Thomas Jonsson

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.

Differences in the Performance of Safety Performance Functions Estimated for Total Crash Count and for Crash Count by Crash Type

In recent years the development and use of crash prediction models for roadway safety analyses have received substantial attention. These models, also known as safety performance functions (SPFs), relate the expected crash frequency of roadway elements (intersections, road segments, on-ramps) to traffic volumes and other geometric and operational characteristics. A commonly practiced approach for applying intersection SPFs is to assume that crash types occur in fixed proportions (e.g., rear-end crashes make up 20% of crashes, angle crashes 35%, and so forth) and then apply these fixed proportions to crash totals to estimate crash frequencies by type. As demonstrated in this paper, such a practice makes questionable assumptions and results in considerable error in estimating crash proportions. Through the use of rudimentary SPFs based solely on the annual average daily traffic (AADT) of major and minor roads, the homogeneity-in-proportions assumption is shown not to hold across AADT, because crash proportions vary as a function of both major and minor road AADT. For example, with minor road AADT of 400 vehicles per day, the proportion of intersecting-direction crashes decreases from about 50% with 2,000 major road AADT to about 15% with 82,000 AADT. Same-direction crashes increase from about 15% to 55% for the same comparison. The homogeneity-in-proportions assumption should be abandoned, and crash type models should be used to predict crash frequency by crash type. SPFs that use additional geometric variables would only exacerbate the problem quantified here. Comparison of models for different crash types using additional geometric variables remains the subject of future research.

Predicting Segment-Intersection Crashes with Land Development Data

Experience with crash prediction modeling has confirmed the importance of traffic volume, not only as exposure but also as a predictive variable. For intersection-related collisions, for example, angle collisions or any collisions involving turning vehicles, traffic volumes on both intersecting roads are necessary for sufficient prediction of crash count. These collisions occur not only at intersections but any place where vehicles turn on or off the roadway, such as driveways. Intersecting traffic volumes at such locations are either not available or labor intensive to acquire. The objective of this study was to investigate the use of geographic information system (GIS) land use inventories to supplement observed traffic volumes as exposure measures for estimating models for predicting segment-intersection crashes, defined as collisions occurring on road segments involving one or more turning or crossing vehicles. Model results for rural two-lane and urban two- and four-lane undivided roads indicate that the number of trips generated and the extent of surrounding land development itself act as excellent predictors for segment-intersection crashes and in fact work better than models using the number of access points. The reason is that those variables better describe the intensity of the traffic accessing the major artery. This is a valuable finding, since access points along a road segment cannot be counted automatically, but many jurisdictions have GIS land use inventories available for all sorts of planning purposes. Such a development will permit better accounting of exposure to segment-intersection crashes in crash prediction modeling.

Motor Vehicle Speeds: Recommendations for Urban Sustainability

This paper explores how vehicle speeds are related to equitable, environmental, and economic sustainability of urban areas. This relationship is manifested primarily through associations between vehicle speeds and road crash casualties, severity of pedestrian crashes, generation of harmful emissions, and relative desirability of neighboring land. Reported research findings describing these associations are presented and discussed. Reported experiences with implementing various methods of influencing vehicle speeds, including automated enforcement, self-explaining roads, and in-vehicle systems, are presented and discussed. To support increased sustainability of urban areas, the following steps are recommended: (a) speed limits should be set to limit casualty risk and not to accommodate driver choices, (b) roadways in developed areas should be designed with 10-ft lanes and on-street parking and sidewalks, and (c) vehicle speeds in downtown and residential areas should be kept below 25 mph (preferably 20 mph). The paper also identifies gaps in knowledge about speed and sustainability.