Road Geometry Research Articles

Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks

Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety-Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both. This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1,140-min of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, weaving segments, expressway merge/diverge segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multivideo stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based trajectory datasets, CitySim had significantly more safety-critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations’ three-dimensional base maps and signal timings.

Using geographically weighted logistic regression (GWLR) for pedestrian crash severity modeling: Exploring spatially varying relationships with natural and built environment factors

Although a large number of studies have tried to explore the relationship between built environment and pedestrian crash severity in developed countries, there is a lack of similar studies in the context of developing countries. Methodologically, the contributory factors influencing pedestrian crash severity are commonly identified through global logistic regression (GLR) models. However, these models are unable to capture the spatial variation in the relationships between the dependent and independent variables. The local logistic regression model, such as geographically weighted logistic regression (GWLR), can potentially overcome this issue. The application of local logistic regression to model pedestrian crash severity is absent in the literature. Therefore, this study aimed to apply the GWLR technique to explore spatially heterogeneous relationships between natural and built environment-related factors and pedestrian crash severity in Dhaka, the capital city of a developing country: Bangladesh. First, using secondary pedestrian crash data, a GLR model was developed to identify significant contributory factors influencing pedestrian crash severity. Results of the model showed that the probability of fatal pedestrian crash occurrence increased at night, in unlit locations, and during adverse weather conditions. In addition, the likelihood of a fatal crash decreases when medians exist on roads and around institutional land use. Also, the chance of fatal crashes increased on straight and flat roads and at locations with more bus stops. Finally, this study explored spatial variation in the effect intensity of these significant variables across the study area using the GWLR technique. High intensity variation across the study area was found for road geometry and institutional land use factors. On the other hand, low intensity variation was found for light conditions and the presence of median factors. This technique can be applied in any area, and the results would help provide insights into the spatial dimension of traffic safety.

Settlement relationships and their morphological homogeneity across time and scale

Homogeneous settlement morphologies negatively impact urban vibrancy, the environment, and emotions. Mainly resulting from the separation of functions such as work and living, homogeneous settlements have often been found around large cities. However, it remains unknown whether this phenomenon occurs in settlements of any size and persisted over time. In this study, we investigated the relationship between the internal structures of settlements and their location within a settlement network at a large spatial scale and a fine resolution, over seven time steps covering 120 years of settlement development. Using building footprints and road geometries from historical maps of the Swiss Plateau in combination with historical travel speeds, we analyzed networks at both the local- (building networks) and the regional-scale (settlement networks). Our findings show that particularly small settlements located near larger settlements exhibit a high degree of morphological homogeneity, and that this pattern persisted since the early twentieth century despite strong changes in mobility. These results suggest that the position of a settlement within a settlement network can have an impact on its morphological homogeneity, which in turn can have consequences for the functionality and livability of the settlement and provides useful insight to the development of settlements.

The impact of microclimate on energy performance of office buildings within urban contexts located in a composite climate, the city of Indore

The energy consumption and thermal performance of a building depend not only on the internal factors, geometry and orientation but also on the external microclimatic factors such as vegetation, solar radiation, air temperature, wind speed and wind direction. Further, it is also influenced by the surrounding built environment characteristics such as street geometry, buildings and surface characteristics. The main objective of the current research paper is to record the energy performance of office buildings within urban contexts, located in a composite climate, for instance, the city of Indore and to determine the influence of the external parameters on energy consumption of the built space so as to optimize the built environment factors and achieve low energy consumption. The current study involves the identification of two office buildings in Indore with different built environmental characteristics. Field measurements were carried out on street canyons along the south and west façades of the buildings and the impact of external microclimatic parameters and the surrounding built environment characteristics on energy consumption of the building was quantified in this study. It was found that the street geometry, H/W ratio, street orientation, building material, urban density and vegetation has significant effects on microclimate and energy consumption of the buildings.

Automatic Calibration for Monocular Cameras in Highway Scenes via Vehicle Vanishing Point Detection

Automatic camera calibration is a fundamental technology for 3D traffic parameter extraction. With the popularity of pan-tilt-zoom cameras, this technique demonstrates great potential to enhance traffic safety and efficiency, especially for highways. This paper aims to present a fully automatic calibration method for surveillance cameras in highway scenes. Our system is divided into two stages. In the first stage, a deep convolution neural network was used to estimate a pair of orthogonal vanishing points from multiple vehicles. This process transformed vanishing point detection into an estimation of vehicle direction, which was further determined by introducing the central residual mechanism. In the diamond space, the straight lines formed by these directions accumulated the final positions of the vanishing points. More importantly, we proposed a novel algorithm for estimating the lane width using vehicle trajectories in the second stage. It can be used to calculate the camera height, making the calibration fully automated. We also corrected the distorted lens using vehicle trajectories. Comprehensive experiments were conducted on the proposed data set and the BoxCars116k data set. The results indicate that the composite mechanism (i.e., classification and central residual) significantly improves the accuracy and robustness of the vanishing point estimation. Combined with automatic camera height estimation, our technology is superior to the most representative methods in calibration performance. Since it does not have any constraints on road geometry and camera placement, our approach applies to most highway surveillance systems.

Pengaruh Usia dan Tingkat Pendapatan Terhadap Potensi Kecelakaan Lalu Lintas dengan Menggunakan Stuctural Equation Modeling (SEM)

Cause traffic accidents include human factors, vehicle factors, road factors and the environment. This study aims to analyze the effect of age, income level, and road geometry on the potential for traffic accidents. The research location is in the Talaud Archipelago Regency, North Sulawesi Province. This research uses a quantitative approach by collecting data using a questionnaire distributed to 200 respondents who use two-wheeled and four-wheeled vehicles. The Likert scale measurement method analysis uses the Structural Equation Modeling (SEM) on the Amos 22.00 program The results showed that age does not directly have a significant effect on the potential for accidents of 0.917 but indirectly age has a significant effect on the potential for accidents through the condition of the vehicle with a statistical value of 3.779, the level of income directly has a significant effect on the potential for accidents with a probability value <0.001 and indirectly has a significant effect through vehicle condition Z statistics value of 2.925, road geometry does not directly influence the potential for accidents with a probability value of 0.184, driving behavior does not directly influence the potential for accidents 0.820 and vehicle conditions directly have a significant effect on potential accidents probability value <0.001.

Statical Analysis of Road Accident in Indapur City

Abstract: Accident analysis studies aim at the identification of high rate accident location, safety deficient areas and the accidents prone zones. For this purpose the road accident data for seven consecutive years, pertaining for a city has to be used. The road geometry will be measured in accident prone location to find out the cause of accident, based on these result, suggestion are to be provided to reduce the frequency of accidents at that particular place. Accidents prediction is done by regressing traffic volume with accident data to obtain an equation which could apprehend the forth coming accidents in the future. Preventive measures regarding the improvement of traffic condition for minimizing accidents rate are suggested

Bivariate-Logit-Based Severity Analysis for Motorcycle Crashes in Texas, 2017–2021

Due to the number of severe traffic collisions involving motorcycles, a comprehensive investigation is required to determine their causes. This study analyzed Texas crash data from 2017 to 2021 to determine who was at fault and how various factors affect the frequency and severity of motorcycle collisions. Moreover, the study tried to identify high-risk sites for motorcycle crashes. Utilizing bivariate analysis and logistic regression models, the study investigated the individual and combined effects of several variables. Heat maps and hotspot analyses were used to identify locations with a high incidence of both minor and severe motorcycle crashes. The survey showed that dangerous speed, inattention, lane departure, and failing to surrender the right-of-way at a stop sign or during a left turn were the leading causes of motorcycle crashes. When a motorcyclist was at fault, the likelihood of severe collisions was much higher. The study revealed numerous elements as strong predictors of catastrophic motorcycle crashes, including higher speed limits, poor illumination, darkness during the weekend, dividers or designated lanes as the principal road traffic control, an increased age of the primary crash victim, and the lack of a helmet. The concentration of motorcycle collisions was found to be relatively high in city cores, whereas clusters of severe motorcycle collisions were detected on road segments beyond city limits. This study recommends implementing reduced speed limits on high-risk segments, mandating helmet use, prioritizing resource allocation to high-risk locations, launching educational campaigns to promote safer driving practices and the use of protective gear, and inspecting existing conditions as well as the road geometry of high-risk locations to reduce the incidence and severity of motorcycle crashes.

Roadside LiDAR Sensor Configuration Assessment and Optimization Methods for Vehicle Detection and Tracking in Connected and Automated Vehicle Applications

This paper develops an assessment and optimization model for configuring roadside LiDAR (Line Detection and Ranging) installation. More specifically, an analytic and a simulation model have been developed to analyze the detection blind zones and their impact on vehicle detection and tracking capabilities in Connected and Automated Vehicle (CAV) applications. The proposed model can derive the area and height of the detection blind zones from a given roadside LiDAR location and road geometry. Evaluation metrics are also proposed to assess the severity of the blind zone including laser beam density, blind zone height and duration, vehicle trajectory missing rate and duration. The simulation model can be used to evaluate and identify optimal configurations for different installation scenarios. To validate the proposed model, the 15-min US101 NGSIM (Next Generation SIMulation) dataset was used for validating the proposed model. Different configuration settings were simulated and compared. The evaluation results demonstrate the capabilities of the proposed models in planning for optimal roadside LiDAR sensor installation for vehicle detection and tracking.

ANALISIS KONSUMSI BAHAN BAKAR ALAT ANGKUT UD QUESTER CWE 370 DALAM KEGIATAN PENGANGKUTAN BIJIH NIKEL

PT. X is a mining contractor company located in North Morowali Regency, Central Sulawesi Province. Transport activities use a combination of excavator PC300 and UD Quester CWE370. Based on the results of the study, there are several haul road geometries that do not meet the standards that have a road slope of more than 8%. Other factors that affect the increase in fuel consumption are; acceleration, RPM, engine brake horsepower and workload. The problem that appear is that the consumption and/or fuel ratio exceeds the company's standards, data recorded in March 2021 that the consumption and fuel ratio of transportation equipment reached 13.63 liters/hour and 0.47 liters/BCM from the company standard of 8 liters/hour and 0.27 liters/BCM. The analysis was conducted to determine the effect of haul road conditions on the fuel consumption of conveyances. The theoretical calculation of fuel consumption is carried out using two methods, that is based on rimpul and RPM. It is known that fuel consumption based on rimpul calculation is 12.10 liters/hour, while based on RPM is 18.84 liters/hour. After making improvements to the geometry of the road will reduce fuel consumption and increase productivity. Based on these calculations and recommendations, fuel consumption uses rimpul calculations, namely; 12.45 liters/hour, while the fuel ratio is 0.28 liters/BCM..

Dielectric characterisation of rock aggregates with different grain size distributions

Aggregates largely influence a road's dielectric properties, which is an important physical parameter when using non-destructive methods such as ground penetrating radar to evaluate a road's geometry. Due to the irregular shapes, the direct measurement of an aggregate piece by piece in the microwave domain is not easy. To address this problem, an epoxy-based composite is used to estimate the dielectric constant of the aggregates by resonant test and mixing law calculation. As a water-free binder, the epoxy composite eliminates the water effect on the dielectric properties. Meanwhile, the distribution of each component can be studied by X-ray computed tomography, which allows knowing the composite's morphology. Moreover, the composite has a smooth surface that avoids the texture effects on the dielectric measurement. In addition, the impedance analysis of individual aggregates at low frequencies was used to evaluate the results obtained with composites and mixing laws.

A real-time collision-free maneuver generation algorithm for autonomous driving

In this paper we propose a real-time maneuver generation algorithm for Autonomous Vehicles (AVs). Given a planar road geometry with static and moving obstacles along it, we are interested in finding collision-free maneuvers that satisfied the AV’s dynamics and subject to physical and comfort limits. Based on longitudinal and transverse coordinates, we propose a novel collision avoidance constraint and formulate a suitable maneuver regulation optimal control problem. Maneuver regulation has intrinsic robustness with respect to standard trajectory tracking that stems from the requirement of following a desired path with a desired velocity profile assigned on it. The optimization problem is solved by using a nonlinear optimal control technique that generates (local) optimal trajectories. We demonstrate the efficacy of the proposed algorithm by providing numerical computations on two different scenarios. Finally, experimental results are presented to demonstrate the efficiency of the proposed algorithm both in terms of computational effort and dynamic features captured.

The influence of LED road stud color on driver behavior and perception along horizontal curves at nighttime

Scotopic lighting conditions (reduced level of natural light or presence of artificial lighting) may impair driving performance and, therefore, impact on road safety. Thanks to technological developments, low-cost light emitting diode (LED) studs are now being considered as an alternative and affordable pavement marking solution to assist drivers in these conditions. By helping them to maintain their vehicle within the marked lane, the studs should prevent any deterioration in driver performance when negotiating curves at nighttime. However, the few studies that investigated the impact of LED studs on driving performance produced inconsistent results, and the question of whether they actively improve driver performance remains open. Furthermore, while international road regulations allow the use of LED studs, they do not provide consistent prescriptions for their lighting color.Here, we assessed the influence of different LED lighting colors (red, white, and unlit) on longitudinal and transversal driver behavior when negotiating road curves with different radii and sense of direction. In the study, thirty-six drivers drove a dynamic virtual scenario featuring twenty-four curves. After the driving simulation, participants completed a static perception test in which they assessed each curve in terms of the perceived levels of risk, pleasantness, and arousal they experienced while driving on it.In comparison with the unlit and red lit curves, those marked with white lighting LED studs were perceived as less risky, less arousing, and more pleasant independently of the radii and curve direction. Furthermore, when entering these curves, participants tended to shift their driving trajectories towards the center of the road. This effect was most evident on the central part of the curve. Further studies are expected to corroborate these results by focusing on different road geometries and LED stud layouts, as well as testing driving behavior in controlled road field studies.

The effect of street design on speed of vehicles along urban residential streets in Johor

Street design plays an important role in controlling vehicular speed. This has a direct impact on enhancing the safety of road users and the quality of the environment for residents living adjacent to the streets. The increasing of road accidents every day is worrisome. The safety of other road users is adversely affected when drivers travel at high speeds. Speeding is also the cause of noise and air pollution, which may negatively impact community livability. The speed of the vehicle is influenced by the physical condition of the road such as uneven roads, sharp turns and narrow lanes. Thus, by tweaking the design of streets, it is possible to achieve speed reduction. The objective of this study is to investigate the influence of road geometry and parking on speed of a moving vehicles in urban residential areas. This study was conducted on local streets and collectors in Johor. The data obtained were vehicle speed, road characteristics (road length, road width, road gradient) and number of parked vehicles. Then, the data were analysed using One-way Analysis of Variance (ANOVA) to find the relationship between street design and vehicle speed. The result from the analysis shows that road characteristics such as traffic calming and road gradient can significantly influence vehicle speed.

Assessing the effects of road geometry and fidelity on driver behaviour in driving simulator experiments

The paper presents a set of programs created specifically to investigate the driver’s behaviour along a road circuit when the driving simulation scenario changes in the quantity and quality of the displayed Level of Details (LOD) (fidelity), as well as when the road curvature ratio (geometry) varies. In the experiments, carried out on a sample of 29 drivers who drove on the same circuit twice, data on vehicle features (speed, trajectory, and gas pedal usage), physiological factors of the drivers (SC, skin conductance), and information from an eye-tracking equipment (gaze) were collected. The programs used in this study are designed to study the complex and position-based information present in the data that cannot be analysed by classical statistical methods. The results are twofold. On one side, they show the applicability and usefulness of the proposed functions, which are capable of extracting information and referring it to a specific point in the circuit. On the other side, they give effective insights on driving behaviour. When driving is more demanding (i.e., along curvilinear stretches), variations between different scenarios are nearly non-existent, and the effect of geometry changes greatly depending on the signal being investigated (speed, trajectory, gas pedal, gaze, skin conductance). The effects of vehicle speed are essentially the same for all drivers and are closely tied to the geometry of the road. Trajectory and gas-pedal usage signals exhibit diverse effects on drivers, indicating that their choice of trajectory and frequency of gas-pedal usage is undoubtedly influenced by their driving style as well as by geometry. Skin conductance appears to be significantly altered where driving is more challenging, but it varies greatly from driver to driver. The results of gaze analysis also demonstrate that the driver’s behaviour strongly depends on geometry and much less on LOD and loop sequence.

Comparing Machine Learning Techniques for Predictions of Motorway Segment Crash Risk Level

Motorways are typically the safest road environment in terms of injury crashes per million vehicle kilometres; however, given the high severity of crashes occurring therein, there is still space for road safety improvements. The objective of this study is to compare the classification performance of five machine learning techniques for predictions of crash risk levels of motorway segments. To that end, data on crash risk levels, driving behaviour metrics, and road geometry characteristics of 668 motorway segments were exploited. The utilized dataset was divided into training and test subsets, with a proportion of 75% and 25%, respectively. The training subset was used to train the models, whereas the test subset was used for the evaluation of their performance. The response variable of the models was the crash risk level of the considered motorway segments, while the predictors were various road design characteristics and naturalistic driving behaviour metrics. The techniques considered were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Among the five techniques, the Random Forest model achieved the best classification performance (overall accuracy: 89.3%, macro-averaged precision: 89.0%, macro-averaged recall: 88.4%, macro-averaged F1 score: 88.6%). Moreover, the Shapley additive explanations were calculated in order to assist with the interpretation of the model’s outcomes. The findings of this study are particularly useful as the Random Forest model could be used as a highly promising proactive road safety tool for identifying potentially hazardous motorway segments.

Impact of Pavement Defects on Traffic Operational Performance

The present research investigates the influence of pavement distresses on traffic operational performance in terms of speed and capacity. The pavement distresses are expressed by the pavement condition index (PCI). Three roads of multi-lane highways located on the road network of Kafrelsheikh governorate, Egypt, were chosen as a case study. Road geometry, pavement condition characteristics, and traffic volume were collected manually from 23 sections for field data surveys. For speed measurements, spot speed data were gathered using the stopwatch method under the free-flow conditions, and the percentile speeds (V50% and V85%) have been calculated for different vehicles classes. Traffic capacity was determined via extrapolation of the quadratic function. Regression analysis models were created to express the relationships of both the percentile speeds and traffic capacity as dependent variables, and three independent variables (PCI, lane, and shoulder widths). These empirical mathematical relations help in developing road design and maintenance works on multi-lane highways. The main conclusion of the study demonstrated that traffic operational performance was more significantly affected by pavement distresses than road geometry. Moreover, the results indicated that pavement distresses have a significant impact on light vehicles more than heavy ones.

Improving Road Segmentation by Combining Satellite Images and LiDAR Data with a Feature-Wise Fusion Strategy

Numerous deep learning techniques have been explored in pursuit of achieving precise road segmentation; nonetheless, this task continues to present a significant challenge. Exposing shadows and the obstruction of objects are the most important difficulties associated with road segmentation using optical image data alone. By incorporating additional data sources, such as LiDAR data, the accuracy of road segmentation can be improved in areas where optical images are insufficient to segment roads properly. The missing information in spectral data due to the object blockage and shadow effect can be compensated by the integration of 2D and 3D information. This study proposes a feature-wise fusion strategy of optical images and point clouds to enhance the road segmentation performance of a deep learning model. For this purpose, high-resolution satellite images and airborne LiDAR point cloud collected over Florida, USA, were used. Eigenvalue-based and geometric 3D property-based features were calculated based on the LiDAR data. These optical images and LiDAR-based features were used together to train, end-to-end, a deep residual U-Net architecture. In this strategy, the high-level features generated from optical images were concatenated with the LiDAR-based features before the final convolution layer. The consistency of the proposed strategy was evaluated using ResNet backbones with a different number of layers. According to the obtained results, the proposed fusion strategy improved the prediction capacity of the U-Net models with different ResNet backbones. Regardless of the backbone, all models showed enhancement in prediction statistics by 1% to 5%. The combination of optical images and LiDAR point cloud in the deep learning model has increased the prediction performance and provided the integrity of road geometry in woodland and shadowed areas.

Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia.

The coronavirus disease 2019 (COVID-19) pandemic led to rapid and unexpected changes across the world, particularly in road safety. Thus, this work assesses the impact of COVID-19 accompanied by government preventive policies on road safety in Saudi Arabia by investigating the crash frequency and crash rates. A 4-year crash dataset relating to 2018-2021 was collected, covering about 71,000km in total road length. It covers all intercity roads and some of the major intercity roads in Saudi Arabia with over 40,000 data logs of involved crashes. We considered three different time phases to observe road safety. These time phases were identified by the duration of government curfew measures against COVID-19 (before, during, and after). The crash frequency analysis showed that the curfew during COVID-19 significantly impacted the crash reduction. At a national level, the crash frequency decreased during 2020 and reached a 33.2% reduction compared to 2019 (the previous year), and it surprisingly continued decreasing in 2021 (the consequent year) to another 37.7% reduction although the government measures were lifted. Moreover, considering the traffic volume and road geometry, we analyzed crash rates for 36 selected segments, and the results showed a significant reduction in the crash rate before and after the COVID-19 pandemic. Additionally, a random effect negative binomial model was developed to quantify the impact of the COVID-19 pandemic. The results showed that the reduction in crashes was significant during and after COVID-19. Also, single roads (two-lane, two-way) were found to be more dangerous than other types of roads.