Safety Effects of Speed Limit Changes: Use of Panel Models, Including Speed, Use, and Design Variables

Abstract

This work estimates the total safety effects of speed limit changes on high-speed roadways using traffic detector data and Highway Safety Information System (HSIS) data from 1993 to 1996. In order to gauge the total effects, this study applies a sequential modeling approach wherein average speed and speed variance models are first estimated, based on roadway design, use and speed limit information. Then, crash counts (of varying severity) are estimated, based on the speed estimates, design, and use variables. The four years of data come from 63,937 "homogeneous" roadway segments along 7 interstates and 143 state highways in Washington State. A random-effects negative binomial model was selected among several alternative panel and non-panel models for count data. Results indicate that the average road segment in the data set can be expected to exhibit lower non-fatal crash rates up to a 55 mph (88 km/h) speed limit. In contrast, fatality rates appear unresponsive to speed limit changes. Fatal and non-fatal rates fall for design reasons, including wider shoulders and more gradual curves, which appear to be key design variables. However, fatal and non-fatal rates move differently when traffic levels rise, with non-fatal rates remaining unchanged and fatal rates falling.Key Words: Speed limit; Traffic Crash; Panel model; Highway Safety Information System (HSIS) -2 - INTRODUCTIONSpeed limit changes in the U.S. have been made at local and national levels without a thorough understanding of traffic safety consequences. These changes affect speed choices, which affect crash frequency and severity. However, speed choices are only barely recognized in studies aimed at evaluating the effects of speed limit changes on roadway safety. This lack of speed consideration may be attributable to a general lack of extensive speed data.Some speed limit studies have attempted to control for chosen travel speeds by including coarse speed averages and/or variance values (e.g., Rodriguez 1990 andLave 1985). They have relied on highly aggregate speed data (e.g., Rodriquez's [1990] data were at the national level, while Lave's [1985] were at the state level). Such aggregation obscures most distributional information about individual traveler speed choices. Golob and Recker (2003) assembled crash and traffic flow data at the 30-second aggregation level on California highways, yet did not include road design variables.In addition to a lack of chosen speed information, roadway design features are rarely accounted for in speed limit safety studies, although, along with speed, they are recognized as a critical factor in traffic safety. Some speed limit studies (e.g. Lave 1985, Lave and Elias 1994, and Greenstone 2002 have attempted to control for overall design effects by separately analyzing crash counts on different road types (e.g., all interstate, arterial, and collector roads in each state). However, they have failed to include detailed geometric information, primarily due to aggregation of non-homogeneous roadway segments. Again...

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

“…The results show that low speed limit will increase the possibility of multivehicle crashes. Some researchers also found that low speed limit will increase crash possibility [36], but they failed to reveal its different effects on SV and MV accidents. The speed gap variable denotes the difference between the average speed and the speed limit, which can represent the congestion level to some extent.…”

“…In this study, the results imply that higher curvature will cause higher multivehicle crash risks. Albeit some researchers found that the degree of curvature can be beneficial for traffic safety [17,39], other researchers found it positively correlated with the crash likelihood [36,40].…”

Speed Limit and Fatalities in the U.S.: Implication for Transportation Policy

“…The model yielded the unresponsiveness of fatality rates to speed limit changes. Hence, the paper did not cite speed as the main contributor to the fatal accidents and concluded that further research was needed to firmly establish the causality between speed limit and accident related fatalities [11].…”

Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

“…For example, Aarts and Shagen (2006) analyzed the effect of driving speed on crash rates, Kweon and Kockelman (2005) studied the impact of roadway design and speed limits on crash rates, and Ivan (2004) explored the relation between traffic volumes and crash rates. Most studies define crash rate as the crash count divided by VMT; here, however, as in Noland and Karlaftis (2005), crash rate is defined as the ratio of crash count to population.…”