Asad J. Khattak

Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions

Collisions between heavy trucks and passenger cars are a major concern because of the severity of injuries. This research has two objectives. One is to examine the impact of various factors on injuries to passenger car occupants involved in such collisions. Due to the complex interaction of factors influencing injury levels in truck-car collisions, the ordered probit model is used to identify specific variables significantly influencing levels of injury in two-vehicle rear-end involvements on divided roadways. Another objective is to demonstrate the use of the ordered probit in this complex highway safety problem. A set of vehicle, occupant, roadway, and environmental factors expected to influence injury severity was developed. Given two-vehicle passenger car-truck rear-end collisions, the variables that increase passenger vehicle occupant injury severity include darkness; high speed differentials; high speed limits; grades, especially when they are wet; being in a car struck to the rear (as opposed to being in a car striking a truck to the rear); driving while drunk; and being female. The interaction effects of cars being struck to the rear with high speed differentials and car rollovers were significant. Variables decreasing severity include snowy or icy roads, congested roads, being in a station wagon struck to the rear (as opposed to a sedan), and using a child restraint. With injuries ordered in five classes from no injury to fatalities, the marginal effects of each factor on the likelihood of each injury class are reported.

A SIMPLE TIME SEQUENTIAL PROCEDURE FOR PREDICTING FREEWAY INCIDENT DURATION

The objective of this study is to develop a methodology for incident duration prediction. First, we develop an understanding of factors that influence incident duration. Then, we use a series of truncated regression models to predict incident duration. The models account for the fact that incident information at a Traffic Operations Center is acquired over the life of the incident. The implications of this simple methodology for incident duration prediction are discussed.

Effects of work zone presence on injury and non-injury crashes.

Work zones in the United States have approximately 700 traffic-related fatalities, 24,000 injury crashes, and 52,000 non-injury crashes every year. Due to future highway reconstruction needs, work zones are likely to increase in number, duration, and length. This study focuses on analyzing the effect of work zone duration mainly due to its policy-sensitivity. To do so, we created a unique dataset of California freeway work zones that included crash data (crash frequency and injury severity), road inventory data (average daily traffic (ADT) and urban/rural character), and work zone related data (duration, length, and location). Then, we investigated crash rates and crash frequencies in the pre-work zone and during-work zone periods. For the freeway work zones investigated in this study, the total crash rate in the during-work zone period was 21.5% higher (0.79 crashes per million vehicle kilometer (MVKM)) than the pre-work zone period (0.65 crashes per MVKM). Compared with the pre-work zone period, the increase in non-injury and injury crash rates in the during-work zone period was 23.8% and 17.3%, respectively. Next, crash frequencies were investigated using negative binomial models, which showed that frequencies increased with increasing work zone duration, length, and average daily traffic. The important finding is that after controlling for various factors, longer work zone duration significantly increases both injury and non-injury crash frequencies. The implications of the study findings are discussed in the paper.

Factors Influencing Bicycle Crash Severity on Two-Lane, Undivided Roadways in North Carolina

Concern over crashes involving bicycles and motor vehicles is largely due to the severity of injuries. The impacts of physical and environmental factors on the severity of injury to bicyclists are examined. North Carolina Highway Safety Information System crash and inventory data for state-controlled, two-lane, undivided roadways are analyzed. The injury severity distribution, measured on the KABCO scale, is as follows: no injury, 1.8 percent; complaint of pain, 24.4 percent; nonincapacitating injury, 42.5 percent; incapacitating injury, 25.5 percent; and fatal injury, 5.9 percent. The total number of involvements in this data set was 1,025, with a majority of the involvements occurring outside urbanized areas (80.5 percent). Using the ordered probit model, the effect of a set of roadway, environmental, and crash variables on injury severity is explored. Variables that significantly increase injury severity include straight grades, curved grades, darkness, fog, and speed limit. Higher average annual daily traffic, an interaction of speed limit and shoulder-width variables, and dark conditions with street lighting significantly lower injury severity. Separate models are estimated for rural and urban locations. Marginal effects of each factor on the likelihood of each injury-severity class are reported. Policy implications and possible countermeasures are then discussed.

ROLE OF ADVERSE WEATHER IN KEY CRASH TYPES ON LIMITED-ACCESS ROADWAYS: IMPLICATIONS FOR ADVANCED WEATHER SYSTEMS

Adverse weather can reduce visibility and road surface friction and thus increase crash frequency and injury severity. However, drivers may compensate for higher crash risk by reducing speeds, maintaining safe spacing, and driving more carefully. The impacts of adverse weather and its interactions with driver and roadway characteristics on the occurrence and injury severity of selected crash types are analyzed. Single-vehicle, two-vehicle sideswipe, and two-vehicle rear-end collisions on limited-access roadways are considered. To analyze differential impacts of adverse weather on crash type, binary probit models are estimated for single-vehicle versus the two types of two-vehicle crashes, and for rear-ends versus sideswipes. To analyze injury severity, ordered probit models are estimated. The 1990–1995 Highway Safety Information System (HSIS) database for North Carolina was used for analysis. The results indicate that, for the selected crash types, drivers appear to compensate for increased injury risks in that in adverse weather crashes are more frequent but injuries are less severe. Some implications for advanced weather systems are discussed.

An accident waiting to happen: a spatial approach to proactive pedestrian planning

There are about 75,000 pedestrian crashes in the United States each year. Approximately 5000 of these crashes are fatal, accounting for 12% of all roadway deaths. On college campuses, pedestrian exposure and crash-risk can be quite high. Therefore, we analyzed pedestrian crashes on the campus of the University of North Carolina at Chapel Hill (UNC) as a test case for our spatially-oriented prototype tool that combines perceived-risk (survey) data with police-reported crash data to obtain a more complete picture of pedestrian crash-risk. We use spatial analysis techniques combined with regression models to understand factors associated with risk. The spatial analysis is based on comparing two distributions, i.e. the locations of perceived-risk with police-reported crash locations. The differences between the two distributions are statistically significant, implying that certain locations on campus are perceived as dangerous, though pedestrian crashes have not yet occurred there, and there are actual locations of police-reported crashes that are not perceived to be dangerous by pedestrians or drivers. Furthermore, we estimate negative binomial regression models to combine pedestrian and automobile exposure with roadway characteristics and spatial/land use information. The models show that high exposure, incomplete sidewalks and high crosswalk density are associated with greater observed and perceived pedestrian crash-risk. Additionally, we found that people perceive a lower risk near university libraries, stadiums, and academic buildings, despite the occurrence of crashes.

Willingness to pay for travel information

Abstract Improved travel information received via electronic sources can inform people about travel conditions and help them make travel decisions. The personal benefits of high quality travel information may motivate individuals to pay for information. This study analyzes travelers’ willingness to pay for better quality information received from a traveler information system offered through a public–private partnership in the San Francisco Bay Area. The data were collected in 1997 through a computer-aided telephone interview of individuals who called traveler advisory telephone system (TATS) and were willing to be interviewed ( N =511). The survey results indicate that the average number of times per month the respondents called TATS was 4.80 (TATS was a free service at the time). The average use of the system would decline if the service was not improved but a service charge was initiated. People indicated that they were more willing to pay for a customized service. The impacts of travel information, travel context and socioeconomic variables on willingness to pay for information were analyzed by estimating a random-effects negative binomial regression model of revealed and stated TATS calling frequency. The results indicate that customized travel information, longer trips, worktrips, and listening to radio traffic reports are associated with higher TATS calling frequency and with greater willingness to pay for information. Overall, the consumer response to purchasing travel information services seems cost-sensitive and future efforts can focus on commercialization of travel information, beginning with where demand for information is relatively inelastic and improvement or customization of travel information is achievable.

Behavioral issues in the design and evaluation of advanced traveler information systems

Decisions about implementing Advanced Traveler Information Systems (ATIS) should be based on the individual and social benefits expected from such technologies, which will be strongly dependent on the ways travelers respond to these new information sources. This paper explores the behavioral issues important to understanding traveler reactions to ATIS; it discusses evaluation strategies, including stated preference methods and observation of revealed behavior in laboratory simulations and field tests with various degrees of control and complexity. Advantages and disadvantages of different approaches are reviewed, and the experimental design challenges of site selection, recruitment of test subjects, and measurement of behavior are explored.

EFFECT OF SPEED LIMIT INCREASES ON CRASH INJURY SEVERITY: ANALYSIS OF SINGLE-VEHICLE CRASHES ON NORTH CAROLINA INTERSTATE HIGHWAYS

The recent congressional action revoking the national maximum speed limits has rekindled the debate over safety and travel time tradeoff. The effect of speed limit increases on the most severe occupant injury in a crash is analyzed here. Single-vehicle crashes on Interstate highways in North Carolina (N = 2729) are examined. Two analysis methods are used: a paired-comparison analysis and an ordered probit model. Increasing speed limits from 88.5 to 96.6 km/h (55 to 60 mph) and from 88.5 to 104.6 km/h (55 to 65 mph) increased the probability of sustaining minor and nonincapacitating injuries, but increasing speed limits from 104.6 to 112.7 km/h (65 to 70 mph) did not have a significant effect on crash severity. There were too few fatal crashes to draw conclusive results for this category of injury severity. Crashes involving the face of a guardrail were more severe on segments where the speed limit was raised than on comparison segments or study segments before the limits were increased. These findings may be conservative because study segments with good safety records were chosen for the speed limit increases.

Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types.