Road Geometry Research Articles

Analysis of Black Site and Black Spot at Jl. Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang Section, Mempawah

Transportation plays a vital role in society by facilitating the movement of people and goods. However, it also brings challenges like traffic congestion and accidents. With urbanization and increased vehicles, road safety has become crucial, as accidents have significant economic and social impacts. Analyzing accident-prone areas helps identify necessary preventive measures. This research was conducted on the 10.1 km Jl. Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang section in Mempawah. Primary data from field surveys includes road conditions, fittings, geometry, and vehicle speeds. Secondary data is traffic accident data (2018-2023) from the Mempawah City Police, analyzed using numerical methods.The Jalan Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang corridor analysis reveals several accident-prone areas (Black Spots) with damaged road features and poor safety facilities. Key findings include deteriorating zebra crossings, speed traps, and traffic signs. High-risk accident locations were identified using Z-Score and Cusum methods. Recommendations include repairing roads, repainting markings, installing better signage, and maintaining clear sightlines. Future studies should focus on more specific interventions and monitoring to improve road safety, following Department of Settlements and Regional Infrastructure guidelines for effective accident reduction

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

Driving Safety and Comfort Enhancement in Urban Underground Interchanges via Driving Simulation and Machine Learning

Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives.

Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.

Analisis Konsumsi Bahan Bakar Alat Muat dan Angkut Pada Pengupasan Overburden di PT Putra Maga Nanditama Bengkulu Utara

The research was conducted at PT Putra Maga Nanditama site Gunung Selan, PT Putra Maga Nanditama is a company engaged in coal mining. The background to the research is that overburden production did not reach the company's target and fuel consumption for overburden loading and transport equipment exceeded the company's fuel target. The research method used is a quantitative method. Data collection consists of primary and secondary data. Primary data includes tool cycle time and work efficiency, actual fuel consumption as well as road geometry and distance to disposal. Secondary data in the form of regional performance maps, equipment specifications, rainfall data, equipment working hours data, drone maps for August 2023 and contours. Based on the results of research in the field, factors that influence fuel consumption such as road geometry, road conditions and distance to overburden disposal are still not in accordance with established standards. The productivity of the DX800LC Excavator is 279.59 BCM/hour, the Doosan DA45 ADT is 60.47 BCM/hour and the Volvo A40G ADT is 59.37 BCM/hour. For Doosan DX800LC Excavator fuel consumption is 48.21 liters/hour, ADT Doosan DA45 is 26.62 and ADT Volvo A40G is 23.72 liters/hour. The actual fuel comparison for the two haulers is that the ADT Volvo A40G is more efficient than the ADT Doosan DA45 both in terms of fuel consumption and fuel costs.

Road Geometry Feasibility for Automated Vehicles with ACC Systems: Vehicle Dynamics on Curved Roads

Road Geometry Feasibility for Automated Vehicles with ACC Systems: Vehicle Dynamics on Curved Roads

Automating construction of road digital twin geometry using context and location aware segmentation

Geometric Digital Twins (GDT) represent a critical advancement in road management, yet their practical implementation encounters a substantial obstacle due to development costs outweighing the expected benefits. This paper addresses this challenge and introduces an automated solution for creating 3D geometric foundation models for road digital twins. The proposed approach utilises point clouds to generate meshed, coloured, and semantically labelled models of road objects. The proposed solution incorporates context- and location-aware segmentation, followed by a 3D representation step via meshing. Experiments showed that the solution achieves a 91.7% mean intersection over union segmentation on road furniture in the Digital Roads dataset and surpasses the current leader on the KITTI360 dataset by +16.93%. As a result, the fully automatic method enables scalable and affordable geometry digital twinning for roads.

Safety, Efficiency, and Mental Workload in Simulated Teledriving of a Vehicle as Functions of Camera Viewpoint.

Teleoperation services are expected to operate on-road and often in urban areas. In current teleoperation applications, teleoperators gain a higher viewpoint of the environment from a camera on the vehicle's roof. However, it is unclear how this viewpoint compares to a conventional viewpoint in terms of safety, efficiency, and mental workload. In the current study, teleoperators (n = 148) performed driving tasks in a simulated urban environment with a conventional viewpoint (i.e., the simulated camera was positioned inside the vehicle at the height of a driver's eyes) and a higher viewpoint (the simulated camera was positioned on the vehicle roof). The tasks required negotiating road geometry and other road users. At the end of the session, participants completed the NASA-TLX questionnaire. Results showed that participants completed most tasks faster with the higher viewpoint and reported lower frustration and mental demand. The camera position did not affect collision rates nor the probability of hard braking and steering events. We conclude that a viewpoint from the vehicle roof may improve teleoperation efficiency without compromising driving safety, while also lowering the teleoperators' mental workload.

Evaluation of the Implementation Status of the Nepal Rural Road Standards (NRRS) Concerning Geometrical Parameters: A Case Study of Morang District of Nepal

Maintaining the effectiveness of the transportation system is essential to economic growth. Poor road upgrading methods in many developing nations, however, are making it impossible for traffic to flow freely. Nepal developed the Nepal Rural Road Standards (NRRS) of 2055, District Transportation Master Plan (DTMP), and Municipality Transportation Master Plan (MTMP) to regulate the construction of rural road infrastructure. These guidelines provide guidelines for rural road planning, design, building, and maintenance, among other aspects. However, each geometric design has certain features, and parameter requirements depending on the region and location to be applied. This study aims to compare the geometrical features of the rural roads that have been built with the Nepal Rural Road Standards (NRRS) 2055, second revision (2071). Using a random sampling approach, six rural roads in the Morang district were selected for the data collection. To gather geometrical data, field observations and measurements were made at random chainages. This study evaluates various geometric parameters using linear and angular field measurements. Mapping conducted via smart road software ensures road geometry compliance, including aspects such as curve radius, shoulder width, gradient, sight distance, carriageway, and additional widening. The findings demonstrate that strict adherence to established standards substantially enhances road safety. Based on the results of the field observation, the geometric elements have been built according to design specifications, except for the additional widening at shoulder width and horizontal.

Supporting driver performance in closely spaced tunnel-interchange structure: Traffic control devices and effects on driving behavior

Objectives With the rapid development of expressways in the mountainous regions of southwestern China, closely spaced tunnel-interchange structures have inevitably emerged due to topographical constraints and environmental limitations. Given the unfavorable road geometry and rapid cross-section transitions, drivers face significant safety concerns. This study aims to investigate drivers’ safety performance at closely spaced tunnel-interchange sections and determine how safety risks can be mitigated through improved traffic control devices design. Methods Thirty-nine participants conducted an experimental study in a fixed-base simulator. The test scenario was modeled on the Xingyan Freeway-S3801 and accurately reproduced in the simulator. For each safety performance metric, the driving simulator experiments yielded a dataset with 780 observations. To address the idiosyncratic variation due to individual driver differences, a series of linear mixed effects models (LMM) were developed to analyze drivers’ behavior responses. Results In closely spaced tunnel-interchange sections, a general impairment of both longitudinal and lateral performance was observed. This study identified potential critical impact variables in traffic control device systems. According to the LMM results: (a) Removing the 0.5 km interchange ramp exit advance guide sign located in the tunnel exit area reduces dangerous behavior in the corresponding impact area. (b) Replacing the 0.5 km interchange ramp exit advance guide sign with arrow pavement markers as an information source supports improved driver performance, promoting driver safety. (c) Adding tunnel exit distance signs within tunnels is recommended to enhance situation awareness for drivers. Conclusions This study addresses the scientific issues related to traffic control devices setup for closely spaced tunnel-interchange sections, focusing on identifying potential critical impact variables. The findings provide guidance on the design of traffic control devices for such sections and support revisions to national engineering standards.

ANALISIS BIAYA OPERASIONAL KENDARAAN BERDASARKAN KECEPATAN RATA-RATA PERJALANAN DI JALAN MAJAPAHIT KOTA MATARAM

The city of Mataram, the center of trade, offices, and education, experienced an increase in population and vehicles. The 2023 data recorded 441.147 thousand residents and 385,579 motor vehicles. The increase in traffic volume on road Majapahit has an impact on traffic performance and vehicle operating costs. The study entitled " Analysis Of Vehicle Operational Costs Based On Average Travel Speed On Jalan Majapahit Mataram City" was conducted to further understand the condition of road section performance and the Operating Cost of Vehicles passing through the road. The MKJI method is a calculation standard used to analyze the performance of road sections, assess capacity and service levels, based on traffic volume and road geometry. while to calculate BOK (Vehicle Operating Costs) used an equation method developed by the PCI 1989 method to measure vehicle costs, including fixed costs and non-fixed costs, which are influenced by the average speed of travel. The results of the study on the Majapahit road section show the service level of the Majapahit road section in category C. For the calculation of the free flow speed, namely 52.68 km / h (east) and 49.10 km / h (west). The speed of the eastbound peak clock is 48 km/h and the westward is 42 km/h. While the calculation results of BOK Group I (Eastward) Free Flow Speed Rp1.049,- / km, Peak Hour Speed Rp1.082,- /km. Group I (West) Free Flow Speed Rp1.072,- /km, Peak Hour Speed Rp1.163,- /km. Group IIA (Eastward) Peak Hour Speed Rp2.921,- /km, Peak Hour Speed Rp3.007,- /km. Group IIA (Westward) Free Flow Speed Rp2.980,- /km, Peak hour speed Rp3.235,- /km

Contribution of congested traffic flow condition to air pollution at intersections in Addis Ababa, Ethiopia

Currently, one of the serious problems in the world is transport-related air pollution. Air pollution from vehicles was not considered properly in the plan, design, and management system of the roads in Addis Ababa city, to solve the problem of traffic congestion. It is influenced by different factors such as road geometry, road surface type, traffic congestion, traffic characteristics, fuel type, and meteorological parameters. This study was aimed to investigate the contribution of congested traffic flow conditions on air pollution at intersection points in Addis Ababa city. To carry out the study, seven intersection points were selected in the city randomly. For the measurement technique of carbon monoxide (CO) and sulfur dioxide (SO2) gases, a portable Aeroqual series 500 instrument was exercised, and for the particular matter (PM2.5 and PM10), Light Amplification by Stimulated Emission of Radiation (LASER) was adapted. To count the number of traffic on the site, a video camera method was utilized. For the data analysis techniques, a multiple linear regression method was practiced, which is very powerful for multiple independent variables with a single dependent one. The study revealed that the level of traffic congestion at each selected intersection is at a severe level. During the congested time, as a base of the steady condition, the pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by an average of 19.10, 51.61, 33.83, and 29.07 % respectively. Based on the analyzed results, when the concentration of pollutants increases, the traffic volume, percentage of heavy vehicles, green time, and approach grade are also increased. On the other hand, the concentration of pollutants decreased when the lane width and wind speed increased. Therefore, any concerned bodies in and around Addis Ababa city should take remedial measures to decrease the emission of the concentration of the pollutants. This investigation is very important for policymakers, municipalities, and planners to do any activities in the city. Researchers can also use it as a reference, for further investigations and remedial measurements.

Vehicle Dynamic Maneuverability through Data-driven Six Sigma Analysis for Enhanced Driver Assistance

This paper presents the concept of a vision-based system to provide intelligent driving assistance for steering, braking, and acceleration maneuvers. The system utilizes vision systems to detect the vehicle's surroundings, including the road, other vehicles, pedestrians, and obstacles. Based on this environmental awareness, the system can intervene to assist the driver to enhance the safety and efficiency of the driving experience by maneuverability access or alert to driver in steering, brake, and acceleration. The steering assist function analyzes the road geometry and trajectory and provides subtle steering adjustments alerts to keep the vehicle centered in the lane thereby smoothly navigate curves. The braking assist function detects potential collision threats and can assist driver on application status of brakes to avoid or mitigate impacts. Similarly, the acceleration assist function monitors the driving conditions and traffic flow to optimize throttle input for improved fuel economy, traffic flow thereby reducing the impact to dynamic parts of vehicles. A detailed six sigma analysis is done by DMAIC method to prove the proposed methodology significance compared to existing techniques.

Modelling the effects of neighbourhood and street geometry on pedestrian thermal comfort in Hong Kong

The severe global warming and rapid urbanization increase the risks of extreme heat stresses for pedestrians. While urban geometry significantly affects pedestrian thermal comfort, limited research has explored the relative impacts of street and neighbourhood design. ENVI-met simulation models were constructed to analyse their effects on pedestrian thermal comfort in an urban street of Hong Kong. Subsequent ANOVA analysis revealed that surrounding building height was a major contributor to thermal comfort variations of a street in addition to street orientation and aspect ratio, while tree coverage ratio and ground albedo played much weaker roles. Based on these, a multivariate model has been developed to predict hourly street thermal conditions for various street orientations, aspect ratios and surrounding building heights. Specific preferred and avoided street configurations for optimizing the thermal environment have been identified. For instance, aspect ratio ≥ 5.0 is preferred for NW-SE Street with surrounding building height of 45–105 m.

Network-level crash risk analysis using large-scale geometry features

Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways’ horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model’s attributes with the Harris Hawks Optimization (HHO) algorithm—referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of “region-road-segment” for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.

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

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

ASSESSMENT OF TRAFFIC CONGESTION AND ITS IMPACT ON ROAD CAPACITY IN URBAN AREAS UNDER MIXED TRAFFIC CONDITIONS USING MULTIPLE REGRESSION ANALYSIS - A CASE STUDY IN ADDIS ABABA CITY

Traffic congestion is a complex phenomenon, which is connected to a number of vehicle movements on the road. The major reasons behind this problem are due to various continuous activities in the urban areas such as education, employment, recreation, business, political, social and cultural. These activities in addition to floating and migration increase congestion on city roads. This will affect the traffic stream characteristics and reduces the road capacity and level of service. The present study is aimed to evaluate and analyze the traffic congestion and its effect on road capacity and level of service at Mebrat haile mid-block location in Addis Ababa city. To achieve the objective, the Mebrat haile mid-block was divided into four segments appropriately and the traffic volume and speed studies were conducted at each segment, for a period of 10 hours in a day at every 15 minutes consecutive interval. The necessary traffic data including road geometry at each segment was recorded. The congestion levels at each segment were then measured by estimating the reduced speed between each segment. The volume to capacity ratio (v/c ratio) was then estimated and found to be exceeding 1 in most of the hours of the day, resulting poor level of service F. Various Multiple Regression Models were developed correlating the reduced speed with traffic volume, composition and road geometry. The results from the analysis indicates that the traffic congestion on the urban roads, have an impact on urban characteristics.

Developing accident frequency prediction models for urban roads: A case study in São Paulo, Brazil

The growing number of vehicles and the evolving behaviour of road users present new and additional challenges to road safety. Study on the variables that influence the frequency of crash occurrences such as road geometry, junction, speed and land use are needed as they have proven effects on the number and severity of crashes. In this paper, we identify and assess the variables, namely road geometry, vehicle speed, traffic volume, land use and junction type, and develop accident frequency prediction models for a main urban transport corridor in São Paulo, Brazil. Crash data was provided by the traffic management company of the city, other datasets were obtained from a mix of primary and secondary sources including roadside cameras, Geographic Information Systems (GIS) and digital mapping tools. The studied road was segmented and the coefficients associated with variables in the segments were obtained using Poisson regression through a stepwise variable selection procedure. Two models with junctions density per type (access/km, T-junction unsignalised/km, T-junction signalised/km and crossroads/km) and junction density per merged type (signalised/km and unsignalised/km) along with land use per type (commercial and residential) are developed. The junction density and land use are found to be significant and positively correlated with crash frequency. The models were evaluated by statistical means for their accuracy of predicting the crashes, and validated with additional information obtained from field observation.

The method for evaluating the coverage rate of roadside monitoring cameras in road areas

Accurately assessing the coverage rate of roadside monitoring cameras in road areas is crucial for intelligent transportation systems and road risk assessment. This paper presents a new method that uses Carsim software to simulate different camera tilt angles and road geometry conditions to evaluate the coverage area of roadside monitoring cameras. By adjusting the camera tilt angles and the curve radius of the road, the coverage effect under different conditions is analyzed, and affine transformation is used to calculate the actual coverage area. The research results show that this method can effectively fit the road area, providing theoretical support for the deployment of monitoring cameras in traffic.

Lifetime cost analysis of concrete barriers and steel guardrails

This study investigates the lifetime costs associated with concrete barriers and steel guardrails. We introduce a cost analysis methodology that incorporates critical factors such as construction costs, maintenance costs, exposure risks during maintenance activities, and the costs imposed to traveling public through the increased traffic and the crash outcomes. We integrate various parameters including economic factors, road geometry, general weather condition, and traffic mix to estimate a location-dependent cost for each type of barrier accurately. A software tool, named CalBarrier, was developed during this study to carry out the calculations and the comparison of lifetime cost of aforementioned barriers. An inherent strength of this research is its reliance on recent real data extracted from various databases of California Department of Transportation (Caltrans), ensuring precision and relevance in accounting for various influential factors. Drawing insights from Caltrans practices and interviews with their personnel, this study emphasizes the intricate decision-making process involved in mitigating safety risks and reducing operational expenses. Although our data originates from California, the methodology for life cycle cost analysis, and our software are applicable for regions with different socio-economic conditions by deploying user input costs, making our findings a valuable resource for other areas facing comparable challenges.