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In this study, 4 classification algorithms were employed to characterize the influence of road determinants on roadway crash severity with actual crash data. The crash data were obtained from crash records in Texas, USA, from January 2020 to April 2021. The prediction model of crash severity utilized 12 road-related features—including shoulder types, shoulder width, and curb types—as well as 10 other features—such as weather and illumination conditions—as input features. Three crash severity levels—"Minor Damage," "Moderate Damage," and "Severe Damage"—were used as output features. Decision tree, support vector machines, and multi-layer perceptron were employed to compare their prediction performance with the XGBoost model. The results show that the XGBoost model yields the best performance among the 4 algorithms. The overall accuracy, average precision, average recall, and average F1 score of the XGBoost model were 82.65%, 0.83, 0.82, and 0.82, respectively. Besides, SHapley Additive exPlanations (SHAP) and partial dependence plots were used to interpret the model results. Among the road-related features, the most influential one is the median width. Greater crash severity is related to paved right shoulder and curb. These findings are helpful for the design and planning of road safety. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control. [ABSTRACT FROM AUTHOR]

The ability to understand the underlying fundamentals of traffic flow behaviour facilitates improved planning and decision‐making for road operators. This paper presents an overview of the various models which can be used to describe the interaction between the different parameters governing traffic flows. 5‐years of measured data from Ireland's M50 motorway are used to demonstrate the application of traffic flow theory using real data, and a detailed investigation of factors affecting the fundamental traffic behaviour is presented. The road capacity is shown to be impacted by different traffic behaviour during morning and evening‐peak periods, during dry vs. wet weather conditions and between lanes on the approach to junctions. It is demonstrated that the mean vehicle length is an important factor to consider when using traffic flow models. A novel 3‐dimensional fundamental diagram model linking mean vehicle speed, mean vehicle length, and density is introduced which enhances capacity estimation and illustrates the importance of considering vehicle length when using the fundamental diagram to interpret traffic flows and estimate the capacity of the motorway. [ABSTRACT FROM AUTHOR]

Urban road traffic network optimization based on the Internet of Things (IoT) is one of the important methods to reduce vehicle exhaust and ease traffic congestion. A two-level planning model on road traffic network optimization in IoT and the IS-APCPSO algorithm is deployed, which can effectively solve the above problems. The experimental results show that the total vehicle emissions of the proposed scheme are lower than those of traditional schemes. Moreover, the real-time perception, interactive coupling, and coordinated control of "people-vehicle-road-environment" can be achieved through the use of the IoT. We discuss the sensitivity of the model to establish a scene selection that takes into account different regional and road traffic conditions, avoiding the subjective randomness in model parameter selection. A novel multi-objective method based on IoT proposed in this paper helps to alleviate "urban diseases," such as traffic congestion, vehicle emissions, and energy waste, and to emphasize the overall benefits of energy-saving and emission reduction in urban road networks. By setting up traffic lights reasonably and guiding the driver behavior, the integrated control and management of traffic flow and exhaust emission under multiple driving cycles are realized. [ABSTRACT FROM AUTHOR]

As one of the important factors affecting regional economic development, expressway construction has been a concern of scholars worldwide. China's highway construction is relatively late compared with Western developed countries, but the speed is faster. The construction is in step with China's high-speed economic growth stage. However, there is temporal and spatial heterogeneity in expressway construction and economic development across different regions of the world, which has not been fully documented in the current literature. Using panel data from 1999 to 2019 encompassing expressway construction and regional economic development indicators of 233 prefecture-level cities in China, this paper delves into the dynamics of expressway infrastructure development and its correlation with regional economic growth. Using regression analysis, the study provides the following key insights: (1) As of 2019, expressway density exhibits a spatial pattern characterized by a higher density in major urban clusters and provincial capitals and a lower density in northeastern China and border regions. Between 1999 and 2009, the eastern region witnessed the fastest increase in expressway density, while the central region surpassed the eastern region in density growth during 2009–2019. Over the past two decades, there has been a significant overall increase in expressway growth nationwide, with the distribution of prefecture-level cities experiencing relatively faster growth rates aligning with the principles of the "National Highway Network Planning". (2) Although there has been an overall increase in per capita GDP among prefecture-level cities, there has also been a widening disparity between these cities. Meanwhile, the total GDP of the central and western regions has experienced notable growth, effectively closing the gap with the eastern region. While total GDP demonstrates balanced development, per capita GDP displays a more varied pattern of growth. (3) Expressway density has played a positive role in fostering regional economic growth across the country over the long term. However, its impact has been more pronounced in the western region from 1999 to 2009 and increasingly significant in the eastern and central regions from 2009 to 2019. [ABSTRACT FROM AUTHOR]

One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road's weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing – conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information. [ABSTRACT FROM AUTHOR]

Urban roads can effectively handle peak flows during extreme storms by serving as surface flood passages, provided the flow remains within safety thresholds for vehicles and pedestrians. However, studies on flow allocation at crossroad intersections are limited. Previous research has overlooked important factors: road transverse slope and turning radius. This study built a "two in, two out" laboratory crossroad intersection to examine flow allocation patterns. Experiments explored the effects of road longitudinal slope, boundary conditions, and the combined influence of turning radius and side slope. The results indicated that at flatter slopes, flow allocation is more influenced by road slope, while at steeper slopes, the inflow Froude number ratio becomes more significant. The combined effect of the turning radius and side slope results in a flow allocation that differs by 44.3% compared to rectangular orthogonal channel intersections. A straightforward formula is proposed to calculate the flow allocation ratio based on experimental results and inflow power ratio. These findings could improve road intersection designs for better flood mitigation, offering a practical tool for planning flood-resilient road networks. [ABSTRACT FROM AUTHOR]

This study introduces a novel method specifically designed to detect clusters of points within linear networks. This method extends a classification approach used for point processes in spatial contexts. Unlike traditional methods that operate on planar spaces, our approach adapts to the unique geometric challenges of linear networks, where classical properties of point processes are altered, and intuitive data visualisation becomes more complex. Our method utilises the distribution of the Kth nearest neighbour volumes, extending planar-based clustering techniques to identify regions of increased point density within a network. This approach is particularly effective for distinguishing overlapping Poisson processes within the same linear network. We demonstrate the practical utility of our method through applications to road traffic accident data from two Colombian cities, Bogota and Medellin. Our results reveal distinct clusters of high-density points in road segments where severe traffic accidents (resulting in injuries or fatalities) are most likely to occur, highlighting areas of increased risk. These clusters were primarily located on major arterial roads with high traffic volumes. In contrast, low-density points corresponded to areas with fewer accidents, likely due to lower traffic flow or other mitigating factors. Our findings provide valuable insights for urban planning and road safety management. [ABSTRACT FROM AUTHOR]

To expand the scale and enhance the comprehensiveness of urban rail transit networks, a strategic framework for urban rail transit network planning has been developed by leveraging the power of dynamic spatiotemporal graph convolution. This innovative approach utilizes a dynamic spatiotemporal graph convolutional network to effectively identify spatiotemporal interdependencies within rail transit sensor data. Through the integration of dynamic spatiotemporal graph convolution and a convolution interaction module, it performs spatiotemporal feature extraction and interactive learning on rail transit data. A physical continuous environment model, which incorporates slope and terrain details, is constructed for urban rail transit network planning. With the aim of minimizing the overall passenger travel time and considering spatiotemporal attributes and supply-demand balance, a comprehensive planning model for urban rail transit networks is established. Experimental results verify the effectiveness of the proposed method. The use of dynamic spatiotemporal graph convolution and related comprehensive planning models provides an efficient method for optimizing urban rail transit network planning. It not only improves network coverage and connectivity, but also significantly reduces transfer time, thereby enhancing the overall efficiency and quality of urban rail transit services. [ABSTRACT FROM AUTHOR]

Urban diversion road network design is crucial as it addresses the growing concerns of urban traffic congestion and environmental pollution by proposing a method to reduce carbon emissions and improve traffic flow efficiency. To optimize the design of urban road networks, this paper provides an environmental impact analysis perspective and investigates a dual-level planning approach for the design of urban diversion road networks. Initially, an upper-level model is developed with the objective of minimizing carbon emissions, environmental impact, and road network congestion impedance time. Subsequently, a lower-level Logit-type random user equilibrium allocation model is constructed to achieve random allocation. Finally, by modifying the spiral position update method and enhancing the search capability of the whale algorithm, an improved algorithm is employed to solve the model. Experimental results indicate that after applying this method, daily CO2 emissions from the road network remained below 600 kg, and the traffic flow in each section was balanced, demonstrating the feasibility and effectiveness of this new approach. [ABSTRACT FROM AUTHOR]

• The effectiveness of the two-stage transition procedure was determined through a simulator study. • The two-stage procedure improved the post-takeover performance during lane change. • Driver's gaze shifts from "busy and widespread" to "stable and centralised" during preparation. • The trend shift in gaze behaviour was observed for approximately 20–30 s after the preparation starts. • The detection of the trend shift can be useful in evaluating the driver's situation awareness. The feasibility of human-system transition in conditional automated driving systems remains an unresolved issue. These advanced systems necessitate support for planned transitions due to their frequent occurrence. Concerns in planned transitions include the lack of situation awareness and physical preparedness; specifically, drivers lack awareness of the traffic situation and their hands are occupied at the moment of transition. In this study, three types of two-stage transition procedures were investigated as potential solutions to these issues. In these procedures, the takeover request was issued after either a prenotification alert (notifying drivers of an impending takeover request) or a preparation request for takeover (requiring drivers to cease non-driving-related activities and monitor the traffic situation with both hands unoccupied for approximately 1 min before the transition). The effectiveness of combining road signs with the preparation request to facilitate takeover was also evaluated. The results indicate that a two-stage transition procedure incorporating a preparation request can mitigate potential hazards in planned transitions. This procedure resulted in shorter takeover reaction times and a lower rate of lane change failures post-takeover compared to the single-stage transition procedure. The addition of road signs to the preparation request further enhanced takeover support. However, the two-stage transition procedure was ineffective when only a prenotification alert was provided without a preparation request. Furthermore, the gaze behaviour of the drivers during the preparation period suggested the development of situation awareness for a safe and smooth takeover. The trend in gaze behaviour shifted from "busy and widespread" to "stably and centralised," occurring within approximately 20 s. These findings may contribute to the design of automated driving systems, the planning of road infrastructures, and the development of driver monitoring systems. [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 the rapidly evolving sphere of infrastructure management, early detection of road damage stands paramount for ensuring both safety and longevity. This research introduces an innovative technique for real-time road damage detection by leveraging the Mask R-CNN (Region-based Convolutional Neural Networks) approach. The primary objective was to discern varied forms of damages – from cracks to potholes, ensuring timely interventions and repairs. Utilizing a robust dataset comprising images of multiple road surfaces under different environmental conditions, the Mask R-CNN model was trained exhaustively. Results reveal a commendable accuracy rate, with the model distinguishing between minor aberrations and significant damages adeptly. A distinctive feature was the model's capability to operate in real-time, aiding in instant damage reporting. Furthermore, a comparative analysis with existing methods demonstrated a marked improvement in terms of both detection speed and precision. The findings suggest promising implications for urban planning and road maintenance. The integration of such an approach can revolutionize the manner in which road monitoring is traditionally undertaken, potentially resulting in substantial economic savings and enhanced safety measures. [ABSTRACT FROM AUTHOR]

This study aims to analyze the usage patterns of rest areas on highways and toll roads using a data mining approach, utilizing popular time information from Google Maps and web scraping techniques. We collected popular time data for rest areas along highways and expressways using web scraping technology and Google Maps API. The research methodology includes a data collection phase that extracts popular time information from Google Maps using web scraping techniques. The collected data was then analyzed using data mining techniques to identify road user visitation patterns, specifically at rest areas. The analysis included understanding the most popular times visitors visit rest areas and providing deep insights into road user preferences. The findings indicate specific time patterns during which visitors visit rest areas. These findings can have important implications in the planning and managing of road infrastructure, enabling improved services at rest areas according to road users' needs. They can also assist in determining appropriate locations for constructing new rest areas or necessary improvements to existing rest areas. [ABSTRACT FROM AUTHOR]

This study examines Vietnam’s state management of public investment in road construction. The development of road transport infrastructure is in extremely high demand as Vietnam undergoes an economic transition. Despite the fact that the effects of the COVID-19 pandemic have caused the economy to grow more slowly recently, public investment in the transport industry has increased significantly. Several modern, large-scale road works that have been finished and put into operation are having a significant positive impact on urbanization, connecting regions, and socioeconomic development. However, public investment in road transport construction still has a lot of issues that need to be fixed. The state’s weakness is one of the primary issues that reduces investment efficiency. This study analyzes the state management of public investment in road construction, according to the following contents: legal system and policies; formulation and implementation of public investment plans; inspection and supervision by state management agencies. The method of collecting secondary data and primary data (through sociological investigation) is used and combined with Statistical Package for the Social Sciences (SPSS) software to determine the influencing factors and find out the problems and their causes. Therefore, this study proposes a number of urgent and practical solutions to improve the state management of public investment in road construction and develop a synchronized road transport network. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Many developing countries have high traffic accident rates due to the mixed traffic environment in which bicycles and motor vehicles travel in the same lanes. This paper examines the perceived risk among road users in mixed traffic. A questionnaire survey was used to obtain demographic information and self-reported risk perception among cyclists, motorcyclists, and drivers. Statistical analyses, including the T-test and ANOVA, were used to examine the relevant differences in risk assessment among the groups. The results demonstrate that the groups differ significantly in sociodemographic characteristics, driving experience, and travel characteristics. For self-reported perceived risk, the respondents' perceptions of road surface conditions, the potential of daydreaming while operating a vehicle, and distraction from passengers or riding companions exhibited significant differences between the groups. The research findings could be beneficial as a guide for policymakers in planning road safety awareness to reduce risk in mixed traffic environments. [ABSTRACT FROM AUTHOR]

Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the "worn-out urban texture" criterion and a meager weight for "noise pollution". Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process. [ABSTRACT FROM AUTHOR]

Vehicle detection in remote sensing images is a crucial aspect of intelligent transportation systems. It plays an essential role in road planning, congestion control, and road construction in cities. However, detecting vehicles in remote sensing images is challenging due to their small size, high density, and noise. Most current detectors that perform well in conventional scenes fail to achieve better results in this context. Thus, we propose a quad-layer decoupled network to improve the algorithm's performance in detecting vehicles in remote sensing scenes. This is achieved by introducing modules such as a Group Focus downsampling structure, a quad-layer decoupled detector, and the GTAA label assignment method. Experiments demonstrate that the designed algorithm achieves a mean average precision (mAP) of 49.4 and operates at a speed of 3.0 ms on the RTX3090 within a multi-class vehicle detection dataset constructed based on the xView dataset. It outperforms various real-time detectors in terms of detection accuracy and speed. [ABSTRACT FROM AUTHOR]

In Japan, the mobility functions of principal arterial roads are hindered by lined-up roadside facilities and densely located signalized intersections. As one of the solutions, it is necessary to consider a functional hierarchy of road networks, based on the structure and traffic operations of each road. The purpose of this study was to propose an index to evaluate the degree to which the road network could be used hierarchically according to their mobility function, based on the location of the bases and the location of roads at each level of the hierarchy, and to propose a method to evaluate the road network using this index. To accomplish this, we proposed a four-level method for the evaluation of the functional hierarchy of road networks based on the derived index values. As a case study, we calculated the degree of hierarchy for the road networks in northern Mie and western Shizuoka. The results showed that the current evaluations of the functional hierarchy in these regions were low, and that introducing mid-level roads was necessary to improve the current situation. The evaluation method proposed in this study will enable us to examine the ideal network configuration for each road in the region to exhibit its mobility function as a preliminary step to studying solutions to various road network issues, such as ensuring the smooth movement between bases and safety. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the World Conference on Transport Research – WCTR 2023. • Proposed a four-level evaluation method for the functional hierarchy of road networks. • Focused on the utilization rate of road hierarchy according to trip length. • Effective for road planning from the viewpoint of hierarchical road use. [ABSTRACT FROM AUTHOR]

Traditional scenic road visual landscape assessment methods struggle to quantify drivers' subjective visual perceptions. This study aims to develop a new method to decipher Scenic Road Visual Landscape Evaluation (SRVLE) of motorists' visual field, reconciling the longstanding subjectivity–objectivity dichotomy in landscape quality research. By adopting binocular visual simulation and image segmentation, this paper conceptualizes a novel "non-scale semantic differential approach" to quantify landscape qualities across the dimensions of naturalness–artificiality (NA), diversity–coherence (DC), and openness–deepness (OD), constructing a three-dimensional visual landscape quality evaluation system. Taking the Nujiang Beautiful Road in Yunnan as a case study, the results show the following: (1) The three indicators reveal the scenic road's distinctive visual landscape characteristics, marked by high naturalness, coherence, and relative openness. (2) SRVLE is found to vary between the two driving directions and different sections. (3) The three-dimensional evaluation cube intuitively displays the comprehensive characteristics of landscape quality, providing a basis for scenic road planning. This method offers a new approach to resolving the subjective–objective divide in SRVLE and can assist road administrations in enhancing policy planning, construction, and management, thereby promoting the high-quality development of scenic roads. [ABSTRACT FROM AUTHOR]

This study aims to provide practical and applicable solutions to improve road safety on the Pasukan- Sindangkasih Road section in Majalengka Regency, West Java, by analyzing road visibility using empirical data and the Bina Marga method. The primary data consisted of looking for a literature review on the classification of road classes based on their functions, calculation of visibility, while for secondary data collected about the location map of this study. The results show that visibility planning can be improved to improve traffic efficiency and reduce the risk of accidents. The research could also consider the influence of different vehicle types and weather conditions on visibility requirements to refine the standards set by the Geometric Planning Procedures for Intercity Roads. It is also hoped that this research will increase awareness of the importance of visibility planning for overall road safety to avoid accidents. [ABSTRACT FROM AUTHOR]

High-precision step feature lines play a crucial role in open-pit mine design, production scheduling, mining volume calculations, road network planning, and slope maintenance. Compared with the feature lines of the geometric model, step feature lines are more irregular, complex, higher in density, and richer in detail. In this study, a novel technique for extracting step feature line from large-scale point clouds of open-pit mine by leveraging structural attributes, that is, SFLE_OPM (Step Feature Line Extraction for Open-Pit Mine), is proposed. First, we adopt the k-dimensional tree (KD-tree) resampling method to reduce the point-cloud density while retaining point-cloud features and utilize bilateral filtering for denoising. Second, we use Point Cloud Properties Network (PCPNET) to estimate the normal, calculate the slope and aspect, and then filter them. We then apply morphological operations to the step surface and obtain more continuous and smoother slope lines. In addition, we construct an Open-Pit Mine Step Feature Line (OPMSFL) dataset and benchmarked SFLE_OPM, achieving an accuracy score of 89.31% and true positive rate score of 80.18%. The results demonstrate that our method yields a higher extraction accuracy and precision than most of the existing methods. Our dataset is available at https://github.com/OPMDataSets/OPMSFL. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, the total mileage and line density of China's highways have increased year by year. It is estimated that by 2026, the total mileage of national highways will exceed 5.74 million kilometers. An efficient highway network is crucial for a country's move towards traffic modernization, economic development, and improvement of people's livelihoods. The highway route is the basic structure of the highway network, determining whether the highway can maximize its economic and traffic effects. Therefore, research on highway route design holds significant engineering value. Highway planning is a complex issue involving a wide range of factors. Especially with the increasing awareness of environmental protection, it is necessary to consider natural problems in addition to technical and economic costs. Methods: This paper first points out the important position of highway route research in highway rules, summarizes the research status at home and abroad, and lists conventional highway planning measures. It then discusses the optimization design based on vehicle running speed and driver comfort, and introduces related theories of deep learning and their applicability to multiobjective optimization problems. Finally, aiming at the problem of highway route planning influenced by many factors, a deep learning strategy based on a multiobjective genetic algorithm is adopted, and its multi-objective optimization model and optimization objective function are presented. Results: The proposed deep learning strategy based on a multi-objective genetic algorithm is a new attempt to combine genetic algorithms with deep learning in highway route planning to solve its multi-objective comprehensive optimization problem. The results indicate that this strategy can determine the best route scheme by optimizing technology while satisfying external constraints, thereby achieving the optimal solution in terms of technology and economy, and improving the overall efficiency and sustainability of the highway. Discussion: This study provides a reference for the application of deep learning and other nonlinear multi-objective optimization research, aiding the research on highway route optimization design. By combining multi-objective genetic algorithms with deep learning, it effectively solves various multi-objective nonlinear problems, providing new methods and tools for highway route planning. [ABSTRACT FROM AUTHOR]

Traffic volume studies are crucial for understanding vehicle quantity, movements, and classifications at specific sites. This study aims to establish a correlation between flow rate and density, providing insights into traffic flow characteristics such as density, velocity, and flow, essential for effective road design. The proposed method combines automated (mobile phones and cars) and manual counts on separate sheets, offering a compelling alternative to traditional traffic study methods. The collected data can be utilized for various purposes, including estimating fuel consumption, road pricing, road user cost, and planning road network improvements. Acquiring precise traffic data using cost-effective, low-tech solutions is vital for comprehending urban traffic dynamics. Evaluating collected data, which encompasses traffic parameters like flow, density, and speed, is crucial for informing urban road design and planning. For instance, traffic flow indicates throughput, density reflects traffic conditions, and speed is essential for calculating travel times. This investigation focused on King Fahad Road in Al-Ahsa, Saudi Arabia, which was chosen among three alternative urban roads based on varying traffic conditions. Smartphones and cars were used to collect traffic data during weekday evening peak hours, analyzing the interrelation between traffic flow, density, and vehicle speed. Manual traffic counts were conducted to determine measured density and speed, which were then used to estimate calculated density. Additionally, a statistical t-test was performed to validate measured density against calculated density at a 5% significance level. The data collection systems utilized in this research provide a cost-effective solution, considering capital, operational, and maintenance expenses while remaining portable and non-intrusive to road users during surveys. These characteristics make the system a practical choice for implementation in developing nations where resources are constrained, rendering costlier alternatives economically unfeasible. [ABSTRACT FROM AUTHOR]

Technology implementation, particularly the use of Unmanned Aerial Vehicles (UAVs) and photogrammetry, is being employed in road works for regional and road planning. The current study deploys aerial photographs and data processing along with software, like Agisoft Metashape, PCI Geomatica, Global Mapper, and Autocad Civil 3D as an efficient and effective way to generate digital maps and perform geometric road reviews. The accuracy test of CE90 performed for horizontal accuracy was 0.003 m and the LE90 carried out for vertical accuracy was 0.006 m. This accuracy level is valuable for road planning, ensuring that the data utilized for decision-making are reliable and precise. The study focused on Wori Street spanning from Pandu to Kima Atas Street Manado, covering the section from Sta. 0+000 to Sta. 5+225, which is a collector road with a designated speed of 50 km/h. Among the 16 bends analyzed, 11 met highways’ standards for the collector road class, certifying compliance with safety guidelines. Furthermore, the existing road slope conforms to standard requirements, remaining below 8%. This adherence to safety criteria is vital for the design and operation of safe roads. [ABSTRACT FROM AUTHOR]

Automated maintenance and motion planning for unpaved roads are research areas of great interest in the field robotics. Constructing such systems necessitates the development of surface maps for unpaved roads. However, the lack of distinctive features on unpaved roads degrades the performance of light detection and ranging (LiDAR)‐based mapping. To address this problem, this paper proposes three‐dimensionalized feature‐based LiDAR‐visual odometry (TFB odometry) for the online mapping of unpaved road surfaces. TFB odometry introduces a novel interpolation concept to directly estimate the three‐dimensional coordinates of the image features using LiDAR. Furthermore, LiDAR intensity‐weighted motion estimation is proposed to effectively mitigate the effects of dust, which significantly impact the performance of LiDAR. Finally, TFB odometry includes pose graph optimization to efficiently fuse global navigation satellite system data and poses estimated from motion estimation. Through field experiments on unpaved roads, TFB odometry demonstrated successful online full mapping and outperformed other simultaneous localization and mapping methods. Additionally, it demonstrated remarkable performance in accurately mapping road surface anomalies, even in dusty regions. [ABSTRACT FROM AUTHOR]

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×106) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads. [ABSTRACT FROM AUTHOR]

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users' travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications. [ABSTRACT FROM AUTHOR]

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Road damage can be attributed to several factors, including high traffic volume, excessive vehicle loads, subgrade bearing capacity, and climatic conditions. Common. methods for road pavement planning include the Road Note 31 method, AASHTO 1993, Component Analysis, and the 2017 Pavement Design Manual. This study aims to review pavement thickness and identify the damage levels on the Palingi-Lansilowo road in Konawe Islands Regency. The analysis results indicate that 50% of the road pavement falls into the Severely Damaged category, while the remaining 50% is classified as Moderately Damaged. The dominant causes of road damage are climatic factors and subgrade bearing capacity. The pavement thickness determined using the Component Analysis method includes subbase course of Class C Sand and Gravel (15 cm), base course of Class C Crushed Stone (15 cm), and surface course of Asphalt Concrete (5 cm). This review is based on traffic survey results from 2023. Therefore, it is recommended to reassess the road condition and pavement thickness in the next 10 years, considering urban development and traffic growth rate. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Educational Psychological Studies (EPS) is the property of Refaad for Studies, Research & Development 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.)

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]

Identifying the relationship between forest roads and wildfires in forest ecosystems is a crucial priority to integrate suppression and prevention within wildfire management. In various investigations, the interaction of these elements has been studied by using road density as one of the anthropogenic dependent variables. This study focused on the use of a broader set of metrics associated with forest road networks, such as road density, the number of links (edges), and access percentage based on two effect zones (road buffers of 75 m and 97 m). These metrics were employed as response variables to assess forest road network suitability in relation to wildfires, specifically the number and size of fires (2000–2021), using the Apulia region (Italy) as a case study. In addition, to enhance the comprehensive understanding of road networks in forest ecosystems in relation to wildfires, this study considered various affecting factors, including land-cover data (forest, maquis, natural grassland), geomorphology (slope, aspect), vegetation (Normalized Difference Vegetation Index (NDVI)), and morphometric indexes (Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI)). We used geographically weighted regression (GWR) and ordinary least squares (OLS) to analyze the interaction between forest road metrics and dependent variables. Results showed that the GWR models outperformed the OLS models in term of statistical results such as R2 and the Akaike Information Criterion (AICc). We found that among road metrics, road density and number of links do not effectively demonstrate the correlation between roads and wildfires as a singular criterion. However, they prove to be a beneficial supplementary variable when considered alongside access percentage, particularly within the 75-m buffer zone. Our findings are used to discuss implications for forest road network planning in a broader wildfire management analysis. Our findings demonstrate that forest roads are not one-dimensional and static infrastructure; rather, they are a multi-dimensional and dynamic structure. Hence, they need to be analyzed from various perspectives, including accessibility and ecological approaches, in order to obtain an integrated understating of their interaction with wildfire. [ABSTRACT FROM AUTHOR]

Forest road planning incorporates crucial strategies essential for sustainable timber extraction, minimizing environmental impacts and ensuring safe access to forest regions. This paper presents a literature review conducted to examine publications related to forest roads to ultimately filter and evaluate the information on the incorporation of classification systems in the planning strategies for forest roads. Using the Scopus database to gather publications, various data points were mapped, such as temporal distribution of publications, citation metrics, keyword inputs and other bibliometric markers. Through the bibliometric visualization software VOSviewer 1.6.19, this study determined that over the years, the forest road research subject has gained increasing attention with different shifts in focus. However, upon investigating the application of systems of classification implemented on forest road networks, it became evident that this approach is not a recent innovation and there is scarce documentation and development regarding this planning strategy. The information collected also reveals that this type of classification can be found more in technical documents, like design manuals. This outcome suggests that the subject under study is not relevantly covered in forest-related journals, but rather by institutions aiming to identify region-specific needs and develop corresponding systems accordingly. [ABSTRACT FROM AUTHOR]

Quality control (QC) is an essential component of quality assurance (QA) programs used to monitor and ensure the highway construction projects' quality. After receiving the contractor's QC plan, state departments of transportation (DOTs) conduct review and acceptance, monitor the QC process, and ensure construction quality by performing inspection activities. Given that state DOTs are increasingly implementing alternative contracting methods (ACMs), including construction manager/general contractor (CM/GC), design-build (D-B), and public–private partnerships (P3), it is essential to consider the impact of shifting more responsibility to the contractor on quality management activities, including developing and monitoring QC plans throughout the project development. The objective of this paper was to investigate the implementation of QC plans for administering QA specifications in highway infrastructure projects delivered using ACMs. The data collected from a national survey questionnaire of 43 DOTs in the US and structured interviews with seven DOT representatives were employed to investigate this research's objective. As a result, eight typical requirements of QC plans in ACM projects were identified in terms of certification and qualification of QC personnel and process control. Additionally, the standard of care of QC staff in ACMs was not found to be higher than that in design-bid-build. Nine risk factors related to the oversight of QC plans in D-B, CM/GC, and P3 highway projects were identified. There was no statistical difference in the nine risk scores among D-B, CM/GC, and P3 projects. The findings from seven case studies provide insights regarding the implementation of QC plans for ACM highway projects. This study contributes to the body of knowledge by advancing the understanding of how state agencies execute and oversee QC activities in ACM projects. The outcomes of this study may help state DOTs identify optimal QC practices to administer the QA process in ACM projects. This study investigates the current state of practice in implementing QC plans to manage and improve QA processes for highway projects delivered using ACMs such as CM/GC, D-B, and P3. This study synthesized and documented typical QC requirements used by state DOTs in reviewing, accepting, and monitoring contractor compliance with their QC plans. Additionally, the descriptive and inferential testing results of nine risk factors in implementing QC plans in highway projects found in this study enable highway agencies to effectively justify their monitoring process of the QC plan implementation. Further, the results from seven case studies provide insights and key lessons learned regarding (1) the guidance and templates for developing a QC plan; and (2) risk perceptions in managing the QC plan implementation in ACM highway projects that can benefit both the contractors and state DOTs. The outcomes of this study also shed light on exploring the optimal allocation of responsibilities between state DOTs and contractors in implementing QC activities to administer QA programs in ACM highway projects. [ABSTRACT FROM AUTHOR]

An essential process in earthworks is projecting effective routes for material allocation. In highway construction, haul road planning is a challenge for practitioners once there are few consolidated methodologies to rationally select existing haul routes or construct new ones. Some authors indicated geographic information system (GIS)-based methods for routing through least-cost algorithms. However, most proposed methodologies overlook the logistics related to allocations, not considering supply–demand relations in their criteria. Consequently, through a case study in Brazil, we present an earthwork optimization system that integrated established mathematical programming techniques for allocation and a GIS-based algorithm for haul road construction. In summary, we formulated a model capable of selecting different routes based on their impact on earthworks logistics cost. Thus, we built several paths between source and demand points utilizing two methods. First, based on recent literature, we used a GIS-based least-cost algorithm using the measuring attractiveness by a categorical-based evaluation technique (MACBETH) as a multicriteria support system incorporating multiple economic and environmental parameters. Second, we built routes based on Euclidian distance between the same source and demand points, analyzing if the savings in operational costs would payoff additional expenses of choosing a shorter route. After applying our methodology to the case study, we observed that the proposed procedure presented a lower budget in a reasonable processing time compared with the original project. Therefore, we inferred that the strategy of proposing path alternatives can be significantly efficient when applied to highways. Based on the results, we also found opportunities for improvement of our GIS step once it generated long paths when the distance between source and demand was short. [ABSTRACT FROM AUTHOR]

Roads are crucial public spaces in cities and serve as a window to showcase the city’s characteristics. They serve not only as a means of urban transportation but also as a crucial spatial carrier for urban communication activities and a significant location for meeting the increasing demands of people for a better quality of life. The current road infrastructure prioritizes the right of way for cars, neglecting the design of sidewalks and green belts within the road’s boundaries, and extension spaces between buildings and boundary lines of roads. There is a pressing need to improve street space in response to the demand for development transformation and the creation of a warmer city. This paper summarizes common problems in current road spaces, draws on the experience of excellent urban road spaces in foreign countries, discusses new ideas for the refined design of road spaces based on the transformation of road planning concepts, and suggests a reference standard for guiding detailed design. Simultaneously, the review of road construction will incorporate the detailed design of road space to enhance the role of planning in guiding and controlling the construction of road works. [ABSTRACT FROM AUTHOR]

The traditional aim in transportation planning is to maximise gains associated with vehicular travel distances or times, indirectly prioritising populations that live near existing or proposed roads—remote populations that first require hours of walking to reach roads are overlooked. In this paper, rural roads optimisation is performed using a new model that estimates proposed roads' accessibility gains, considering reductions in vehicular travel time and reductions in walking time required by remote populations to reach them. This ensures that even the most remote populations that may benefit from new roads are included in their evaluation. When presented with a large number of proposed roads and the requirement of determining a plan within a suitable budget, it is often infeasible to construct all proposed roads. In such instances, subsets of well-performing road-combinations that are evaluated with respect to multiple objectives need to be identified for analysis and comparison–for which multi-objective optimisation approaches can be employed. Traditional optimisation approaches return a small number of road-combination plans only, limited to user-specified budget levels and objective weight sets. This paper presents an innovative heuristic solution approach that overcomes such limitations by returning thousands of well-performing solutions scattered across a budget span, and not limited in number to user-specified objective weight sets at fixed budget levels. The heuristic is employed along with a more traditional weighted-sum integer-linear programming approach to determine high-quality road-combination plans selected from 92 roads recently proposed for construction in Nepal's remote Karnali province. Using these two approaches with inputs from the new multi-modal accessibility model, it is illustrated how rural roads planning can be performed to the benefit of rural populations regardless of their proximity to roads. New planning and analysis benefits of the heuristic are demonstrated by comparing its solutions to those determined by the weighted-sum approach, providing a level of detail and sophistication not previously possible for rural roads planning and analysis. [ABSTRACT FROM AUTHOR]