Stretch Of Highway Research Articles

A Multicriteria Approach for Landslide Hazard Zonation in the Lesser Kumaun Himalaya

ABSTRACTThe socioeconomic prosperity of a region is intricately tied to its infrastructure development, particularly the connectivity provided by highways. Preserving the economic well‐being of an area demands meticulous planning and the execution of construction projects with minimal risk. Landslide hazard zonation (LHZ) is a crucial tool for assessing the likelihood of landslide occurrences in specific regions. This study delves into the LHZ of a 171.2 km2 area, focusing on critical causative factors such as slope inclination and aspect, slope mass material, Land Use/Land Cover (LULC), distance from fault lines, roads, and drainage systems. The classification resulted in four distinct zones along the National Highway (NH‐109) stretch from Bhowali to Almora. Notably, the moderate‐hazard zone encompasses 60.4% of the area, followed by the high‐hazard zone at 24.4%, the low‐hazard zone at 11.9%, and the very high‐hazard zone at 3.3%. To enhance the reliability of the study, 63 previously identified landslide locations were marked based on diverse sources, including field studies and landslide inventory data from the Geological Survey of India (GSI). In the validation process, 12 past landslide occurrences were situated within the very high‐hazard zone, 32 in the high‐hazard zone, 18 in the moderate‐hazard zone, and only one in the low‐hazard zone. The receiver operating characteristic (ROC) curve yielded a commendable quality with a % area under the curve (AUC) of 71.2%. In summary, this research underscores the importance of LHZ in assessing and mitigating landslide risks along a stretch of the NH‐109 corridor. The findings provide valuable insights for informed decision‐making in infrastructure development and risk management, contributing to the sustainable growth and protection of the region's livelihood.

Evaluation of Flexible Pavement Friction Coefficients: A Case Study of East-West Highway Near Mahendranagar City, Nepal

Friction is a crucial factor in highway engineering, as it affects the traction between a vehicle's wheels and the road surface. This traction significantly influences the movement, speed, and efficiency of vehicles on highways. The coefficient of friction depends on various factors, such as pavement materials, traffic volume, road age, temperature, and weather conditions. The Skid Resistance Test Method is adopted for the study, which involves using a Portable Skid Resistance Tester to measure the British Pendulum Number (BPN) and then calculating the coefficient of friction by dividing the BPN by 10. An evaluation of a 5 km stretch of the East-West highway near Mahendranagar Bazaar revealed a longitudinal coefficient of friction of 0.354 and a lateral coefficient of friction of 0.184 under both dry and wet conditions. These findings were relevant for design speeds ranging from 60 to 80 km/h and considered an Annual Average Daily Traffic (AADT) of 10,321 Passenger Car Units (PCU) during the year 2023/2024. The assessment also indicated a variance of 0.19% and a poor International Roughness Index (IRI) value of 2.05. These aggregates had specific gravities ranging from 2.6 to 2.67 and were sized at 13.2 mm. The application rate for the aggregates ranged from 12 to 15 kg per square meter. It is important to note that a higher coefficient of friction leads to reduced vehicle efficiency. Thus, it is crucial to carefully consider the coefficient of friction values and detailed specifications to ensure traffic safety and road integrity.

Impact of BR-282 highway on the mortality of wild felids in the extreme west of Santa Catarina, Brazil: threat to conservation

Among human impacts, road kills are an important source of mortality for wild animals, as highways and roads result in the fragmentation of natural habitats. Felids are strongly negatively impacted and, although roads are mentioned as potential threats, few studies address how felids interact with these environments. We aimed to describe the richness and composition of wild felids, analyze the spatial and temporal effect, and identify road-kill hotspots of these species on a stretch of the BR-282 highway between the cities of São Miguel do Oeste and Paraíso, in the extreme west of the State of Santa Catarina, southern Brazil. From December 2021 to November 2022, we traveled a 28-km stretch weekly to collect data on road-killed wild felids, and at each carcass sighted, the vehicle was parked to collect information, photographs, and geographical coordinates. During this period, 26 specimens of three different species were found, viz. Leopardus guttulus (southern tiger cat), Leopardus wiedii (margay), and Herpailurus yagouaroundi (jaguarundi). In addition to the significant number of wild felids found, a hotspot for road kills of these species was identified.

Seasonal analysis of particulate matter and its exposure on urban bikers in Nashik city, India

Introduction: Exposure to Particulate Matter (PM) can cause ill health effects such as coughing, allergies, decreased lung function, chest discomfort and pain. The current study aims to monitor particulate matter concentrations on the highways in Nashik, India and to estimate its exposure to the bikers in the form of Respiratory Deposition Doses (RDDs) with its seasonal variation.
 Materials and methods: Low-cost air quality monitor was mounted on the bike to measure Particulate Matter (PM1, PM2.5 and PM10) concentrations at breathing level. Extensive mobile monitoring was performed on seven highway stretches passing through city limits at morning and evening peak hours for all the weekdays for three seasons.
 Results: The PM concentrations differed on each route seasonally as well as at peak hours in morning and evening. The maximum PM1, PM 2.5 and PM10 concentrations monitored were 119.84 μg/m3, 218.85 μg/m3 and 239.25 μg/m3 respectively on Route R3 in Winter morning. The maximum RDDHD exposure on R5 and R3 in Winter mornings was due to PM10. While maximum RDDTB and RDDAL exposure on R5 in Winter morning and evening was due to PM2.5. Also, the seasonal and particle size effect on RDD has been studied which exhibits higher rise in exposure in Winter mornings due to PM2.5.
 Conclusion: This study reveals that maximum exposure was observed during Winter mornings. The results recommend that seasons have a substantial effect on PM concentrations and their exposure. The minimum exposure was observed in monsoon, followed by summer and the maximum exposure was seen in winters.
 

Freeway congestion management on multiple consecutive bottlenecks with RL‐based headway control of autonomous vehicles

AbstractAdaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively.

Joint Vehicle Path Planning for Interruptible 1-D Snake-like Platoons and 2D Flocks in Lane-free Traffic

This article presents a joint trajectory optimization algorithm for connected and automated vehicles forming 1-D snake-like interruptible platoons or 2-D deformable and interruptible flocks in a lane-free traffic environment on highways. A double double-integrator model is employed for the longitudinal and lateral movements of each vehicle. An appropriately defined Euclidian distance to track the front vehicle is utilized to establish and operate a 1-D platoon of vehicles, at selectable distance from each other. The Euclidian distance is used as a soft constraint, which allows for platoons to divide, if required to avoid obstacles, and reunite eventually. Moreover, the distance from a selectable frame (e.g. a triangle) are employed as soft constraints to form and maintain a 2-D deformable and interruptible flock of vehicles. The flock may feature a leader vehicle, e.g. at the front corner of a triangle. A collision avoidance function considering constant time-gaps or space-gaps, is used based on the elliptical distance between each two vehicles or external obstacles. A joint multi-objective function is formulated and minimized using an efficient feasible direction algorithm with respect to constant and state-dependent bounds on control inputs, including road boundary constraints. This leads to creating a flexible platoon or deformable flock accounting for low fuel consumption, passenger convenience, collision avoidance, achievement of desired speed and prevention of infeasible maneuvers. Challenging scenarios are examined on a lane-free straight motorway stretch, producing promising results for further exploration of the proposed concepts within an MPC (model predictive control) framework.

Size Distribution as a Forensic Tool to Source Apportion Ambient PM

The particulate matter (PM) emitted from multiple sources contribute to air pollution near highways, affecting, mainly the commuters on road, dwellers and shopkeepers nearby. In this study, the size distribution of the PM near a highway, obtained using real time laser aerosol spectrometer, was used to apportion the contributions from vehicular traffic and road dust. Data on vehicle count and type were obtained for the selected highway stretch. The size distribution of emission from petrol and diesel vehicles and the size distribution of re-suspended dust were obtained through independent studies. Size based distribution of PM in the study area contributed by vehicles, were modelled using AERMOD using the vehicle emissions and vehicle count data. Aggregation of the modelled concentrations of PM of various size ranges with the contribution from re-suspended dust gave size based concentration distributions closely matching with that of PM in ambient air. The size based PM analyses showed that in the study area, 47.36%, 15.74%, and 15.46% of PM2.5, were contributed by diesel vehicles, petrol vehicles and suspended dust, respectively. The corresponding contributions to PM10 were, 14.42%, 10.59% and 68.35%. The emission studies on vehicles showed diesel vehicles contributing significantly higher concentration of PM to ambient air than petrol vehicles, with condition of vehicle influencing the amount of PM emitted. The use of particle size distribution pattern for preliminary source apportionment, demonstrated in this study, has promising environmental forensic applications as it provides a cheap and fast corroborative evidence for responsibility allocation.

Priodontes maximus (Cingulata: Chlamyphoridae) using a highway wildlife underpass in a fragmented Cerrado landscape from Eastern Goias State, central Brazil

We monitored a stretch of the BR-050 highway in the State of Goias that crosses several natural environments used by the giant armadillo, a peculiar and endangered species whose survival is threatened by vehicle collisions. Here, we present the record of highway underpass use by Priodontes maximus in a fragmented Cerrado landscape in Ipameri, Goias. The finding confirms the use of highway underpasses by the giant armadillo and highlights that these structures should be promoted at points where P. maximus occurs in the habitats isolated by the BR-050.

Assessment of slope-cut landslides along Pokhara-Baglung Highway, Nepal

The study addresses the ramifications of development initiatives in delicate mountainous terrains, unveiling significant economic constraints and multifaceted environmental challenges. It focuses on investigating substantial landslides triggered by excavations along a specified stretch of Nepal's Pokhara-Baglung Highway. This research holds significance for policymakers engaged in devising highway development strategies that mitigate future landslides, minimizing both costs and the toll on life and assets. The investigation encompassed on-site cataloging of landslides, lab test of sampled soils and a structured questionnaire distributed among local residents. This comprehensive approach facilitated a thorough assessment of landslide occurrences and their consequential effects. The identified landslides exhibited a consistent rotational pattern, characterized by abundant quartzite and phyllite rock formations. The predominant soil composition consisted of fine-to-medium sands, exhibiting a Plasticity Index (PI) range of 0.5 to 3, indicating marginal plasticity. Significantly, a substantial portion (70%) of the populace reported tangible impacts from landslides, with about 32% of affected individuals confirming an average agricultural productivity loss of 4330 kg/km2. Statistical analysis using the Chi-square test indicated a uniform impact across various demographic categories, including gender, education, proximity to the affected site, and social caste. Although fluctuations in the region, the observed temporal precipitation consistency over decades suggests as an accelerating rather than primary causative factor for landslides. Thus, principal causes of slope failures predominantly link to inadequately managed bedrock excavations and suboptimal road drainage systems underscoring the necessity for systematic inquiries into soil stability post-slope incisions. These measures are pivotal in guiding the construction and expansion of road networks within Nepal's Himalayan region.

A sequence-to-sequence model for joint bridge response forecasting

Knowledge of the structural response of bridges is extremely important for highway asset management and bridge structural health monitoring. Instrumenting every bridge in a road network and maintaining the monitoring instrumentation over decades of service can be financially infeasible. Mechanical intuition suggests a significant relationship exists between responses of two sets of bridges reasonably similar in design exposed to an identical load. This study explores the use of data-driven models to forecast the response of one bridge to a given truck load using the response of another bridge to the same loading profile. By deploying a modern monitoring system in multiple bridges in the same highway corridor integrated in a cyber-physical systems (CPS) framework, and utilizing advanced computer vision algorithms, the authors have gathered a unique dataset consisting of pairs of bridge responses to the same truck load from live traffic moving across a 32.2 km (20 miles) stretch of the I-275 highway in southeast Michigan. Signal processing techniques have been employed to isolate the response of the bridges to a single truck load in a time series of recorded responses. Then, a deep-learning-based time series forecasting framework using the encoder-decoder architecture with gated recurrent unit (GRU) and long short-term memory (LSTM) cells has been used for bridge response forecasting. Baseline models based on linear time series models are also developed to which the deep-learning forecasting models can be compared. After training the models, it is observed that deep-learning-based models can accurately forecast the response of one bridge using the response of another and reduce the forecasting root-mean-squared error (RMSE) by at least 20% relative to baseline linear models. The forecasting capabilities of the encoder-decoder architecture proposed herein outperform traditional approaches to response forecasting. Trained versions of the encoder-decoder forecasting model can be used to provide reliable estimates of bridge response using a single instrumented bridge in a corridor, thereby enhancing the value of data from instrumented bridges for asset management of bridge networks.

Night icing prediction using vehicle-mounted pavement temperature sensor and atmospheric data

Faced with high accident rates on ice-covered roads at night, notifying road managers of the most dangerous locations would allow them the opportunity to treat the icy roads by applying de-icing chemicals. In addition, drivers can be better prepared for the impending danger. In this regard, this study developed a web-based night icing potential information system and a deep learning-based night icing forecasting model. Input data included pavement temperature, collected by patrol cars operating between 23:00 and 07:00 on a daily basis in the winter season, and atmospheric data provided by the Korea Meteorological Administration. For evaluating the forecasting model, baseline data were generated based on the physical principle that ice is formed when pavement temperature is negative and lower than the dew point temperature. The forecasting model was assessed using pavement temperature and atmospheric data obtained on a 40 km stretch of a rural highway, passing through a mountainous region in South Korea. As a result, the model showed favorable performances, with 94% and 90% accuracies for bridge and roadway segments, respectively. Considering the increasing emphasis on preventive maintenance, the developed forecasting model can be applied to a preventive anti-icing measure, in turn, increasing traffic safety on winter roads.

Assessment and Prioritization of Highway Stretch Deploying Functional and Structural Characteristics

Asphalt pavements deteriorate over time when subjected to fatigue; hence, it is critical to evaluate in-service pavements to keep them in good condition. The goal of pavement evaluation is to assess the functional and structural performance of a pavement section, with a view to improving both the pavement's serviceability and riding quality. Through field and laboratory experiments, the current study attempts to evaluate the performance of pavement stretches. The sections are ranked and prioritized based on the test results. Medchal-Dabilpur national highway was evaluated for pavement roughness, skid resistance and deflections. In addition, core samples with diameters of 100mm and 150mm were collected and tested in the laboratory for moisture susceptibility, tensile strength, stiffness and fracture characteristics. The current study also intends to establish a unique technique for prioritizing pavement maintenance sections based on functional and structural performance in the field and laboratory. The Artificial Neural Network (ANN) approach was used to develop the relationship between pavement functional and structural performance. Heat maps were created and ranked in Python Jupyter notebook to better visualize the performance of functional and structural characteristics of the pavement. The final evaluation results can be validated by supporting them with laboratory investigations based on the pavement-section field samples. The results demonstrate that there is a strong relationship between the structural and functional properties of the pavement. The developed Maintenance Priority Index (MPI) will be useful in rating the maintenance and rehabilitation actions depending on the level of necessity. KEYWORDS: Functional and structural evaluation, Resilient modulus, Tensile strength ratio, Fracture properties, ANN technique, Maintenance priority index.

Immediate impact of fires on roadkilling of wild vertebrates on a highway in southeast Brazil

Fires cause wild vertebrates to try to escape, thereby leading to an increase in attempts to cross roads and consequently an increase in the number of roadkills. However, the study of the impact of fires on roadkilling of wild vertebrates is practically nonexistent. In this study, we analyzed the relationship between fires near a 180 km highway stretch in Southeast Brazil and wild vertebrate roadkills using a 7-year dataset on roadkills and satellite data on fires, hypothesizing that roadkills would increase after fires. We found that the number of roadkilled wild vertebrates increased by about 144% (P = 0.020) in highway sections up to 1 km from fires, within 7–8 days after the fires. The number of roadkills in highway sections affected by fires was also higher than that of neighboring (control) section (P = 0.028). Taken together, these results show there was a significant increase in the number of roadkills on road sections close to or within burned areas. This work emphasizes the importance of analyzing the synergistic effects of fires and roadkill of wild vertebrates and guides research on adopting an experimental design which allows for temporal and spatial controls using retrospective data on fires and roadkilling. We recommend decreasing traffic flow and/or vehicle speed near areas recently affected by fires, as a preventive management strategy.

Strategic Approach for Electric Vehicle Charging Infrastructure for Efficient Mobility along Highways: A Real Case Study in Spain

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip.

Wild fauna as roadkill on a highway in the semiarid region of northeastern Brazil

The death of wild vertebrates run over by motor vehicles on highways is one of the main causes of the loss of wildlife diversity. The aim of the present study was to investigate the diversity of wild fauna in roadkill incidents on a stretch of highway in the semiarid region of Brazil. The survey was conducted between March 2020 and May 2021 through active searches using a motorcycle. Killed vertebrates were analyzed and photographed in situ. Wild fauna was studied based on ecological descriptors, speed of movement and average rate of roadkill incidents. We compared total observed richness to that expected using nonparametric estimators. A total of 464 road-killed vertebrates were represented by two species of amphibians, 17 reptiles, 17 birds and 10 mammals. Amphibians were the most affected, totaling 245 individuals. Colubridae was the family with the largest number of species affected. Boiruna sertaneja and Boa constrictor were the only medium-size to large snakes killed. Herpailurus yagouaroundi and Leopardus tigrinus are respectively categorized as “least concern” and “vulnerable” according to the IUCN. The species accumulation curves continued to rise, showing no sign of approaching to an asymptote. Chao 2 and Jack 2 were the main estimators for road-killed wild vertebrates considering total richness. The diversity reported here is considered to be underestimated and greater monthly sampling efforts are required. Conservation strategies are urgently needed for the protection of wild vertebrate fauna to mitigate the staggering loss of biodiversity caused by the construction and operation of roads and highways in Brazil.

Hotspots and hotmoments of wildlife roadkills along a main highway in a high biodiversity area in Brazilian Amazonia

ABSTRACT Wildlife roadkills have become a concern in the Amazon biome due to the opening of major roads in recent decades. In this study, we aimed to describe wildlife roadkills in a 100-km stretch of the BR-163 highway, in western Pará state, determining which vertebrate groups are most affected and whether there are spatial (hotspots) and temporal (hotmoments) aggregations of roadkills. From July 2019 to June 2020, we carried out 25 surveys at 15-day intervals, from a vehicle at a maximum speed of 40 km h-1. We recorded 351 individuals at an observed rate of 0.14 ind km-1 day-1. Despite their lower detectability and faster carcass removal rate from the road due to small size, most recorded roadkills were amphibians (0.066 ind km-1 day-1). We mapped several hotspots along the study stretch considering the total number of animals recorded, and separately for amphibians and reptiles. Multiple linear regression analyses indicated that the number of roadkills of all vertebrates, amphibians and reptiles recorded are influenced by temperature and precipitation. Information on places with the highest incidence of roadkills can support actions such as the installation of underpasses and fences, aimed at reducing the impacts on wild vertebrates of this Amazonian highway.

An artificial intelligence based framework to analyze the landside risk of a mountainous highway

The present study entails an artificial intelligence-based framework for landslide risk analysis of a highway infrastructure in the Himalayan region. In total, 241 landslide polygons that were inventoried for the study area. The spatial component of landslide susceptibility map was prepared by incorporating drainage density, TWI, geology, elevation and slope gradient as major contributing factors, in the certainty factor–random forest (CF-RF) hybrid model with accuracy of 0.928. The landslide hazard analysis was carried out by multiplying landslide spatial and temporal probabilities. The landslide vulnerability analysis of the highway stretch was carried out by integrating the elements at risk. The built-up area was extracted by using U-Net deep learning algorithm with an accuracy of 0.964. The landslide risk map of the highway stretch prepared by the multiplication of landslide hazard and vulnerability maps depicts that 16.78% and 6.25% of the study area falls in high and very high-risk zones, respectively.

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Overlapping internal boundary control of lane-free automated vehicle traffic

Lane-free vehicle driving has been recently proposed for connected automated vehicles on highways or arterials. Lane-free traffic implies that incremental changes of the road width lead to corresponding incremental changes of traffic flows and capacity. In these conditions, internal boundary control (IBC) was proposed for flexible sharing of the total road width and capacity among the two traffic directions of the highway in real-time in order to maximize the cross-road infrastructure utilization and minimize congestion and delays. Centralized solutions , e.g. LQ (linear quadratic) regulators, requiring information from the whole highway stretch under consideration, have already been proposed, which, however, may be cumbersome for long highways with respect to the offline controller design effort; the extent of real-time communications; and the physical system architecture. This paper investigates two different overlapping decentralized control schemes for IBC in lane-free automated vehicle traffic. The first approach is based on a contractible controller, which is designed and employed in a decomposed way, for each subsystem of an extended highway system. In the second approach, the overall discrete-time LQ control problem is transformed into a linear matrix inequalities (LMI) problem. After selecting the overlapping feedback structure and solving the overall LMI problem, the correspondingly structured gain matrix is obtained, enabling a decentralized overlapping control deployment in real-time. Simulation investigations, involving a realistic highway stretch and demand scenarios, demonstrate that the proposed decentralized regulators are almost as efficient as the centralized control solutions for the problem at hand.

A 10-year collection of roadkilled avifauna in a stretch of the BR-040 highway in southeastern Brazil

ABSTRACT In Brazil, the number of animals killed by vehicle collision surpasses 475 millions/year, but the actual number may be even higher since roadkill data in the country is scarce and punctual. Using a standard protocol, the project Caminhos da Fauna monitored since 2006 the avifauna roadkill in the BR-040 (RJ/MG) highway, southeastern Brazil. Here, we present a checklist of the avifauna registered during the 10-year monitoring program, including the list of roadkills, indicating threatening status at regional, national, and global levels, and the number of records/species. We registered 1,317 individuals belonging to 180 species, 21 orders, and 48 families. Another 247 individuals could not be identified. For seven families roadkills were higher than 50 individuals: Thraupidae, Cuculidae, Strigidae, Tyrannidae, Psittacidae, Columbidae, and Turdidae. The most collected species were Crotophaga ani (n = 106; Cuculidae), followed by Psittacara leucophthalmus (n = 61; Psittacidae), Piaya cayana (n = 56; Cuculidae), and Asio clamator (n = 44; Strigidae). Five species were under threat: Penelope obscura, Primolius maracana, Pteroglossus bailloni, Ramphastos vitellinus, and Sicalis flaveola. Most species were geographically widespread, while other 13 (7.2%) were endemic. Given the considerable amount of data obtained, further efforts of the same motivation in other Brazilian roads and express transportation systems should be encouraged.