Road Geometry Research Articles

Hazard perception test among young inexperienced drivers and risk analysis while driving through a T-junction

In this article, the hazard-based accident duration model and the reaction time due to various types of distractions are considered. This analysis is mainly considered T-junction or T-junctions like all other forms of road geometries which are more prone to accident. The model proposed is constructed by using a electronics devices along with driving simulator, to study the behavior of the yang inexperienced drivers as well as an experienced driver about their reaction by calling their phone from unknown numbers intentionally. The results show that drivers distracted by mobile phones uses the hard breaking due to least available time to respond after identification of an event. Some of the research and theory bearing on decision making and risk perception, driver situation awareness, and possible mediators of risk-taking is also analyzed here.

Statistical modeling of traffic noise at intersections in a mid-sized city, India

Abstract The modeling of traffic noise is more debated around intersections due to traffic flow and road geometry complexity. The available intersection-specific traffic noise models cannot be transferred to predict the traffic noise at intersections in the mid-sized Indian cities due to traffic heterogeneity, variety in driving conditions, and vehicle compositions. This article aims to develop an intersection-specific traffic noise model by collecting data at 19 intersections in Kanpur, India. The data include a wide range of traffic, road, and weather-related variables. Furthermore, significant input variables are determined and used in the statistical regression model to develop an intersection-specific traffic noise model for the mid-sized Indian cities. This study develops a separate entrance and exit arm model based on the corresponding influencing variables. The coefficient of determination (R 2) value is 0.74 and 0.69 for the developed model at the entrance and exit arms, respectively, whereas these models achieve R 2 values of 0.73 and 0.67 in the validation step. Also, the performance of developed models is evaluated on the standard and mean absolute errors as performance metrics. This study finds that traffic volume and receiver distance are relatively the most important variables in the entrance and exit arm noise models.

Strategies for Overcoming Environmental Noise in Recreational Area (Case Study in Tibubeneng Village, Kuta Utara District, Badung Regency)

This researcha ims to analyze noise due to traffic volume what happened in the village of Tibubeneng. Quantitative data collection was obtained by calculating the noise level due to traffic volume which is measured for 1 (one) hour and calculating the noise level due to human activity Which dB(A) sound pressure level measurement for 10 minutes for each measurement and readings are taken every 5 seconds. The results are set forth in a traffic volume, noise and road geometry survey form.Traffic Noise Level Prediction Method (CoRTN). The research results show that Noise levels due to human activities at the study site on weekdays and holidays have exceeded the noise quality standards listed in KMLH no 48 of 1996 concerning Noise Level Standards, where the noise level in recreational areas is 70 dB (A). The strategy for noise sources from human activities is to regulate the operating hours of recreational areas, arrange the implementation of activities that cause higher noise levels such as launching fireworks, adding silencers and planting trees in recreation areas. While the strategy for noise sources from traffic activities is to carry out traffic engineering, regulate traffic speed and add noise reduction from natural materials such as planting trees.

Effects of design consistency on run-off-road crashes: An application of a Random Parameters Negative Binomial Lindley model

Run-off-road crashes are one of the most common crash types, especially in rural roadway environments contributing significantly to fatalities and severe injuries. These crashes are complex and multi-dimensional events, and factors like road geometry, driver behaviour, traffic characteristics and roadside features contribute to their occurrence, separately or interactively. Sudden changes in road geometry, in particular, can influence driver behaviour, and therefore, in developing a micro-level crash risk model for run-off-road crashes, one of the challenges is incorporating the effects of driver behaviour (disaggregated information) that may arise from the variations in road geometry (aggregated information). This study aims to examine the interaction between road geometry and driver behaviour through a set of measures for design consistency on two-lane rural roads. Multiple data sources, including crash data for 2014–18, traffic data, probe speed data and roadway geometric data, for twenty-three highways in Queensland, Australia, have been fused for this study. Seventeen types of design consistency measures with regard to alignment consistency, operating speed consistency and driving dynamics are tested. A run-off-road crash risk model is estimated by employing the Random Parameters Negative Binomial Lindley regression framework, which accounts for excess zeros in the crash counts and captures the effects of unobserved heterogeneity in the parameter estimates. Results indicate that the geometric design consistency capturing the interaction between driver behaviour and operational factors better predicts run-off-road crashes along rural highways. In addition, roadside attributes like clear zone width, infrastructures, terrain, and roadway remoteness also contribute to run-off-road crashes. The findings of the study provide a comprehensive understanding of the influence of variations in roadway geometry on driver behaviour and run-off-road crashes along rural highways.

AACO: Aquila Anti-Coronavirus Optimization-Based Deep LSTM Network for Road Accident and Severity Detection

Globally, traffic accidents are of main concern because of more death rates and economic losses every year. Thus, road accident severity is the most important issue of concern, mainly in the undeveloped countries. Generally, traffic accidents result in severe human fatalities and large economic losses in real-world circumstances. Moreover, appropriate, precise prediction of traffic accidents has a high probability with regard to safeguarding public security as well as decreasing economic losses. Hence, the conventional accident prediction techniques are usually devised with statistical evaluations, which identify and evaluate the fundamental relationships among human variability, environmental aspects, traffic accidents and road geometry. However, the conventional approaches have major restrictions based on the assumptions regarding function kind and data distribution. In this paper, Aquila Anti-Coronavirus Optimization-based Deep Long Short-Term Memory (AACO-based Deep LSTM) is developed for road accident severity detection. Spearman’s rank correlation coefficient and Deep Recurrent Neural Network (DRNN) are utilized for the feature fusion process. Data augmentation method is carried out to improve the detection performance. Deep LSTM detects the road accident and its severity, where Deep LSTM is trained by the designed AACO algorithm for better performance. The developed AACO-based Deep LSTM model outperformed other existing methods with the Mean Square Error (MSE), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 0.0145, 0.1204 and 0.075%, respectively.

Comparison of Unmanned Aerial Vehicle-LiDAR and Image-Based Mobile Mapping System for Assessing Road Geometry Parameters via Digital Terrain Models

Road condition analysis is an important research topic in many fields (such as intelligent transportation, road safety, road design analysis, and traffic analysis) and depends on road geometry parameters such as longitudinal profile and cross-slope. In this study, the extraction of road geometry parameters by unmanned aerial vehicle (UAV) with LiDAR and by a mobile photogrammetric system (MPS) designed by our research group was investigated. The purpose of this study was to obtain geometric parameters (such as road longitudinal profile and cross-slope) by using digital terrain model (DTM) surfaces derived from point cloud data acquired using UAV-LiDAR and MPS. For this purpose, a framework was developed for the extraction and comparison of longitudinal and cross-sectional profiles. First, the ground filtering approach was used to extract ground points and DTM surfaces generated from an appropriate interpolation algorithm by using ground points. Cross-sectional/longitudinal profiles of the road sections were extracted and compared with reference data. A comparison of the longitudinal profiles obtained from DTMs derived from the MPS and from UAV-LiDAR revealed root mean square error values of 1.8 cm and 2.3 cm, respectively. The average deviation of cross-slopes for both surfaces was 0.19% and 0.18%, respectively. These results show that road geometric parameters can be obtained from DTM surfaces with high accuracy. It can be concluded from the results of this study that MPS can be a favorable alternative for studies on road geometry parameters extraction.

An evaluation of the passing process through road - vehicle parameters assessment

PurposeThis paper investigates the interaction between vehicle dynamics parameters and road geometry during the passing process.Methodology The methodology is based on a realistic representation of the passing task with respect to roadway’s posted speed and the ability of the passing (examined) vehicle to perform such maneuvers. Regarding passing distance outputs, an existing vehicle dynamics model was utilized, where aiming to assess the model’s accuracy, instrumented field measurements were performed. The analytical model is computationally demanding. Therefore statistical models were worked out, in line with the German rural road design guidelines, to determine passing sight distances (PSDs) by arranging combinations of 4 critical vehicle—roadway parameters; namely, vehicle horsepower rates, variations between the passed vehicle’s speed and roadway’s posted speed, peak friction supply coefficients and grade values.Results The analysis revealed that the difference between the speed of the passed vehicle and the posted speed value, as well as certain interactions of the assessed parameters impact excessively PSD, especially for values below 20 km/h. The lognormal modelling approach for predicting PSDs was found efficient and may be useful to researchers and practitioners aiming to evaluate the interaction of the utilized road—vehicle parameters in terms of determining PSDs as well as passing zones.Conclusions Although more advanced communication between vehicles or between vehicles and road environment seems a prerequisite in order integrated guidance during passing maneuvers to be enabled, the present research consists an opening paradigm of how the passing process can be standardized and therefore deployed in advanced driver assistance systems (ADAS).

Integration of Traction Electricity Consumption Determinants with Route Geometry and Vehicle Characteristics

Traction electricity (TE) consumption in rail transportation (rail transport) is determined by factors (determinant) related to the characteristics of railway lines and vehicles. They have an impact on driving speeds, which, in turn, affect energy consumption. The scientific research presented here combined the results of expert, direct and indirect measurement methods, including brainstorming, mind mapping, system approach, heuristics, failure mode and effect analysis. The main objective was to demonstrate the influence of the determinants of TE consumption, depending on the route (road) geometry and characteristics of the traction of electric vehicles and whole trains (catenary-supplied electric vehicles, non-autonomous electric vehicles, and network traction vehicles, especially electric locomotives and electric multiple units, electric multiple-units (EMUs)). Using a new approach, the TE consumption equation, we applied values for the movement resistances of electric locomotives during braking for a jointed railway track Mres JRT braking and continuous welded rail tracks Mres CWRt braking. The values of the movement resistances of the electric locomotives during startup on the jointed railway track Mres JRT startup and continuous welded rail tracks Mres CWRt startup were also applied. They showed a strong correlation with the existing speeds of catenary-supplied electric vehicles. The implementation of the new innovative approach is an important contribution to the development of engineering and technical sciences, in particular, the disciplines of civil engineering, surveying/geodesy, and transport.

Acoustic sensing of wind generated vortices in urban environments

In this work, generated vortices within an urban microspaces are investigated numerically and experimentally. Of particular interest are vortices within an urban street canyon. Computational fluid dynamics simulations of wind vortices are compared to experimental observation of similar urban street geometries within the District of Columbia. Additionally, the vortex and ground interaction are investigated for acoustic propagation. Preliminary results are presented and discussed.

Exploiting Surrogate Safety Measures and Road Design Characteristics towards Crash Investigations in Motorway Segments

High quality data on road crashes, road design characteristics, and traffic are typically required to predict crash frequency. Surrogate Safety Measures (SSMs) are an alternative category of indicators that can be used in road safety analyses in order to quantify various unsafe traffic events. The objective of this research is to exploit road geometry data and SSMs toward various road crash investigations in motorway segments. To that end, for this analysis, a database containing data on injury and property-damage-only crashes, road design characteristics, and SSMs of 668 segments was compiled and utilized. The results of the developed negative binomial regression model revealed that crash frequency is positively correlated with the average annual daily traffic volume, the length of the segment, harsh accelerations, and harsh braking. Moreover, four distinct clusters representing crash risk levels of the examined segments emerged from the hierarchical clustering procedure, ranging from more risk-prone, potentially unsafe locations to more safe locations. These four clusters also formed the response variable classes of a random forest model. This classification model used various road geometry data and SSMs as predictors and achieved high classification performance for all classes, averaging more than 88% correct classification rates.

Numerical Modelling of Free Surface Agitation in a Coastal Lagoon by Roadway Path Influence

The development and construction of highway infrastructure are essential in developing countries, whereas its layout and construction sometimes interact with the coastal environment. One problem to attend to is that the outline and geometry designs impact as little as possible on the hydrodynamic circulation of coastal bodies in order to alter the associated ecosystem as little as possible. The study area is located in the north of Colombia, and is made up of a continental coastal zone (Mallorquín Lagoon) and a marine zone (Caribbean Sea), in which a highway is projected that provides communication between two locations. This study presents the application of a numerical model previously developed and modified by the Berkhoff equation, which is developed in a finite difference scheme and has been validated and applied in different works in coastal and fluvial shallow water areas. The application of the model was carried out in a hydrodynamic circulation research project for a one-way highway through a coastal lagoon, where the knowledge of the magnitude of the incident wave height in the structure of the road body is necessary for the design, protection elements, and road geometry. Two numerical simulation scenarios were carried out, specifying normal conditions and extraordinary wave conditions in the month of November with a simulation time of 15 days, obtaining the velocity field associated with coastal currents, waves, and wave modification phenomena, such as refraction, diffraction, and reflection, which provide the height of the incident wave on the highway and the recirculation patterns in the coastal lagoon to identify alterations in the ecosystem. The results of the wave height in each scenario and the velocity field provide values to be used in the design, type of armor, and dimensions of the protection works required for the proper functioning of the road structure.

Road geometrical design out of standards: a preliminary study in a simulated context

When a road design solution is quite out of standards for the presence of insurmountable constraints, there is the need for an objective procedure aimed at achieving a certain level of safety for drivers. To avoid issues on judicial responsibility, designers aim to fully satisfy the existing standards, possibly without any exception. Traditional methodologies based on previous experience or road administrators’ guidelines generally caused problems due to the high subjectivity involved in the analysis. In this paper, to overpass these issues, a rational procedure based on vehicles telemetry data in a simulated environment is proposed. This process, through synthetic indices, allows the analysts to compare two road geometries, similar but different, because one includes curves with shorter residual circular arcs than threshold values imposed by Italian standards. The main results, derived from a two-way ANOVA with subsequent contrast analysis, suggest that a certain deviation respect to the standards did not determine any decay in the driver’s performance. Compared to the existing literature, in this study, a full objective procedure was proposed, based on a totally new indicator, which can be easily adapted to any context, involving driver, road and vehicle at the same time.

Safety Comparison of Simple and Spiral Horizontal Curves Based on Side Friction Factor Dynamic Modeling

A horizontal curve’s geometric design is considered an important factor in highway accidents, and simple and spiral curves are regarded as the most common types of horizontal curves. Various factors affect the safety of horizontal curves, one of the most important of which is the side friction factor in the horizontal curves. Therefore, in this study, the safety of simple and spiral horizontal curves was investigated for the E-class sedan, E-class SUV, and two-axle conventional truck based on the side friction factor. In this regard, CarSim and TruckSim vehicle dynamic simulation software were utilized using 360 scenarios, including vehicle speed, vehicle type, curve radius, and road geometry. It was revealed that the maximum side friction factor for all vehicles in the simple horizontal curve was higher than the spiral horizontal curve. Also, the process of increasing the side friction factor was carried out with a gentler slope in the spiral horizontal curve. Except for the radius of 0.7 times the maximum radius of the spiral horizontal curve (R) for the truck and the radii of 0.7 R and 0.9 R for the sedan and SUV, the maximum side friction factor in simple and spiral horizontal curves was lower than the AASHTO recommended values, which shows that the spiral horizontal curve was better and safer compared to the simple horizontal curve based on the side friction factor.

Surrogate Evaluation Model for Assessing Lane Detection Reliability of Automated Vehicles in Complex Road Environments

The reliability of environmental perception is the key to ensuring the stability and safety of autonomous vehicles. Complex road environments pose attentional challenges to automated driving, especially for vehicle perception systems. Therefore, evaluating the performance of autonomous driving algorithms in complex road environments is of great significance. However, as road geometry is complex, with many factors, including curvature, slope, lane marking conditions, and so on, the existing approach in assessing an algorithm through detection tests is of low efficiency. Therefore, this study relies on failure prediction modeled by extreme gradient boosting and recursive feature elimination. An evaluation model that could accomplish a quick set of road evaluations is developed. The proposed model generates recognition reliability merely by such features as road design parameters. In addition, road curvature is the most significant environmental factor affecting the perception of autonomous vehicles. The failure rates of the algorithm in different scenarios demonstrate a negative correlation between the radius of curvature and failure perception. The partial dependence involving the radius of curvature and the perception ability is obtained. Furthermore, other features, such as missing lane lines and illumination conditions, are taken into consideration to explore the potential failure perception mode. The proposed method has a particular significance for finding the failure mode of an autonomous driving perception algorithm.

Interpolation-Based Framework for Generation of Ground Truth Data for Testing Lane Detection Algorithm for Automated Vehicle

Automated vehicles, predicted to be fully electric in future, are expected to reduce road fatalities and road traffic emissions. The lane departure warning system, an important feature of automated vehicles, utilize lane detection and tracking algorithms. Researchers are constrained to test their lane detection algorithms because of the small publicly available datasets. Additionally, those datasets may not represent differences in road geometries, lane marking and other details unique to a particular geographic location. Existing methods to develop the ground truth datasets are time intensive. To address this gap, this study proposed a framework for an interpolation approach for quickly generating reliable ground truth data. The proposed method leverages the advantage of the existing manual and time-slice approaches. A detailed framework for the interpolation approach is presented and the performance of the approach is compared with the existing methods. Video datasets for performance evaluation were collected in Melbourne, Australia. The results show that the proposed approach outperformed four existing approaches with a reduction in time for generating ground truth data in the range from 4.8% to 87.4%. A reliable and quick method for generating ground truth data, as proposed in this study, will be valuable to researchers as they can use it to test and evaluate their lane detection and tracking algorithms.

Curve Path Prediction and Vehicle Detection in Lane Roads using Deep Learning for Autonomous Vehicles

The development of autonomous vehicles has seen significant progress in recent years. One of the key challenges in this field is the ability of the vehicle to accurately predict the path of the road and detect other vehicles in real-time. In this research paper, we propose a deep learning-based approach for curve path prediction and vehicle detection in lane roads for autonomous vehicles. The proposed approach utilizes convolutional neural networks to detect vehicles and predict their trajectory, taking into account road geometry and traffic conditions. The model will be trained on a large dataset of road scenes and tested in realistic simulations. The findings of this study will contribute to the advancement of autonomous vehicle technology, particularly in terms of improving the accuracy and reliability of curve path prediction and vehicle detection in lane roads.

Ranking Crossing Scenario Complexity for eHMIs Testing: A Virtual Reality Study

External human–machine interfaces (eHMIs) have the potential to benefit AV–pedestrian interactions. The majority of studies investigating eHMIs have used relatively simple traffic environments, i.e., a single pedestrian crossing in front of a single eHMI on a one-lane straight road. While this approach has proved to be efficient in providing an initial understanding of how pedestrians respond to eHMIs, it over-simplifies interactions which will be substantially more complex in real-life circumstances. A process is illustrated in a small-scale study (N = 10) to rank different crossing scenarios by level of complexity. Traffic scenarios were first developed for varying traffic density, visual complexity of the road scene, road geometry, weather and visibility conditions, and presence of distractions. These factors have been previously shown to increase difficulty and riskiness of the crossing task. The scenarios were then tested in a motion-based, virtual reality environment. Pedestrians’ perceived workload and objective crossing behaviour were measured as indirect indicators of the level of complexity of the crossing scenario. Sense of presence and simulator sickness were also recorded as a measure of the ecological validity of the virtual environment. The results indicated that some crossing scenarios were more taxing for pedestrians than others, such as those with road geometries where traffic approached from multiple directions. Further, the presence scores showed that the virtual environments experienced were found to be realistic. This paper concludes by proposing a “complex” environment to test eHMIs under more challenging crossing circumstances.

Impact of combined alignments and adverse weather conditions on vehicle skidding

Roadways in Wyoming are characterized by challenging horizontal profiles, vertical profiles, a combination of the two and adverse weather conditions, all of which affect vehicle stability. In this study, we investigated the impact of different operating speeds when negotiating combined horizontal and vertical curves under unfavorable environmental conditions on Wyoming's interstates via vehicle dynamics simulation software. The simulation tools provided the acting forces on each tire of the vehicle and the side friction (skidding) margins. This allowed for examining the interaction between vehicle dynamics and road geometry in such alignments. Also, linear regression analysis was implemented to investigate the skidding margins based on the simulation results to demonstrate when a vehicle is more likely to deviate from its desired trajectory. Specifically, this examines the contributing factors that significantly influence the skidding margins. The results indicated that: 1)the skidding margins are dramatically decreased by adverse weather conditions even with lower degree of curvature and gradient values of combined curves and more particularly at higher operating speeds conditions. Increasing the vehicle speed on the curve by 10%, the skidding margin dropped by 15%. 2) Compared to heavy trucks and sports utility vehicles (SUVs), passenger cars require the highest side friction demand. 3) The effect of applying brakes on vehicle stability depends on the road surface condition; applying the brakes on snowy road surfaces increases the potential of vehicle skidding especially for heavy trucks. This study assessed the curve speed limits and showed how important to assign safe and appropriate limits speed since the skidding likelihood is significantly sensitive to the vehicle speeds. This study is beneficial to Wyoming's roadway agencies since hazardous sections having combined horizontal and vertical curves are identified. Also, critical situations that require additional attention from law enforcement agencies are pinpointed. Finally, recommendations that are valuable to roadway agencies are made based on this study's findings.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems, path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers. In the literature, the algorithms that provide driver intent belong to two categories: those that use physics based models with some type of filtering, and machine learning based approaches. In this paper we employ barrier functions (BF) to decide driver intent. BFs are typically used to prove safety by establishing forward invariance of an admissible set. Here, we decide if the “target” vehicle is violating one or more possibly fictitious (i.e., non-physical) barrier constraints determined based on the context provided by the road geometry. The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives. The predicted intent is then used by a control barrier function (CBF) based collision avoidance system to prevent unnecessary interventions, for either an autonomous or human-driven vehicle.

Using 3D road texture measurements for tyre/road noise modell

When investigating the generation of rolling noise, profound knowledge of the tyre/road interaction is inevitable. Research projects addressing the road surface influence on the noise generation are commonly based on standardized road surface texture parameters, e.g. mean profile depth or spectral analysis of surface profiles. Such quantities are calculated from line texture measurements or equally sized parts of 3D-road surface samples. AIT has recently developed a high-speed continuous 3D texture scanner which provides heightened spatial information down to 60µm. This allows the determination of a multitude of additional parameters of road surface geometries. In this paper, comparisons of line texture, statistical analyses of the variability of 3D texture parameters on real road sections, as well as basic statistical models to investigate correlations between these parameters and coupled Close-Proximity Method (CPX) measurements will be described.