Karen Dixon

Influence of Land Use and Driveway Placement on Safety Performance of Arterial Highways

Characterizing driveway safety is a relevant and relatively complex topic in transportation safety research. This research studied the safety link of driveways abutting Oregon highways and considered various factors proposed in the current literature for design and evaluation of the safety performance of roadside elements. On the basis of two probability samples from rural and urban arterial state highways, this research developed alternative safety performance functions to evaluate the safety impacts of various driveway configurations. These safety performance functions were intended to explore driveway safety beyond the average driveway density treatment commonly encountered in the literature. The statistical models and methodologies in this research are comparable with those in the Highway Safety Manual. The proposed models exhibited different ranges of effects for urban and rural conditions, but type of land use proved a prominent factor for both the urban and the rural models. The analysis showed that roadside safety is influenced mainly by driveways associated with commercial and industrial land uses in the urban environment. Similarly, industrial driveways are more influential for safety than other types in rural environments. In addition, the rural model uncovered a safety connection to clusters of driveways rather than to driveways alone. This research indicated that after driveway land use in rural environments was accounted for, clustered driveways tended to have fewer crashes compared with isolated driveways.

Investigation on the wrong way driving crash patterns using multiple correspondence analysis

Wrong way driving (WWD) has been a constant traffic safety problem in certain types of roads. Although these crashes are not large in numbers, the outcomes are usually fatalities or severe injuries. Past studies on WWD crashes used either descriptive statistics or logistic regression to determine the impact of key contributing factors. In conventional statistics, failure to control the impact of all contributing variables on the probability of WWD crashes generates bias due to the rareness of these types of crashes. Distribution free methods, such as multiple correspondence analysis (MCA), overcome this issue, as there is no need of prior assumptions. This study used five years (2010-2014) of WWD crashes in Louisiana to determine the key associations between the contribution factors by using MCA. The findings showed that MCA helps in presenting a proximity map of the variable categories in a low dimensional plane. The outcomes of this study are sixteen significant clusters that include variable categories like determined several key factors like different locality types, roadways at dark with no lighting at night, roadways with no physical separations, roadways with higher posted speed, roadways with inadequate signage and markings, and older drivers. This study contains safety recommendations on targeted countermeasures to avoid different associated scenarios in WWD crashes. The findings will be helpful to the authorities to implement appropriate countermeasures.

Trends in Transportation Research: Exploring Content Analysis in Topics

Proceedings of journal and conference papers are good sources of big textual data to examine research trends in various branches of science. The contents, usually unstructured in nature, require fast machine-learning algorithms to be deciphered. Exploratory analysis through text mining usually provides the descriptive nature of the contents but lacks quantification of the topics and their correlations. Topic models are algorithms designed to discover the main theme or trend in massive collections of unstructured documents. Through the use of a structural topic model, an extension of latent Dirichlet allocation, this study introduced distinct topic models on the basis of the relative frequencies of the words used in the abstracts of 15,357 TRB compendium papers. With data from 7 years (2008 through 2014) of TRB annual meeting compendium papers, the 20 most dominant topics emerged from a bag of 4 million words. The findings of this study contributed to the understanding of topical trends in the complex and evolving field of transportation engineering research.

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

Motorcyclists are vulnerable highway users. Unlike passenger vehicle occupants, motorcycle riders do not have either protective structural surrounding or the advanced restraints that are mandatory safety features in cars and light trucks. Per vehicle mile traveled, motorcyclist fatalities occurred 27 times more frequently than passenger car occupant fatalities in traffic crashes. In addition, there were 4,976 motorcycle crash-related fatalities in the U.S. in 2014—more than twice the number of motorcycle rider fatalities that occurred in 1997. It shows that, in addition to current efforts, research needs to be conducted with additional resources and in newer directions. This paper investigated five years (2010–2014) of Louisiana at-fault motorcycle rider-involved crashes by using deep learning, which is a competent tool for mapping a high-multidimensional input into a smaller multidimensional output. The current study contributes to the existing injury severity modeling literature by developing a deep learning framework, named as DeepScooter, to predict motorcycle-involved crash severities. The final deep learning model can predict severity types with 100% accuracy with training data, and with 94% accuracy with test data, which is not attainable by using a statistical method or machine learning algorithm. The intensity of severities was found to be more likely associated with rider ejection, two-way roadways with no physical separation, curved aligned roadways, and weekends. It is anticipated that the DeepScooter framework and the findings will provide significant contributions to the area of motorcycle safety.

Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

ABSTRACT In 2011, 4,432 pedestrians were killed (14% of total traffic crash fatalities), and 69,000 pedestrians were injured in vehicle-pedestrian crashes in the United States. Particularly in Louisiana, vehicle-pedestrian crashes have become a key concern because of the high percentage of fatalities in recent years. In 2012, pedestrians were accounted for 17% of all fatalities due to traffic crashes in Louisiana. Alcohol was involved in nearly 44% of these fatalities. This research utilized ‘a priori’ algorithm of supervised association mining technique to discover patterns from the vehicle-pedestrian crash database. By using association rules mining, this study aims to discover vehicle-pedestrian crash patterns using eight years of Louisiana crash data (2004–2011). The results indicated that roadway lighting at night helped in alleviating pedestrian crash severity. In addition, a few groups of interest were identified from this study: male pedestrians’ greater propensity towards severe and fatal crashes, younger female drivers (15–24) being more crash-prone than other age groups, vulnerable impaired pedestrians even on roadways with lighting at night, middle-aged male pedestrians (35–54) being inclined towards crash occurrence, and dominance of single vehicle crashes. Based on the recognized patterns, this study recommends several countermeasures to alleviate the safety concerns. The findings of this study will help traffic safety professionals in understanding significant patterns and relevant countermeasures to raise awareness and improvements for the potential decrease of pedestrian crashes.

Use of Access Travel Time to Estimate the Impact of Driveway Restrictions on Corner Lot Developments

The types of data needed to assess the economic impacts of access management treatments on businesses, such as net profits or tax revenue, are difficult to obtain in a consistent and reliable manner. As a result, most research efforts on the topic have used subjective data collected through surveys and interviews, as well as less direct data variables, such as long-term land values. Although these efforts are valuable and insightful, their findings are difficult to apply to small-scale access decisions about a specific parcel of land. It is assumed that the travel time required to access a development is a contributing factor in the decision to patronize a business. Therefore, these travel time values could be a useful measure in evaluating potential effects of access management decisions on specific developments. This study used microsimulation to explore the use of travel time into and out of a corner lot development as a measure of the potential impact of driveway restrictions on business vitality. Results indicated that when only one access point was provided, a driveway on the minor road required less travel time than a driveway on the major road, although the relative difference in travel times decreased as volume decreased. In addition, under many of the scenarios tested, the provision of additional access (either additional driveways or fewer movement restrictions) did not reduce the amount of time required for potential customers to access a corner lot development.

Validation Technique Applied to Oregon Safety Performance Function Arterial Segment Models

The Oregon Department of Transportation developed segment arterial safety performance functions (SPFs) to help quantify the safety performance of driveways located on state urban and rural arterial highways. The research team determined that the crash reporting indicating that a driveway may have been involved in the crash was not a dependable variable, so the team developed SPFs for all non-intersection-related arterial crashes (many were likely the result of vehicle interactions at driveway locations). The information included in this paper reviews the subsequent validation effort and highlights innovative techniques used for the analysis. A common validation approach is assessing model performance for spatial transferability. For this effort, however, the authors evaluated spatial transferability, spatial–temporal transferability, and individual coefficient stability and significance. These procedures were highlighted and applied to an example urban model. The model performed well with the spatial transferability resulting in statistically equivalent values. The spatial–temporal transferability provided similar values but was not statistically equivalent at the 95% level, and all but one of the model variables were determined to be statistically significant.

Identifying low-volume road segments with high frequencies of severe crashes

Low-volume two-lane highways can be characterized by a wide range of physical features. Often the available crash data for these facilities are limited to fatal and injury crash information. It can be a challenge, therefore, to determine whether the select number of severe crashes observed along a corridor merits detailed safety evaluations and the associated investment of limited funds. The technique identified in this paper uses predictive method concepts developed with procedures consistent with those included in the AASHTO Highway Safety Manual but targeted only to low-volume roads and the number of observed fatal and injury crashes. For the purposes of this analysis, safety models are based on detailed crash and site data for low-volume highways in the state of Washington. A simplified procedure is then demonstrated to determine whether the number of severe crashes (fatal and injury) is significant enough to justify a detailed safety assessment and potential special safety enhancements on roadway construction projects or stand-alone safety enhancement projects for low-volume (≤1,000 vehicles per day) rural two-lane highways.

Exploring the Effects of Important Predictors of Ramp Speed Choice

Traditional measures of speed obtained through traffic observations are not based on detailed information about the related drivers and vehicles. Data from naturalistic studies, such as SHRP2 – NDS, can mitigate this issue by combining the key data on driver, roadway, and speed choice behavior. The objective of this study is to assess drivers’ speed choice on freeway ramps as a function of ramp design, trip summary, and driver characteristics. The data analysis provides insights into various spatial and temporal factors. To conduct the data analysis time series reduction, matching, and clustering methods were implemented to define a new speed choice behavior response variable denoted as driving state. Using the resulting response variable and the three groups of predictors, neural network analysis was conducted to identify the most influential predictors and their effects on the speed choice behavior of drivers during on-ramp and off-ramp travels. Results of this analysis of speed choice behavior on freeway ramps indicate that the speed choice at these locations is indeed a complex process and is mainly influenced by the temporal and traffic conditions. Personal characteristics of drivers were also found to influence speed choice in these locations.

A Comparative Analysis on Performance of Severe Crash Prediction Methods

The objective of this paper is to compare the performance and tradeoffs between two alternative analysis methods for developing crash prediction models for severe crashes: a direct estimation of severe crashes using frequency models, and an indirect but popular approach of combining frequency of total crashes models and some form of severity distribution functions (SDFs). The researchers conducted a comprehensive comparison of these modeling methods to illustrate the strengths and weaknesses of each alternative, and to inform future research that intends to develop such models. An examination of the theoretical characteristics of the modeling approach is presented and discussed. The performance of the two modeling alternatives is compared using two different datasets. The results of those comparisons showed very similar performances by both techniques. Finally, a sensitivity analysis is presented to explore how the performance of these techniques vary by degree of dispersion and observed correlation levels of total and severe injury crashes (KAB; injury scale in which K = fatal [killed], A = incapacitating injury, B = nonincapacitating injury) with potential explanatory variables. The results from these analyses tended to favor the use of SDFs in combination with total crashes safety performance functions (SPFs), as the prediction tended to show reduced dispersion under most conditions. However, performance of the KAB SPF model outperformed the combination of SDF and SPF for total crashes when KAB and non-KAB crashes had a common predictor but with effects in opposite directions.