Driver Speed and Lane Keeping Behaviors in Adverse Weather Conditions

Driver Speed and Lane Keeping Behaviors in Adverse Weather Conditions
Author: Ali Ghasemzadeh
Publisher:
Total Pages: 135
Release: 2017
Genre: Automobile drivers
ISBN: 9780438515581
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This dissertation consists of five published or presented papers in which addresses different gaps in the knowledge by presenting innovative methods to identify and analyze weather-related naturalistic driving data to better understand driver behavior and performance in adverse weather conditions. An innovative methodology introduced in Chapter 4 helped to effectively identify weather-related trips in real-time using vehicle wiper status and other complementary methodologies introduced in chapter 5 helped to identify naturalistic driving weather-related trips using external weather data sources. In addition, a semi-automated data reduction procedure was developed and introduced in chapter 5 to process raw trip data files into a format that further analyses and modeling techniques could be easily applied. The novel approaches developed in this dissertation for NDS trip acquisition and reduction could be extended to other naturalistic driving studies worldwide. In addition to the contributions in data extraction and reduction, preliminary analysis as well as advanced modeling techniques were utilized in this study. These analyses were used to explain the relationship between different levels of speed selection/lane keeping behaviors and a set of contributing factors including roadway characteristics, environmental and traffic conditions and driver demographics on a trajectory level. These modeling techniques ranged from common parametric approaches such as binary logistic regression and ordinal logistic/probit regression models to a more advanced non-parametric/data mining modeling techniques such as Classification and Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS). The results from this study suggest that both parametric and non-parametric modeling approaches are important to analyze driver behavior and performance. In fact, this study attempted to maximize the benefits out of the advantages of parametric models, such as the ability of interpreting the marginal effects of various risk factors, as well as the advantages of using non-parametric models, including but not limited to the ability of providing high prediction accuracy, handling of missing values automatically, and their capability of handling large number of explanatory variables in a timely manner, which might be extremely beneficial specifically for assessing traffic operations and safety in real-time considering weather and traffic data to be directly fed into the model. The results of the developed speed selection models revealed that among various adverse weather conditions, drivers were more likely to reduce their speed in snowy weather conditions compared to other adverse weather conditions. Specifically, the odds of drivers reducing their speed were 9.29 times higher in snowy weather conditions, followed by rain and fog with 1.55 and 1.29 times, respectively (compared to clear conditions). In addition, variable importance analysis using CART method revealed that weather conditions, traffic conditions, and posted speed limit are the three most important variables affecting driver speed selection behavior. In addition, the results of the developed lane-keeping models revealed that drivers in heavy rain conditions were 3.95 times more likely to have a worse lane-keeping performance compared to clear weather conditions. The developed speed selection model is a key example of a derived mechanism by which the SHRP2 database can be leveraged to improve Weather Responsive Traffic Management (WRTM) strategies directly. Moreover, the results may shed some light on driver lane keeping behavior at a trajectory level. Moreover, a better understanding of driver lane-keeping behavior might help in developing better Lane Departure Warning (LDW) systems. Evaluating driver behavior and performance under the influence of reduced visibility due to adverse weather conditions is extremely important to develop safe driving strategies, including Variable Speed Limits (VSL). Many roadways across the U.S. currently have weather-based VSL systems to ensure safe driving environments during adverse weather. Current VSL systems mainly collect traffic information from external sources, including inductive loop detector, overhead radars and Closed Circuit Television (CCTV). However, human factors especially driver behavior and performance such as selection of speed and acceleration/deceleration behaviors during adverse weather are neglected due to the lack of appropriate driver data. The findings from this study indicated that the SHRP2NDS data could be effectively utilized to identify trips in adverse weather conditions and to assess the impacts of adverse weather on driver behavior and performance. With the evolution of connected vehicles, Machine Vision and other real-time weather social crowd sources such as WeatherCloud®, more accurate real-time data similar to the NDS data will be available in the near future. This study provided early insights into using similar data collected from NDS.

Investigation of Lane-keeping and Lane-changing Characteristics in Fog Using the SHRP2 Naturalistic Driving Study Data

Investigation of Lane-keeping and Lane-changing Characteristics in Fog Using the SHRP2 Naturalistic Driving Study Data
Author: Anik Das
Publisher:
Total Pages: 101
Release: 2018
Genre: Automobile driving
ISBN: 9780438387867
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Driving in foggy weather conditions has been recognized as a major safety concern for many years. Driver behavior and performance can be negatively affected by foggy weather conditions due to limited visibility and shorter available perception-reaction time. In addition, random and unusual patterns of fog affect driver behavior greatly. A number of previous studies focused on driver performance and behavior in simulated environments. However, very few studies have examined the impact of foggy weather conditions on specific driver behavior in naturalistic settings. The second Strategic Highway Research Program (SHRP2) has conducted the largest Naturalistic Driving Study (NDS) between 2010 and 2013 on six US states to observe drivers performance and their interactions with roadway features, traffic, and other environmental conditions. The study conducted in this thesis utilized the SHRP2 NDS dataset to evaluate driver lane-keeping behavior in clear and foggy weather conditions. A total of 62 drivers involved in 124 trips in fog with their corresponding 248 matching trips in clear weather were selected for investigating lane-keeping behavior. Preliminary descriptive analysis was performed and a lane-keeping model was developed using ordered logistic regression approach to achieve the study goals. Individual variables such as visibility, traffic conditions, occurrence of lane-changing maneuver, driver marital status, geometric characteristics, among other variables, as well as some interaction terms (i.e., weather and gender, surface condition and driving experience, speed limit and mileage last year) have been found to significantly affect lane-keeping ability. An important finding of this study illustrated that affected visibility caused by foggy weather conditions decreases lane-keeping ability significantly. More specifically, drivers in affected visibility conditions showed 1.37 times higher Standard Deviation of Lane Position (SDLP) in comparison with drivers who were driving in unaffected visibility conditions. The outcome of this research may provide a better understanding of driver lane-keeping behavior and their perception of foggy weather conditions. This thesis also provided valuable insights into lane-changing characteristics based on driver behavior in fog and clear weather conditions. While a few studies focused on lane-changing maneuvers based on driver type, the impact of adverse weather conditions (especially in fog) was not addressed. This thesis examined lane-changing maneuvers in fog and clear weather conditions using the SHRP2 NDS dataset. A total of 125 drivers involved in 214 trips in fog with their corresponding 214 trips in clear weather were selected for analyzing the lane-changing characteristics. These participants performed 92 lane changes in heavy fog, 445 in distant fog, and 1,163 in clear weather conditions. The study tested several hypotheses to identify significant differences in number of lane-changing events per mile and lane-changing durations in fog and clear weather in different traffic conditions. In addition, different distributions of lane-changing durations were fitted to identify common trends. Using K-means cluster analysis technique and based on lane-changing behaviors, drivers were classified into two categories, conservative and aggressive. It was found that in heavy fog the mean lane-changing durations were significantly higher than clear weather in mixed-flow conditions. The cluster analysis results revealed that both conservative and aggressive drivers in heavy fog conditions had longer lane-changing durations than in clear weather. The comparison between the SHRP2 administrated survey questionnaires and the cluster analysis suggested that drivers’ responses related to foggy weather were more consistent with survey questionnaires compared to their responses in clear weather during free-flow conditions. The findings of this study have several practical implications. The result of lane-keeping behavior might be used to improve Lane Departure Warning (LDW) systems algorithm considering affected visibility by fog. The outcomes of lane-changing analysis could be used to classify drivers in real-time based on their lane-changing behaviors in a connected vehicle (CV) environment. The results might also be used in microsimulation model calibration and validation related to lane change in reduced visibility due to fog and various traffic conditions.

Investigating Driver Lateral Behavior in Adverse Weather Conditions

Investigating Driver Lateral Behavior in Adverse Weather Conditions
Author: Anik Das
Publisher:
Total Pages: 302
Release: 2021
Genre: Aggressiveness
ISBN:
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The presence of adverse weather has a significant negative impact on driving. This research investigated driver lateral behavior under adverse weather via Big Data analytics, Machine Learning, Data Mining in addition to traditional parametric modeling using trajectory-level SHRP2 Naturalistic Driving Study datasets. Initially, driver lane-keeping behavior in adverse weather was examined using ordered logistic regression approach, which indicated that environmental, traffic, driver, and roadway characteristics affect lane-keeping ability. The following study leveraged association rules mining that demonstrated a high association of affected visibility with poor lane-keeping performance. This research was then extended to investigate lane-changing characteristics, which revealed that conservative drivers had longer lane-changing durations in heavy fog compared to clear weather. Moreover, the research provided extensive evaluation into another lateral behavior, named lane-changing gap acceptance, using Multivariate Adaptive Regression Splines. The findings illustrated that relative speed between lane-changing and lead vehicle, acceleration of lane-changing and following vehicle, traffic conditions, and roadway geometries have effects on gap acceptance behavior. Subsequently, emphasis has been provided on developing reliable, accurate, and efficient Machine Learning-based lane change detection and prediction models through a data fusion approach considering different data availability. Finally, the research focused on developing weather-based microsimulation lane change models indicating that weather-specific lane changes were unique and hence, microsimulation models should be weather-specific. The outcomes of this research have significant implications, which could be used in microsimulation model calibration related to lateral behavior and safety improvements in Connected and Autonomous Vehicles, especially in adverse weather.

Human Factors of Visual and Cognitive Performance in Driving

Human Factors of Visual and Cognitive Performance in Driving
Author: Candida Castro
Publisher: CRC Press
Total Pages: 298
Release: 2008-11-21
Genre: Technology & Engineering
ISBN: 142005533X
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Human error is involved in more than 90 percent of traffic accidents, and of those accidents, most are associated with visual distractions, or looking-but-failing-to-see errors. Human Factors of Visual and Cognitive Performance in Driving gathers knowledge from a human factors psychology standpoint and provides deeper insight into traffic -user beh

Speed Selection Behavior During Adverse Winter Weather Conditions

Speed Selection Behavior During Adverse Winter Weather Conditions
Author: Sandeep Thapa
Publisher:
Total Pages: 179
Release: 2016
Genre: Automobile drivers
ISBN: 9781369182019
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As road conditions become more hazardous during winter events, individual drivers often react by modifying their headway and speed. Every driver chooses their speed according to their own perception of the weather severity and their own comfort level. This may lead to higher speed variations within the traffic flow, which is correlated to increases in crash frequencies. About 24 percent of all reported motor vehicle crashes in the United States are related to weather conditions which result in more than 673,000 injuries and about 7,400 fatalities per year. In the Elk Mountain corridor on Wyoming Interstate 80 (I-80), crashes during the winter season are 2.82 times higher than the number of summer season crashes. A better understanding of the impacts of adverse weather conditions on traffic flow would be beneficial for identifying methods to reduce crashes and improve safety. The objectives of this study are to address the relationship and the impacts of adverse weather conditions on speed selection and car following behavior at a microscopic level. Data collected along corridors on I-80 in Wyoming during winter storms were used for the analysis. Models were developed for speed selection behavior based on different weather parameters. The results show that pavement surface conditions were found to have a significant negative impact on reduction of average speed. Truck percentage was also included in the model because of its importance for this roadway with truck percentages exceeding 50 percent. The impact of truck percentage was found to have a significant impact on reducing average speed. It was also found that headways and spacing were decreased during non-ideal weather conditions. Furthermore, an analysis was done to identify how sensitive weather-related parameters were on traffic variables during adverse weather conditions. A model was developed in VISSIM tool using observed traffic parameter data during ideal and different storm events. Measures of Effectiveness (MOEs) obtained from the base and adjusted model were compared to measure the level of sensitivity. Average speed was found to be more sensitive than average spacing. Guidelines for calibrating adverse weather conditions in VISSIM were developed as part of this project. An important conclusion from this project is that VISSIM can be used to calibrate the impacts of adverse weather conditions on traffic operations which allows for its use in testing the impacts of adverse weather conditions in a simulation environment.

Identification of Surrogate Measures of Safety and Means to Assess the Performance of Connected Vehicles

Identification of Surrogate Measures of Safety and Means to Assess the Performance of Connected Vehicles
Author: Elhashemi Mohammed Ali
Publisher:
Total Pages: 167
Release: 2018
Genre: Automobile driving in bad weather
ISBN: 9780438806702
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Sudden changes in weather conditions might have a tremendous impact on traffic operation and safety. Previous studies investigated the impact of adverse weather conditions on traffic safety and to what extent these conditions may increase crash risks on roadways. The increase in weather-related crashes has motivated researchers to study driver behavior and performance under different weather conditions. Adverse weather affects driver decisions and may result in taking improper actions while facing a crash/near-crash event in comparison with clear weather conditions. While driver behavior and performance are considered among the key contributing factors to crashes, little research have been conducted to fully understand the difference between normal driving and safety critical scenarios for developing crash prevention means. Monitoring driver behavior and performance during a safety critical event has been a challenging task for researchers due to the lack of detailed event records. Moreover, the issues associated with traditional police records of crashes have limited a comprehensive analysis of how the deviation from normal driving may lead to a culmination of crashes. In addition, one of the main reasons for the increase number of crashes on roadways is that drivers may not appropriately adapt their behaviors to compensate for adverse weather conditions. The lack of real-time trajectory-level weather information and the sporadic data collected from weather stations have limited researchers from conducting sound safety studies. This study attempts to fulfill some of the research gaps to assist transportation agencies and traffic safety researchers to improve safety and mobility. In general, the research efforts conducted in this dissertation aims to improve traffic safety in adverse weather conditions on freeways. In addition, this dissertation aims to provide practical recommendations to transportation agencies that can efficiently enhance traffic safety in Connected and Automated Vehicle (CAV) environments. The dissertation goal was achieved through utilizing different subsets of the Second Strategic Highway Research Program (SHRP2) – Naturalistic Driving Study (NDS) data. The utilization of the NDS real-time trajectory dataset would open a new horizon in traffic safety research related to connected and automated vehicles. In this study, five main research objectives, each with multiple tasks, were set to enhance traffic safety in adverse weather conditions. The first objective was to provide a better understanding of what happened before and during a near-crash event and comparing it with normal matched trips. This objective would help to develop effective countermeasures that reduce crash risks on freeways. The second objective was to detect Surrogate Measures of Safety (SMoS) on freeways by comparing environmental conditions and vehicle kinematics signatures of near-crash events to their matched normal driving trips. A time-chunking technique was used with different aggregation levels to monitor changes in vehicle kinematics on a timescale. This approach established a comparative study of parametric and non-parametric techniques to estimate near-crashes on freeways. A Binary Logistic Regression model was used as a parametric prediction model, while the Decision Tree (DT), k-Nearest Neighbors (k-NN), and Deep Learning Artificial Neural Network (ANN) were used as non-parametric prediction models. The results showed that the logistic regression model has provided an excellent fit to the input data and can predict near-crashes with an outstanding accuracy. In addition, DT and Deep Learning ANN machine learning algorithms showed higher prediction accuracy of near-crashes compared to the k-NN algorithm. The third objective was to investigate normal and risky driving condition patterns under both rainy and clear weather conditions. The fourth objective was to distinguish between normal driving and risky driving condition patterns in rainy and clear weather conditions using real-time trajectory-level datasets. To achieve the third and fourth objectives, the SHRP2 - NDS data were employed to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started one second earlier in rainy weather condition than in clear weather condition. Furthermore, risky driving patterns extended three seconds in rainy weather condition, while it was two seconds in clear weather condition.

Improving Traffic Safety and Drivers' Behavior in Reduced Visibility Conditions

Improving Traffic Safety and Drivers' Behavior in Reduced Visibility Conditions
Author: Hany Mohamed Ramadan Hassan
Publisher:
Total Pages: 210
Release: 2011
Genre: Automobile driving in bad weather
ISBN:
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This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers' behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers' responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers' behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers' stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds' ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that would achieve the best perceived safety and drivers' compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that "Caution-fog ahead-reduce speed" was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers' responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers' compliance and drivers' satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers' satisfaction with VSL/CMS was the most significant factor that positively affected drivers' compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers' compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers' familiarity with VSL signs and driver factors were the significant factors affecting drivers' satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers' behavior in such reduced visibility conditions by enhancing drivers' compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed.

The Effect of Advisory Messages on Driver Behavior During Inclement Weather

The Effect of Advisory Messages on Driver Behavior During Inclement Weather
Author: Desirée Dulcinéa Carron
Publisher:
Total Pages: 100
Release: 2015
Genre: Automobile driving in bad weather
ISBN:
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This research examines the effectiveness of advisory variable speed limit (VSL) and advisory variable message sign (VMS) messaging on reducing traffic speeds during inclement weather conditions. Regulatory variable speed limit infrastructure is costly to install, whereas advisory messaging enables state transportation departments to utilize existing infrastructure in an effort to slow traffic during winter storms and improve safety. This study utilized roadway sensor data in southern New Hampshire, roadway grip data obtained from a Road and Weather Information System (RWIS) station located in Derry, New Hampshire, and New Hampshire Department of Transportation (NHDOT) winter weather logs obtained from the Transportation Management Center (TMC). The data were used to determine the impact the advisory messages had on reducing traffic speeds as compared to the impact roadway grip has on speed reduction. Overall, this analysis indicates that, while drivers do adjust their rates of speed based on the roadway grip value, the presence of both prescriptive and descriptive messages appears to cause them to reduce their rate of speed even further than they otherwise would, especially during storm events with large amounts of accumulating precipitation. Speed reductions were found to be more significant while prescriptive messages were displayed, although significant reductions in speed were also noted while descriptive messages were displayed. After controlling for the slowdown caused by a reduction in roadway grip, it was determined that the presence of prescriptive messages reduces the mean speed by 9.5 mph, and descriptive messages reduces the speed by 2.5 mph.