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Planning the renewal and repair of aging bridges is a pressing issue for road managers in Japan. The refurbishment of old structures in urban expressways is fraught with difficulties due to their constant use and central location within Tokyo metropolitan area. Currently, a long-term bridge replacement project is being executed in Tokyo by Metropolitan Expressway Co., Ltd. As part of this project, the Daishi Bridge expressway, which was opened in 1967 in southern Tokyo, was renewed in 2015 owing to multiple fatigue cracks throughout its superstructure. The damaged, aging bridge was taken apart and a new bridge spanning 292 m and weighing approximately 4,500 tons (including attachments) was erected over a short period of merely two weeks. The expressway, which supports heavy traffic, was temporarily closed during this period. Various measures were devised and implemented to complete the renewal project as quickly as possible, while accounting for special construction conditions for bridges over rivers. This report provides additional details of this renovation project. [ABSTRACT FROM AUTHOR]

In the context of autonomous driving on expressways, the issue of ensuring safe and efficient ramp merging remains a significant challenge. Existing systems often struggle to accurately assess the status and intentions of other vehicles, leading to a persistent occurrence of accidents despite efforts to maintain safe distances. This study proposes a novel spatiotemporal cooperative control approach integrating vehicle-road coordination to address this critical issue. A comprehensive methodology is developed, beginning with the calculation of safe distances under varying spatiotemporal conditions. This involves considering multiple factors, including vehicle speed differentials, positioning errors, and clock synchronization errors. Subsequently, an advanced vehicle conflict risk evaluation model is constructed. By incorporating collision acceleration and emergency acceleration as key parameters, this model offers a more accurate and detailed evaluation of potential risks during the ramp merging process. Based on the calculated safe distances and conflict risk evaluations, a mainline priority coordinated control method is formulated. This method enables the pre-planning of vehicle trajectories, effectively reducing conflicts among vehicles. Through rigorous simulations using diverse traffic volume and speed scenarios, the efficacy of the proposed strategy is validated. The results demonstrate remarkable improvements, with the average delay time reduced by an impressive 97.96% and fuel consumption decreased by 6.01%. These outcomes indicate that the proposed approach not only enhances the speed of vehicle merging but also significantly reduces latency and fuel consumption, thereby enhancing the overall performance of ramp merging operations.

Abstract The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12 s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway.

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Predicting conflict risk from a microscopic perspective is crucial for understanding traffic dynamics and preventing crashes. However, previous studies often missed this perspective and lacked methods for applying it in spatial contexts. This study introduces a method that predicts conflict risk using both spatial and temporal segments, using real-time data for practical implications. Employing a hybrid model that combines long short-term memory (LSTM) and a convolutional neural network (CNN), the research utilizes three hours of vehicle trajectory data from the Shanghai Inner Ring Expressway. Specifically, a 10 s data window is employed to forecast conflict risk within the subsequent 3 s, 4 s, and 5 s intervals, considering a 10 m spatial section. Modified time to collision (MTTCL) is adopted to assess conflicts between vehicles. The study incorporates 12 factors to predict conflict risk, prioritizing them based on SHAP values. The results highlight that most conflicts occur within the 3 s prediction horizon, with prediction accuracy decreasing as the forecast horizon extends. Notably, the study identifies key contributing factors, including the speed difference between vehicles, the number of lane changes, inter-vehicle distance, distance variability, and the number of cars. Spatially, the research identifies entry and exit ramps and their surrounding areas on the expressway as hotspots for sideswipe conflicts. [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]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & 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.)

Distracted driving is a significant factor influencing traffic accidents. Current research primarily focuses on identifying distracted behaviors among drivers, while the investigation into accident mechanisms remains limited. To improve highway driving safety, the coupling relationships among drivers, vehicles, roads, and environments were explored, based on traffic accident data in a specific region. Utilizing association rule algorithms, seasonal variations in accident occurrences were analyzed, and potential patterns of distracted driving accidents were explored from multiple perspectives. The results show that accidents caused by driver distraction exhibit certain temporal and spatial patterns, with a higher frequency during weekday commuting peak hours and identifiable accident-prone locations. Regardless of season, distracted driving accidents tend to occur more frequently on straight road segments under favorable weather conditions. The severity of accidents predominantly involves property damage or injuries, with a low proportion of fatalities. Differences in accident causation and occurrence patterns are observed between summer and winter distracted driving accidents. These findings provide valuable insights for traffic managers to develop innovative policies and enhance road safety. [ABSTRACT FROM AUTHOR]

The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12 s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway. [ABSTRACT FROM AUTHOR]

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks. [ABSTRACT FROM AUTHOR]

High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods. [ABSTRACT FROM AUTHOR]

Abstract Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

An accurate, fast and real-time expressway automatic incident detection (AID) algorithm can not only reduce the burden of expressway management personnel, but also increase the safety and reliability of expressway travel. Aimed at the accuracy from surveillance video, FairMOT is initially transferred from detecting humans to abnormal incidents for expressways, while UA-DETRAC vehicle dataset is employed to train and evaluate YOLOv3 + DeepSORT, YOLOv5 + DeepSORT and JDE. The comparison on evaluation indexes demonstrates that FairMOT improves vehicle tracking effect, and the accuracy is better than the current mainstream algorithms listed above. In the case study, the length change of the track vector is employed to determine whether the car is stopped, and the relationship between the track vector and the center dividing line vector is adopted to decide whether the vehicle is in reverse. The real surveillance video verifies that the proposed FairMOT can detect parking and reversing quickly and accurately. The results can provide an alternative and benefit the automatic incident detection. [ABSTRACT FROM AUTHOR]

Copyright of Rozwój Regionalny & Polityka Regionalna is the property of Uniwersytetu im. Adama Mickiewicza (IH UAM) 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.)

An integrated construction station for an expressway is characterized by complex carbon emission sources and high carbon emission intensity. Conducting carbon emissions accounting makes possible a comprehensive understanding of these characteristics, enabling targeted and guided carbon reduction efforts, which is crucial for advancing the low-carbon development of expressway construction. This paper, based on an in-depth analysis of the carbon emission structure during the operational period of an integrated expressway construction station identifies, as calculation boundaries, eight categories: residential areas, station transportation, mixing stations, precast beams, steel bar yards, artificial carbon emissions, chemical reactions during construction, and construction conditions. The study adopts a "bottom-up" approach to carbon emission measurement and constructs a carbon emission model for the production and operational period of the integrated construction station, based on the carbon emission factor method. Using the SG-2 section of the Fengqiu to Xiuwu stretch of the Changxiu Expressway as an engineering case, carbon emissions accounting for each component of the integrated construction station's operational period was conducted, and the results were compared with the station's monitoring system. High-precision characterization and calculation of total carbon emissions, as well as emissions from each process and piece of equipment during the operational period, were achieved. The results indicate that: (1) the relative error between the overall calculation results and actual monitoring is 3.6%, verifying the model's accuracy; (2) the monthly carbon emissions of the integrated construction station during the operational period reached 72.15 tons; (3) there is a significant difference in carbon emissions among the different processes, with the highest emissions coming from transportation and residential areas, accounting for 43.4% and 23.7%, respectively. Therefore, electrification of transportation equipment could significantly reduce the overall carbon emissions of the integrated construction station. [ABSTRACT FROM AUTHOR]

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout. [ABSTRACT FROM AUTHOR]

Seeking a straightforward and efficient method to predict expressway traffic noise, this study selected three expressway segments in Guangdong Province, China and conducted noise monitoring at ten different sites along these expressways. Data analysis revealed that the mean sound levels and standard deviations were significantly positively and negatively correlated with traffic volume, respectively, and the frequency distribution of sound levels closely resembled a normal distribution. A probability prediction model for expressway traffic noise, based on a normal distribution, has been constructed utilizing these characteristics. The mean and standard deviation of the model were determined using a linear regression method, and the relationship between the mean, standard deviation, and various noise evaluation indices was derived from the characteristics of the normal distribution. The proposed model enables the direct prediction of the statistical frequency distribution of sound levels and various noise evaluation indices. Despite using only two five-minute segments of monitoring data for training, the model's average prediction error for Leq, L10, L50, and L90 was only 1.06, 1.07, 1.04, and 1.32 dB(A). With increased sample data for modeling, the model's predictive accuracy notably improved. This study provides a highly effective predictive tool for assessing traffic noise for residents near expressways. [ABSTRACT FROM AUTHOR]

The construction of cast-in-place concrete curved beams in the soft foundation area faces problems such as weak foundation bearing capacity, many uncontrollable factors, and difficulty in controlling the alignment of the bridge. Relying on the cast-inplace concrete curve beam project of the Dazong Lake Interchange C ramp bridge of Fuli Expressway, the reinforcement measures for the soft foundation in the bridge construction area were proposed. The curved beam model was established through the finite element software Midas Civil, the changing rules and influencing factors of the vertical and radial displacements of the curved beam were analyzed, and construction measures for linear control of curved beams were proposed. The results show that the maximum deflection of prestressed continuous box girder caused by shrinkage creep, prestress and dead load is 2 mm, 13 mm and 12. 5 mm respectively, the change in prestress has the greatest influence. The plane radial displacement of the curved beam is more sensitive to the temperature effect, and the radial displacement caused by the system temperature rise reaches 14. 11 mm. The monitoring of the box girder shows that the elevation deviation of the concrete box girder meets the specification requirements, and the monitoring results are in good agreement with the finite element simulation values. The research results can provide reference for similar projects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology 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 small-spacing interchanges (SSI) appear with the improvement of the expressway network. To investigate the speed and mental workload characteristics in the SSI and acquire the mechanism of the influence of speed on the drivers' workload, 37 participants were recruited to perform a field driving test. Each driver performed four driving conditions (i.e. ramp-mainline, mainline-ramp, mainline driving, and auxiliary lane driving). The speed and drivers' electrocardiogram (ECG) data were collected using SpeedBox speed acquisition equipment and PhysioLAB physiological instrument. The heart rate increase (HRI) index was used to analyse the drivers' mental workload regularity. The relationship model between speed and HRI was developed to examine the impact of speed on HRI. The results show that the speed variation in the SSI displayed two patterns: 'decrease - increase and continuous decrease.' The drivers' HRI variation presented four patterns: 'convex curve, continuously increasing, continuously decreasing and concave curve'. SSI's influenced area length is given based on the speed and HRI variation regularity. HRI is significantly higher when driving in the ramp-mainline condition in the SSI than when driving in other conditions, indicating that drivers are more nervous when merging with the mainline traffic. HRI increases significantly in the first 50% of the weaving area in four driving conditions, indicating that vehicle weaving greatly influences the drivers' mental workload. A positive correlation exists between vehicle speed and drivers' HRI without interference from other vehicles and road alignment. The shorter spacing of the interchange will result in a more difficult driving task for the drivers. This study shows that drivers have the highest mental workload in ramp-mainline driving condition at small-spacing interchanges. The first half of the weaving area is the area where drivers' mental workload increases significantly, and is a high-risk section for small-spacing interchanges. This study can provide a reference for the revision of the allowable minimum interchange spacing in the corresponding specification, and the calibration of the simulation test parameters for similar scenarios. [ABSTRACT FROM AUTHOR]

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.)

Accurate prediction of electric vehicle (EV) charging loads is a foundational step in the establishment of expressway charging infrastructures. This study introduces an approach to enhance the precision of expressway EV charging load predictions. The method considers both the battery dynamic state-of-charge (SOC) and user charging decisions. Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow features. A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow. An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values. Based on this foundation, an EV charging decision model was developed which considers expressway node distinctions. EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model. Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions. Finally, the proposed method is applied to a case study of the Lian-Huo expressway. An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways, thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.

São Paulo's Minhocão (Big Worm) is a 3.5 km elevated expressway that cuts across a dense part of the central city. Opened in 1971, it was controversial from the start, and widely held responsible for the decline of the city's historic centre in the 1970s and 1980s. However, it has been gradually tamed over the years, first closed to traffic at night, and then at weekends and on holidays, becoming an impromptu park, the Parque Minhocão, which has had official status since 2014. Those informal closures have been accompanied by numerous architectural schemes over the years to make the Minhocão a permanent park on the lines of New York's High Line. The Parque Minhocão in its present condition represents a stand-off between various interest groups, all of whom have claims on it as public space. The paper explores the history of the Parque Minhocão since 1969 through different forms of visualisation, arguing that its present condition, however imperfect, keeps multiple and contradictory interests in balance. [ABSTRACT FROM AUTHOR]

The prediction of expressway vehicle trajectories is a crucial aspect in the development of intelligent expressways. This paper proposes a novel approach, namely the W-GRU-Attention (WGA) model, which utilizes ETC transaction data to predict trajectory selection based on historical traffic paths and previous passed gantry information. In this study, we apply the concept of word embedding models to extract contextual semantics from the historical trajectories on expressways. Additionally, we introduce an average pooling technique for converting the historical vehicle trajectory into a fixed-length Historical Trajectory Vector (HTV), enabling us to capture dependency relationships within experience paths. By combining proximity gantry vectors during transit, we accurately predict the next gantry location. Finally, our proposed method is evaluated using a real-world expressway ETC dataset. It achieves an impressive accuracy rate of 96.14% in capturing the relationship between historical trajectories and adjacent gantries, surpassing other models in path prediction. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

For temperate desert ecosystems, understanding the maintenance mechanisms of biodiversity has become a central concern of policymakers, practitioners, and academicians, especially in the context of human disturbances. However, most of the past desert research is based on a single survey and single functional traits or phylogeny, and may lead to incomplete estimates of community assembly. To assess climate factors, soil factors, and human interference in the processes of dispersal assembly, filtering, and limiting similarity in community assembly, we selected 50 sites along the Beijing‐Xinjiang Expressway and conducted plant surveys at sites 0.5, 1.0, and 1.5 km away from the expressway twice, one in spring and one in autumn, and collected 31 functional traits and phylogenetic information of 74 species. We found that incomplete detection can lead to bias in biodiversity estimation due to seasonal surveys. Road disturbance at different distances had no significant effect on species composition and community assembly processes. The trade‐off between dispersal limitation and environmental filtering is the process of maintaining desert plant diversity. Precipitation directly determined the dispersal limitation and environmental filtering. Soil pH mediated the effect of temperature on biotic interaction in the dimension of phylogeny, while soil pH and organic carbon acted as intermediate factors affecting biotic interaction in functional traits. Our findings suggest that climate change, notably precipitation, has a more pronounced impact on desert plant communities than human disturbances. A multifaceted approach considering functional traits and phylogeny across seasons is essential for understanding species coexistence in desert ecosystems. [ABSTRACT FROM AUTHOR]

The formulation of expressway traffic policy needs to accurately predict the traffic volume. Based on this, Long short-term memory (LSTM) machine learning model was selected to study it. Aiming at the problem of parameter determination in LSTM model,Particle swarm optimization (PSO) algorithm was selected to improve it. At the same time for the PSO algorithm in the particle position update problem, the meaning of each parameter in the formula for the entry point for improvement, the PSO algorithm formula for the original static inertia weights and learning weights into the iteration number and the particle position will change with the change of the dynamic value, so as to achieve the purpose of searching for the purpose of improving accuracy. Based on this, the improved PSO-LSTM model is constructed. Finally, through the calculation and analysis of an example, the working days and rest days of expressway are predicted respectively. The results show that the root mean square error of the evaluation index is increased by 12. 19% and 10. 97%, the average absolute error is increased by 17. 06% and 15. 17%, and the square absolute percentage error is increased by 24. 56, respectively. The algorithm shows that the improved PSO-LSTM model plays a significant role in traffic volume forecasting, and has strong anti-interference ability. It can provide a more reliable basis for the rational formulation of policies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology 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.)

With rapid urbanization, traffic growth has accelerated in some cities in China. Due to strict urban construction land utilization policies, many high-rise apartment buildings have been constructed adjacent to expressways. To better understand the impact of urban traffic noise on the residents of high residential buildings adjacent to expressways and the differences in noise impacts on different floors, on-site noise monitoring and questionnaires for building residents have been conducted. The characteristics of traffic noise were analyzed based on the measured data, and factors, including time periods and building floors, were considered. According to the results of the questionnaire survey, 56.06% of the male respondents and 54.55% of the female respondents think that the impact of traffic noise on high-rise buildings is "high" or "very high"; 50.53% of the respondents who were in "good" or "very good" condition thought that the traffic noise has a "significant impact" or "very significant impact" on their sleep and daily life. In addition, 25% of respondents living on floors 4–10 and 62.5% of respondents living on floors 11–20 considered the traffic noise to have a "significant impact" or "very significant impact" on their sleep and daily life. The on-site noise monitoring results show that the noise levels (LAeq) outside the windows of the studied buildings remain significantly elevated, with daytime noise on working days ranging from 56 to 70 dB(A), and on weekends ranging from 50 to 65 dB(A). During the four time periods on weekdays from 7 a.m. to 9 a.m., 11 a.m. to 1 p.m., 5 p.m. to 7 p.m., and 10 p.m. to 12 a.m., the average LAeq levels on floors 11–20 are higher than those on floors 4–10 by 4.04 dB(A), 4.92 dB(A), 4.06 dB(A), and 2.67 dB(A), respectively. Similarly, during these time periods on weekends, the levels on floors 11–20 are higher than those on floors 4–10 by 4.96 dB(A), 6.32 dB(A), 5.28 dB(A), and 5.24 dB(A), respectively. This indicates that floors 4–10 of the building experience relatively lower noise levels, while floors 11–20 are subjected to comparatively higher levels of noise disturbance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology 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.)