2013 Session: 433

• Effects of Public Rest Areas on Fatigue-Related Crashes
  Abstract: Fatigue-related crashes account for 2.2 to 2.6 percent of all fatal crashes in the United States on an annual basis. These types of crashes are prevalent in rural areas and often result in severe injuries to crash-involved occupants. Public roadside rest areas were developed largely to alleviate motorist fatigue and reduce the opportunity for fatigue-related crashes by providing safe parking areas for tired drivers. However, research as to the safety effects of rest areas has been limited. This paper presents the results of a spatial analysis to investigate the effects of a road segment’s proximity to a rest area on the frequency of fatigue-related crashes. Poisson and negative binomial models are estimated for freeways and two-lane highways in order to isolate the effects of proximity while control for other relevant factors, such as traffic volumes. The results of these models indicate that the proximity of a road segment to the nearest rest area significantly influences crash frequencies on both types of facilities. Traffic volumes tended to have similar effects on both facility types while the effects of proximity were slightly more pronounced on two-lane highways. The study results suggest that roadside rest areas provide a safety benefit and the crash prediction models developed as a part of this research provide a simple, practical tool for use by road agencies in quantifying these impacts.
  Authors: McArthur, Adam; Savolainen, Peter Tarmo; Gates, Timothy J.
  Authors: McArthur, Adam; Savolainen, Peter Tarmo; Gates, Timothy J.
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-0162
• On the commonly accepted assumptions regarding observed motor vehicle crash counts at transport system locations
  Abstract: Readily accepted knowledge regarding crash causation is consistently omitted from efforts to model and subsequently understand motor vehicle crash occurrence and their contributing factors. For instance, distracted and impaired driving accounts for a significant proportion of crash occurrence, yet is rarely modeled explicitly. In addition, spatially allocated influences such as local law enforcement efforts, proximity to bars and schools, and roadside chronic distractions (advertising, pedestrians, etc.) play a role in contributing to crash occurrence and yet are routinely absent from crash models. By and large, these well-established omitted effects are simply assumed to contribute to model error, with predominant focus on modeling the engineering and operational effects of transportation facilities (e.g. AADT, number of lanes, speed limits, width of lanes, etc.)The typical analytical approach—with a variety of statistical enhancements—has been to model crashes that occur at system locations as negative binomial (NB) distributed events that arise from a singular, underlying crash generating process. These models and their statistical kin dominate the literature; however, it is argued in this paper that these models fail to capture the underlying complexity of motor vehicle crash causes, and thus thwart deeper insights regarding crash causation and prevention. This paper first describes hypothetical scenarios that collectively illustrate why current models mislead highway safety researchers and engineers. It is argued that current model shortcomings are significant, and will lead to poor decision-making. Exploiting our current state of knowledge of crash causation, crash counts are postulated to arise from three processes: observed network features, unobserved spatial effects, and ‘apparent’ random influences that reflect largely behavioral influences of drivers. It is argued; furthermore, that these three processes in theory can be modeled separately to gain deeper insight into crash causes, and that the model represents a more realistic depiction of reality than the state of practice NB regression. An admittedly imperfect empirical model that mixes three independent crash occurrence processes is shown to outperform the classical NB model. The questioning of current modeling assumptions and implications of the latent mixture model to current practice are the most important contributions of this paper, with an initial but rather vulnerable attempt to model the latent mixtures as a secondary contribution.
  Authors: Washington, Simon; Haque, Md. Mazharul
  Authors: Washington, Simon; Haque, Md. Mazharul
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-1841
• Verb-Based Text Mining of Road Crash Report
  Abstract: Traffic accident report is usually completed by police officers at the scene and contains important information on the cause and outcome of automobile accidents. However, a significant part of the report is stored in unstructured textual format. In the existing literature, there is only a handful of studies on extracting useful information from the crash report. In this research, we developed a verb-based text mining method. This method identifies and extracts the main verbs representing the vehicle actions in a sentence. Using those verbs, we are able to extract the sequence of events of the crash accident. The vehicle action entities are identified through using Natural Language Processing (NLP) techniques to identify both syntactic and semantic units in the text. The developed verb-based approach can effectively handle complex sentence structures such as clauses and conjunctive sentences. In the case study, we evaluated the proposed method using a total of 945 accidents records published by Missouri State Highway Patrol during the period from May 19, 2012 to June 27, 2012. The obtained results show that the extracted information is useful not only to crash classifications but also to help understand the causes of crashes.
  Authors: Gao, Lu; Wu, Hui
  Authors: Gao, Lu; Wu, Hui
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-2292
• Developing a Truck Corridor Crash Severity Index
  Abstract: According to the United States Department of Transportation (USDOT) estimates, over 500,000 truck accidents occur every year. Of that number, approximately 5,000 trucking accidents result in fatalities. Compared to extensive studies conducted on freeway truck safety, the research on arterial streets is considerably disproportionate. Making the connections between truck traffic generators, arterial streets are key links in door-to-door deliveries. There is an urgent need to study truck safety on arterial streets because of the strong growth of truck traffic. Truck related crashes are expected to be reduced through the careful planning of the location, design, and operation of driveways, median openings, street connections and street sections. By collecting extensive data on selected arterial corridors that are heavily used by trucks, truck crash frequency and severity contributing factors have been identified using negative binomial model and multinomial logit (MNL) model, respectively. Subsequently, a crash severity index (CSI) for the truck arterial corridors was developed. The findings from the study will not only benefit state and local agencies in planning, design, and manage a safer truck arterial corridor, but also help carriers to optimize their routes from the safety perspective.
  Authors: Qin, Xiao; Sultana, Most Afia; Chitturi, Madhav V.; Noyce, David A.
  Authors: Qin, Xiao; Sultana, Most Afia; Chitturi, Madhav V.; Noyce, David A.
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-3047
• Effects of Public Rest Areas on Fatigue-Related Crashes
  Authors: Savolainen, Peter
  Authors: Savolainen, Peter
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-0162
• On the commonly accepted assumptions regarding observed motor vehicle crash counts at transport system locations
  Authors: Washington, Simon
  Authors: Washington, Simon
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-1841
• Developing a Truck Corridor Crash Severity Index
  Authors: Qin, Xiao
  Authors: Qin, Xiao
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-3047
• Verb-Based Text Mining of Road Crash Report
  Authors: Gao, Lu
  Authors: Gao, Lu
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Safety and Human Factors
  Session: 433
  Paper Number: 13-2292