John N. Ivan

Selecting exposure measures in crash rate prediction for two-lane highway segments

A critical part of any risk assessment is identifying how to represent exposure to the risk involved. Recent research shows that the relationship between crash count and traffic volume is non-linear; consequently, a simple crash rate computed as the ratio of crash count to volume is not proper for comparing the safety of sites with different traffic volumes. To solve this problem, we describe a new approach for relating traffic volume and crash incidence. Specifically, we disaggregate crashes into four types: (1) single-vehicle, (2) multi-vehicle same direction, (3) multi-vehicle opposite direction, and (4) multi-vehicle intersecting, and define candidate exposure measures for each that we hypothesize will be linear with respect to each crash type. This paper describes initial investigation using crash and physical characteristics data for highway segments in Michigan from the Highway Safety Information System (HSIS). We use zero-inflated-Poisson (ZIP) modeling to estimate models for predicting counts for each of the above crash types as a function of the daily volume, segment length, speed limit and roadway width. We found that the relationship between crashes and the daily volume (AADT) is non-linear and varies by crash type, and is significantly different from the relationship between crashes and segment length for all crash types. Our research will provide information to improve accuracy of crash predictions and, thus, facilitate more meaningful comparison of the safety record of seemingly similar highway locations.

Factors influencing injury severity of motor vehicle-crossing pedestrian crashes in rural Connecticut

The ordered probit model was used to evaluate the effect of roadway and area type features on injury severity of pedestrian crashes in rural Connecticut. Injury severity was coded on the KABCO scale and crashes were limited to those in which the pedestrians were attempting to cross two-lane highways that were controlled by neither stop signs nor traffic signals. Variables that significantly influenced pedestrian injury severity were clear roadway width (the distance across the road including lane widths and shoulders, but excluding the area occupied by on-street parking), vehicle type, driver alcohol involvement, pedestrian age 65 years or older, and pedestrian alcohol involvement. Seven area types were identified: downtown, compact residential, village, downtown fringe, medium-density commercial, low-density commercial, and low-density residential. Two groups of these area types were found to experience significantly different injury severities. Downtown, compact residential, and medium- and low-density commercial areas generally experienced lower pedestrian injury severity than village, downtown fringe, and low-density residential areas.

Explaining two-lane highway crash rates using land use and hourly exposure.

This paper describes the estimation of Poisson regression models for predicting both single and multi-vehicle highway crash rates as a function of traffic density and land use, as well as ambient light conditions and time of day. The study focuses on seventeen rural, two-lane highway segments, each one-half mile in length with varying land use patterns and where actual hourly exposure values are available in the form of observed traffic counts. Land-use effects are represented by the number of driveways of various types on each segment. Hourly exposure is represented for single-vehicle crashes as the total vehicle miles traveled and volume/capacity ratio; for multi-vehicle crashes it is the product of the hourly volumes on the main highway and the roads intersecting it along the study segment. For single-vehicle crashes, the following variables were found to be significant, with a positive or negative effect as noted: daytime (06:00-19:00 h, negative effect), the natural log of the segment volume/capacity ratio (negative), percent of the segment with no passing zones (positive), shoulder width (positive), number of intersections (negative), and driveways (mixed effects by type). Good multi-vehicle crash prediction models had quite different variables: daylight conditions from 10:00-15:00 and 15:00-19:00 h (positive), number of intersections (negative), and driveways (positive for all types). The results show that traffic intensity explains differences in crash rates even when controlling for time of day and light conditions, and that these effects are quite different for single and multi-vehicle crashes. Suggestions for future research are also given.

Roadway safety in rural and small urbanized areas.

Police Accident Reports (PAR) reveal that in a 5-year period between 1993 and 1997, there were 892 crashes at 87 two lane, undivided roadway sites in Strafford County, NH, a county consisting of suburban and rural communities. The purpose of this paper is to describe: (1) logistic regression model building efforts to identify statistically significant factors that predict the probabilities of crashes and injury crashes; and (2) to use these models to perform a risk assessment of the study region. The models are functions of factors that describe a site by its land use activity, roadside design, use of traffic control devices and traffic exposure. Comparative risk assessment results show village sites to be less hazardous than residential and shopping sites. Residential and shopping sites, which are distinctly different from village sites, reside in single-purpose, land-use zones consisting mostly of single-family dwelling units and roadside shopping units with ample off-street parking. Village sites reside in multi-purpose, land-use zones permitting a combination of activities found in residential, shopping and commercial areas. They are pedestrian friendly, that is, have sidewalks and crosswalks, permit onstreet parking, have speed limits and other amenities that promote walking. Adjusted odds ratios and other comparative risk measures are used to explain why one site is more hazardous than another one. For example, the probability of a crash is two times more likely at a site without a sidewalk than at a site with one. The implications on roadway design to improve safety are discussed.

Differences in causality factors for single and multi-vehicle crashes on two-lane roads

Past research has found a non-linear relationship between traffic intensity or level of service (LOS) and highway crash rates. This paper investigates this relationship further by including the effects of site characteristics and estimating Poisson regression models for predicting single and multi-vehicle crashes separately. Analysis focuses on rural two-lane highways, with hourly LOS, traffic composition, and highway geometric characteristics as independent variables. The resulting models for single and multi-vehicle crashes have different explanatory variables. Single-vehicle crash rates decrease with increasing traffic intensity (lower LOS), shoulder width and sight distance. Multi-vehicle crash rates increase with the number of signals, the daily single-unit truck percentage, and the shoulder width, and decreased on principal arterials compared to other roadway classes. LOS does not significantly explain variation in the number of multi-vehicle crashes. Ongoing research by the authors is aimed at identifying other site factors, such as driveway density and intersection LOS, that can better explain the differing effects reported here and predict crash rates of both types better.

Effects of Geometric Characteristics on Head-On Crash Incidence on Two-Lane Roads in Connecticut

Negative binomial generalized linear models were used to evaluate the effects of roadway geometric features on the incidence of head-on crashes on two-lane rural roads in Connecticut. Six hundred fifty-five highway segments-each with a uniform length of 1 km and no intersections with signal or stop control on the major road approaches-were selected for analysis. Head-on crash data were collected for these segments from 1996 through 2001. Variables found to influence the incidence of head-on crashes significantly were speed limit, sum of absolute change rate of horizontal curvature, maximum degree of horizontal curve, and sum of absolute change rate of vertical curvature. Three models were estimated with different combinations of these four variables, and the performance of the models was tested by using Akaikes information criterion. The number of crashes was found to increase with each of these variables except for speed limit. Variables such as lane and shoulder width were not found to be significant f...

Crash Prediction Models for Intersections on Rural Multilane Highways: Differences by Collision Type

Accident prediction models are often used to estimate the number of accidents on segments and at intersections in the road network. Most often the models are developed for a total number of crashes for the facility or for crashes by severity. However, the frequency and severity of crashes of different types can be expected to vary according to underlying phenomena that cause them. To account for this variation better, modeling of accidents at intersections on rural four-lane highways in California is described separately for four different collision types: opposite-direction crashes, same-direction crashes, intersecting-direction crashes, and single-vehicle crashes. The findings from this modeling are reported with a special focus on the differences in crash types by (a) severity distribution, (b) dependence on traffic flow, and (c) variables that best explain between-site variations in the occurrence of different crash types. Evident differences exist in severity as well as the relationship of flow between several of the crash types. Intersecting and opposite-direction crashes are more severe than same-direction crashes. Same- and opposite-direction crashes exhibit similar relationships with traffic flow, but there are differences compared with intersecting-direction crashes and single-vehicle crashes. In addition, the variables that turn out to be good predictor variables differ somewhat for each crash type.

New approach for including traffic volumes in crash rate analysis and forecasting

A critical part of any risk assessment is identifying the appropriate measure of exposure to the risk in question. Incorporating vehicle miles traveled per year into highway crash analysis was an important step forward for identifying truly hazardous locations, as opposed to locations with high volumes but a low number of crashes per vehicle. This exposure measure is now ubiquitous in highway crash reporting, although it has not been proved to be the most accurate representation of highway crash risk. Research results demonstrate that the relationship between traffic volume and crash count is more complex than this and that it relates to quantities such as the distribution of traffic through the day and the types of crashes experienced. A proposed specification for models of crash count incorporates all these considerations in a form that is accessible to most highway crash analysts and permits comparison of crash rates between various highway locations in a way that accounts for the nonlinearity of the crash rate for traffic volume.

NEURAL NETWORK REPRESENTATIONS FOR ARTERIAL STREET INCIDENT DETECTION DATA FUSION

Abstract This research considers four neural network representations for detecting incidents on signalized arterials using multiple data sources. Two incident detection algorithms process unique data sources separately: inductive loop detectors, and travel times collected from vehicle probes travelling through the street network. The networks then combine the algorithm inferences about traffic conditions to identify highway links on which incidents are occurring. The four networks consider the following input and structure representations, added incrementally: (1) the two algorithm output values alone; (2) a weighted geometric sum of previous network output values; (3) algorithm scores from links immediately upstream and downstream of the subject link; and (4) weighted geometric sums of previous input values. The four representations were trained as feed-forward networks using error back propagation. Time series inputs were represented with extra processing units and fixed weight connections. The networks were trained with data generated by traffic simulation permitting deliberate control of traffic demand, operation and incident conditions. Each network was trained until performance began to degrade on a reserved data set not used for training. Adding the output time series permitted two of the 24 incidents to be detected sooner than with the network that did not include this input. Similarly, using information from adjacent links in time series permitted all of the incidents to be detected by at least the third time period.

Data Fusion of Fixed Detector and Probe Vehicle Data for Incident Detection

This paper describes research conducted for ADVANCE, an ATIS (advanced traveler information system) demonstration sponsored by the Federal Highway Administration, Illinois Department of Transportation, Motorola, Inc., and the Illinois University Transportation Consortium. This research has developed an incident-detection system to use data provided in real time by three distinct data sources: Fixed detectors, which provide occupancy and volume data averaged over a limited time period, for a specific section of roadway; Probe vehicles, specially equipped vehicles that travel freely on the network and report link travel times; and Anecdotal sources, or reports of particular events affecting traffic flow provided by people traveling on or monitoring the road network.