Two-lane Rural Roads Research Articles

From Radar Sensor to Floating Car Data: Evaluating Speed Distribution Heterogeneity on Rural Road Segments Using Non-Parametric Similarity Measures

Rural roads, often characterized by winding paths and nearby settlements, feature frequent curvature changes, junctions, and closely spaced private accesses that lead to significant speed variations. These variations are typically represented by average speed or v85 profiles. This paper examines complete speed distributions along rural two-lane roads using Floating Car Data (FCD). The Wasserstein distance, a non-parametric similarity measure, is employed to compare speed distributions recorded by a radar Control Unit (CU) and a selected FCD sample. Initially, FCD speeds were validated against CU speeds. Subsequently, differences in speed distributions between the CU location and specific sections identified by sharp curves, intersections, or accesses have been assessed. The Wasserstein Distance is proposed as the most effective synthetic indicator of speed distribution variability along roadways, attributed to its metric properties. This measure offers a more concise and immediate assessment compared to an extensive array of statistical metrics, such as mean, median, mode, variance, percentiles, v85, interquartile range, kurtosis, and symmetry, as well as qualitative assessments derived from box plot trends.

Developing Safety Performance Functions for Severe Distraction-Related Crashes along Kentucky’s Rural and Urban Two-Lane Roadways

This study develops safety performance functions (SPFs) for severe (“KA” or “fatal and suspected serious injury”) distraction-related crashes along Kentucky’s rural and urban two-lane undivided roadway segments using recent four-year (2018–2021) crash records. Additional efforts were made to meticulously scrutinize crash narratives categorized as non-distracted and correct those cases. To account for crash under-dispersion, the Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) models were fitted and compared. The ZI-HTCMP model outperformed the other models with respect to several goodness-of-fit measures (e.g., mean absolute deviance and mean square prediction error). From the developed SPFs for rural and urban two-lane roads, wider lanes and higher speed limits (55 mph) were associated with increased severe distraction-related crash frequencies. Furthermore, some variables were found to be significant in rural areas, but insignificant in urban areas, and vice versa. For example, major collector roads, minor collector/local roads, the presence of roadside guardrails, wider right-hand shoulders, the presence of horizontal curves, and the presence of vertical grades were associated with increased crash frequencies along rural two-lane roads. In addition, the proportion of heavy vehicles (>5%) and the existence of paved shoulders were associated with increased crashes along urban two-lane roads. The empirical Bayes method was then used to rank the top 10 distraction-related high crash locations (HCLs) for both rural and urban two-lane segments. In-depth investigation of HCLs highlighted single-vehicle distraction-related crashes as the most common collision type. Countermeasures were proposed to help reduce severe distraction-related crashes, for example, installing chevron signs along rural two-lane roads.

Safety Performance Functions for Mountain Two-lane Rural Roads in Ecuador

The two-lane rural road on mountainous roads has various design and operational challenges. This complexity could produce a road with specific geometric configurations or segments where the probability of a road accident increases. The Highway Safety Manual (HSM) offers a predictive model to detect such sites using Safety Performance Functions (SPF). These functions predict the number of road crashes at the different roadway elements and apply to all project phases. Many functions or equations have been developed worldwide; however, in Ecuador, they have not yet been estimated. Therefore, this study aims to propose Safety Performance Functions for two-lane rural roads in Ecuador in mountainous environments. To do this, design and operation variables were collected from 25 roads in the Ecuadorian Andes. The SPFs were developed with crash data from 2017-2019 and validated with information from 2020-2021. As a result, 12 SPFs were developed for three types of crashes (total, fatal + injury, property damage only) and various ranges of annual average daily traffic (AADT). These equations estimate the average expected frequency of accidents for existing conditions, alternatives for existing conditions, or proposed new road projects. This study will improve the road safety of the built roads and new projects.

Speed limit management on two-lane rural roads shared by drivers and cyclists to improve safety and traffic operation

Two-lane rural roads require management that integrates all road users, especially cyclists, who have reached significant traffic levels. Management measures must focus on increasing safety and, wherever possible, also improving traffic operation. Speed limit management can be a solution, however it has to be based on scientific criteria. In this study, various speed limits were simulated on a narrow rural road using a traffic microsimulation model and considering a set of scenarios combining motor and cycle traffic levels. As a results of simulations, performance measures were obtained to characterise safety and traffic operation. The optimal speed limit for each traffic level was obtained by a Multi Criteria Decision Making method, using performance measures as criteria. The results point to the groups of cyclists to ride two-abreast, and to a dynamic management of the road speed limit, reducing the speed during periods of peak motorised and bicycle traffic. It is recommended to adapt the study to each rural road considering its traffic and geometric characteristics, calibrating the traffic microsimulation model and obtaining a specific dynamic speed limit management model.

Traffic microsimulation for road safety assessments of vehicle automation scenarios: Model comparison and sensitivity analysis

The possible implementation of Automated Vehicles (AVs) in the traffic flow implies that future safety conditions should be assessed by considering promiscuous traffic scenarios (composed of traditional vehicles and AVs). However, real safety performance data related to AVs are still scarce and unreliable. Thus, road safety assessments of future scenarios should necessarily be based on traffic simulations and surrogate safety measures (i.e., related to traffic conflicts, which are different from observed crashes). In the context of traffic simulation, the first step consists in assessing the currently available traffic models to match their characteristics with the possibility of reliably simulating AVs and their behavior in traffic, according to the specific variables of the investigated site (i.e., road type, road geometric design, number of intersections). After the model selection stage, a sensitivity analysis is necessary to identify the most crucial model parameters and their influence on the simulation output, with particular regard to those affecting safety performances. The sensitivity analysis paves the way for making traffic modelers aware of the effects of possible modifications to the simulation parameters in case of AV scenarios. In this research, this procedure was tested on a two-way two-lane rural road network. Among the analyzed traffic models, the Gipps model was highlighted as the most suitable to account for AVs in this specific context. The sensitivity analysis revealed that the most influencing parameters of the Gipps model in the context of safety assessments are: clearance, safety margin factor and sensitivity factor, which were revealed to be greatly influential on all types of traffic conflicts. Two other control tests with different simulated vehicle types were run to highlight the stability of the results coming from the sensitivity analysis in other scenarios.

Crash modification factors of rumble strips on horizontal curves of two-lane rural roads: A propensity scores potential outcomes approach

Horizontal curves are known to be more crash-prone than tangent sections particularly with respect to roadway departure crashes. Rumble strips are an effective countermeasure to mitigate various types of roadway departure crashes. While existing studies on the safety effectiveness of rumble strips have primarily used before-after study designs or cross-sectional methods for crash modification factor (CMF) estimation, these methods often suffer from imbalanced datasets and larger standard errors, especially when the sample size is small. To address this, this study applies the propensity score potential outcome (PSPO) framework to estimate CMFs for centerline rumble strips, shoulder rumble strips, and their combined application on horizontal curves. In addition to contributing to the development of CMFs by crash severity, this study also examines the effects of rumble strips on collision types, highlighting their impact on vehicle maneuvering and collision characteristics. The analysis is conducted on horizontal curves on two-lane rural roads in Pennsylvania, utilizing crash data from 2017 to 2021. The PSPO method effectively reduces bias between sites with and without rumble strips, and the resulting statistical models align with engineering judgment. The findings indicate that centerline rumble strips reduce opposite direction sideswipe and head-on crashes but increase run off the road and hit fixed object crashes. Shoulder rumble strips, either alone or in combination with centerline rumble strips, decrease crash frequencies for most types except opposite direction sideswipe and head-on crashes. However, shoulder rumble strips alone are more effective at reducing crash frequencies on horizontal curves than when combined with centerline rumble strips.

Operational and Infrastructure Readiness for Semi-Automated Truck Platoons on Rural Roads

On highways, truck platooning may reduce fuel consumption, improve road safety and streamline trucking operations. However, most roads worldwide are two-way, two-lane rural roads, i.e., conditions for which truck platooning should be tested to explore the extent of those advantages. This paper reports findings from a field study undertaken in Northern Norway, testing a platoon of three semi-automated trucks on rural roads with tunnels, mountain passes and adverse geometries. Fleet management and distance data, videos, interviews and conversations between participants were used to assess whether platooning was feasible on such roads. The platooning system was used without interventions through most road conditions, and worked well on flat and wide roads with 90 km/h speed limits. However, it struggled in sharp horizontal curves, where the following trucks would speed up before regaining connection to their preceding truck and then brake abruptly to regain the prescribed distance. Moreover, steep uphills were problematic due to inconsistent gear shifting between the trucks. Seemingly, no fuel savings were achieved, due to excessive following distances and suboptimal speed profiles on crest curves. To obtain further insights into the benefits of truck platooning on rural roads, we suggest redoing the field study with V2V-communication, allowing for shorter following distances, and also performing a manual-driven baseline first.

Overtake or not – A computer-based driving simulation experiment on drivers’ decisions during transitions in automated driving

The increase in automated driving changes the role of the human driver. Under SAE level 3 conditions, the driver has to be able to take back the driving task at any time. However, drivers are allowed to engage in non-driving related tasks during the automated drive, which can result in drivers being out-of-the-loop when they have to take back manual control. This raises the question of whether drivers are able to make safe decisions in such situations. In a computer-based driving simulation experiment, N = 37 participants drove in automated mode on a two-lane rural road and were distracted by a non-driving related task. Because of blocked sensors due to a slower preceding vehicle, the automated system asked the driver if an automated overtaking maneuver should be carried out or not. The selected maneuver was executed by the automated vehicle in automated mode. In a within-subject design with repeated measures, the time budget (with time pressure vs. without time pressure) and the criticality of the situation (TTC low vs. TTC middle vs. TTC high) were manipulated, and the overtaking decision, the reaction time, the certainty about the decision and the perceived criticality were measured. As a result, drivers decided more frequently and faster for the automated overtaking maneuver with time pressure. In addition, in more critical situations, drivers decided less frequently for the automated overtaking maneuver and took more time for the decision. The results provide insights into the decision-making of drivers in automated driving and can be used in the design of take-overs as well as in the development of automated driving systems to enhance traffic safety.

Effect of Grade on Operating Speed and Capacity of Two-Lane Rural Roads

Effect of Grade on Operating Speed and Capacity of Two-Lane Rural Roads

Bayesian Networks for the Driver Overtaking Assistance System on Two-lane Roads

Unsuccessful overtaking maneuvers on two-lane rural roads are one of the major causes of road accidents in the 21st century. The complexity of this maneuver merits the adoption of a thorough method for developing a proposed assistance system to prevent accidents and consequently reduce the high number of fatalities and the associated economic costs. This study aims to introduce an intelligent Driver Overtaking Assistance System (DOAS) to assist drivers in performing overtaking maneuvers safely. The study also will introduce a method to assess the impact of all the influential variables related to the driver, vehicle, traffic, road, and the surrounding environment. In momentary driving situations, the DOAS uses the communicated information via Hello beacon messages (HBM) and a set of input sensors to measure the possibility of overtaking the preceding vehicle(s) proactively by considering whether the distance gap to the oncoming vehicle is sufficient for overtaking. Besides, the proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity to acquire all relevant information regarding the ambient driving environment and the vehicles involved in the overtaking. To do this, DOAS uses a Bayesian Network (BN) to model overtaking maneuvers. The work presented shows high accuracy and promising results in aiding safe overtaking, with significant improvements to overtaking maneuvers on two-lane rural roads.

Calibration Measurements and Computational Models of Sensors Used in Autonomous Vehicles

Unsuccessful overtaking maneuvers on two-lane rural roads are one of the major causes of road accidents in the 21st century. The complexity of this maneuver merits the adoption of a thorough method for developing a proposed assistance system to prevent accidents and consequently reduce the high number of fatalities and the associated economic costs. This study aims to introduce an intelligent Driver Overtaking Assistance System (DOAS) to assist drivers in performing overtaking maneuvers safely. The study also will introduce a method to assess the impact of all the influential variables related to the driver, vehicle, traffic, road, and the surrounding environment. In momentary driving situations, the DOAS uses the communicated information via Hello beacon messages (HBM) and a set of input sensors to measure the possibility of overtaking the preceding vehicle(s) proactively by considering whether the distance gap to the oncoming vehicle is sufficient for overtaking. Besides, the proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity to acquire all relevant information regarding the ambient driving environment and the vehicles involved in the overtaking. To do this, DOAS uses a Bayesian Network (BN) to model overtaking maneuvers. The work presented shows high accuracy and promising results in aiding safe overtaking, with significant improvements to overtaking maneuvers on two-lane rural roads.

Using Low-Cost Radar Sensors and Action Cameras to Measure Inter-Vehicle Distances in Real-World Truck Platooning

Many modern vehicles collect inter-vehicle distance data from radar sensors as input to driver assistance systems. However, vehicle manufacturers often use proprietary algorithms to conceal the collected data, making them inaccessible to external individuals, such as researchers. Aftermarket sensors may circumvent this issue. This study investigated the use of low-cost radar sensors to determine inter-vehicle distances during real-world semi-automated truck platooning on two-way, two-lane rural roads. Radar data from the two follower trucks in a three-truck platoon were collected, synchronized and filtered. The sensors measured distance, relative velocity and signal-to-noise ratio. Dashboard camera footage was collected, coded and synchronized to the radar data, providing context about the driving situation, such as oncoming trucks, roundabouts and tunnels. The sensors had different configuration parameters, suggested by the supplier, to avoid signal interference. With parameters as chosen, sensor ranges, inferred from maximum distance measurements, were approximately 74 and 71 m. These values were almost on par with theoretical calculations. The sensors captured the preceding truck for 83–85% of the time where they had the preceding truck within range, and 95–96% of the time in tunnels. While roundabouts are problematic, the sensors are feasible for collecting inter-vehicle distance data during truck platooning.

Passing Path Assessment Based on Nighttime Field Driving Experiment

The overtaking process on two-lane rural roads is a critical task for drivers and directly impacts safety. This paper investigates the vehicle’s passing path from the road-engineering point of view, consisting of a series of successive parametric spiral arcs, based on a real driving experiment with respect to the roadway’s posted speed. Passing maneuvers were recorded utilizing global navigation satellite system (GNSS) receivers. The curved paths were determined for two different and mostly typical posted speed values (90 and 110 km/h), where the impeding (passed) vehicle was assumed to travel under steady-state conditions (20 km/h below the respective posted speed values). The authors, by examining these curved paths, intend to quantify their trajectories and respective critical parameters, such as overtaking distance, headway distance, and acceleration performance. Four predictive lognormal models for each speed scenario were developed to estimate the radius of each overtaking curved section with high accuracy. Headway distance observed in this study is very close, while total overtaking distance is greater than those reported in the published literature. The present research is a source for road authorities to assess overtaking sight distance with critical overtaking parameters such as posted speed and vehicle speed differences as well as an opening paradigm of how the passing process can be standardized and therefore deployed in future advanced driver assistance systems.

Drivers’ behaviour when overtaking cyclists on rural roads: Driving simulator validation using naturalistic data

Driving simulator is a useful tool for obtaining data on driver behaviour efficiently and quickly. However, to ensure the reliability of the data gathered by the simulator, it is necessary to check the differences between drivers’ behaviour in the simulator and in reality. In this study, an existing two-lane rural road was replicated on the driving simulator under the same traffic conditions of groups of cyclists and oncoming motorised vehicles. For this purpose, a naturalistic field data collection was developed on the real road using instrumented bicycles and static video recordings. A total of 30 volunteers participated in the driving simulator tests. The objective validation of the driving simulator was based on three operational variables: average travel speed, overtaking vehicle speed, and lateral clearance. As a result, higher average travel speeds and lower lateral clearances were obtained in the real world compared to those observed in the simulator. It was also found that overtaking vehicle speed depends on the group of cyclists. Overall, the data obtained in the field and in the driving simulator did not present statistically significant differences. The analysis of drivers’ perception in the simulator tests concluded that the simulator reflected reality in an accurate way, achieving the subjective validation of the driving simulator. Thus, this study validates the driving simulator for bicycle safety research on rural roads.

Safety of horizontal curves on rural two-lane roads in Norway

Horizontal curves are found on all roads. The relationship between the characteristics of horizontal curves and the number of accidents is complex and no previous study has included all factors that are potentially influential for accidents. This paper presents a study of 63 969 horizontal curves on rural two-lane roads in Norway. Accident prediction models including more characteristics of horizontal curves than used in any previous study were developed. Most of the characteristics were found to be related to the number of accidents. Most coefficients were consistent with those found in previous studies, but some findings were surprising. Compound curves, which are curves in which radius varies throughout the curve, were found to be safer than circular or nearly circular curves. A steeper slope of a vertical grade before a curve is associated with fewer accidents.

An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions

Illegal running into the opposite lane (IROL) on curve sections of two-lane rural roads is a frequently hazardous behavior and highly prone to fatal crashes. Although driving behaviors are always determined by the information from drivers’ visual perceptions, current studies do not consider visual perceptions in predicting the occurrence of IROL. In addition, most machine learning methods belong to black-box algorithms and lack the interpretation of prediction results. Therefore, this study aims to propose an interpretable prediction model of IROL on curve sections of two-lane rural roads from drivers’ visual perceptions. A new visual road environment model, consisting of five different visual layers, was established to better quantify drivers’ visual perceptions by using deep neural networks. In this study, naturalistic driving data was collected on curve sections of typical two-lane rural roads in Tibet, China. There were 25 input variables extracted from the visual road environment, vehicle kinematics, and driver characteristics. Then, XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive exPlanation) methods were combined to build a prediction model. The results showed that our prediction model performed well, with an accuracy of 86.2% and an AUC value of 0.921. The average lead time of this prediction model was 4.4 s, sufficient for drivers to respond. Due to the advantages of SHAP, this study interpreted the impacting factors on this illegal behavior from three aspects, including relative importance, specific impacts, and variable dependency. After offering more quantitative information on the visual road environment, the findings of this study could improve the current prediction model and optimize road environment design, thereby reducing IROL on curve sections of two-lane rural roads.

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

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

Effects of a rural speed management pilot program in Bishopville, Maryland, on public opinion and vehicle speeds

Introduction: In summer 2021, a speed management pilot program was conducted on a rural two-lane road (MD 367) in Bishopville, Maryland, that combined countermeasures from engineering, enforcement, and communications. The study evaluated public awareness of the program and its effects on speeds. Method: Telephone surveys of drivers in Bishopville and neighboring communities, and of drivers in control communities across the state that had no program, were conducted before and after the program started. Vehicle speeds were collected at treatment sites on MD 367 and at control sites before, during, and after the program. Log-linear regression models estimated changes in speeds associated with the program; separate logistic regression models estimated changes in odds of vehicles exceeding the speed limit and exceeding the speed limit by more than 10 mph during and after the program. Results: The percentage of interviewed drivers in Bishopville and neighboring communities who thought speeding was a major problem on MD 367 declined from 31.0% (before) to 6.7% (after). The program was associated with a 9.3% reduction in mean speeds, a 78.3% reduction in odds of exceeding the speed limit by any amount, and a 79.6% reduction in odds of exceeding the speed limit by more than 10 mph. After the program ended, the mean speeds at the MD 367 sites were 1.5% lower than expected had the program not occurred, odds of exceeding the speed limit by any amount was 37.2% lower, and odds of exceeding the speed limit by more than 10 mph was 11.7% higher. Conclusions: The program was well publicized and reduced speeding, but the effect did not last after the program ended for those traveling at higher-end speeds. Practical applications: Comprehensive speed management programs that leverage multiple proven strategies, similar to the program in Bishopville, are recommended in other communities to reduce speeding.

Validation of a Driving Simulator for Speed Research on Two-lane Rural Roads

Validation of a Driving Simulator for Speed Research on Two-lane Rural Roads

A speed limit advisory system provided by in-vehicle HMI considering auditory perception characteristics for connected environment

In-vehicle HMI (Human Machine Interface) systems are well studied due to their great application prospect, but the recent development of connected technologies raises more possibilities that this technology provides more accurate speed limit warnings to ensure driver safety according to specific vehicle and road parameters. To capitalize on the potential of in-vehicle HMI systems to provide auditory and visual warnings to drivers and to improve adoption and utilization of this warning information. To this end, we designed a speed limit advisory system (SLAS) that recommended reduced speeds for hazardous sections of road, communicated to drivers by their in-vehicle HMI systems. Frontal distance models of single and gradient auditory warning were implanted into the systems to determine the warning timing. We then tested these SLASs in a high-fidelity simulator in order to evaluate their utility. 70 participants (mean age = 28.7 years; 36 male) drove through a simulated 15 km stretch of one-way two-lane rural road. We evaluated five different warning modes: single visual (SV), single audio visual (SAV), gradient visual (GV), gradient audio visual (GAV) or control (roadside sign). Participants using the SLAS slowed down significantly compared to the control group before entering the curve, with drivers receiving GAV warnings driving lowest speeds. Less experienced drivers exhibited higher compliance with GAMs (GV or GAV). The SLAS was generally considered useful though not by drivers with>10 years of driving experience. Drivers tended to prefer singular warnings to gradated warnings, which were rated as equally preferable to roadside signage. Eye tracking data indicated that none of the SLAS modes were overly distracting to the drivers. Overall, we find that the SLAS improves driver safety while producing no obvious distraction. The main difficulty lies in persuading experienced drivers to use the system. In a word, the results of this study provide validation for the concept of HMI warning system using connected technology in hazardous road areas and help determine the appropriate timing of in-vehicle warnings.