Road Geometry Research Articles

PERFORMANCE ANALYSIS OF JALAN PIET A. TALLO IN KUPANG CITY

Jalan Piet A. Tallo is a major arterial road connecting several areas with the city center of Kupang. The large traffic flow that passes through Jalan Piet A. Tallo, resulting in traffic congestion during rush hour, is the background of this research. This research aims to determine the right alternative solution to reduce traffic congestion on Jalan Piet A. Tallo. This research uses a descriptive method by obtaining data directly from surveys and analyzing the data according to the guidelines of the 1997 Indonesian Road Capacity Manual (MKJI). Then, the analysis produces values for the degree of saturation (DS) and level of service (LOS) of the road. Overall, from the results of this research, traffic jams are caused by several factors; namely, there is a narrowing of the road from road type 4/2 D to type 2/2 UD, many vehicles enter and exit from Jalan M. Sabaat to Jalan Piet A. Tallo, and many drivers still violate traffic signs. Therefore, an alternative that can be done is to contact the relevant authorities about the prohibition of right turns onto Jalan M. Sabaat and the need to install road dividers from STA 0+300 to STA 0+550. Apart from this, the road can be widened by 1.7 m in the inner city lanes so that the degree of saturation (DS) during the afternoon rush hour can be reduced by 12.67% to 0.875. Another alternative is to change the road geometry from type 2/2 UD to 4/2 D and build a new connecting bridge. Thus, the degree of saturation (DS) during the afternoon rush hour decreases by 52.89% to 0.472. This value shows that the traffic flow is stable and the speed is controlled. Keywords: Road Performance, Congestion, Piet A. Tallo Road, Kupang City

Drivers’ Steering Behavior in Curve by Means of New Indicators

The design of road geometry is based on a rather elementary assumption that the user strictly follows the lane axis. Based on this hypothesis, the ideal trend of some factors related to the driver’s performance, such as steering angle and speed, can be derived to optimize the most appropriate design choices. In practice, driving behavior differs from the assumed one and produces trends in these variables, which are very different from the ideal functions. The purpose of this research is therefore to propose synthetic performance indicators useful for highlighting the real characteristics of users’ driving behavior during road travel. Toward this aim, some driving experiments along four different curves in a simulated environment were studied in order to evidence possible criticisms. The proposed indicators showed a remarkable ability to represent and synthesize even very complex performance function trends. The proposed performance indicators can have multiple uses, such as, for example, in statistical analyses—which are generally carried out at a later stage—or constitute sufficient information to guide the decisions of infrastructure managers. In the long term, in a “smart road” perspective, they can be used by road administrators for information exchange among users (with each other and with the infrastructure) to improve road operation and safety.

Neural Network-Driven Reliability Analysis in Safety Evaluation of LiDAR-Based Automated Vehicles: Considering Highway Vertical Alignments and Adverse Weather Conditions

Complex road environments threaten the safe operation of automated vehicles. Among these, adverse weather conditions and road geometries have particularly significant impacts. This study investigates LiDAR-based automated vehicles (LAVs) driving safety on vertical curved roads in adverse weather. A key methodology involves constructing a failure function that incorporates both the available sight distance (ASD) and the required stopping sight distance (RSD). This function is analyzed using a combined approach of neural networks and Monte Carlo simulations to quantitatively evaluate and generalize the reliability of LAVs under various conditions. The results reveal that variations in weather conditions and vertical curve radii significantly impact the ASD of LAVs, while the influence of speed is relatively minor. Notably, dense fog and rainfall can substantially reduce LAVs’ ASD on vertical curves. Furthermore, the vehicle automation level and speed have a significant impact on driving safety, emphasizing the need for road and operational domain design tailored to LAVs under adverse weather conditions and vertical curve radii.

Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller

This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby reducing passenger discomfort and the risk of vehicle rollover. An innovative aspect of our work is the adaptive adjustment of lookahead distance based on real-time vehicle dynamics and road geometry, which ensures optimal path following under varying conditions. A quasi-feedback control algorithm constructs optimal clothoids at each time step, generating the appropriate steering input. A lead filter compensates for the vehicle’s lateral dynamics lag, improving control responsiveness and stability. The effectiveness of the proposed controller is validated through a comprehensive co-simulation using TruckSim® and Simulink®, demonstrating significant improvements in lateral control performance across diverse driving scenarios. Future directions include scaling the controller for higher-speed applications and further optimization to minimize off-track errors, particularly for articulated vehicles.

Adopting the Performance Margin in Horizontal Curve Design

A vehicle negotiating a curve experiences a centripetal force that is partially supplied by the tire–pavement friction. The American Association of State Highway and Transportation Officials’ design handbook, A Policy on Geometric Design of Highways and Streets (“Green Book”), dictates the design of horizontal curve segments according to the side friction (demand) factor, which is a fraction of the available friction used during a maneuver. However, the Green Book casts the side friction factor not as a measure of tire–pavement friction but as a measure of lateral acceleration. In this way, the Green Book conflates these independent concepts (tire–pavement friction versus lateral acceleration). It is shown in this work that keeping these curve design parameters independent has meaningful benefits. A rigorous mathematical clarification of the differences among the side friction (demand) factor, lateral acceleration, and coefficient of friction is developed. It is shown that changing the coefficient of friction affects the side friction factor, but the opposite is not necessarily true. An alternate metric is proposed, the performance margin [Formula: see text], that has several advantages for assessing friction margins. Currently, horizontal curves are designed only with a lateral friction reserve. Excluding longitudinal dynamics may not be realistic given the manner in which drivers react in limit handling maneuvers. The [Formula: see text] accounts for both bidirectional dynamics and road geometry, and highlights regions where the Green Book’s standards are inconsistent with vehicle performance capabilities. The work concludes by posing recommendations aimed at implementing the [Formula: see text] in future Green Book editions.

Enhancing Traffic Flow and Safety at LMU Nurtanio Road Crossings - Abdul Rahman Saleh Intersection in Bandung

Bandung is the largest metropolitan city in West Java Province and serves as its provincial capital. One of the locations where vehicle queues or traffic congestion occurs is at the level crossings between the highway and railway tracks, including the direct crossing (JPL) at Andir on LMU Nurtanio Street - Abdul Rahman Saleh Street. The purpose of this research is to assess the performance of the existing road section, simulate it using the PTV VISSIM program, and find alternative solutions to the identified problems. In this study, primary data was obtained through direct field measurements, including vehicle volume, queue length, and road geometry data. Additionally, secondary data on the number of trains passing through the crossing was obtained from PT Kereta Indonesian Fire. The PTV VISSIM program used in this analysis is a student-licensed program, which implies limitations and simplifications in the analysis. The performance calculation of the road section yielded a degree of saturation (Ds) value of 0.73 for LMU Nurtanio Street and 0.77 for Abdul Rahman Saleh Street. According to the road service levels set by the government, both roads fall below service level C. The PTV VISSIM modeling results indicated a queue length of 66.59 m for LMU Nurtanio Street and 105.18 m for Abdul Rahman Saleh Street. Bandung study uses PTV VISSIM to assess LMU Nurtanio - Abdul Rahman Saleh Streets, revealing congestion issues. Queue lengths highlight improvement needs.

Driving Behaviour Estimation System Considering the Effect of Road Geometry by Means of Deep NN and Hotelling Transform

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving.

Analysis of Road Safety in terms of Management and Traffic Safety in Palembang (Case Study of Colonel H. Burlian Road, Palembang)

Road traffic accidents are the ninth leading cause of death and are responsible for 2.2% of all deaths globally. Road safety audits are one way to anticipate traffic accidents. These accidents generally occur due to various causes collectively, including: humans, road conditions, and road completeness. The objective of this study is to conduct a Road Safety Analysis in the Management and Traffic Safety Aspects of Kolonel H. Burlian Street in Palembang. The research used a descriptive method with a qualitative approach as well as interview methods and HIRARC analysis. There are six informants with two main informants and four regular informants. The results showed that along the Kolonel H. Burlian Street, the road geometry was still not suitable for vehicles due to the road conditions that had many holes to the bumps, the completeness of traffic signs was sufficiently fulfilled because they had poor conditions such as road markings in faded conditions or covered with patches.

Intelligent speed adaptation for visibility technology affects drivers’ speed selection along curves with sight limitations

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time. This is a particular issue along curves with limited available sight, where speed management is necessary to avoid unsafe situations (e.g., driving off the road or invading the other traffic lane). To solve this issue, we proposed a novel intelligent speed adaptation (ISA) system for visibility, called V-ISA (intelligent speed adaptation for visibility). It estimates the real-time safe speed limits based on the prevailing sight conditions. V-ISA comes with three variants with specific feedback modalities (1) visual and (2) auditory information, and (3) direct intervention to assume control over the vehicle speed.Here, we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances, using a driving simulator. We also considered curve road geometry (curve direction: rightward vs. leftward). Sixty active drivers were recruited for the study. While half of them (experimental group) tested the three V-ISA variants (and a V-ISA off condition), the other half always drove with the V-ISA off (validation group). We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points, with no discernible negative impact on driver lateral behaviour. On rightward curves, the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations. Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and, consequently, establishes safer driving conditions.

Geometric field model of driver’s perceived risk for safe and human-like trajectory planning

Autonomous driving systems must provide safe, predictable, and consistent behaviors across diverse scenarios to enhance user experience. The geometric driver risk field (GDRF) method is proposed for human-like, interpretable, and fast risk estimations. The method models the driver’s subjectively perceived risk with fields formed by sets of geometric shapes. Unlike current safety measures adopted in trajectory planning algorithms, the proposed method aligns with driver risk perception properties. It provides risk estimations based on probable future vehicle states and the risk consequences at different locations. Compared with existing potential field methods for risk estimations, the mathematical form of the method has a high calculation efficiency while keeping the ability to model the details of the risk information. Influences of different obstacle types, multiple regions with different risk levels, and the relative motions between vehicles are modeled in a unified manner. The graph-search and optimization trajectory planning algorithms with a hierarchy framework are also designed to attain desired risk objectives in the planned trajectories. The framework mitigates the possible suboptimality problems in previous hierarchy trajectory planning algorithms. The proposed GDRF method and planning algorithms are examined in driving scenarios with different traffic states and road geometries. Results showed that the proposed risk estimation method outperforms previous risk-oriented potential field methods by more human-like and safer trajectories with evidence from existing studies and safety measures like time headway (THW), time to collision (TTC), and time to lane crossing (TLC). It also provides more consistent behaviors while maintaining a short computation time. The integration of the proposed risk estimation method and trajectory planning algorithms holds great potential for improving the safety and user experience of autonomous driving systems in real-world scenarios.

Leveraging Generative Design and Point Cloud Data to Improve Conformance to Passing Lane Layout.

Inadequate highway design is a leading cause of traffic accidents, underscoring the importance of adhering to guidelines and regulations for highway design. These standards exist to safeguard road users by addressing crucial factors, like road geometry, signage, and lane markings. Thus, emphasis is placed on computational methods that can optimize towards higher levels of safety, capacity, efficiency, and sustainability in highway designs. Building Information Modeling (BIM) enhances this process by creating a digital model with physical and operational attributes. In this study, a user-friendly, logic-based language is utilized to encode rules for designing highway passing lanes by which designs are automatically evaluated and generated in the BIM-kit software toolkit. This approach is applied to 16 real-world passing lanes in Alberta, showcasing its utility in transportation. The analysis reveals significant enhancements, with rule compliance increasing from 61.82% to 91.31% after employing generative design techniques. These findings underscore the significance of generative design in transportation, offering engineers an efficient tool to create innovative, compliant solutions for highway projects.

Nonlinear effects of traffic statuses and road geometries on highway traffic accident severity: A machine learning approach.

The purpose of this study is to explore nonlinear and threshold effects of traffic statuses and road geometries, as well as their interactions, on traffic accident severity. In contrast to earlier research that primarily defined road alignment qualitatively as straight or curved, flat or slope, this study focused on the design elements of road geometry at accident locations. Additionally, this study considers the traffic conditions on the day of the accident, rather than the average annual traffic data as previous studies have done. To achieve this, we collected road design documents, traffic-related data, and 2023 accident data from the Suining section of the G42 Expressway in China. Using this dataset, we tested the classification performance of four machine learning models, including eXtreme Gradient Boosting, Gradient Boosted Decision Tree, Random Forest, and Light Gradient Boosting Machine. The optimal Random Forest model was employed to identify the key factors infulencing traffic accident severity, and the partial dependence plot was introduced to visualize the relationship between severity and various single and two-factor variables. The results indicate that the percentage of trucks, daily traffic volume, slope length, road grade, curvature, and curve length all exhibit significant nonlinear and threshold effects on accident severity. This reveals sepecific road and traffic features associated with varying levels of accident severity along the highway section examined in this study. The findings of this study will provide data-driven recommendations for highway design and daily safety management to reduce the severity of traffic accidents.

Exploring the Utility of Auto-machine Learning in Predicting Traffic Accident Severity in Jordan

The World Health Organization (WHO) estimates that approximately 1.35 million annual fatalities are caused by road traffic accidents, representing a significant global challenge. This study focuses on creating a machine-learning model to forecast traffic accident severity in Jordan. The objective of this study is to help reduce fatalities and economic losses caused by accidents, which can achieve using various machinelearning classifiers, like Decision Trees, Random Forests, Light Gradient Boosting Machines, Extra Trees, Bagging Classifiers, and Gradient Boosting. These models were assessed before and after down-sampling to deal with imbalance in accuracy, balanced accuracy, recall, precision, and F1-score metrics with and without hyper parameter tuning. The study emphasized the importance of advanced analytics in improving road safety measures and reducing accident severity. The researchers analyzed a dataset of 115,148 accidents. Factors considered included traffic volume, environmental conditions, and road geometry features. The data was segmented into urban and rural categories for customized modeling. Down-sampling improved the models' ability to detect injuries and deaths in both urban and rural areas. Hyper parameter tuning offered an additional improvement in balanced recall and F1-score, particularly after down-sampling. In urban areas, the LGBM and Gradient Boosting classifiers showed the most significant gains in recall for minority classes, while in rural areas the Random Forest, Bagging, and Extra Trees classifiers maintained a better balance between precision and recall. All classifiers achieved high accuracy (above 0.97) in both urban and rural areas, so that these models can accurately forecast the severity of accidents, including property damage, fatalities, and injuries. The authors have also recommended incorporating variables of temporal patterns, driver behavior, demographic data, and geographic location into the models of accident prediction to make them more efficient and stronger. Keywords: Accident severity prediction, Auto-machine learning, Accident classifiers, Python, Jordan.

Assessing pedestrian thermal comfort to improve walkability in the urban tropical environment of Nagpur city

Walking can be an efficient and sustainable mode of transportation for "last mile" connectivity. However, the willingness to walk largely depends on the availability of infrastructure, safety, and comfort. Improving thermal comfort on streets connected to transit stations is crucial for encouraging walking and public transit use. This study assesses seasonal and spatiotemporal variations in pedestrian thermal comfort (PTC) on an N-S-oriented street in Nagpur (India). Thermal walk surveys simultaneously monitored environmental conditions and human thermal perception (thermal sensation vote-TSV). The findings revealed that urban geometry significantly influences PTC and TSV, and the level of influence varied spatiotemporally in both seasons. This study shows the relationship between urban street geometry, microclimate, and PTC, emphasizing the necessity of a multidimensional assessment approach.

Investigating Autonomous Vehicle Accidents at Urban Intersections based on Road Geometry Data

Investigating Autonomous Vehicle Accidents at Urban Intersections based on Road Geometry Data

Pemodelan Kondisi Geometrik Jalan Terhadap Potensi Kecelakaan Lalu Lintas

Road infrastructure has an impact on the occurrence of road traffic accidents. The geometry of a road is a variable that influences the occurrence of road traffic accidents. Interpreting the observed crashes into an equation that can describe the crashes is one of the most difficult problems faced by scientists and engineers in any research. The traffic accident research conducted is still limited to analyzing the causes of accidents and jumping to handling strategies that do not reveal how much geometric influence on accidents at the location. This research was developed using a comprehensive modeling approach to illustrate how highway geometric design, in this case the alignment, influences crash rates. This study aims to analyze the influence of road geometrics on the incidence of accidents. The method used is multiple liner regression measurement model using R-Studio. The variables to be studied include road geometry planning parameters, especially horizontal alignments which include stopping sight distance (X1), radius of horizontal curve (X2), shoulder width X3, and transition curve length (X4). The results of the study obtained a relationship in the mathematical equation Y = 99.55508-0.02634X1+1.17647X2-0.06999X3-0.03906X4 with the most significant correlation value produced is the transition curve length and radius of horizontal curve. Based on the multiple R-squareed value of 0.8775 this model has a very strong correlation category. Where the ability of X1 to X4 is able to explain Y by 80%.

AI-Based Evaluation of Streets for People in Bangkok: Perspectives from Walkability and Lingerability

Bangkok, the capital of Thailand, grapples with several urban challenges, including traffic accidents, congestion, and underdeveloped sidewalks. These issues stem from the escalating use of private cars and motorcycles driven by rapid urbanization, accompanying economic growth, and the unique street geometry of the city. Furthermore, despite the development of railroads, issues persist with the poor walking environment for pedestrian access to stations, posing a challenge to the promotion of safe and environmentally friendly public transportation. This study evaluated streets’ spatial performance, focusing on the performance of soi, the intricate network of narrow urban pathways in Bangkok. Leveraging an AI model capturing pedestrian value, this study subsequently assessed accessibility from typical residential areas to public transportation stations. The findings shed light on the challenges faced by sois in Bangkok. Based on the findings, practical implications were discussed and provided to inform urban designers, with the aim of promoting public transportation usage and encouraging a more sustainable urban form.

COST-EFFICIENT METHODS OF DERIVING SLOPE INFORMATION FOR ROAD SEGMENTS IN DRIVER-ASSISTANCE APPLICATIONS

Abstract. An advanced driver-assistance system (ADAS) is any of a group of technologies that assist drivers in driving and parking functions. Through a safe human-machine interface, ADAS increase car and road safety. These Advanced driver-assistance systems rely on special maps with extended geometry and attribute information. This extra information includes slope, curvature, and speed limit. ADAS-enabled maps are usually rather expensive in the industry. This paper is focused on finding cost-efficient alternatives for generating the slope aspect of ADAS maps. Slope and height information is not only used in ADAS but is a critical aspect of calculating electronic vehicle (EV) ranges, and truck fuel-efficiency calculations as well. ADAS slope information usually requires high-accuracy surveys. This paper researches the possible generation of slope information for road segments with the use of digital elevation models (DEM) or crowdsourcing with low-cost sensors and Kalman filtering. The first approach is based on globally available DEMs with interpolation and filtering with road geometry. DEMs have variable accuracy depending on the type of technology used in producing them. Such technologies include photogrammetry, aerial and terrestrial laser scanning (ALS, TLS), or aerial or space radar measurements. The other method is by using low-cost GPS and IMU sensors for generating altitude profiles. These produced altitude profiles are compared with a profile generated from a high-accuracy survey using large-resolution DEMs produced by aerial photogrammetry or aerial laser scanning. This paper proposes metrics with which these datasets can be compared, one is using the height differences, and the other compares the slope values at discrete common points. In the conclusion, the paper tries to find use cases for the low-accuracy data.

Identification of necessity behind the construction of the third Shitalakhya bridge based on level of service (LOS) measurement parameters applicable for developing countries

With the expansion of industrial activities in developing countries like Bangladesh, the purchase of motorized vehicles is also growing in recent days. This increased number of motorized vehicles is creating massive pressure on the logistic movement in industrial areas, and the performance level of roadways is deteriorating day by day. It has become quite difficult to meet the need for a reliable transportation system that can provide good compatibility for road users and logistical operations. Kanchpur Industrial Area can be classified as an industrial hub of Narayanganj District due to the presence of jute trading and plant processing industries. Over the years two bridges have been constructed in the proximity of this area named, the Kanchpur Bridge and Sultana Kamal Bridge to decrease the pressure on N2 National Highway; however, the situation is still not satisfactory and construction of a third bridge over the Shitalakhya River is under consideration by the Government of Bangladesh (GOB). Dhaka-Sylhet Highway is a section of the N2 National Highway system which connects the industrial district Kanchpur with Dhaka City. The main objectives of the study were to develop a methodology which can be used to measure the Level of Service (LOS) of roadways located in developing countries and use it to understand the necessity of the construction of the third Shitalakhya Bridge. LOS measurement procedures suitable for developed countries are not useable for developing countries due to the difference in road geometry, presence of non-motorized vehicles, and control delay at intersections. A procedure has been developed to measure LOS by combining standards of the National Institute of Technical Teachers Training and Research, Chandigarh, India; Highway Capacity Manual (HCM), USA; and Roads and Highway Department (RHD), Bangladesh. LOS measurement was based on four parameters: Peak Hour Factor (PHF), Travel Speed, Load Factor, and Volume/Capacity Ratio (VCR). The data analysis shows that the LOS value of the Dhaka-Sylhet Highway varies within the range from “E" to “F", and construction of the third Shitalakhya Bridge will significantly improve the roadway condition.

A multi-fidelity approach for reliability-based risk assessment of single-vehicle crashes

Road vehicles are highly susceptible to single-vehicle crashes (SVCs) under complex road geometry and inclement weather, which can significantly threaten traffic safety and mobility of the whole traffic system. Most existing studies involve various simplifications and approximations to assess the associated SVC risks promptly, and therefore the assessment accuracy is often compromised. A novel multi-fidelity approach is developed for the reliability-based risk assessment of SVCs to balance the simulation accuracy and efficiency. Specifically, a high-fidelity transient dynamic vehicle model is introduced for a robust estimation of the vehicle dynamics under various driving environments, assisted by a low-fidelity simplified physics-based vehicle model to improve the computational efficiency. Based on the simulations of the two models, a new multi-fidelity improved cross entropy-based importance sampling (MFICE) algorithm is proposed for integrating multi-fidelity information and facilitating accurate and efficient reliability analysis. Five demonstrative cases are studied to evaluate the performance of the proposed approach, including the comparison with existing representative approaches. The results show that the proposed innovative multi-fidelity approach can provide a reliability evaluation of SVCs both accurately and efficiently, with obviously superior performance over typical state-of-the-art counterparts. Therefore, the proposed approach bears great potential on developing proactive and near real-time intelligent traffic operation and management strategies against SVCs in both normal and hazardous conditions.