Evaluating the Impacts of Driver Behavior in the Speed Selection Process and the Related Outcomes PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Evaluating the Impacts of Driver Behavior in the Speed Selection Process and the Related Outcomes PDF full book. Access full book title Evaluating the Impacts of Driver Behavior in the Speed Selection Process and the Related Outcomes by Cole D. Fitzpatrick. Download full books in PDF and EPUB format.
Author: Cole D. Fitzpatrick
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In the United States, traffic crashes claim the lives of 30,000 people every year and is the leading cause of death for 5-24 year olds. Driver error is the leading factor in over 90 percent of motor vehicle crashes, with the roadway and the vehicle each only accounting for about 2 percent of crashes. In the United States, nearly a third of fatal crashes are due to speeding and therefore, a critical step in improving traffic safety is research aimed to reduce speeding, such as crash data analysis, outreach campaigns, targeted enforcement, and understanding speed selection. In this dissertation, a multi-faceted approach was taken to improve roadway safety by examining the speeding-related crash designation, improving speed limit setting practices, and understanding the causes of speeding. Multiple experiments were conducted under this overarching goal. These experiments included an analysis of speeding-related crashes in Massachusetts, a naturalistic driving study, and a driving simulator study which investigated the causes of speeding. Collectively, the findings from these experiments can expand upon existing speed prediction models, improve crash data influence speed limit setting practices, guide speed management programs such as speed enforcement, and be used in public safety outreach campaigns.
Author: Cole D. Fitzpatrick
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In the United States, traffic crashes claim the lives of 30,000 people every year and is the leading cause of death for 5-24 year olds. Driver error is the leading factor in over 90 percent of motor vehicle crashes, with the roadway and the vehicle each only accounting for about 2 percent of crashes. In the United States, nearly a third of fatal crashes are due to speeding and therefore, a critical step in improving traffic safety is research aimed to reduce speeding, such as crash data analysis, outreach campaigns, targeted enforcement, and understanding speed selection. In this dissertation, a multi-faceted approach was taken to improve roadway safety by examining the speeding-related crash designation, improving speed limit setting practices, and understanding the causes of speeding. Multiple experiments were conducted under this overarching goal. These experiments included an analysis of speeding-related crashes in Massachusetts, a naturalistic driving study, and a driving simulator study which investigated the causes of speeding. Collectively, the findings from these experiments can expand upon existing speed prediction models, improve crash data influence speed limit setting practices, guide speed management programs such as speed enforcement, and be used in public safety outreach campaigns.
Author: Ali Ghasemzadeh
Publisher:
ISBN: 9780438515581
Category : Automobile drivers
Languages : en
Pages : 135
Get Book Here
Book Description
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.
Author: Oren Musicant
Publisher: Frontiers Media SA
ISBN: 2889713962
Category : Science
Languages : en
Pages : 161
Get Book Here
Book Description
Author: Anshu Bamney
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 0
Get Book Here
Book Description
Distracted driving is among the leading causes of motor vehicle crashes worldwide, though the magnitude of this problem is difficult to quantify given the limitations of police-reported crash data. A more promising approach is to evaluate the impacts of distraction in real-world driving events. To that end, this study leverages data from the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) to gain important insights into the risks posed by driver distraction. The objectives of this study are to assess the risk of crash and near-crash events under different contextual environments based upon whether the driver was engaged in any secondary (i.e., non-driving related) tasks. The research also compares speed profiles of distracted drivers in low-speed and high-speed environments, providing important insights into how driver behavior changes based upon the type and intensity of distraction. The first analysis uses the standard SHRP 2 data to compare the differences between near-crash risks on limited access freeways and two-lane highways. Mixed-effects logistic regression models were estimated to discern how the risks of near-crash events varied by distraction type while controlling for the effects of driver, roadway, and traffic characteristics. In general, the risks were more pronounced for those distractions that were a combination of cognitive, visual, and manual distractions (for e.g., cell phone texting). While the same factors tended to increase near-crash risk on both types of facilities, the impacts of several factors tended to be more pronounced on two-lane highways where interaction with other vehicles occurred more frequently. The second analysis uses a subset of the NDS data that were focused on naturalistic engagement in secondary tasks (NEST). The NEST data were used to assess how the type and duration of distraction impacted the likelihood of crash and near-crash events. Separate comparisons were made between crashes and near-crashes with "normal" baseline driving events. The results show the duration of distraction to be a strong predictor of both crash and near-crash risk and were found to have similar relationships with crashes and near-crashes. The risks were highest for those secondary tasks that introduce a combination of visual and manual distractions that provides evidence that distractions requiring higher levels of engagement have more pronounced impacts on safety. The third and final analysis uses NEST data to analyze how driver speed selection varies based upon the types of secondary tasks that a driver is engaged in. Comparisons are made as to differences between high-speed and low-speed environments. Two-way random effects linear regression models were estimated for both speed regimes while controlling for driver, roadway, and traffic characteristics. In general, engagement in all tasks was found to decrease speeds in high-speed environments, while the effects were mixed in low-speed settings. These changes in speeds are much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, handheld cellphone talking was associated with highest speed changes in both environments followed by reaching/manipulating an object and holding an object. Ultimately, the results of this study provides further motivation for more aggressive legislation and enforcement against distracted driving. This can be achieved by enforcing strict laws and fines, graduated licensing process, public campaigns, modified infrastructure (rumble strips and tactile lane marking), and other such measures.
Author: Cole D. Fitzpatrick
Publisher:
ISBN:
Category : Automobile drivers
Languages : en
Pages : 58
Get Book Here
Book Description
Roadside vegetation provides numerous environmental and psychological benefits to drivers. Previous studies have shown that natural landscapes can effectively lower crash rates and cause less stress and frustration to the driver. However, run-off-the-road crashes resulting in a collision with a tree are twice as likely to result in a fatality, thus reinforcing the need to examine the placement of vegetation within the clear zone. This study explores the relationship between the size of the clear zone and the presence of roadside vegetation on selected driver attributes, including both driver speed and lateral positioning. To evaluate the effect on the driver speed selection process, a static evaluation was employed. Completed by more than 100 drivers, the static evaluation was utilized to gather speed selections on both real and virtual roads containing four combinations of clear zone size and roadside vegetation density. Additionally, field data was collected to validate the findings of the static evaluation and to determine the extent to which roadside vegetation impacts driving attributes. When presented with a large clear zone, drivers positioned the vehicle further from the edge of the road as the vegetation density increased. Furthermore, the speeds observed in the field correlated with the speeds that participants selected when watching a video of the same road. Finally, the UMassSafe Traffic Safety Data Warehouse was utilized to link crash and roadway data, allowing for an in-depth analysis of run-off-the-road (ROR) crash severity. The results of this study further demonstrate the nature of the relationship between clear zone design and driver behavior.
Author: Brian Ray
Publisher: Transportation Research Board
ISBN: 0309099358
Category : Technology & Engineering
Languages : en
Pages : 110
Get Book Here
Book Description
TRB¿s National Cooperative Highway Research Program (NCHRP) Report 613: Guidelines for Selection of Speed Reduction Treatments at High-Speed Intersections explores the effectiveness of geometric design features as well as signage and pavement markings to reduce vehicle speeds at high-speed intersections.
Author: Huizhong Guo
Publisher:
ISBN:
Category :
Languages : en
Pages : 113
Get Book Here
Book Description
Driver-related factors have long been an important component in traffic safety. Studies to assess driver behavior and the related safety concerns have primarily used data that does not capture the dynamic nature of driving tasks. The widespread use of naturalistic driving data in recent years allows researchers the capability to capture real-time driver behavior and be able to infer an individual's driving style. However, current studies focus largely on at-risk safety behavior that is often incomplete (e.g., does not consider all types of at-risk safety behavior) and broadly defined regardless of the driving environment. The goal of this dissertation is to assess driver behavior in the context of the driving environment. This is accomplished using data from the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study, which includes more than 3,000 drivers on the road from 2010 to 2013. The concept of "abnormal" driving style is proposed as a complement to "normal" driving style. More specifically, the "abnormality" measures how much a driver deviates from the average driving behavior given the driving context. In this study, the average driving behavior is defined as the average of different vehicle kinematics for drivers that participated in SHRP2 and for a specific environmental context. The study thus aims to examine the association between driving "abnormality" and driver safety. Environmental factors that contribute to the formation of "normal" driving styles were identified in a systematic way through multivariate functional data clustering method and decision trees. The "abnormality" were described by a composite score as well as a set of statistical features that capture the different aspects of a driving style. Path analysis and Structural Equation Modeling method were used to reveal associations between driver safety and driving "abnormality". Results from the study provide insights into driver behavior and implications on driver safety in different environmental contexts. For example, the study showed that drivers who were more likely to crash were also more likely to have unstable lateral control on Urban Interstates. These findings can be integrated in autonomous vehicle algorithms where individual driving styles are considered. It can also provide insights on the development of new technologies to identify risky drivers and to quantify their risky levels.
Author: Curt P. Harrington
Publisher:
ISBN:
Category :
Languages : en
Pages : 77
Get Book Here
Book Description
Static speed limits are the norm across the world's roadway networks. However, advances in technology and increased applications in intelligent transportation systems (ITS) provide a mechanism for upgrading traditional speed limits into an active traffic management system. More specifically, variable speed limits (VSLs) can be used in high crash severity locations and in real-time congestion and weather events to increase traffic safety and operations. Much of the available literature on VSLs focuses upon crash prediction algorithms for VSLs, simulations, and effectiveness of real-world VSL implementations. One noticeable gap in the existing literature is related to driver compliance under varied configurations of alerting drivers of the variable speeds. An additional gap in literature is related to existence of a conceptual framework for identifying optimal corridors for potential VSL implementation. Within this thesis drivers' willingness to comply with VSLs was investigated via focus groups and static surveys during the experimental process. Connections are made between driver speed choice and type of speed limit condition including uniform speed vi limit (USL) versus VSL, overhead mount versus side mount, presence of an explanatory message, and the numerical speed limit value. An analysis of the survey results was completed to isolate critical factors in VSL compliance. Opinions and perspectives on VSLs are derived through the focus group sessions Lastly, a case study approach is presented in which a region is chosen, and implementation metrics are analyzed on the major roadway networks using a GIS platform to create a composite ranking system for potential optimal VSL corridors. The study aims to be used as a foundation to justify use of certain types of VSLs in addition to creating a conceptual framework for VSL implementation zone identification.
Author: United States. National Highway Traffic Safety Administration
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
Get Book Here
Book Description
Author: L. Flynn (comp)
Publisher:
ISBN:
Category :
Languages : en
Pages : 332
Get Book Here
Book Description
