Association of intersection approach speed with driver characteristics, vehicle type and traffic conditions comparing urban and suburban areas

Abstract

A mobile recording system, with integrated laser speed gun, video from CCD-cameras and auxiliary battery system, was used to observe driving behavior at an urban intersection and a suburban analog. After removal of instances of interference, 1538 driving behaviors were recorded. Multiple regression was then utilized to examine the factors affecting approaching speed. Speed limit violation was considered a dichotomous variable with two categories, violation and compliance. Binary logistic regression was also used to examine the risk of speeding as a function of covariates and interaction terms. The results of analysis revealed that the major contributing factors for approaching speed were site, rush-hour-status, traffic light condition, vehicle type and driver gender. In particular, light status was the highest contributor to speed. In addition, the results of logistic regression showed significant sites and rush-hour effects on speeding, with the risk of limit violation in the suburbs nearly six-fold that in urban areas. The relative risk of speeding for travelling in non-rush hours is three times higher than that for rush-hour. In terms of driver characteristics, male drivers under 55 years of age had the greatest speeding propensity in our sample. The results of the present study may provide meaningful information applicable to the design and operation of signalized intersections.

Introduction

Accident modeling and empirical research have demonstrated a strong relationship between vehicle speed, and the frequency and severity of road accidents (Finch et al., 1994, Comte and Jamson, 2000). Due to the high number of vehicle accidents caused by inappropriate speed, law enforcement is obviously a major priority for all agencies involved in road safety (Horberry et al., 2004, Hirst et al., 2005). Increases in speed lead to increases in accident risk and outcome severity because of the greatly increased demands in terms of perception and decision making, the increased braking distances required, and the resultant severity of the physical impact in the event of accident (Navon, 2003). Richter et al. (2004) reported that a small increase in the Israeli speed limit (from 90 to 100 km/h) dramatically increased the number of road accident fatalities; in particular, deaths from serious injury increased significantly. Further, a progressive relationship has been demonstrated between reductions in the number of accidents and decreases in mean speed (Taylor et al., 2000). In their study of self-reported attitudes towards speeding, Lawton et al. (1997) found that the mean speed on most roads might be at least 10 mph over the limit. In general, imposition of speed limits reduces the proportion of vehicles travelling at higher speeds (Evans, 1991). A 5% reduction in accidents for each 1-mph reduction in mean speed is widely quoted, however, this decrement depends on the nature of the road and the previous mean speed (Taylor et al., 2000). Lower speed variance is associated with fewer accidents, and controlling speed consistency is an effective way of reducing the number of accidents and mitigating the outcome of the collisions (Finch et al., 1994, Várhelyi and Mäkinen, 2001). The effect of average traffic speed is far less clear, however. Therefore, an observational study to determine the relationship between vehicle speed and road conditions should be conducted, and the road-specific speed distributions examined.

According to Harms (1991), higher speeds are associated with greater risk of cognitive overload, and a driver will typically compensate for increments in traffic demand by reducing speed. Most drivers spontaneously decrease their forward progress in difficult traffic situations and, complimentarily, accelerate once these critical conditions disappear. Åberg and Haglund (1989) observed vehicle speed on urban roads with and without enforcement where speeds were temporarily reduced from 50 to 30 km/h, noting mean reductions from 51.9 to 42.8 km/h and from 55.2 to 36.4 km/h, respectively. Accident rates vary with road class, and a substantial proportion of urban vehicle impacts occur at intersections (Retting and Williams, 1996, Retting et al., 1999, Al-Ghamdi, 2003). Further, it has been demonstrated that the principal contributing factor in urban vehicle crashes is failure of drivers to obey traffic control devices. In another study, about 22% of urban accidents resulted from drivers failing to halt at traffic controls (Retting et al., 1995). In addition, driver inattention appears to be a major contributing factor in rear-end impacts involving stopping vehicles, and it has been proposed that accidents could be reduced by enforcing speed limits on arterial streets and reducing the numbers of stops at traffic signals (Retting et al., 1995).

In general, traffic conditions are the results of many, varied and complex interactions among the four primary elements of the traffic system: drivers, vehicles, roadways and controls. However, traffic engineers pay scant attention to the effects of their constructions on road users and vehicle performance. By contrast, their designs focus on creating the optimum system of roadways and controls from an engineering perspective. Therefore, the objective of the present study was to determine the factors associated with speed control, especially driver characteristics, vehicle type and traffic conditions. Particular investigative emphasis was placed on the process of risk compensation during the approach to signalized intersections, incorporating both individual processes and situational factors. Previous reported findings indicate that: the young are over-represented in drivers travelling at higher speeds; females tend to drive slower; heavy-laden vehicles progress more slowly than others. Selection of driving speed is governed by traffic lights, and determines driver acceleration/deceleration on approach to an intersection. However, relatively few studies have used naturalistic observation at signalized intersections to explore the risk of speeding violation as a function of covariates and interaction terms. Thus, in the present investigation, multiple regression models were utilized to examine the study hypothesis and identify the significant factors contributing to driver speed control.