Effects of Adverse Winter Weather Conditions on Highway Traffic and Driver Behavior
Effects of Adverse Winter Weather Conditions on Highway Traffic and Driver Behavior

Author: Ting Fu

Publisher:

Published: 2014

Total Pages:

ISBN-13:

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"This research looks into the impact of adverse winter weather conditions on highway driver behaviors using microscopic data from loop detectors and video cameras (e.g., hourly average speed, trajectories, lane changes, time-to-collisions measures). This thesis is composed of two main sections in addition to the introductory section: i) direct and lagged effects of adverse weather on hourly speeds and volumes; and ii) direct effect of adverse weather on driver behaviors (microscopic) measured at the vehicle level using video data. The first part of the thesis presents a review of literature related to past research on the topic. The second part investigates the direct and lagged effects of adverse winter weather conditions on the operating speed in a number of highway segments in Ontario using a time-series approach. This is complemented by the analysis of hourly traffic volumes in the region of Montreal, Canada, using data from magnetic loop detectors as well. In speed modeling, the effect of adverse weather was studied using data from multiple sites including both urban and rural highways, considering weekdays versus weekends separately. For this purpose, a large dataset containing hourly traffic data, weather variables (e.g., temperature, snow, wind speed), and surface conditions was used. A few previous studies have examined the effect of snowstorms on traffic parameters; however, little research has been done regarding the spillover effects (lagged effects) that adverse weather conditions may have on travel demand and traffic patterns. Extreme events or weather conditions might have a strong effect on traffic conditions not only during the events, but also before and after the events. In this study, time-series regression techniques -- in particular, Autoregressive Integrated Moving Average (ARIMA) models were used to model the highway operating speed. These methods are able to consider the serial correlation among error terms. The results indicate that snowstorms have a statistically significant effect on the speed. The lagged effects are however offset by the time and intensity of winter maintenance operations during and after the event. The effect of weather also varies depending on the type of site (urban or rural) and day of the week. Similarly, the effects of different weather variables including their lagged effects were analyzed using hourly traffic volume data. Despite the fact that information of the road surface condition was not available, this analysis is in accordance with previous finding, showing the utility of ARIMA approaches in modeling the highway volume as well. The results of this study can be applied in quantifying the mobility effect of winter weather and benefits of winter road maintenance. In recent years, driver behavior analysis using microscopic (vehicle level) data is a topic that is attracting more attention in road safety analysis. This popularity has brought about research in many different innovative techniques and microscopic measures used to quantify and analyze driver behavior. In the second part of this thesis, it demonstrates a method of analyzing driver behavior using video data approach. This thesis elucidates both a manual and an automated, computer-based method to analyze driver behavior. It also uses the computer-based method to evaluate the effect of adverse winter weather conditions on the driver behavior of highway users. Both the manual and the automated approaches have been used with 15 video recordings obtained from three different locations on the Don Valley Parkway (DVP) in Toronto, Ontario. The results demonstrate the effectiveness of the automated method in analyzing driver behavior, as well as in evaluating the impact of adverse winter weather conditions on driver behavior." --

Where the Weather Meets the Road
Where the Weather Meets the Road

Author: National Research Council

Publisher: National Academies Press

Published: 2004-03-31

Total Pages: 189

ISBN-13: 0309166578

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Weather has broad and significant effects on the roadway environment. Snow, rain, fog, ice, freezing rain, and other weather conditions can impair the ability of drivers to operate their vehicles safely, significantly reduce roadway capacity, and dramatically increase travel times. Multiple roadway activities, from roadway maintenance and construction to shipping, transit, and police operations, are directly affected by inclement weather. Some road weather information is available to users currently, however a disconnect remains between current research and operations, and additional research could yield important safety and economic improvements for roadway users. Meteorology, roadway technology, and vehicle systems have evolved to the point where users could be provided with better road weather information through modern information technologies. The combination of these technologies has the potential to significantly increase the efficiency of roadway operations, road capacity, and road safety. Where the Weather Meets the Road provides a roadmap for moving these concepts to reality.

Speed Selection Behavior During Adverse Winter Weather Conditions
Speed Selection Behavior During Adverse Winter Weather Conditions

Author: Sandeep Thapa

Publisher:

Published: 2016

Total Pages: 179

ISBN-13: 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.

Driver Speed and Lane Keeping Behaviors in Adverse Weather Conditions
Driver Speed and Lane Keeping Behaviors in Adverse Weather Conditions

Author: Ali Ghasemzadeh

Publisher:

Published: 2017

Total Pages: 135

ISBN-13: 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.

Sustainable Winter Road Operations
Sustainable Winter Road Operations

Author: Xianming Shi

Publisher: John Wiley & Sons

Published: 2018-08-27

Total Pages: 476

ISBN-13: 1119185068

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The first and only comprehensive guide to best practices in winter road operations Winter maintenance operations are essential to ensure the safety, mobility, and productivity of transportation systems, especially in cold-weather climates, and responsible agencies are continually challenged to provide a high level of service in a fiscally and environmentally responsible manner. Sustainable Winter Road Operations bridges the knowledge gaps, providing the first up-to-date, authoritative, single-source overview and guide to best practices in winter road operations that considers the triple bottom line of sustainability. With contributions from experts in the field from around the world, this book takes a holistic approach to the subject. The authors address the many negative impacts on regional economies and the environment of poorly planned and inadequate winter road operations, and they make a strong case for the myriad benefits of environmentally sustainable concepts and practices. Best practice applications of materials, processes, equipment, and associated technologies and how they can improve the effectiveness and efficiency of winter operations, optimize materials usage, and minimize cost, corrosion, and environmental impacts are all covered in depth. Provides the first up-to-date, authoritative and comprehensive overview of best practices in sustainable winter road operations currently in use around the world Covers materials, processes, equipment, and associated technologies for sustainable winter road operations Brings together contributions by an international all-star team of experts with extensive experience in designing, implementing, and managing sustainable winter road operations Designed to bring professionals involved in transportation and highway maintenance and control up to speed with current best practice Sustainable Winter Road Operations is essential reading for maintenance professionals dealing with snow and ice control operations on highways, motorways and local roads. It is a valuable source of information and guidance for decision makers, researchers, and engineers in transportation engineering involved in transportation and highway maintenance. And it is an ideal textbook for advanced-level courses in transportation engineering.

Investigating The Impacts of Adverse Road Weather Conditions on Free-Flow Speed and Lane Utilization
Investigating The Impacts of Adverse Road Weather Conditions on Free-Flow Speed and Lane Utilization

Author: Navoda Yasanthi Rillagodage

Publisher:

Published: 2018

Total Pages: 0

ISBN-13:

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Winter driving poses additional safety challenges to drivers and other road users due to reduced visibility, snow and ice accumulation on travel lanes, and variations in transportation infrastructure such as reduced lane width due to snow plow operations and coverage of lane markings by snow. On the other hand, road-weather related variations in transportation infrastructure characteristics and driver behavior negatively impact traffic operations in winter. Therefore, it is crucial to understand the anticipated consequences of extreme road-weather conditions on traffic flow parameters and driver behavior. Thus, in this study, the free-flow speed and the lane utilization patterns under adverse roadweather conditions were studied. Road-weather data were collected from a Road-Weather Information System (RWIS) and traffic data were collected from a Weigh-In-Motion (WIM) station located in Highway 16 in Alberta from October, 2014 to December, 2015. The first part of the study attempts to investigate drivers' choice of desired speed under adverse road-weather conditions with two main objectives: i) to model the impacts of road-weather events on free-flow speed of light and heavy vehicles, and ii) to explore the variability in free-flow speed models caused by adopting different data analysis methods. Separate linear and nonlinear regression models were developed by adopting three distinct statistical approaches i.e. i) with dependent variable as individual vehicle speeds, ii) with dependent variable as 20 minute aggregate vehicle speeds, and iii) with sampled data. The study results revealed that slight, moderate and heavy snow will reduce the free-flow speed of light vehicles travelling in shoulder lane by 0.2%, 3.4% and 0.8% and the free-flow speed of heavy vehicles by 1.7%, 0.1% and 1.3% respectively. Further, it was unveiled that the drivers' reaction to ice warning pavement surface conditions is maximum. Linear regression models with aggregated speeds as the dependent variable was concluded as the best fit for the data among the other statistical approaches tested. The second part of the study aims to model lane utilization patterns of light and heavy vehicles under different road-weather conditions in macroscopic and microscopic facets. Two main types of lane utilization models were developed exerting a linear and a logistic regression approach in modelling. Each main model contains three sub models with refined data corresponding to all light and heavy vehicles. Interestingly, moderate rain and nighttime cause the vehicles to prefer shoulder lane irrespective of the vehicle type as suggested by both models. The findings indicated that heavy vehicles tend to choose the median lane under icy pavement conditions as estimated by both model types. Light vehicles tend to utilize the median lane under ice watch and frost pavement conditions in macroscopic and microscopic contexts, respectively. Unlike the other similar studies conducted so far, this study estimates the free-flow speeds and lane utilization patterns of vehicles more accurately because of the unique location configuration of the data collectors. In addition, the study results direct the weather responsive traffic management systems to a new path by introducing more dimensions to be considered about, such as variable speed limits depending on different road-weather conditions and traffic composition, and dynamic lane utilization schemes.