High-speed Roads Research Articles

A comparison between bridge-related and non-bridge crashes: severity, vehicle involvement, and motorists’ characteristics

Objectives Bridges, though representing a small portion of transport networks by length, are still prone to traffic crashes. Despite extensive research on bridge-related crashes globally, there’s a scarcity of studies exploring differences between bridge- and non-bridge-related crashes. Thus, this paper attempts to add to the current knowledge by comparing bridge-related crashes and non-bridge crashes using a case study in Victoria, Australia. Methods By utilizing crash data in Victoria between January 2006 and April 2023; the Chi-squared test is conducted to examine differences between crash types. Partial proportion odds models are then employed to establish if bridge-related crashes tend to be more severe. Results In general, the analysis reveals that bridge-related crashes tend to result in more severe injury outcomes compared to non-bridge crashes. Furthermore, there is evidence that bridge-related crashes are more likely to be single-vehicle, to involve a heavy vehicle as well as young/male motorists, and to occur on high-speed roads, regional areas and at nighttime than non-bridge crashes. Conclusions A targeted program aiming at enhancing traffic safety at bridges is recommended due to the higher severity of bridge-related crashes. Considering the presence of bridges on the road network, and particularly in regional areas where many are in poorer conditions, it is essential to consider retrofitting as well as low-cost solutions in order to complement motorist education and awareness programs. Specific groups with higher risks of being involved in bridge-related crashes, such as young male motorists, should be targeted in these programs.

Expressway Network Model Construction and Road Section Impedance Estimation Based on Mixed Data

The acquisition of high-speed road network information and traffic environment information is of great significance for traffic management and planning, and at the same time, an accurate and efficient road network model is also the basis for the acquisition and application of traffic parameters. In order to quickly obtain high-speed road network information and road section traffic state information, this paper takes Chongqing Highway as an example, analyzes the traffic condition and characteristics of the highway network, and establishes the topology of the high-speed road network using network data and actual high-speed toll data from the graph theory and complex network theory. At the same time, based on the topology theory, fully exploiting the high-speed toll data, a more accurate estimate of different time periods, vehicles in the high-speed road section of the travel time, for highway traffic analysis to provide data reference, to improve the traffic planning program to enhance the efficiency of traffic management, to promote traffic planning and decision-making, as well as to enrich the theoretical basis of transportation research is of great significance.

The effects of built environments on bicycle accidents around bike-sharing program stations using street view images and deep learning techniques: The moderating role of streetscape features

With the global rise of bike-sharing programs (BSP), planners and traffic experts have raised concerns as to whether the rapid growth of BSP ensures cycling safety. Despite numerous studies on built environments encouraging bike usage, there is limited knowledge whether streetscape environments around BSP stations affect bicycle accidents. We address this gap by investigating the relationships between various built environments and bicycle accidents around BSP stations, with a particular focus on the moderating effects of comprehensive streetscapes. Streetscape environments, estimated through semantic segmentation and k-means clustering, were used in two-level negative binomial regression models to clarify how street- and station-level environments affect different types of bicycle accidents. The findings indicate increased crash likelihoods on higher-speed roads, streets with traffic facilities, in proximity to public transportation infrastructure, and specific streetscape types. In particular, streetscape features like green oasis streets and open-sky roadways positively contribute to mitigating the negative effects of traffic facilities, such as bike networks and crosswalks, on cycling safety. This study aids in developing comprehensive strategies and guidelines to retrofit built environments for better traffic safety and thereby promote urban cycling.

Construction and optimisation of watershed scale ecological network: a case study of kuye river basin

IntroductionCreating an ecological space network is essential for safeguarding the core structure of ecological space.MethodsMorphological spatial pattern analysis was used to locate ecological sources in the Kuye River Basin. Using the least cumulative resistance model and gravity model, the resistance surface, ecological corridor, and ecological space management network are determined.Results and discussionThe study revealed that in 2022, the predominant land use types in the Kuye River Basin were wood land and grassland, cultivated land, and construction land. MSPA model software identifies a substantial portion of the landscape pattern as consisting of core and marginal areas, which encompass 30324.05 hm2 and 15088.24 hm2. High ecological resistance surface factors dominate the socioeconomically vibrant zone and northern regions. Resistance values ranging from 0.02 to 0.87, and high and law resistance zones alternate. The minimal cumulative resistance approach found 171 ecological corridors. And gravity model using the interaction matrix of 19 primary ecological sources discovered 8 first-level ecological corridors with the highest interaction force. There are 137 core and 23 subsidiary ecological corridors with significant affects. Overlying the road factors and ecological corridors of national highways, provincial roads, railways, and high-speed roads creates a total of 38 ecological breakpoints, each characterized by specific barrier effects and legal ecological stability.

Road Design and Traffic Detection Methods for Autonomous Driving Scenarios

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities

The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

Perceived effectiveness of traditional and technology-based speeding-related countermeasures

Introduction: Speeding behaviour contributes significantly to road crashes and subsequent injuries and fatalities. The purpose of this study was to examine which traditional countermeasures (i.e., police enforcement and on-road signs) and technology-based countermeasures (i.e., advanced driver assistance systems [ADAS] and in-vehicle speed audio alerts) drivers perceived as effective in assisting them to comply with posted speed limits. Methods: Participants (N = 680; Mage = 49.34 years) who held a current driver’s licence completed a 20-minute online survey. Participants in the experimental condition were randomly assigned to read one of four scenarios which differed based on location (urban or regional) and posted speed limit (60 km/hr or 100 km/hr) or to the control condition (no scenario), before answering questions about the perceived effectiveness of the traditional and technology-based interventions. For the experimental conditions, participants were instructed to respond to these questions based on how they would drive in the situation outlined in the scenario. Results: Low-level speeding behaviour was common, with 40.7 % reporting regularly driving 5 km over the posted speed limit in a 60 km/hr speed zone and 50.4 % reporting regularly driving 5 km over the posted speed limit in a 100 km/hr speed zone. A mixed ANOVA revealed that participants perceived police enforcement activities to be the most effective at assisting them to comply with posted speed limits when compared to other traditional and technology-based approaches. Further, ADAS was rated by participants as significantly more effective at assisting them to comply with the posted speed limit in the 100 km/hr urban condition compared to the 60 km/hr urban condition. Conclusions: Low-level speeding behaviour remains common practice and there is a need for continued roadside police presence to discourage this behaviour. Drivers with ADAS-equipped vehicles could also be encouraged to use systems, such as adaptative cruise control, to assist with speed management on high-speed roads.

Impacts of noise pollution from high-speed rail and road on bird diversity: a case study in a protected area of Italy

The disturbance of infrastructures may affect biological communities that are exposed to them. This study assesses the impact of high-speed (highway and railway) infrastructures in a protected study site, the Natural Reserve Fontanili di Corte Valle Re (Emilia–Romagna, Italy). We compared bird diversity with sound intensity and frequency in three sampling areas, increasingly distant from the infrastructures at the border with the reserve, during the last 4 years (2019–2022), monitoring sedentary, nesting, and migratory bird species. We hypothesize a decreasing diversity closer to the source of disturbance, which is mostly attributable to noise pollution. Our findings confirmed this trend, and we show that, in particular, disturbance seems to influence species richness more than the total abundance of birds. We also discovered that highway disturbance was much higher than railway in terms of frequency and duration. In light of these results, we suggest that some species, which have a behavioral ecology strongly based on singing to communicate with each other for their reproductive and defensive strategies, may suffer more from constant acoustic disturbance. The installation of effective noise barriers to shield the sound produced by the highways should be considered a mandatory request not only in proximity to houses but also in the vicinity of protected areas.

Optical fiber sensing and tensor AP clustering for high-speed road tunnel vehicle detection

Aiming at the problems of signal acquisition, feature extraction and vehicle recognition in highway tunnel vehicle detection, a new tunnel vehicle detection method is proposed by combining optical time-domain reflectometry distributed fiber sensing technology and tensor affine propagation clustering algorithm. Firstly, the distributed optical fiber system designed by optical time domain reflectometry was used to collect the running signals of tunnel vehicles and obtain the measurement data. Secondly, a high-order tensor sample set is constructed by using the spatial resolution of optical fiber as channel number and combining the feature number, time domain and frequency domain. Finally, tensor affine propagation clustering method and other clustering methods are used to test the accuracy. The test results show that the proposed method can better classify vehicles without destroying the original high-dimensional data structure. Meanwhile, the unsupervised clustering algorithm also reduces manual intervention in the identification process, increases the intelligence level of the whole vehicle detection model, and effectively improves the detection accuracy rate of tunnel vehicles.

A New Strategy for Planning Urban Park Green Spaces by Considering Their Spatial Accessibility and Distributional Equity

Urban park green spaces (PGSs) are crucial social public resources that provide various ecological services and enhance human health and well-being. However, with the acceleration of urbanization, the accessibility and equity of urban PGS resources are increasingly threatened. This study aims to propose an integrated framework that considers accessibility and equity simultaneously to optimize the planning and layout of urban PGS within the high-speed ring road of Hefei City. This study first used an improved two-step floating catchment area (2SFCA) method to quantify the level of accessibility of urban PGS within the ring road. Then, with the use of Lorenz curves, Gini coefficients, and bivariate correlation coefficients, the equity characteristics of these PGSs were quantified and evaluated, followed by an analysis of their relationships to the accessibility levels. Based on this comprehensive evaluation, the particle swarm optimization (PSO) algorithm was employed to the areas with low accessibility and equity levels to propose targeted PGS optimization strategies. The results showed that the accessibility of PGS was unevenly distributed, exhibiting a clear spatial difference of “east–west clustering”. The number of subdistricts with good (52.24–94.78) and best (94.79–283.58) accessibility was four, which was less than one-tenth of the total number of subdistricts in the study area. At the subdistrict level, the Gini coefficients for the accessible area of all types of PGSs were substantially higher than the international warning line of 0.4, indicating a substantial inequity in the population’s access to PGS. The implemented PSO algorithm resulted in eight new parks being planned at the specific optimized locations. Based on the actual land use status of the selected sites, recommendations are provided for the planning and layout of PGS. This proposed framework offers valuable data and theoretical insights for urban public green space planning and design in similar regions.

Integrative Approach for High-Speed Road Surface Monitoring: A Convergence of Robotics, Edge Computing, and Advanced Object Detection

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries.

Factors influencing the road accidents in low and middle-income countries: a systematic literature review

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

The system of formation of a fleet of vehicles for traffic safety management on high-speed roads

The article discusses the issue of increasing the requirements for road safety in the Russian Federation. The data on the growth of the fleet and the increase in transport mobility of the population are presented, while it is noted that the death rate as a result of road accidents remains very high. Special attention is paid to the analysis of winter road maintenance, the causes of mass accidents, as well as the assessment of the quality of road maintenance and possible ways to improve the safety system. It is noted that at the moment there is wear and tear of existing road equipment, which is being replaced with new imported ones. The necessity of improvement has been established and an algorithm for the formation and management of a fleet of road and utility vehicles servicing highways has been proposed to minimize accident-hazardous road operating conditions and ensure road safety at all levels. Keywords road safety, winter maintenance of roads, motorways, road vehicles, a fleet of vehicles for the maintenance of highways

Latest Development of Low Noise Road Surfacing in Hong Kong - Application on Local Roads with Different Road Characteristics

Like other metropolitan cities in the world, road traffic noise is a major problem in Hong Kong. The Hong Kong Government endeavors to pre-empt road traffic noise issue through comprehensive noise planning process for new road projects or new residential developments. For traffic noise from existing roads, the Government has adopted various noise mitigation strategies, for example, resurfacing with Low Noise Road Surface (LNRS) material, retrofitting of noise barrier, etc., to combat the noise problem. As of today, Polymer Modified Friction Course (PMFC) is the most commonly adopted LNRS in Hong Kong, which serves as a standard road surface material for high-speed roads and expressways. Nonetheless, it is found to be less durable for local roads due to its open-texture, especially for common road characteristics such as frequent start stop, sharp turn, roadside parking, etc. To explore suitable LNRS material for local roads, a trial programme on a newly developed LNRS material - Polymer Modified Stone Mastic Asphalt (PMSMA6), has been launched to evaluate its noise reduction performance and engineering feasibility on local roads with common road characteristics. This paper attempts to share the experience and latest technical findings of the above trial programme on LNRS in Hong Kong.

Reasons Why Only Kuwaiti Citizens Drive Electric Vehicles despite Being Only a Quarter of the Population

The adoption of fully battery-powered electric vehicles (EVs) in Kuwait is currently at less than one percent of the population, and hence Kuwait has the lowest index rank among countries around the globe. Effectively, only Kuwaiti nationals have the opportunity to own EVs, as there are no fast direct-current charging stations in Kuwait, and Kuwaiti landlords (expats cannot own real estate) do not allow the installation of EV-charging home wall boxes at their rented properties. Given these facts, this paper only focuses on the preferences of Kuwaiti nationals, even though they only constitute 25 percent of the population of Kuwait. To accomplish the present objectives, a quantitative descriptive method (closed-ended questions) was used to collect data from a sample of 227 Kuwaiti nationals who were representative of the owners of half a million internal combustion engine (ICE) cars and that were categorized as early majority consumers. The findings of the present study indicate that over 50 percent of the respondents would prefer to buy an EV in the following three years when certain criteria were satisfied, including government-controlled pricing policies and recharging point availability, high-speed roads, and free EV-dedicated parking spaces. Furthermore, over 40 percent of respondents stated that they would contemplate purchasing an electric vehicle if the price of gasoline or diesel increased by 19 to 50 percent. The findings also indicated that more than 40 percent of respondents believed that EVs are fire- and crash-safe, and roughly 50 percent of the respondents would be willing to pay between 6 and 20% more for an EV because they believe that EVs are ecofriendlier vehicles and are significantly faster than conventional petrol vehicles. Additionally, respondents rewarded those vehicles with an excellent mark because of their ecological, economical, and technological attributes and benefits.

The Surface Accelerations Reference—A Large-Scale, Interactive Catalog of Passenger Vehicle Accelerations

There is a need for a large-scale, real world, diverse, and context rich vehicle acceleration catalog that can be used to design, analyze, and compare various intelligent transportation systems. This paper fulfills three primary objectives. First, it provides such a catalog through the Surface Accelerations Reference, which is openly available as an interactive analytics tool as well as an open and downloadable dataset. The Surface Accelerations Reference statistically describes the driving profiles of about 3,500 individuals contributing 34 million miles of continuous driving data collected in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). These profiles were created by summarizing billions of longitudinal and lateral acceleration epochs experienced by the participants. Second, this paper introduces a standardized methodology for creating such a catalog so that similar acceleration profiles can be produced for other human cohorts or automated driving systems. Finally, the data are used to analyze the effect of roadway speed category on the rates of lateral and longitudinal acceleration epochs at various thresholds. It is observed that, for the median driver, the rates of epochs are up to three orders of magnitude higher on low-speed roads as compared to high-speed roads. This catalog will facilitate intelligent vehicle system designers to compare and tune their systems for safer driving experiences. It will also allow agencies with similar data to create comparable catalogs facilitating safety and behavioral comparisons between populations.

Seat belt usage in buses – An observation study of usage and travellers’ perspectives

This study aims to evaluate seat belt usage in buses and to understand travellers’ incentives of seat belt usage. Methods used are observational studies (10 cities, with 328 bus observations), focus group discussion (7 groups with a total of 32 participants) and a web survey (n = 1737 respondents). The results show that the seat belt use among bus passengers can be improved especially in regional and commercial bus traffic. It is more common to buckle up on long trips than on short trips. However, even though observations show high usage during long trips, travellers report that they remove the seat belt after a while if they want to sleep or for comfort reasons. For the bus drivers it is not possible to control passengers’ usage. Dirty seat belts and technical malfunction might deter some passengers from using them and therefore systematic cleaning and control of seats and belts are recommended. On short trips one reason for not using the belt is related to worries about getting stuck and not being ready to get off in time. In general, it is most important to increase the usage on high-speed roads (>60 km/h), in lower speed it might be more important to provide a seat for each passenger. Based on the results a list of recommendations is presented.

Adaptive Stability Control Based on Sliding Model Control for BEVs Driven by In-Wheel Motors

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control.

Investigating pedestrian crash patterns at high-speed intersection and road segments: Findings from the unsupervised learning algorithm

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at high speeds means that the driver has less time to react and make evasive maneuvers to avoid a pedestrian crash. On top of this, other crash-contributing factors such as humans (pedestrians or drivers), vehicles, roadways, and surrounding environmental factors actively interact together to cause a crash at high-speed locations. The pattern of pedestrian crashes also differs significantly according to the high-speed intersection and segment locations which require further investigation. This study applied association rules mining (ARM), an unsupervised learning algorithm, to reveal the hidden association of pedestrian crash risk factors according to the high-speed intersection and segments separately. The study used Louisiana pedestrian fatal and injury crash data (2010 to 2019). Any crash location with a posted speed limit of 45 mph or above is classified as a high-speed location. Based on the generated association rules, the results show that pedestrian crashes at a high-speed intersection are associated with the intersection geometry (3-leg) and control (1 stop, no traffic control device), driver characteristics (careless operation, failure to yield, inattentive-distracted, older, and younger driver), pedestrian-related factors (violations, alcohol/drug involvement), settings (open country, residential, business, industrial), dark lighting conditions and so on. Most pedestrian crashes at high-speed segments are associated with roadways with no physical separation, dark-no-streetlight conditions, open country locations, interstates and so on. The findings of the study may help to select appropriate countermeasures to reduce pedestrian crashes at high-speed locations.