential for significant reductions in delivery costs per parcel, up to 26%. These findings contribute valuable insights into optimizing truck platooning configurations, showcasing the potential benefits of connected freight operations, and improving environmental sustainability.

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引用次数: 0

Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques 调查影响印度长途卡车司机超速行为的促成因素：二元对数和机器学习技术的启示

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2024.01.008

Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

Speeding is one of the most common aberrant driving behaviors among the driving population. Although research on speeding behavior among drivers has increased over the decades, little is known about the motivating factors associated with speeding behavior among long-haul truck drivers (LHTDs), especially in developing nations like India. This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India. A cross-sectional study was conducted among LHTDs in Salem City, Tamil Nadu, India. The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic, work, vehicle, health-related lifestyle, and speeding-related characteristics. A total of 756 valid samples were collected and utilized for analysis purposes. While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). From the befitting RF model, the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot. Pressured delivery of goods, sleeping duration per day, age of truck, size of truck, monthly income, driving experience, driving duration per day, and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs. Based on the developed RF model, the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots (PDPs). The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.

{"title":"Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques","authors":"Balamurugan Shandhana Rashmi, Sankaran Marisamynathan","doi":"10.1016/j.ijtst.2024.01.008","DOIUrl":"10.1016/j.ijtst.2024.01.008","url":null,"abstract":"<div><div>Speeding is one of the most common aberrant driving behaviors among the driving population. Although research on speeding behavior among drivers has increased over the decades, little is known about the motivating factors associated with speeding behavior among long-haul truck drivers (LHTDs), especially in developing nations like India. This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India. A cross-sectional study was conducted among LHTDs in Salem City, Tamil Nadu, India. The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic, work, vehicle, health-related lifestyle, and speeding-related characteristics. A total of 756 valid samples were collected and utilized for analysis purposes. While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). From the befitting RF model, the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot. Pressured delivery of goods, sleeping duration per day, age of truck, size of truck, monthly income, driving experience, driving duration per day, and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs. Based on the developed RF model, the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots (PDPs). The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 194-211"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140525287","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

Impacts of characteristics of service facilities in metro station area on housing prices 地铁站区服务设施特征对房价的影响

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2024.01.009

Minqing Zhu , Chengwang Zhou , Yizhe Yang , Hongjun Cui , Xinwei Ma

Transit-oriented development is extensively employed for the development of metro stations. Service facilities are key components of the regional built environment of metro stations, and the arrangement and design of these facilities significantly impact regional economic efficiency, particularly housing prices. Data from Tianjin, China are analyzed using the gradient boosting decision tree model to explore the impacts of service facilities on housing prices at various operational stages. The results showed that: (1) the development of metro stations encourages the evolution of housing prices and service facilities, and there is a more pronounced concentration effect of service facility allocation intensity on the urban central circle; (2) the characteristics of service facilities in metro station areas have a nonlinear effect and threshold effect on housing prices; (3) the gradient boosting decision tree model explains a premium effect, which is better than the ordinary least square model; (4) kernel the density is the most significant, and affects housing prices, with per capita quantity decreasing as diversity increases over time. The results encourage government departments to enhance the construction of improved rail transit links and to optimize public service facilities in station areas.

{"title":"Impacts of characteristics of service facilities in metro station area on housing prices","authors":"Minqing Zhu , Chengwang Zhou , Yizhe Yang , Hongjun Cui , Xinwei Ma","doi":"10.1016/j.ijtst.2024.01.009","DOIUrl":"10.1016/j.ijtst.2024.01.009","url":null,"abstract":"<div><div>Transit-oriented development is extensively employed for the development of metro stations. Service facilities are key components of the regional built environment of metro stations, and the arrangement and design of these facilities significantly impact regional economic efficiency, particularly housing prices. Data from Tianjin, China are analyzed using the gradient boosting decision tree model to explore the impacts of service facilities on housing prices at various operational stages. The results showed that: (1) the development of metro stations encourages the evolution of housing prices and service facilities, and there is a more pronounced concentration effect of service facility allocation intensity on the urban central circle; (2) the characteristics of service facilities in metro station areas have a nonlinear effect and threshold effect on housing prices; (3) the gradient boosting decision tree model explains a premium effect, which is better than the ordinary least square model; (4) kernel the density is the most significant, and affects housing prices, with per capita quantity decreasing as diversity increases over time. The results encourage government departments to enhance the construction of improved rail transit links and to optimize public service facilities in station areas.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 212-221"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139832558","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

Mixing temperature optimization and modification mechanism of medical masks modified asphalt: Insights from computational chemistry 医用口罩改性沥青的混合温度优化和改性机理：计算化学的启示

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2024.01.007

Heyang Ding, Hongren Gong, Lin Cong

Masks modified asphalt (MMA) provides a potential solution to pollution from discarded medical masks. Mixing temperature significantly affects storage stability and rheological performance of MMA. Traditional selection method overly relies on trial-and-error experiment, neglecting the convenience offered by computational chemistry. Furthermore, previous literature lacks precise elucidation of MMA’s physical modification mechanism, especially concerning the binding mode and energy composition. To address these issues, the optimal mixing temperature for MMA was recommended based on molecular dynamics (MD). The rationality of recommended temperature was validated through laboratory tests, simultaneously investigating the impact of heating time. Fluorescence microscopy and multi-band spectroscopy were employed to acquire the microstructure. Binding modes in MMA were determined using binding sites exploration, evaluating the energy composition of each binding mode through quantum chemistry (QC). The interaction mechanism was explained based on surface properties of isolated molecules. Results indicated that 170 ℃ was the recommended optimal mixing temperature derived from mixing free energy and Flory-Huggins interaction parameter. The fluctuations in softening point difference (ΔT_R&B) and separation ratio (R_S) concurrently tended towards stability, thereby validating the reliability of recommended temperature. Moreover, even after 72 h heating, MMA prepared at recommended temperature remained within a reasonable range concerning ΔT_R&B, R_S, and microscopic structure. Perpendicular, parallel, toroidal, and spherical modes emerged in MMA. Perpendicular and parallel modes exhibited the highest binding energy, while circular mode demonstrated the lowest. Binding energy is primarily governed by van der Waals interaction, attributed to the dominance of dispersion term on MMA’s molecular surface. Besides, due to the presence of polycyclic aromatic hydrocarbons in asphalt molecules, electrostatic interaction contributed to specific molecular bindings.

{"title":"Mixing temperature optimization and modification mechanism of medical masks modified asphalt: Insights from computational chemistry","authors":"Heyang Ding, Hongren Gong, Lin Cong","doi":"10.1016/j.ijtst.2024.01.007","DOIUrl":"10.1016/j.ijtst.2024.01.007","url":null,"abstract":"<div><div>Masks modified asphalt (MMA) provides a potential solution to pollution from discarded medical masks. Mixing temperature significantly affects storage stability and rheological performance of MMA. Traditional selection method overly relies on trial-and-error experiment, neglecting the convenience offered by computational chemistry. Furthermore, previous literature lacks precise elucidation of MMA’s physical modification mechanism, especially concerning the binding mode and energy composition. To address these issues, the optimal mixing temperature for MMA was recommended based on molecular dynamics (MD). The rationality of recommended temperature was validated through laboratory tests, simultaneously investigating the impact of heating time. Fluorescence microscopy and multi-band spectroscopy were employed to acquire the microstructure. Binding modes in MMA were determined using binding sites exploration, evaluating the energy composition of each binding mode through quantum chemistry (QC). The interaction mechanism was explained based on surface properties of isolated molecules. Results indicated that 170 ℃ was the recommended optimal mixing temperature derived from mixing free energy and Flory-Huggins interaction parameter. The fluctuations in softening point difference (Δ<em>T</em><sub>R&B</sub>) and separation ratio (<em>R</em><sub>S</sub>) concurrently tended towards stability, thereby validating the reliability of recommended temperature. Moreover, even after 72 h heating, MMA prepared at recommended temperature remained within a reasonable range concerning Δ<em>T</em><sub>R&B</sub>, <em>R</em><sub>S</sub>, and microscopic structure. Perpendicular, parallel, toroidal, and spherical modes emerged in MMA. Perpendicular and parallel modes exhibited the highest binding energy, while circular mode demonstrated the lowest. Binding energy is primarily governed by van der Waals interaction, attributed to the dominance of dispersion term on MMA’s molecular surface. Besides, due to the presence of polycyclic aromatic hydrocarbons in asphalt molecules, electrostatic interaction contributed to specific molecular bindings.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 177-193"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139876908","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

Lane number and its impact on commercial motor vehicle crash safety: An econometric perspective 车道数及其对商用机动车碰撞安全的影响：计量经济学视角

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2024.01.002

Jaekook Kim , Nabeel Saleem Saad Al-Bdairi , Salvador Hernandez

This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle (CMV) drivers. Many studies have addressed crash determinants, but the safety implications of differing numbers of lanes remain insufficiently examined, especially during the highway planning stages. Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios. Employing a random parameter logit modeling framework, we differentiated injury levels for 2–4 lanes and 6–10 lanes. Key factors were identified for each number of lanes, with older, loss of vehicle control, non-collision crashes, and crashes, on locations where grade or hill existed, being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways. For 4-lane highways, factors such as non-Oregonian drivers, older drivers, crashes that occurred during the spring season, and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes. Regarding highways with 6 lanes and higher, driving too fast for conditions and driver error (drowsy, fatigued, inattentive, or reckless) increases the odds of being involved in higher levels of injury crashes. To enhance truck driver safety, we recommend the implementation of electronic stability control in CMVs, with moderated speeds on graded sections, improved curve markers, and robust public safety campaigns.

{"title":"Lane number and its impact on commercial motor vehicle crash safety: An econometric perspective","authors":"Jaekook Kim , Nabeel Saleem Saad Al-Bdairi , Salvador Hernandez","doi":"10.1016/j.ijtst.2024.01.002","DOIUrl":"10.1016/j.ijtst.2024.01.002","url":null,"abstract":"<div><div>This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle (CMV) drivers. Many studies have addressed crash determinants, but the safety implications of differing numbers of lanes remain insufficiently examined, especially during the highway planning stages. Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios. Employing a random parameter logit modeling framework, we differentiated injury levels for 2–4 lanes and 6–10 lanes. Key factors were identified for each number of lanes, with older, loss of vehicle control, non-collision crashes, and crashes, on locations where grade or hill existed, being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways. For 4-lane highways, factors such as non-Oregonian drivers, older drivers, crashes that occurred during the spring season, and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes. Regarding highways with 6 lanes and higher, driving too fast for conditions and driver error (drowsy, fatigued, inattentive, or reckless) increases the odds of being involved in higher levels of injury crashes. To enhance truck driver safety, we recommend the implementation of electronic stability control in CMVs, with moderated speeds on graded sections, improved curve markers, and robust public safety campaigns.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 119-133"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458255","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

Discovering latent themes in aviation safety reports using text mining and network analytics 利用文本挖掘和网络分析发现航空安全报告中的潜在主题

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-12-01 DOI: 10.1016/j.ijtst.2024.02.009

Yingying Xing , Yutong Wu , Shiwen Zhang , Ling Wang , Haoyuan Cui , Bo Jia , Hongwei Wang

Aviation accidents, referring to unexpected and undesirable events involving aircraft, often cause great damage to property and human life. Learning from historical accidents is pivotal for improving safety in aviation. However, aviation accidents are typically documented and stored as unstructured or semi-structured free-text, rendering the ability to analyze such data a difficult task. This study presents a novel framework that combines text mining and network analytics techniques to provide the ability to analyze aviation accident reports automatically. The framework comprises a four-step modelling approach to: (1) the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix; (2) the identification of aviation accident topics using a structural topic model (STM); (3) the production of a word co-occurrence network (WCN) to determine the interrelations between aviation safety risk factors; and (4) quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety events. The proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board (NTSB). The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional latent dirichlet allocation (LDA). Among the identified topics, “Fuel and Power” and “En-route Phase” have the highest occurrence rate according to STM. Additionally, “Aircraft Crash” is the most prevalent topic in aviation accidents that resulted in fatal injuries, whereas the “Landing phase” is the most prevalent topic in non-fatal injuries on accidents. Based on the WCN, three centrality measures highlight “inspection of equipment” and “take off” as the most important risk factors in aviation safety. The proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports, offering decision support for aviation safety management and accident prevention, thereby reducing risks and strengthening safety measures.

{"title":"Discovering latent themes in aviation safety reports using text mining and network analytics","authors":"Yingying Xing , Yutong Wu , Shiwen Zhang , Ling Wang , Haoyuan Cui , Bo Jia , Hongwei Wang","doi":"10.1016/j.ijtst.2024.02.009","DOIUrl":"10.1016/j.ijtst.2024.02.009","url":null,"abstract":"<div><div>Aviation accidents, referring to unexpected and undesirable events involving aircraft, often cause great damage to property and human life. Learning from historical accidents is pivotal for improving safety in aviation. However, aviation accidents are typically documented and stored as unstructured or semi-structured free-text, rendering the ability to analyze such data a difficult task. This study presents a novel framework that combines text mining and network analytics techniques to provide the ability to analyze aviation accident reports automatically. The framework comprises a four-step modelling approach to: (1) the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix; (2) the identification of aviation accident topics using a structural topic model (STM); (3) the production of a word co-occurrence network (WCN) to determine the interrelations between aviation safety risk factors; and (4) quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety events. The proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board (NTSB). The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional latent dirichlet allocation (LDA). Among the identified topics, “Fuel and Power” and “En-route Phase” have the highest occurrence rate according to STM. Additionally, “Aircraft Crash” is the most prevalent topic in aviation accidents that resulted in fatal injuries, whereas the “Landing phase” is the most prevalent topic in non-fatal injuries on accidents. Based on the WCN, three centrality measures highlight “inspection of equipment” and “take off” as the most important risk factors in aviation safety. The proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports, offering decision support for aviation safety management and accident prevention, thereby reducing risks and strengthening safety measures.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 292-316"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468501","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