Osama A Osman

Experimental modeling of the effect of hurricane wind forces on driving behavior and vehicle performance

While traffic planning is important for developing a hurricane evacuation plan, vehicle performance on the roads during extreme weather conditions is critical to the success of the planning process. This novel study investigates the effect of gusty hurricane wind forces on the driving behavior and vehicle performance. The study explores how the parameters of a driving simulator could be modified to reproduce wind loadings experienced by three vehicle types (passenger car, ambulance, and bus) during gusty hurricane winds, through manipulation of appropriate software. Thirty participants were then tested on the modified driving simulator under five wind conditions (ranging from normal to hurricane category 4). The driving performance measures used were heading error and lateral displacement. The results showed that higher wind forces resulted in more varied and greater heading error and lateral displacement. The ambulance had the greatest heading errors and lateral displacements, which were attributed to its large lateral surface area and light weight. Two mathematical models were developed to estimate the heading error and lateral displacements for each of the vehicle types for a given change in lateral wind force. Through a questionnaire, participants felt the different characteristics while driving each vehicle type. The findings of this study demonstrate the valuable use of a driving simulator to model the behavior of different vehicle types and to develop mathematical models to estimate and quantify driving behavior and vehicle performance under hurricane wind conditions.

Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments

Lane changing is a complex decision-making process that is affected by factors such as vehicle features, driver characteristics, network attributes, and traffic conditions. Understanding the changes in driver behavior and vehicle trajectory before the lane change initiation process is essential to the design of a safe and reliable crash avoidance system. The recently introduced connected vehicle (CV) technology provides opportunities for real-time, high-resolution data exchange capability between vehicles. This study explored the high-resolution vehicle trajectory data attainable in CV environments for detecting the onset of lane change maneuvers. The observed change in behavior before the initiation of such a maneuver was examined to identify the associated driving pattern. This pattern was used to develop two lane change detection models: an artificial neural network (ANN) model and a multiple logistic regression (MLR) model. The two models were trained and tested with Next Generation Simulation data collected from a weaving freeway segment in Arlington, Virginia. The results show 80% detection accuracy for the ANN model, compared with 72% for the MLR model. The developed models identified the vehicle speed, acceleration, and speed relative to the lead vehicle as the most significant attributes for lane change detection. Drivers’ intentions could be detected early and potential crashes could be prevented by training these models to capture similar driving behavior patterns.

A Comparative Analysis of Tree-Based Ensemble Methods for Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments

Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the decision-making process associated with lane changing makes such maneuvers prone to driving errors, and hence, increases the possibility of car crashes. Thus, researchers have been investigating ways to model and predict lane changing maneuvers for optimally designed crash avoidance systems. Such systems rely on the accuracy of detecting the onset of lane-change maneuvers, which requires comprehensive vehicle trajectory data. Connected Vehicles (CV) data provide opportunities for accurate modeling of lane changing maneuvers, especially with the variety of advanced tools available nowadays. The review of the literature indicates that most of the implemented modeling tools do not achieve reliable accuracy for such critical safety application of lane-change prediction. Recently, eXtreme Gradient Boosting (XGB) became a well-recognized algorithm among the computer science community in solving classification problems due to its accuracy, scalability, and speed. This study implements the XGB in predicting the onset of lane changing maneuvers using CV trajectory data. The performance of XGB is compared to three other tree-based algorithms namely, decision trees, gradient boosting, and random forests. The Next Generation SIMulation trajectory data are used to represent the high-resolution CV data. The results indicate that XGB is superior to the other algorithms with a high accuracy value of 99.7%. This outstanding accuracy is achieved when considering vehicle trajectory data two seconds prior to a potential lane change maneuver. The findings of this study are promising for detection of lane change maneuvers in CV environments.

Accounting for Driver Distraction and Socioeconomic Characteristics in a Crash Risk Index: Naturalistic Driving Study

Distracted driving has long been acknowledged as one of the main contributors to crashes in the United States. According to past studies, driving behavior proved to be influenced by the socioeconomic characteristics of drivers. However, few studies attempted to quantify that influence. This study proposed a crash risk index (CRI) to estimate the crash risk associated with the socioeconomic characteristics of drivers and their tendency to experience distracted driving. The analysis was conducted with data from the SHRP 2 Naturalistic Driving Study. The proposed CRI was developed on a grading system of three measures: the crash risk associated with performing secondary tasks during driving, the effect of socioeconomic attributes (e.g., age) on the likelihood of engagement in secondary tasks, and the effect of specific categories within each socioeconomic attribute (e.g., age older than 60) on the likelihood of engagement in secondary tasks. Logistic regression analysis was performed on the secondary tasks, socioeconomic attributes, and specific socioeconomic characteristics. The results identified the significant secondary tasks with high crash risk and the socioeconomic characteristics with significant effect on determining drivers’ involvement in secondary tasks in each tested parameter. These results were used to quantify the grading system measures and hence estimate the proposed CRI. This index indicates the relative crash risk associated with the socioeconomic characteristics of drivers and considers the possibility of engagement in secondary tasks. The proposed CRI and the associated grading system are plausible methods for estimating auto insurance premiums.

Prediction of Travel Time Estimation Accuracy in Connected Vehicle Environments

Effective management of transportation networks requires accurate travel time information that is largely determined by the quality of collected real-time traffic data. In connected vehicle (CV) environments, wherein equipped vehicles may be the primary source of reliable travel time data, accuracy and reliability of travel time estimates present a challenge due to the low market penetration at the early deployment stages of CV technology. The absence of ground truth data presents another challenge for quantifying the accuracy and reliability of travel time estimates. Therefore, CV infrastructure should be well planned in transportation networks to achieve acceptable and reliable estimates of travel times. Recent research shows that the accuracy of travel time estimates is influenced by traffic density, CV market penetration, and transmission range. These factors also impact the vehicle-to-vehicle and vehicle-to-infrastructure communication stability in a transportation network. This suggests correlation between the accuracy and reliability of travel time estimates and the communication stability. This study develops regression models to measure the accuracy and reliability of travel time estimates as a function of communication stability. Such models can help transportation planners assess the anticipated accuracy and reliability of travel time estimates in CV environments, as well as make better infrastructure investment decisions to ensure an acceptable level of accuracy and reliability of travel time estimates.

Crash Risk Aanlysis of Distracted Driving Behavior: Influence of Secondary Task Engagement and Driver Characteristics

Distracted driving has long been acknowledged as one of the main contributors to crashes in the US. According to past studies, driving behavior proved to be influenced by the socioeconomic characteristics of drivers. However, only few studies attempted to quantify that influence. The study proposed a Crash Risk Index to estimate the crash risk associated with the socioeconomic characteristics of drivers and their tendency to experience distracted driving. The analysis is conducted using data from the SHRP 2 Naturalistic Driving Study (NDS). The proposed Crash Risk Index (CRI) is developed based on a grading system of three measures: the crash risk associated with performing secondary tasks during driving, the effect of socioeconomic attributes (e.g. Age) on the likelihood of engagement in secondary tasks, and the effect of specific categories within each socioeconomic attribute (e.g. Age>60) on the likelihood of engagement in secondary tasks. Logistic Regression analysis was performed on the secondary tasks, socioeconomic attributes, and the specific socioeconomic characteristics. The results identified the significant secondary tasks with high crash risk and the socioeconomic characteristics with significant effect on determining drivers’ involvement in secondary tasks among each tested parameter. These results were used to quantify the grading system measures and hence estimate the proposed CRI. This index indicates the relative crash risk associated with the socioeconomic characteristics of drivers and considering the possibility of engagement in secondary tasks. The proposed CRI and the associated grading system are plausible methods for estimating auto insurance premiums.

Genetic Algorithm–Based Approach for Optimal Deployment of Roadside Units in Connected and Automated Vehicle Environments

Traffic management strategies play a critical role in connected and automated vehicle (CV/AV) environments in which vehicles will be the primary source of traffic information. In CV/AV environments, traffic data are collected from vehicles, and decisions are made at the transportation management centers (TMCs) and communicated back to vehicles via deployed roadside units (RSUs). This process poses a challenge at the early deployment stages of the technology because of the anticipated low market penetration. Under such conditions, RSUs must be optimally located throughout the network to provide continuity and expand the coverage of vehicle-to-vehicle communication systems. This study presents a genetic algorithm–based approach for determining the optimal locations of RSUs by maximizing the connectivity robustness measure, by taking into consideration vehicle clustering (groups of vehicles in transmission range of each other), network size, and other factors. Traffic simulation data were generated from a microscopic simulation platform (VISSIM) and used to test the proposed approach for different penetration rates. The results show that the proposed approach identified locations where more vehicles can communicate. The optimized RSU locations enabled communication between more vehicles in the network, which was identified by the increased robustness of connectivity. This aspect can maximize the amount of exchanged information between vehicles and the RSUs. Consequently, better traffic monitoring can be achieved by collecting more representative data of the traffic conditions in the network. Thus, optimal decisions, such as vehicle rerouting, are made at the TMCs and disseminated to as many vehicles as possible, which helps in achieving better management.

Statistical Evaluation of Ramp Metering for a Dual Freeway Corridor

This study evaluates the effectiveness of ramp metering on two corridors of I-10 and I-12 in Baton Rouge, Louisiana. This is achieved by simulating both corridors with and without ramp metering. Geometric and traffic data were collected to build the network in the simulation model (VISSIM). Simulation results for travel times and delays from 25 runs were obtained for two simulation scenarios, one with and one without ramp meters. The simulation results were then analyzed statistically to investigate the impact of ramp meters on the corridors operational conditions. The comparative evaluation showed a statistically significant improvement in the corridor travel times and delays with ramp meters. Based on the simulation results, the study endorses the use of ramp metering as a successful control strategy.