Many studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of ...lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009–2011) crash data of two-lane rural roads of the state of Washington.
Separate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified.
The modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions).
This paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions.
•We have modeled injury severity based on different lighting conditions.•Injury severity in specific lighting conditions is associated with contributing factors in different ways which such differences cannot be uncovered by focusing merely on one aggregate model.•Speed limit is significantly associated with increased levels of injury severity in daylight conditions, but not in darkness.•Injury severity highly increases when crashes occur at intersections or access points in darkness, but not in a sufficient condition.
Roads have numerous negative impacts for mammals, but may also serve as attractants due to altered vegetation or provisioning of resources.
We reviewed the use of roads and their associated features ...by mammals, in order to understand the ecological factors contributing to road use.
We documented 129 studies that recorded road use by 116 mammalian species, spanning 15 orders and 35 families, in six continents. Carnivora was the most common order (40 species, 34% of all 116 species), followed by Artiodactyla (24 species, 20%) and Rodentia (21 species, 18%). The species were placed in the IUCN categories Least Concern (88 species, 76% of all 116 species), Vulnerable (11 species, 9%), Near Threatened (9 species, 8%), Endangered (6 species, 5%), and Critically Endangered (2 species, 2%).
We assigned road use to five ecological categories, reflecting the reason for it, with subcategories where appropriate: (1) communication; (2) foraging (subcategories: anthropogenic food, herbivory, predation, salt, scavenging, water); (3) movement (subcategories: bridges, environmental alterations, habitat connectivity); (4) refuge (subcategories: avoidance, burrowing and denning, cover, roosting); and (5) thermoregulation. Foraging, movement, and refuge were the most common uses.
Roads provide a variety of resources to mammals, but road use is highly dynamic in time and space. We suggest that the use of roads by mammals is extensive, both geographically and taxonomically. Road use is likely to influence mammalian ecology while contributing to the risk of collisions with vehicles.
We reviewed the ecological factors that contribute to use of roads by mammals. We documented 129 studies that recorded road use by 116 mammalian species, spanning 15 orders and 35 families, in six continents. Reasons for road use included communication, foraging, movement, refuge and thermoregulation. We suggest that use of roads by mammals is geographically and taxonomically widespread, and likely influences mammalian ecology while contributing to risk of vehicle collisions.
Traffic forecasts are employed in the toll road sector, inter alia, by private sector investors to gauge the bankability of candidate investment projects. Although much is written in the literature ...about the theory and practice of traffic forecasting, surprisingly little attention has been paid to the predictive accuracy of traffic forecasting models. This paper addresses that shortcoming by reporting the results from the largest study of toll road forecasting performance ever conducted. The author had access to commercial-in-confidence documentation released to project financiers and, over a 4-year period, compiled a database of predicted and actual traffic usage for over 100 international, privately financed toll road projects. The findings suggest that toll road traffic forecasts are characterised by large errors and considerable optimism bias. As a result, financial engineers need to ensure that transaction structuring remains flexible and retains liquidity such that material departures from traffic expectations can be accommodated.
This publication presents a three-part road classification system that utilises the vehicle's onboard signals of two-wheeled vehicles. First, a curve estimator was developed to identify and classify ...road curves. In addition, the curve estimator continuously classifies the road curviness. Second, the road slope was evaluated to determine the hilliness of a given road. Third, a modular road profile estimator has been developed to classify the road profile according to ISO 8608, which utilises the vehicle's transfer functions. The road profile estimator continuously classifies the driven road. The proposed methods for the classification of curviness, hilliness, and road roughness have been validated with measurements. The road classification system enables the collection of vehicle-independent field data of two-wheeled vehicles. The road properties are part of the customer usage profiles which are essential to define vehicle design targets.
Two-vehicle crashes resulting from distracted driving led to a higher number of fatalities and serious injuries over time. This study utilized machine learning and econometric models to investigate ...two-vehicle-involved distracted driving crashes from the Crash Report Sampling System within the United States. XGBoost and Random Forest were utilized to identify the top variables based on SHAP value, although mixed logit with unobserved heterogeneity was used to model injury severity. The model results indicate that there is a complex interaction of driver characteristics, such as demographics (male drivers), driver actions (careless driving, driving more than the speed limit of more than 15 mph, hitting a stopped vehicle), a driver without violation history, turning violation, drinking, roadway characteristics (non-interstate highways, undivided and divided roadways with positive barrier, curved roadways, dry surface), environmental conditions (rainy weather), vehicle attributes (motorcycle, displacement volume up to 2500 cc, newer vehicle within five years of crash-involvement), temporal characteristics (4-6 PM, July-September, and year 2017). These findings underscore the importance of driving behavior and roadway design. As such, prioritizing efforts to address distracted driving behavior through driver training and law enforcement, as well as considering its implications for roadway design and maintenance, becomes crucial.
This contribution demonstrates how inner ring roads change the location pattern of shops in urban areas with the application of the space syntax method. A market rational behaviour persists, in that ...shop owners always search for an optimal location to reach as many customers as possible. If the accessibility to this optimal location is affected by changes in a city’s road and street structure, it will affect the location pattern of shops. Initially, case studies of inner ring road projects in Birmingham, Coventry, Wolverhampton, Bristol, Tampere, and Mannheim show how their realisation affect the spatial structure of the street network of these cities and the location pattern of shops. The results of the spatial integration analyses of the street and road network are discussed with reference to changes in land-use before and after the implementation of ring roads, and current space syntax theories. As the results show, how an inner ring road is connected to and the type of the street network it is imposed upon dictates the resulting location pattern of shops. Shops locate and relocate themselves along the most spatially-integrated streets. Evidence on how new road projects influence the location pattern of shops in urban centres are useful for planning sustainable city centres.
The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure ...inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.
A wide range of new possibilities in the area of intelligent transportation systems (ITS) emerged when sensors, such as accelerometers, were introduced in practically every smartphone. A clear ...example is using a driver's smartphone to detect the vertical movement experienced by the vehicle when passing over a pothole or bump; in other words, sensing the quality of the road. To this end, several approaches have been proposed in the literature, most of them based on thresholds applied to accelerometer readings. Nonetheless, no fair comparison of these approaches had been done until now, mainly because of the lack of public datasets. In this paper, we propose a platform to create road data sets that could be used by the community to create their own roads with their own requirements. Using this platform, we assembled a data set of 30 roads plagued with potholes and bumps, which we used to evaluate the most popular heuristics previously reported. From our study, a heuristic, called STDEV(Z), based on standard deviation analysis proposed by Mednis et al. obtained the best results among the considered reference methodologies. This finding suggests that measures of dispersion, specifically standard deviation, are among the best indicators to identify disruptions on accelerometer readings. From this point, we fused features used by all these heuristics within our own feature vector, which we used with a support vector machine. We show that the proposed methodology clearly outperforms all other evaluated methods. To support these conclusions, results were statistically validated. We expect to lay the first steps to homogenize future comparisons as well as to provide stronger baselines to be considered in subsequent works.
Vehicular networks have emerged as a promising technology for the development of traffic management systems in smart cities. They are expected to revolutionize a variety of applications such as ...traffic monitoring and pay-as-you-drive services. Recently, the notion of road pricing has become crucial in most big cities as it contributes in road congestion avoidance, fuel consumption saving, and pollution reduction. However, as the road pricing systems need trip data to invoice citizens, it is vital to ensure geolocation privacy while keeping drivers honest. In this paper, we propose a security approach for smart road pricing systems, which prevents toll evasion violations. The proposed approach operates under a fully distributed threshold-based control system to detect fraudulent drivers trying to cheat on their tolls. The accused drivers are reported to the toll server in order to take the appropriate countermeasures. Through the security analysis, we show the robustness of the proposed approach against a range of potential attacks. We also evaluate the proposed approach through simulations considering important metrics, namely, the storage and communication overheads. The proposed approach shows better performance results in comparison to the existing approaches. Furthermore, we evaluate the proposed approach efficiency in terms of detection precision, where it demonstrates promising results.
Complex road environments threaten the safe operation of automated vehicles. Among these, adverse weather conditions and road geometries have particularly significant impacts. This study investigates ...LiDAR-based automated vehicles (LAVs) driving safety on vertical curved roads in adverse weather. A key methodology involves constructing a failure function that incorporates both the available sight distance (ASD) and the required stopping sight distance (RSD). This function is analyzed using a combined approach of neural networks and Monte Carlo simulations to quantitatively evaluate and generalize the reliability of LAVs under various conditions. The results reveal that variations in weather conditions and vertical curve radii significantly impact the ASD of LAVs, while the influence of speed is relatively minor. Notably, dense fog and rainfall can substantially reduce LAVs’ ASD on vertical curves. Furthermore, the vehicle automation level and speed have a significant impact on driving safety, emphasizing the need for road and operational domain design tailored to LAVs under adverse weather conditions and vertical curve radii.
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