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Spillages may cause traffic congestion and incidents and seriously affect the efficiency of traffic operation. Due to the changeable shape and scale of a spill on a highway, the location of the spill is random, so the current background extraction and object detection methods cannot achieve good detection results for the spill. This paper proposes a highway spill detection method using an improved STPM anomaly detection network. The method is based on the STPM network and achieves detection through FFDNet image filtering, calculation of the global correlation features of the student and teacher networks, contour positioning of spillages in the feature map, and automatic collection of positive samples to train and update the model, achieving high-precision identification and positioning of the spillages. The experimental results of the custom-built top-view road surface spillage dataset and the MVTec anomaly detection dataset show that the method proposed in this paper can obtain an AOC-ROC value of 0.978 and a PRO score of 0.965 and can distinguish between spillages and reflective cones, avoiding the problem of false detection when spills are similar in appearance. Therefore, the proposed method has value in the research and engineering application of spill detection in special highway scenes. [ABSTRACT FROM AUTHOR]

The features and patterns of the development of the country's land transport road network in the 16th to mid-19th centuries are examined. Particular attention is paid to the main objective factors that had a decisive influence (both positive and restraining) on the development of land communications. Without considering them, it is impossible to assess objectively road construction and the functioning of the Russian road network, as well as to abandon a number of persistent stereotypes that are not so much a reflection of objective processes as their interpretation in the context of ideological positions and interpretations of different times. This work covers the period from the formation of Russian statehood to the victory of the concept of mass railway construction. The study provides a description of both the entire road network and the main highways, shows the process of their improvement within the framework of existing capabilities and tasks, and assesses the place of land communication routes in the unified transport system of Russia. [ABSTRACT FROM AUTHOR]

Transport activities are one of the important indicators that determine the degree of development of a country. Transport, which is at the centre of many factors such as the continuation of commercial activities within the country, communication mobility to every point of the country and individual activities, is also one of the main actors that determine the relations between countries. Due to the availability of access to every point by road and the high variety of vehicles that can be used in transportation, policies have been adopted to improve road transport in Türkiye as in the whole world. The Istanbul - Izmir Motorway, which is a result of these policies and has both national and international characteristics with its level of impact, was tendered in 2009 and was fully put into service as of 2019. This study aims to examine the impact of this motorway on agricultural areas in Manisa. The fact that Manisa is a national and international agricultural region was effective in the selection of the sample and in this context, data obtained from Google Earth, OpenStreetMap, Coordination of Information on the Environment (CORINE, 2018), General Directorate of Mapping and General Directorate of Rural Services were used. In the study, it was found out that as a result of the motorway project, a total of 842 ha of agricultural land, in the categories of complex cropping patterns, dry agricultural land, olive land, irrigated agricultural land and vineyards, was lost in Manisa. With the study, the necessity of taking measures to protect agricultural areas, which are food production areas, on the basis of integrated transportation policies was emphasized. [ABSTRACT FROM AUTHOR]

Applying light-transmitting concrete (LTC) to highway lane markings is a novel idea to increase the visual recognition distance and promote traffic safety. However, compared to its excellent visual recognition performance, its service performance has received little attention in the current research. This paper conducted an accelerated pavement test to investigate the behaviour of rutting depth, British Pendulum Number (BPN) and water permeability coefficient of the LTC lane markings. The results show that rutting depth of LTC is lower than that of asphalt pavement. As a result, small height steps appeared at the seam of the two parts. For the anti-sliding performance represented by the BPN, the BPN of LTC gradually exceeds asphalt pavement and maintains a slightly higher value. For the water permeability coefficient, the LTC is almost un-permeable, avoiding water damage during service. According to these findings, the LTC lane markings meet the requirements of highway service performance.. [ABSTRACT FROM AUTHOR]

Green areas around motorways can be potential sources of toxic substances, especially risk elements, which come from internal combustion engines. The aim of this study was to determine the level of soil contamination in the vicinity of the largest motorway in the Czech Republic, the D1. Soil contamination indices such as the contamination factor (C_f^i), Potential ecological risk index E_f^I and Potential toxicity response index (RI). Lead (Pb), copper (Cu), and mercury (Hg) were selected as elements of interest. The soil contains, on average, 0.18 mg/kg Hg, 34.32 mg/kg Pb, and 15.48 mg/kg Cu. The calculations of the above factors showed that the content of the measured elements does not represent a serious problem for the environment. Hg can be considered the most risky element, as ecosystems are very sensitive, even to small amounts. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today’s society and one of its main pillars, mobility, is undergoing significant changes due to accelerated technological development. The previous mobility criteria may require revision due to changes. The work presents a study about the urban-rural-highway separation, which is widely used and crucial for the sustainability assessment of road mobility devices. Its characteristics are presented, and the test procedures based on them are considered. By comparing these with mobility trends, these need to be complemented due to the changing mobility use of space. The introduction of a new category, whose main dimensions are indicated, is proposed, and we highlight the areas where further research is needed.

We studied species composition and monthly patterns of roadkill of large and medium sized mammal species along 778 km of Slovenian highways in the period 2018–2020. Apart from providing the first comprehensive data on monthly patterns of roadkill as a proxy of wildlife-vehicle collisions at Slovenian highways and identifying linear collision hotspots (using KDE + methodology), we also tested the impact of acoustic deterrents installed at highway interchanges in late 2018 on WVC frequency. In the three-year period, we registered in total 2046 roadkill cases, with red fox (Vulpes vulpes), (N = 790; 39.6%), roe deer (Capreolus capreolus) (N = 592; 28.9%), and European badger (Meles meles), (N = 423; 20.7%) being the most exposed species. Temporal collision patterns with evident species-specific monthly peaks reflect the behaviour and activity of these three species. We identified 268 KDE + hotspots, accounting for 48.5% of all roadkill and covering 9.3% (72.6 km) of the highway network. Recognising these hotspots is crucial for implementing mitigation measures to reduce WVC on Slovenian highways. However, our findings indicate no decrease in roadkill after installation of acoustic deterrents (2019–2020), therefore additional effort is needed to find adequate and effective approach for reducing roadkill and hence increasing road safety. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction is one of the most significant research topics in traffic engineering. It is instrumental in designing a more modern transport network to manage traffic signals and reduce congestion. Short-term traffic flow is a challenge that a third-world country like Bangladesh is all too familiar with. Like the other cities of Bangladesh, Khulna Metropolitan City is gradually becoming more aware of this situation. The Khulna-Jashore National Highway (N-7), which runs through the city and provides a linear shape, serves as the backbone of the Khulna Metropolitan City traffic flow. This study developed an Artificial Neural Network (ANN) model for the short-term Traffic Flow Prediction on Two-Lane Highway in Khulna Metropolitan City, Bangladesh. Data was collected from March 1, 2021, through June 30, 2021, during 600–900 hours and 1200–1500 hours. Good-quality electronic cameras recorded the vehicles at the full designated length. The regression graphs displayed the network outputs with targets for the training, validation, and test sets. The various speed level parameters for which the fit is reasonable for all data sets, with R values of 0.98426 in each case. The various traffic volume parameters for which the fit is reasonable for all data sets, with R values of 0.96758 in each case. The model's superiority is indicated by its low mean squared error values. This study demonstrated that the neural network has a good prediction effect on specific road traffic flow, which can achieve the goal of short-term prediction and has improved practicability through testing on real traffic data. This study provides an opportunity to provide a suitable alternative for short-term traffic flow forecasting in Khulna Metropolitan City with traffic flow conditions for two-lane undivided highways.

Engineering management is an extremely important aspect of construction engineering, and a better management approach can greatly enhance the production profits of enterprises. Traditional management optimization schemes cannot adapt to current technological needs due to their inability to effectively consider the impact of each factor. Therefore, a construction management optimization scheme combining improved particle algorithm and multi-objective optimization was proposed. The improved particle algorithm enhances its performance by introducing adaptive weight and multi-objective optimization ideas. These studies confirmed that the predicted direct cost savings for the project were around 1 million yuan. The total construction period of project was optimized to 380 days, saving 34 days. The optimization technology not only reduced construction costs, but also reflected the problems that could be improved during this construction process. This study contributes to achieving multi-objective balance in the construction management process, effectively improving project efficiency, reducing project costs and risks, and providing scientific support for construction decision-making.

Existing studies often overlook the nuanced differences between various road classifications and their respective crash dynamics, hindering the development of targeted interventions to mitigate crash severity. To address this gap, this study investigates factors influencing the likelihood of fatality in road crashes across highways, collector roads, and local roads in Thailand using crash data from 2015 to 2021. Highways connect regions with high-speed traffic and large volumes, collector roads link smaller communities with lower traffic density but allow higher speeds, and local roads primarily pass through villages, with narrow pathways, two traffic lanes, and frequent motorcycle use. The study employs machine learning methodologies utilizing tree-based algorithms, including Decision Trees, Random Forest, Gradient Boosting, AdaBoost, Extra Trees, XGBoost, LightGBM, and CatBoost. The XGBoost model delivered superior performance for highways, while Gradient Boosting slightly outperformed XGBoost for local and collector roads. Both models consistently achieved a test accuracy of 0.70, with precision between 0.66 and 0.67, recall ranging from 0.59 to 0.61, and F1-scores from 0.58 to 0.61. The AUC values also consistently ranged from 0.59 to 0.61. SHAP values reveal key factors influencing fatality risk across road types, including speeding, gender disparities, driving under the influence of alcohol, inadequate lighting, and elderly drivers. Specific concerns include reversing on highways, collisions in poorly lit areas on collector roads, and helmet non-use on local roads. The findings support policy recommendations to address speeding, target male and older drivers, prevent reversing incidents, enhance lighting, and promote helmet use. This research deepens our understanding of factors affecting road crash severity and offers valuable insights for improving road safety across various environments.

Construction and paving of road infrastructures is on the rise globally. Roads have many negative impacts on the environment, including changes and damages to landscape connectivity, fragmentation and disrupting of ecological corridors, loss of habitats, air pollution, noise pollution and light pollution . Here we aimed to examine the impacts of the construction of a major road (national Highway 6 in Israel, 188 km long) on landscape fragmentation. To this end we mapped the land cover at three-time steps: 1997 (before the construction of the road began), 2009 and 2019, quantifying both patch-based metrics (using Frgastats), the continuous metrics of landscape connectivity (using Circuitscape) and landscape continuity, adapting a before-after-control-impact methodology. Analyzing changes in those metrics for sections of the road based on their time of construction and on the distance from the highway, we found that most changes took place within a distance of up to 1 km from the new highway, starting after road construction began. We also found that the patch-based metrics and the continuous metrics were weakly correlated. We conclude that each of those different approaches has its merits and limitations, that they are complementary and that jointly they provide us with better understanding of landscape changes.

[Introduction] The rapid development of new energy vehicles (NEVs) brings higher requirements for the power demand of highways. Based on the analysis of the power loads of highways, the photovoltaic endowment, and the energy storage technologies suitable for highway service areas in China, this paper explores the self-consistency of the highway transportation and energy integration mode of the PV-Storage-Charging integrated development path, and combines practical engineering project to demonstrate and analyze the feasibility of the highway transportation and energy integration mode. [Method] By analyzing the total mileage of highways in China, the market penetration rate of NEVs, and the PV & energy storage resources of highway transportation, based on the potential increase in highway power load due to the growth of NEVs and the utilization space of photovoltaic resources in highway transportation, a comprehensive energy utilization mode of PV-Storage-Charging for highway service areas was proposed. Combined with existing projects of self-consistent modes of transportation and energy integration, suggestions were proposed for the integrated development mode of highway PV-Storage-Charging. [Result] Data indicates that by 2025, the national stock of NEVs will exceed 25 million, consuming 37.5 TWh of electricity, showing tremendous potential for electricity demand. At the same time, the abundant photovoltaic resources along and around roads in China have a photovoltaic generation potential of approximately 1022.8 TW, while the calculated annual energy consumption of highway infrastructure is approximately 17.99 TW. [Conclusion] The main path of integration between transportation and energy should start with developing wind and photovoltaic natural resource endowments based on the infrastructure of road transportation itself, and forming a self-consistent supply of transportation energy systems, thereby constructing a distributed, green, renewable, and net zero-emission energy supply system for transportation systems. The integrated development path of PV-Storage-Charging transportation and energy integration can consume renewable energy locally, alleviate grid pressure while promoting the clean energy utilization of highways, showing immense potential. This paper can provide theoretical support and reference for the development of highway transportation and energy integration in China.

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks.

Commercially available crowdsourced connected vehicle (CV) trajectory data have recently been used to provide stakeholders with actionable and scalable roadway mobility infrastructure performance measures. Transportation agencies and automotive original equipment manufacturers (OEMs) share a common vision of ensuring the privacy of motorists that anonymously provide their journey information. As this market has evolved, the fleet mix has changed, and some OEMs have introduced additional fuzzification of CV data around 0.5 miles of frequently visited locations. This study compared the estimated Indiana market penetration rates (MPRs) between historic non-fuzzified CV datasets from 2020 to 2023 and a 5–11 May 2024, CV dataset with fuzzified records and a reduced fleet. At selected permanent interstate and non-interstate count stations, overall CV MPRs decreased by 0.5% and 0.3% compared to 2023, respectively. However, the trend in previous years was upward. Additionally, this paper evaluated the impact on data characteristics at freeways and intersections between the 5–11 May 2024, fuzzified CV dataset and a non-fuzzified 7–13 May 2023, CV dataset. The analysis found that the total number of GPS samples decreased 10% statewide. Of the evaluated 54,284 0.1-mile Indiana freeway, US Route, and State Route segments, the number of CV samples increased for 33.8% and decreased for 65.9%. This study also evaluated 26,291 movements at 3289 intersections and found that the number of available trajectories increased for 28.3% and decreased for 70.4%. This paper concludes that data representativeness is enough to derive most relevant mobility performance measures. However, since the change in available trajectories is not uniformly distributed among intersection movements, an unintended sample bias may be introduced when computing performance measures. This may affect signal retiming or capital investment opportunity identification algorithms.

One of the significant challenges encountered during the implementation of highway projects today is the inability to perform accurate and timely inspections. This results in both loss of time and the potential for uncontrolled construction at various points due to insufficient control capabilities at every site. During the preparation of highway projects, crosssections of only specific points can be obtained, which limits the scope of inspection during implementation. The ability to inspect points outside these cross-sections and control the areas beyond the axis line of the cross-section points during the project execution would provide a significant advantage. Additionally, this approach would result in time and cost savings, enabling the swift execution and control of construction at points outside the cross-section axis line according to project requirements. In this context, an algorithm has been designed to instantly calculate the distance of any point from the starting point, its distance from the road axis, and the required elevation along the designed elevation profile. This algorithm has been utilized to develop a computer program (interface software) to instantly calculate all relevant data for any given point during the project construction phase. For instance, in a completed highway project, using this interface facilitated the analyses of cost benefits achieved. [ABSTRACT FROM AUTHOR]

In this study, the rockfall potential of the rock slopes on the Tortul-Kürtün Highway was evaluated by three-dimensional rockfall analyses. Based on the results of these analyses, different slope protection methods were proposed. To this end, in the first stage, flights were made with LIDAR-integrated unmanned aerial vehicles in order to establish a digital terrain model. This was followed by the preparation of a digital terrain model of the rock slopes using the photogrammetric data obtained. In the study, the block sizes and shapes to be used as a source in 3D analyses were determined by taking into account the data obtained from discontinuity measurements and the blocks that had previously fallen and accumulated on the slope bases. Furthermore, the normal coefficient of restitution was accepted as 0.75, the tangential coefficient of restitution as 0.75, and the friction coefficient as 0.4 by considering the literature data. In the 3D rockfall analyses, the following parameters were calculated: block energy, bounce height, stoop points, and block velocity values. The resulting 3D visuals of these values were prepared. The results of the analyses indicate that the average block velocity values are 15 m/s, with maximum values of block energies varying between 50 and 200 kJ in different areas. The results of the 3D rockfall analysis, the location and height of the slopes, the width of the buffer zones between the slopes and the highway were evaluated as a whole. It was recommended that the rock slopes be coverred using "wire rope net" after stone/rock cleaning in areas with block energy values less than 100 kJ and "high capacity steel wire ring net" over the "wire rope net" in areas with block energy values higher than 100 kJ. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In Brazil, the maintenance and expansion of the federal road network conflict with the country's climate neutrality goals. The National Department of Transportation Infrastructure (DNIT), responsible for Brazil's federal highways, lacks tools to assess and mitigate greenhouse gas (GHG) emissions from its projects, which can be achieved through the life cycle assessment (LCA) methodology. Additionally, the scarcity and insufficient quality of environmental data on Brazilian road materials led to using foreign databases in LCA studies conducted in Brazil, generating uncertainties in the results. This research aims to apply LCA to analyze the GHG emissions from highway projects developed by DNIT. The analysis covers the phases of material production, transportation, and construction. Additionally, this study investigates the influence of the leading project disciplines on the results and the differences in outcomes resulting from the use of local Portland cement compared to its equivalent produced in the United States of America (USA) and Europe. The results indicate that the materials production phase has the most significant impact on the project's global emissions, contributing over 78% in all scenarios. The transportation phase accounts for more than 16% of global emissions, with the potential for greater contributions depending on project transportation distances. Environmental data showed that foreign cement increased the project's global emissions by 7.31% (Europe) and 12.91% (USA), with the most significant differences observed in pavement disciplines. In all scenarios, the discipline of new pavement presented the highest unitary emissions, followed by the discipline of restored pavement, which showed average values 58% lower than the former, and earthworks. Other services, drainage, and special structures disciplines showed minimal influence on the results. In conclusion, this research proposes an approach to estimate the environmental impact of Brazilian federal highway projects, enabling early mitigation of GHG emissions during the design phase, focusing on critical disciplines and materials, and highlighting potential distortions associated with the use of environmental data from foreign cement in LCA studies of Brazilian highways. Finally, considerations are presented regarding improving and expanding the scope of the calculation methodology used in this study, allowing for a more comprehensive and accurate analysis of the environmental impacts associated with the Brazilian federal road network. [ABSTRACT FROM AUTHOR]

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks. [ABSTRACT FROM AUTHOR]

Public–private partnerships (P3s) have gained prominence in both developed and developing nations over recent decades. P3 contracts have been used in a broad spectrum of infrastructure sectors in the United States. However, it is important to demonstrate how P3 projects compare in terms of cost and schedule to traditional project delivery methods to make more informed choices when selecting a delivery method in infrastructure construction. This research focused on benchmarking the project performances of P3 highway projects against design-build (DB) and design-bid-build (DBB) highway projects by analyzing cost growth and schedule growth for highway projects undertaken using P3, DB, and DBB project delivery. Statistical tests revealed that P3 highway projects had a mean cost growth of 2.12%, whereas DB projects experienced 8.95% growth, and DBB projects 7.27%. Furthermore, the mean schedule growth for P3 highway projects was 0.59%, compared to 37.94% for DB projects and 31.39% for DBB projects. The research results can provide valuable insights to assist decision-making processes for future projects, which can be particularly useful for government agencies, private companies, and other stakeholders involved in infrastructure development. Furthermore, the stakeholders can make more informed choices when selecting a delivery method with the identified performance comparison findings, potentially reducing the likelihood of project disputes and failures. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems. [ABSTRACT FROM AUTHOR]

To investigate highway vehicle lane-changing behavior, we utilized the publicly available naturalistic driving dataset, HighD, to extract the movement data of vehicles involved in lane changes and their proximate counterparts. We employed univariate and multivariate Cox proportional hazards models alongside random survival forest models to analyze the influence of various factors on lane change duration, assess their statistical significance, and compare the performance of multiple random survival forest models. Our findings indicate that several variables significantly impact lane change duration, including the standard deviation of lane-changing vehicles, lane-changing vehicle speed, distance to the following vehicle in the target lane, lane-changing vehicle length, and distance to the following vehicle in the current lane. Notably, the standard deviation and vehicle length act as protective factors, with increases in these variables correlating with longer lane change durations. Conversely, higher lane-changing vehicle speeds and shorter distances to following vehicles in both the current and target lanes are associated with shorter lane change durations, indicating their role as risk factors. Feature variable selection did not substantially improve the training performance of the random survival forest model based on our findings. However, validation set evaluation showed that careful feature variable selection can enhance model accuracy, leading to improved AUC values. These insights lay the groundwork for advancing research in predicting lane-changing behaviors, understanding lane-changing intentions, and developing pre-emptive safety measures against hazardous lane changes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Previous research has reported that driving on a familiar roadway can influence speed choice. However, the findings have not been extensively discussed in simulated environments, which are frequently used for assessments of driving behavior and traffic safety. This study assesses the effects of familiar roadways on drivers' speed behavior in a driving simulator environment. During testing, 120 individuals drove through two blocks of four scenarios, each representing a real stretch of a mountainous Brazilian highway, with differences among the scenarios in advisory signs but with the same regulated speed. The participants could drive during the first, second, third, or fourth round, as established by random sorting. Afterwards, a Kruskal–Wallis Analysis of Variance (ANOVA) test was applied to search for significant differences in average speed between the rounds and scenarios. The results showed no significant differences in average speed (p-value < 0.05; α = 0.05); moreover, the drivers' ability and time licensed were not necessarily correlated with average speed, supporting future research with repeated scenarios towards maximizing the sample's utility for speed analysis in driving simulators. [ABSTRACT FROM AUTHOR]

Abstract Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

The aim of this study was to map and model the noise pollution level (NPL) of a highway in Iran by using statistical techniques, geographic information system (GIS) Software, and the Traffic Noise Model version 2.5 (TNM2.5). The equivalent noise level in A weighting network (L eq(A)) was measured in 28 stations in different seasons using the sound level meter of the TES-1358 model. The traffic noise data and indices L eq (equivalent noise level), L max and L min (the maximum and minimum time-weighted noise levels), L n (noise pressure level exceeded for n percent of the time), NPL, and TNI (traffic noise index) were measured and calculated to determine the daily sound level. They were then statistically analyzed by SPSS and Sigma Plot Software. Finally, the GIS software and TNM2.5 techniques were used to map and predict the traffic NPL in the region and the distribution of noise levels. The highest noise level was measured at about 80 and 79.61 dB in Kouhsangi and Abadgaran stations, respectively. The lowest level was measured at around 58 dB in the Hafez Square station. In addition, the results show that the highest and lowest L eq were almost in the same range (81–85 dB). It appears that the TNM2.5 model can reasonably predict traffic noise levels as well as the dispersion and nature of noise pollution on the highway.

According to traffic accident statistics, the main causes are violations of traffic rules. Based on scientific research, it is clear that the main reason for these events is the drivers' incorrect assessment of the road situation and their inadequate response. Assessing road conditions depends on drivers' qualifications, skills, work experience, reaction time, and other physiological factors. The functional state of drivers is important to consider. It reflects their ability to work reliably and affects both fatigue levels and how fatigue builds up. Factors that impact decision-making speed and quality are crucial when evaluating the risks of the transportation process, particularly those associated with human behavior. This study investigates the safety of freight transportation for drivers, road users, third parties, and the environment. The study focuses on road traffic characteristics in mountainous conditions at high altitudes. The study aims to analyze how drivers' performance is affected while navigating challenging sections of mountain roads in the Ukrainian Carpathians. Four different routes will be examined. The research will provide graphic analysis of the results. It will show the main patterns of changes in drivers' functional state based on the route's complexity. The research aims to analyze indicators of drivers' functional state and their impact on their performance reliability. It allows for a more detailed evaluation of transport routes and planning of truck drivers' work and rest schedules on suburban routes.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance. [ABSTRACT FROM AUTHOR]

To improve the low traffic flow efficiency on highways, this study focuses on digital infrastructure construction, specifically on real-time traffic flow prediction. A multi-level framework for digital traffic infrastructure is designed and implemented. Various sensors are utilized for data acquisition. The framework involves preprocessing to eliminate redundant information, and data management through a database system. Prediction analysis is conducted using a combination of wavelet neural networks and a Shuffled Frog Leaping algorithm. The Shuffled Frog Leaping short-term traffic flow prediction algorithm and radial basis function neural networks are employed for feature extraction and model optimization. In the experiment, the traffic flow prediction model developed in this study was compared with various advanced models for performance evaluation. The average absolute error ranged from 1.6235 to 2.7482, with a mean of approximately 1.9793. The average absolute error increased by 28.53%, the average absolute percentage error decreased by about 45.8%, and the root mean square error improved by nearly 26.9%. In 15 experiments, the predictive performance showed significant enhancements in error control, fitting degree, stability, and accuracy, demonstrating the superiority and practicality of the traffic flow prediction. [ABSTRACT FROM AUTHOR]

Bibliometric analysis of the use of PCI methods on roads, main corridors, airports and ports is a network of research from around the world on related topics. The main objective of this study is to see trends that continue to develop in various points of view. By capturing various studies from 2000 to 2023 using Crossref sources, 1000 studies with related topics were produced. This research accompaniment uses the help of Publish or Perish, which is then processed in the VOSviewer application with the Research Information Systems (RIS) format. There is a reasonably broad relationship in research keywords produced with the dominance of rigid pavement keywords (299 studies), pavement condition index (112 studies), road damage (79 studies), rehabilitation (63 studies), and other keywords with different number variations. The results of the conclusion data regarding the development of research on pavement condition index continue to increase continuously, with the highest number in 2022 (86 studies). [ABSTRACT FROM AUTHOR]

Increased daily exposure to fine particulate matter air pollution (PM2.5) is associated with increased morbidity, yet high exposures over shorter timeframes (e.g., hourly) may also play a role. Transportation is a milieu for increased transient pollution exposures. Both the road traveled and nearby roadways (i.e., surrounding road density) may play a role in increased PM2.5 exposure during commutes. For 2311 min of commutes, corresponding to 25 participants, we obtained in-vehicle PM2.5 exposures using personal monitors and, through GPS data, road features, including road density and road type (e.g., highway vs. local roads). We considered the density of both the surrounding highways and the local roads at 500 m and 1000 m resolutions. We estimated associations of road features with minute-averaged in-vehicle PM2.5 by applying linear mixed-effects models with random intercepts and autoregressive errors. The difference in log PM2.5, comparing the highest vs. lowest quartile of highway road density at 1 km resolution, was 0.09 log μg/m3 (95% confidence interval: 0, 0.19), which was similar to the difference between driving on highways vs. local roads (0.07 log μg/m3 (95% confidence interval: 0.00, 0.14)). Estimated differences were attenuated for local road density and 500 m resolution. The results were robust to adjustment for meteorology and ambient PM2.5. Unlike road features such as speed and road type, the surrounding road density is less modifiable during transportation. Therefore, road choice may not have a large impact on personal PM2.5 exposures. [ABSTRACT FROM AUTHOR]

Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management. [ABSTRACT FROM AUTHOR]

The problems of information isolation, inefficiency, and paper-based data archiving in traditional highway survey and design methods are investigated in this paper. A novel digital sandbox platform framework was developed to promote the efficiency of route design, model data integration, and information sharing. Under the presented framework, an integrated application method for both the Building Information Modeling (BIM) and Geographic Information System (GIS) technologies was designed by using Unreal Engine technology. Firstly, a digital base model was established by integrating multi-disciplinary BIM model data and GIS three-dimensional (3D) multi-scale scene model data. On this basis, using Unreal Engine technology for visualization development, a digital sandbox platform with the data visualization, traffic organization simulation analysis, 3D spatial analysis, component information query, and scene switching functions was developed, which satisfies the 3D visualization and digitalization needs in the current highway planning and design. Additionally, the Analytic Hierarchy Process (AHP) was employed to analyze the impact of digital base model on the development and application of platform modules, including five crucial factors: data accuracy, data representation, multi-source data fusion, data management capability, and scene semantic representation. Finally, the research results indicate that the proposed digital sandbox platform framework provides users with a platform for integrated data management, information sharing, and 3D data visualization, while reducing design time by 30%, total design cost by 12%, and land occupancy rate by 10%. [ABSTRACT FROM AUTHOR]

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