Road Properties Research Articles

An efficient and automatic method based on monocular camera and GNSS for collecting and updating geographical coordinates of mileage pile in highway digital twin map

Abstract The high-precision positioning of mileage piles on a digital map ensures accurate data for high-speed event releases, toll audits, and road condition monitoring. This paper introduces an efficient and automatic system for positioning highway mileage piles. The self-developed data acquisition system collects mileage pile images and road trajectory. Considering the limited variety of mileage piles, a Simplified-YOLOv5m (S-YOLOv5m) is proposed. Then a high-precision character detection network of S-YOLOv5m is proposed to enhance feature extraction and improve the accuracy of character detection on mileage piles. Thirdly, the end-to-end monocular distance measurement combines the target detection with the distance estimation, enabling simultaneous object detection and distance measurement. Fourthly, by combining with the geographical coordinates of the acquisition point, the direct solution to geodetic problems is applied to calculate the spatial coordinates of the mileage piles. Finally, the missing mileage piles are compensated for using the road curve and equal distance interpolation. The complete mileage piles and geographical information list of the inspection trajectory are output. Through a series of verification tests, the average positioning error of the mileage pile system is 1.265 m. The contribution of the automatic positioning system of mileage piles is to construct the relationship between mileage piles and geographical coordinates, match mileage piles with digital maps to realize the full-featured, real-scene, and high-dynamic management of road attributes.

WITHDRAWN: Evaluating Collaborative Emission Reduction Efforts in Urban Road Passenger Transportation: insights from the City Scale

Road passenger transportation contributes significantly to carbon dioxide (CO2) and pollutant emissions, posing challenges for urban sustainability, particularly in rapidly urbanizing cities such as Fuzhou, which was recognized as a global sustainable development city for 2023. This study addresses the research gap by integrating road green factors, which refer to the ecological attributes of roads, such as green space, vegetation, and tree coverage that contribute to improving air quality and reducing pollution, into emission reduction strategies for city-scale road passenger transportation. This novel approach has not been extensively explored in the existing literature. The purpose of this study was to identify a rational pathway for emissions reduction by analyzing the economic, demographic, transportation infrastructure, road green space, and policy factor impacts on emissions. Employing a system dynamics (SD) model, this research evaluates the emissions from road passenger transportation in Fuzhou from 2010 to 2030 under various scenarios, including a baseline scenario (BS), controlled private cars travel demand (CPD), promotion of new energy vehicles (PVE), improvement in energy efficiency (IEE), increase in road capacity (IRC), and improvement in road green level (IRG). The principal results indicate that by optimizing the traffic structure and integrating other emission reduction measures, peak CO2 emissions can be achieved by 2024 under the three scenarios or by 2023 under the four scenarios. Furthermore, significant reductions in pollutants of 84.62% and 86.07% are projected, respectively. The study concluded that the economic benefits of CO2 emissions reductions consistently exceeded those of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) reductions, highlighting the importance of a holistic approach to emissions reduction strategies. Based on these findings, policy recommendations were proposed to guide the formulation of effective road passenger transportation policies aimed at achieving sustainable urban development.

Predicting the roadway width of forest roads by means of airborne laser scanning

Modern forestry relies on an extensive network of well-maintained forest roads. However, the trafficability of forest roads can be limited by poor construction or by insufficient load-bearing capacity caused by, for example, weather conditions. In all forestry operations, the forest roads should be able to support heavy vehicles during the transport of roundwood and harvesting machinery, so logistics must be carefully planned to ensure good road trafficability. To facilitate this planning, it is necessary to know the various characteristics of the forest road in terms of its width, structure and load-bearing capacity. Obtaining such information using remote sensing data would be beneficial. In this study, we used airborne laser scanning (ALS) data to assess the width of the roadway on forest roads. We developed several algorithms that were used to assess roadway width based on the cross-sectional data (derived with ALS) of the roads in 8-m long segments. The ALS echoes within the segments were classified into 20 cm wide strips which the algorithms then analyzed in a stepwise manner. Both height and intensity information from the ALS data were utilized. The algorithm results were then used as predictors in linear models to predict the roadway width. The main focus was on the road level, i.e., aggregated values from 25 different roads were used in model fitting. In terms of root mean square error (RMSE) values, accuracies of approximately 20–30 cm (or 7–10 %) were obtained for the predicted roadway widths with a separate validation data. This level of accuracy can be considered adequate given the characteristics of the ALS data used in the modelling. The results indicate that the presented approach has potential for practical applications to predict the width attributes of forest roads.

Multi-crowdsourced data fusion for modeling link-level traffic resilience to adverse weather events

Climate change leads to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data present new opportunities to monitor and investigate road traffic changes during adverse weather. This study utilizes two types of crowdsourced data: passively collected location data (PCLD) from mobile devices and actively collected user reports (ACUR) from the WAZE app, to examine the impact of floods, winter storms, and fog on road traffic. Three metrics derived from PCLD—speed change, event duration, and area under the curve (AUC)—along with an additional metric from ACUR, user-perceived severity, are utilized to assess link-level traffic resilience. All metrics are correlated with road characteristics such as functional class, number of lanes, divider types, road geometry, and historical traffic, to analyze underlying relationships and make forecasting. Using the Dallas–Fort Worth (DFW) metroplex in 2022 as a case, the study finds that overall, winter storms have the most substantial impact on road traffic, followed by floods and fog. Travel demand metrics, such as POI visits and traffic volume, experience a more significant decrease than traffic speed. The impact of weather also varies by road characteristics, with higher-class roads with greater volume and higher speed experiencing more substantial changes. Road geometry, such as minimal altitude and slopes, also significantly influences road traffic resilience under floods but not during winter storms. Moreover, the study reveals that ACUR may not accurately reflect the actual impact during extreme weather events, as few users are able to travel outside to actively act as “sensors”. In sum, this study evaluates a set of crowdsourced data for assessing link-level traffic resilience, designs various resilience metrics, and examines how they vary across road attributes and weather events, which are essential for guiding disaster preparedness, response, and recovery in road transportation systems.

Measuring solar radiation and spatio-temporal distribution in different street network direction through solar trajectories and street view images

Under the dual pressures of rapid urbanization and global warming, the management of solar radiation has become an urgent issue to address. In this context, the potential regulatory role of urban street networks on solar radiation has garnered widespread attention. However, existing research lacks an understanding of the impact of street direction. By utilising street view data and solar trajectory simulation technology, it becomes possible to facilitate the revelation of spatiotemporal differences in solar radiation across streets with different direction. The results show that solar radiation is generally lower on north–south streets than on east–west streets. In terms of spatial differences, the average solar radiation in the fifth ring of Beijing is 25.38% higher than that in the second ring. Temporally, the average solar radiation in August, the highest month, is 15.98% greater than in October, the lowest month. Additionally, solar radiation on east–west street direction shows more intense variations in summer. A periodic variation in solar radiation was also discovered in relation to street angles, with different frequencies and amplitudes at 30° and 180°. This study is the first to focus on the directional attributes of roads at an urban scale. By utilising 100,000 street view images, it calculates and analyses the spatiotemporal distribution of solar radiation in Beijing during summer across different direction. The findings provide new insights into the relationship between the distribution of solar radiation and the morphology of urban roads.

Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach

Road traffic accidents pose a significant global health concern, with an alarming 1.19 million fatalities reported in 2021. Traditionally, strategies to address this challenge have relied on expert input and subjective evaluations. This study introduces an adaptive and novel two-stage model that minimizes expert interference by integrating multi-objective optimisation with association rule mining. This innovative approach provides a systematic framework to enhance the efficiency of decision-making and optimise resource allocation outcomes in road infrastructure management, facilitating adaptability to diverse objectives. A case study in Utrecht from iRAP validates the efficiency of the approach, demonstrating significant improvements across various objectives by using the non-dominant sorting genetic algorithm and enabling road local authorities to tailor investment plans to their specific road network characteristics by crash database mining. However, the methodology requires refinement, particularly in identifying risk levels considering the interactive effects of multiple road attributes. In conclusion, while representing a substantial advancement, further refinement is necessary to fully realize its potential in enhancing road safety.

Wildlife mortality risk posed by high and low traffic roads.

Wildlife mortality due to collisions with vehicles (roadkill) is one of the predominant negative effects exerted by roads on many wildlife species. Reducing roadkill is therefore a major component of wildlife conservation. Roadkill is affected by various factors, including road attributes and traffic volume. It is theorized that the effect of traffic volume on roadkill probability should be unimodal. However, empirical evidence for this theory is lacking. Using a large-scale roadkill database of 18 wildlife species in Israel, encompassing 2846km of roads over 10years, we explored the effects of traffic volume and road attributes (e.g., road lighting, verge vegetation) on roadkill probability with a multivariate generalized linear mixed model. A unimodal effect of traffic volume was identified for the striped hyena (Hyaena hyaena), whereas 5 species demonstrated a novel quadratic U-shaped effect (e.g., golden jackal [Canis aureus]). Four species showed a negative linear effect (e.g., wild boar [Sus scrofa]). We also identified varying effects of road attributes on roadkill. For instance, road lighting and roadside trees decreased roadkill for several species, whereas bus stops and concrete guardrails led to increased roadkill. The theorized unimodal effect of traffic volume may only apply to large, agile species, and the U-shaped effect could be related to intraspecies variability in traffic avoidance behavior. In general, we found that both high-traffic and low-traffic roads can pose a high mortality risk for wildlife. It is therefore important to monitor roadkill on low-traffic roads and adapt road attributes to mitigate roadkill. Road design for effective roadkill mitigation includes reducing the use of concrete guardrails and median barriers where possible and avoiding dense bushes in verge landscaping. These measures are complemented by employing wildlife detection systems, driver warnings, and seasonal speed reduction measures on low-traffic roads identified as roadkill hotspots.

Novel method for ecosystem services assessment and analysis of road-effect zones

To explore the cumulative ecological effects of roads in ecologically fragile areas, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) tool was applied to assess the spatiotemporal changes in habitat quality, water yield, and soil erosion in road-effect zones of the Western Sichuan Plateau, China. Then, generalized estimating equations were formulated to analyze the impact of synergies among road attributes, climate, topography, land cover, and other factors on ecosystem service changes. The results showed that the habitat quality within the road-effect zones was mostly affected by road grade and structure, and water yield and soil erosion were attributed to the factors of road structure, rainfall, and topography. Roadbed sections had the greatest negative impact on ecosystem services, followed by bridge sections and tunnel sections. Overall, the results of this study address habitat encroachment and soil and water loss in ecologically fragile areas, contributing to knowledge on green infrastructure planning.

A Functional Observer Approach to Accurate Road Bank Estimation

Understanding critical situations and risks while driving ensures a safe drive, this will only be achieved by vehicle dynamic behavior and a good knowledge of road attributes, an unknown inputs functional observer is developed and presented in this study to estimate the vehicle’s unmeasured states in real-time and the unknown input, it is mainly about the road bank angle and it is based on a measured output and a known input. The design conditions for functional observer proposed are expressed by Lyapunov-Krasovskii stability theory using convergence conditions and solving linear matrix inequalities (LMIs). The solutions to these LMIs are used to determine the observer parameters.

Multimodal Deep Learning for Robust Road Attribute Detection

Automatic inference of missing road attributes (e.g., road type and speed limit) for enriching digital maps has attracted significant research attention in recent years. A number of machine learning-based approaches have been proposed to detect road attributes from GPS traces, dash-cam videos, or satellite images. However, existing solutions mostly focus on a single modality without modeling the correlations among multiple data sources. To bridge this gap, we present a multimodal road attribute detection method, which improves the robustness by performing pixel-level fusion of crowdsourced GPS traces and satellite images. A GPS trace is usually given by a sequence of location, bearing, and speed. To align it with satellite imagery in the spatial domain, we render GPS traces into a sequence of multi-channel images that simultaneously capture the global distribution of the GPS points, the local distribution of vehicles’ moving directions and speeds, and their temporal changes over time, at each pixel. Unlike previous GPS-based road feature extraction methods, our proposed GPS rendering does not require map matching in the data preprocessing step. Moreover, our multimodal solution addresses single-modal challenges such as occlusions in satellite images and data sparsity in GPS traces by learning the pixel-wise correspondences among different data sources. On top of this, we observe that geographic objects and their attributes in the map are not isolated but correlated with each other. Thus, if a road is partially labeled, then the existing information can be of great help on inferring the missing attributes. To fully use the existing information, we extend our model and discuss the possibilities for further performance improvement when partially labeled map data is available. Extensive experiments have been conducted on two real-world datasets in Singapore and Jakarta. Compared with previous work, our method is able to improve the detection accuracy on road attributes by a large margin.

Development of a Road Protection Value Model as an Indicator for National Road Safety Performance Assessment from the Perspective of Drivers of Four or More Wheeled Motorized Vehicles

This paper introduces the model of Star Rating Scores (SRS) or Road Protector Scores (RPS) for Indonesian National Roads from the perspective of motorized vehicle drivers with four or more wheels, which are explored from the characteristics of traffic accidents along Indonesia's national roads. This SRS model takes into account 2 different main parameters and 4 main parameters that are the same as the main parameters of the SRS International Road Assessment Program (iRAP) and a total of 51 road attributes. The two main parameters that differ from the SRS iRAP model are the parameters for front-rear crashes and fore-side crashes when turning around. While the 4 parameters are the same as front-side collision accidents at property access, single accidents leaving the road body, front-to-front collision accidents, and accidents at intersections. In the early stages, the National Road SRS model was designed using 51 road attributes. After analysis using the Importance and Performance Analysis (IPA) method, 43 road attributes were successfully formulated for the SRS National Road model, consisting of 30 likelihood factor attributes, 10 severity factor attributes, 2 external traffic influence factor attributes, and 1 factor attribute operational speed. In addition, it is proven that the SRS National Road model is significantly different from the SRS iRAP model. The three main parameters of the National Road SRS model, namely the front-rear hit accident parameter, the front-side hit accident parameter when turning around, and the front-side hit accident parameter at property access are significantly different from the parameters of the SRS iRAP model.

Attention based spatio-temporal graph convolutional network with focal loss for crash risk evaluation on urban road traffic network based on multi-source risks

The urban road transportation has presented a high probability of crash occurrence, and the aim of the present study is to evaluate the crash risk for urban road networks. However, the irregular structure of urban road networks, the high-dimensional spatio-temporal correlations among multi-source risks (i.e., the contributing risks from traffic flow, meteorological conditions, road design, and so forth), and the issue of data imbalance have brought challenges to this topic. To solve these issues, an Attention based Spatio-Temporal Graph Convolutional Network (ASTGCN) model with focal loss function is used for the first time to evaluate crash risk on an urban road network. This work can be summarized as (1) adopting the spatio-temporal graph convolution structure to capture the spatio-temporal properties and characterize the multi-source risks; (2) utilizing an attention mechanism network to address the critical contributing risks during crash risk evaluation; (3) introducing the focal loss function to improve the model performance impacted by the imbalanced data; and (4) investigating the different contributions of multi-source risks to model performance. The evaluation performance is tested in a real-world urban road traffic network. The raw data consists of 1239 crash records with corresponding datasets of traffic flow characteristics, meteorological conditions, road attributes and the topological structure of the road network. At the same time, three baseline models Artificial Neural Network (ANN), Random Forest (RF), and Deep Spatio-Temporal Graph Convolutional Network (DSTGCN) are compared to the proposed ASTGCN on the same datasets. Overall, the results show that ASTGCN outperforms the baseline models in several evaluation metrics. ASTGCN with focal loss function further improves performance by tackling the issues of dataset imbalance. Additionally, it is also found that the traffic flow risk is most crucial to model performance. The findings of the present study indicate that the proposed model can efficiently evaluate dynamic crash risk for urban road networks, which will benefit the safety management of urban road transportation.

Analyzing the Risk Factors of Traffic Accident Severity Using a Combination of Random Forest and Association Rules

This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents.

Pre-Disaster Resilient Road Network Investment Strategy With Uncertainty Quantification

Extreme events, such as disasters and severe road accidents, have frequently happened in recent years, threatening thousands of people’s lives. Efficient evacuation after extreme events is of utmost importance, which requires transport authorities to plan for and develop solutions to ensure transport infrastructure resiliency to disasters. In this study, we develop a modeling framework for optimizing road network pre-disaster investment strategy under different disaster damage levels. A bi-level multi-objective optimization model is formulated, in which the upper-level aims to maximize the capacity-based functionality and reliability of the road network, which are measured by the resilience polygon area, while minimizing the total budget costs, and the lower-level is the user equilibrium problem. To efficiently solve the model, the Shapley value is used to select candidate edges and obtain a near-optimal project order. Realistic speed data collected from crowd sources is used to explore the dependency between the vulnerability and new steady state with copula functions. The numerical study finds that the dependency of road capacities does affect measurements of road network reliability. The investment strategy is significantly affected by the characteristics of road edges and disaster damage levels. Critical sections that can significantly improve the overall functionality of the network are identified. This paper provides a theoretical basis for authorities to make pre-disaster road network investments based on different disaster damage levels, road attributes and budgets.

Road Network Representation Learning: A Dual Graph-based Approach

Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is named road network representation learning (RNRL). While several models have been proposed for RNRL, they capture the pairwise relationships/connections among roads only (i.e., as a simple graph), and fail to capture among roads the high-order relationships (e.g., those roads that jointly form a local region usually have similar features such as speed limit) and long-range relationships (e.g., some roads that are far apart may have similar semantics such as being roads in residential areas). Motivated by this, we propose to construct a hypergraph , where each hyperedge corresponds to a set of multiple roads forming a region. The constructed hypergraph would naturally capture the high-order relationships among roads with hyperedges. We then allow information propagation via both the edges in the simple graph and the hyperedges in the hypergraph in a graph neural network context. In addition, we introduce different pretext tasks based on both the simple graph (i.e., graph reconstruction) and the hypergraph (including hypergraph reconstruction and hyperedge classification) for optimizing the representations of roads. The graph reconstruction and hypergraph reconstruction tasks are conventional ones and can capture structural information. The hyperedge classification task can capture long-range relationships between pairs of roads that belong to hyperedges with the same label. We call the resulting model HyperRoad . We further extend HyperRoad to problem settings when additional inputs of road attributes and/or trajectories that are generated on the roads are available. We conduct extensive experiments on two real datasets, for five downstream tasks, and under four problem settings, which demonstrate that our model achieves impressive improvements compared with existing baselines across datasets, tasks, problem settings, and performance metrics. CCS Concepts: • Information systems → Data mining ; • Urban computing ; • Spatial-temporal systems ;

Utilizing High Resolution Satellite Imagery for Automated Road Infrastructure Safety Assessments.

The European Commission (EC) has published a European Union (EU) Road Safety Framework for the period 2021 to 2030 to reduce road fatalities. In addition, the EC with the EU Directive 2019/1936 requires a much more detailed recording of road attributes. Therefore, automatic detection of school routes, four classes of crosswalks, and divided carriageways were performed in this paper. The study integrated satellite imagery as a data source and the Yolo object detector. The satellite Pleiades Neo 3 with a spatial resolution of 0.3 m was used as the source for the satellite images. In addition, the study was divided into three phases: vector processing, satellite imagery processing, and training and evaluation of the You Only Look Once (Yolo) object detector. The training process was performed on 1951 images with 2515 samples, while the evaluation was performed on 651 images with 862 samples. For school zones and divided carriageways, this study achieved accuracies of 0.988 and 0.950, respectively. For crosswalks, this study also achieved similar or better results than similar work, with accuracies ranging from 0.957 to 0.988. The study also provided the standard performance measure for object recognition, mean average precision (mAP), as well as the values for the confusion matrix, precision, recall, and f1 score for each class as benchmark values for future studies.

Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics.

This paper proposes a new observer approach used to simultaneously estimate both vehicle lateral and longitudinal nonlinear dynamics, as well as their unknown inputs. Based on cascade observers, this robust virtual sensor is able to more precisely estimate not only the vehicle state but also human driver external inputs and road attributes, including acceleration and brake pedal forces, steering torque, and road curvature. To overcome the observability and the interconnection issues related to the vehicle dynamics coupling characteristics, tire effort nonlinearities, and the tire-ground contact behavior during braking and acceleration, the linear-parameter-varying (LPV) interconnected unknown inputs observer (UIO) framework was used. This interconnection scheme of the proposed observer allows us to reduce the level of numerical complexity and conservatism. To deal with the nonlinearities related to the unmeasurable real-time variation in the vehicle longitudinal speed and tire slip velocities in front and rear wheels, the Takagi-Sugeno (T-S) fuzzy form was undertaken for the observer design. The input-to-state stability (ISS) of the estimation errors was exploited using Lyapunov stability arguments to allow for more relaxation and an additional robustness guarantee with respect to the disturbance term of unmeasurable nonlinearities. For the design of the LPV interconnected UIO, sufficient conditions of the ISS property were formulated as an optimization problem in terms of linear matrix inequalities (LMIs), which can be effectively solved with numerical solvers. Extensive experiments were carried out under various driving test scenarios, both in interactive simulations performed with the well-known Sherpa dynamic driving simulator, and then using the LAMIH Twingo vehicle prototype, in order to highlight the effectiveness and the validity of the proposed observer design.

Remote Sensing Image Road Extraction Network Based on MSPFE-Net

Road extraction is a hot task in the field of remote sensing, and it has been widely concerned and applied by researchers, especially using deep learning methods. However, many models using convolutional neural networks ignore the attributes of roads, and the shape of the road is banded and discrete. In addition, the continuity and accuracy of road extraction are also affected by narrow roads and roads blocked by trees. This paper designs a network (MSPFE-Net) based on multi-level strip pooling and feature enhancement. The overall architecture of MSPFE-Net is encoder-decoder, and this network has two main modules. One is a multi-level strip pooling module, which aggregates long-range dependencies of different levels to ensure the connectivity of the road. The other module is the feature enhancement module, which is used to enhance the clarity and local details of the road. We perform a series of experiments on the dataset, Massachusetts Roads Dataset, a public dataset. The experimental data showed that the model in this paper was better than the comparison models.

Assessment Performance of Flexible Pavements for Surface Deflection Measure by Benkelman Beam Method

AbstractThe condition of roads assumes significant part since it straightforwardly influence traveler just as cargo traffic. The decayed street having undesired diversion or deficient harshness makes inconvenience rider and may even bring about road mishaps. This insufficiency in road attributes can be classifications as both useful and underlying. The evaluation of street conditions is needed to distinguish the explanations behind disintegration before any restoration work is completed. The fundamental boundaries including in the appraisal of street conditions are avoidance and unpleasantness estimation which decided utilizing different test, for example, FWD, Benkelman shaft test, profilometer, and slide obstruction test. This appraisal additionally helps in focusing on the upkeep works as indicated by the necessity. A few procedures are utilized to evaluate the road conditions and contrast and standard standards. In this concentrate on a stretch of road is break down for similar where techniques utilized are visual strategy, Benkelman beam test, and slide opposition test. Utilizing last two methods, surface diversion and skid resistance is surveyed, and restorative measure are recommended for the road stretch.

Correction, update, and enhancement of vectorial forestry road maps using ALS data, a pathfinder, and seven metrics

Accurate information about forestry roads is a key aspect of forest management in terms of economy (e.g. accessibility, cost, optimal path) and ecology (e.g. wildfire and wildlife protection). In Canada, and in fact, globally, most provincial, state or territory governments maintain vectorial information on the forestry roads under their jurisdiction. However, official maps are not always accurate, may lack road attributes of interest and are not always up-to-date. Airborne Laser Scanning (ALS) has become an established technology to accurately characterize and map broad territories by providing high density 3D point-clouds with, at least, 3 or 4 measurements per square meter. This paper addresses the problem of the automatic updating, fixing, and enhancement of vectorial forestry road maps over large landscapes (¿10000 km 2 ). For this purpose, we developed a production ready, documented and open-source software. From metrics derived from the point-cloud the method produces a raster of road probability. It then uses an existing, inaccurate, map of the road network to define approximate start and end points for each road. Then, a pathfinder retrieves the accurate road shape by computing the least cost path between the two points on the probability raster. Using the accurate road position given by the algorithm, road width and road state are then estimated based the on characteristics of the point-cloud. We demonstrate that our algorithm retrieves the centrelines of roads in a natively vectorial form with an error below 3 m in 95% of the roads using a fully automatic method. The accuracy of the road location allows us to derive other accurate measurements, including the state of the roads. • We accurately relocated roads from governmental database using ALS point-clouds. • We measured the width and estimated the state of each road segment. • The method is natively vectorial and uses pathfinding instead of pixel classification. • The method is open-source, fully packaged, documented, and production-ready.