Shashi Nambisan

What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors

Crashes at highway-rail grade crossings can result in severe injuries and fatalities to vehicle occupants. Using a crash database from the Federal Railroad Administration (N=15,639 for 2004-2013), this study explores differences in safety outcomes from crashes between passive controls (Crossbucks and STOP signs) and active controls (flashing lights, gates, audible warnings and highway signals). To address missing data, an imputation model is developed, creating a complete dataset for estimation. Path analysis is used to quantify the direct and indirect associations of passive and active controls with pre-crash behaviors and crash outcomes in terms of injury severity. The framework untangles direct and indirect associations of controls by estimating two models, one for pre-crash driving behaviors (e.g., driving around active controls), and another model for injury severity. The results show that while the presence of gates is not directly associated with injury severity, the indirect effect through stopping behavior is statistically significant (95% confidence level) and substantial. Drivers are more likely to stop at gates that also have flashing lights and audible warnings, and stopping at gates is associated with lower injury severity. This indirect association lowers the chances of injury by 16%, compared with crashes at crossings without gates. Similar relationships between other controls and injury severity are explored. Generally, crashes occurring at active controls are less severe than crashes at passive controls. The results of study can be used to modify Crash Modification Factors (CMFs) to account for crash injury severity. The study contributes to enhancing the understanding of safety by incorporating pre-crash behaviors in a broader framework that quantifies correlates of crash injury severity at active and passive crossings.

Analyzing the effectiveness of implemented highway safety laws for traffic safety across U.S. states

ABSTRACT Since highway safety laws vary greatly from state to state in the U.S., there is a need to analyze the effectiveness and performances of the implemented highway safety laws. The random-parameter zero-truncated negative binomial (RZTNB) models are proposed to analyze the effects of highway safety laws on fatal crashes at state levels. The results show that the proposed models are useful in describing the relationships between the fatal crashes and the explanatory variables with better goodness of fit. By accounting for the heterogeneities, the RZTNB model outperforms the negative binomial model and reveals new insights. The findings indicate that (1) compared to the secondary ban, the primary handheld cellphone ban is more effective; (2) establishing reasonable and acceptable speed limits can enhance the traffic safety; and (3) the implemented speed camera system and ignition interlock device have weaknesses and alternative methods should be considered when upgrading laws and regulations.

Analyzing injury crashes using random-parameter bivariate regression models

ABSTRACT This paper proposes a random-parameter bivariate zero-inflated negative binomial (RBZINB) regression model for analyzing the effects of investigated variables on crash frequencies. A Bayesian approach is employed as the estimation method, which has the strength of accounting for the uncertainties related to models and parameter values. The modeling framework has been applied to the bivariate injury crash counts obtained from 1000 intersections in Tennessee over a five-year period. The results reveal that the proposed RBZINB model outperforms other investigated models and provides a superior fit. The proposed RBZINB model is useful in gaining new insights into how crash occurrences are influenced by the risk factors. In addition, the empirical studies show that the proposed RBZINB model has a smaller prediction bias and variance, as well as more accurate coverage probability in estimating model parameters and crash-free probabilities.

Modeling Route Choice of Utilitarian Bikeshare Users with GPS Data

To understand a bicyclist’s route choice is difficult, given the many factors that influence the attractiveness of different routes. The advent of low-cost GPS devices has made route choice analysis more precise. Bikeshare, with instrumented bikes, allows for better assessment of revealed route preference of a large subpopulation of cyclists. This study used GPS data obtained from 9,101 trips made by 1,866 users of Grid Bikeshare, Phoenix, Arizona. This unique bikeshare system relied on Social Bicycles’ onboard telematics, which allowed nonstation origins and destinations, and operated on a grid street network. The system enabled unique route choice analysis. The trips studied included only direct utilitarian trips. Circuitous trips that could have included multiple destinations or could have been recreational trips were removed. The analysis focused on facility use assessment and route choice behavior. The results were compared between two categories of bikeshare users: registered users and casual users. Registered users made shorter trips on roads with low volume and preferred bike-specific infrastructure. A path size logit model was used to model route choice. Riders were sensitive to travel distance, with little deviation from the shortest path to use more bike-friendly infrastructure. Travel on the bike-specific facilities was equivalent to a decrease in distance by 44.9% (compared with 53.3% for casual users). Left turns imposed higher disutility than right turns did for casual users. The proportion of one-way segments, annual average daily traffic values, and length of trip had a negative influence on route choice, and a number of signalized intersections had a positive influence on route selection. The results were also compared with those of earlier studies.

Exploring the effects of state highway safety laws and sociocultural characteristics on fatal crashes.

ABSTRACT Objective: Distinguished from the traditional perspectives in crash analyses, which examined the effects of geometric design features, traffic factors, and other relevant attributes on the crash frequencies of roadway entities, our study focuses on exploring the effects of highway safety laws, as well as sociocultural characteristics, on fatal crashes across states. Methods: Law and regulation related data were collected from the Insurance Institute for Highway Safety, State Highway Safety Offices, and Governors Highway Safety Association. A variety of sociodemographic characteristics were obtained from the U.S. Census Bureau. In addition, cultural factors and other attributes from a variety of resources are considered and incorporated in the modeling process. These data and fatal crash counts were collected for the 50 U.S. states and the District of Columbia and were analyzed using zero-truncated negative binomial (ZTNB) regression models. Results: The results show that, in law and regulation–related factors, the use of speed cameras, no handheld cell phone ban, limited handheld cell phone ban, and no text messaging ban are found to have significant effects on fatal crashes. Regarding sociocultural characteristics, married couples with both husband and wife in the labor force are found to be associated with lower crash frequencies, the ratios of workers traveling to work by carpool, those driving alone, workers working outside the county of residence, language other than English and limited English fluency, and the number of licensed drivers are found to be associated with higher crash frequencies. Conclusions: Through reviewing and modeling existing state highway safety laws and sociocultural characteristics, the results reveal new insights that could influence policy making. In addition, the results would benefit amending existing laws and regulations and provide testimony about highway safety issues before lawmakers consider new legislation.

Effects of Roadway Geometric Design Features on Frequency of Truck-Related Crashes

Because truck-related crashes are a socioeconomic concern that can result in tremendous loss of life and property, unbiased, relatively accurate estimations of crash frequency are essential. A data set from Tennessee was used to examine the effects of roadway geometric design features and other relevant attributes on the frequency of truck-related crashes. Negative binomial (NB) and zero-inflated NB (ZINB) models were proposed to identify the risk factors that had significant effects on the frequency of crashes that involved large trucks. Differences in truck-related crashes were investigated across collision vehicle types, and three crash count models—total truck related, car–truck, and truck only—were developed under the ZINB and NB frameworks. Elasticities were estimated for these crash count models to identify the most critical variables contributing to crashes. Findings suggest that the ZINB models have most of the desirable statistical properties (i.e., better goodness of fit and more significant variables identified). Model results revealed seven factors significant for the frequency of truck-related crashes regardless of crash type: annual average daily traffic, percentage of trucks, segment length, degree of horizontal curvature, terrain type, median type, and posted speed limit. Results of elasticity estimation reveal that the percentage of trucks is the most critical variable of all explanatory variables for the frequency of truck-related crashes.

Pilot Initiative in Iowa for an Intern Development and Management Program

This paper summarizes the efforts and the outcomes of a pilot student internship program developed jointly in 2012 by the Iowa Department of Transportation (DOT) and the Institute for Transportation at Iowa State University. In 2012, Iowa was the only state DOT to use federal funds for the internship program, which was initiated as a summer program with 56 interns assigned to DOT offices and projects across the state. The paid internship program was extended into the fall semester with 22 students assigned to offices in Ames, Iowa. The program had engineering and nonengineering positions and was developed to provide experiential learning opportunities, to address Iowa DOTs workforce needs, and to attract undergraduate students to transportation careers. More than 80% of the interns and more than 90% of the supervisors, choosing from multiple choice responses, rated the program outcomes as great or okay. The interns valued opportunities to work in professional settings where they had responsibilities entrusted to them, interactions with other professionals and practitioners, an opportunity to understand real-world application of their course work, and the chance to gain first-hand experience working on transportation projects, as well as to learn about expectations in a professional setting. Supervisors noted the importance of establishing a continuation of the internship and cooperative education programs for future students. Nearly 100% of the interns and fully 100% of the supervisors said that they would recommend the program to students. Because of the student contributions during the internship, Iowa DOT was able to complete many activities that would not have been completed.

Estimating Factors Contributing to Frequency and Severity of Large Truck–Involved Crashes

AbstractUnderstanding the factors that contribute to crash frequency and severity will assist better highway design and develop appropriate countermeasures for hot spots, thereby improving the safe...

Fair Representation of Transportation Research Record Impacts

This paper presents results of a qualitative survey and a quantitative assessment of the relative impacts of transportation journals. The survey involved 80 research university professors, government employees, and practitioners randomly chosen to rate a list of peer-reviewed transportation journals. The quantitative assessment consisted of an extensive examination of the impact factor (IF), a widely used metric for comparing scholarly publications, of 30 major transportation journals. Although the Transportation Research Record (TRR) was ranked by far as the most “useful and important” to the work of all those surveyed in the field of transportation, the TRRs IF is lower than those of most other transportation journals. However, compared with other transportation journals, the TRR publishes the most scholarly papers and receives the most citations annually. It is also the most widely accessed by practitioners. This paper explores key factors that affect IF calculations and identifies strategies to enhance the TRRs IF with nominal and major changes to the current process and procedures. These strategies focus on increasing exposure time of papers published in the TRR and increasing timely access to TRR issues, as well as exploring the potential to subdivide the TRR into several thematic sections, which would lead to substantial increases in the IFs of several of the parts.