Christopher S. Hanson,
Robert B. Noland
Charles Brown
 Alan M. Voorhees Transportation Center, Edward J. Bloustein School of Planning and Public Policy, Rutgers University, 33 Livingston Ave., New Brunswick, NJ 08901, United States
 
 
 Highlights

•Google Street View is used to obtain pedestrian and road infrastructure features.
•Logit models of the severity of pedestrian casualties are estimated with this data.
•The probability of pedestrian crashes cannot be analyzed with this data.
•Sidewalks and buffers reduce severity, high speed roads increase severity.
•Crashes during darkness increase the severity of pedestrian casualties.


Keywords
Pedestrian safety; Logit analysis; Urban design; Street network


Abstract
Data derived from visual inspection of Google Street View imagery associated with a variety of pedestrian and road infrastructure features are analyzed with a database of pedestrian casualties. These features include the presence of sidewalks, buffers between the road and the sidewalk, street lighting, number of travel lanes and the presence of medians, traffic controls at intersections, and posted speed limits. The analysis focuses on how these features affect the severity of a pedestrian casualty once it has occurred. Other controls used in the analysis include the age of the victim, ambient lighting conditions, and whether the vehicle driver was intoxicated. Results suggest that severity of pedestrian casualties is associated with the lack of sidewalks and buffers, high-speed roads, roads with six or more lanes and a median, and lack of traffic lighting when it is dark. Speed is a critical factor in determining the severity of crash outcomes, and most road characteristics affect crash outcomes to the extent that they moderate or facilitate high speeds. Casualties are more severe when it is dark than when it is daylight. Older pedestrians tend to have more severe casualties. A key contribution of this work is the use of Google Street View imagery; however, a limitation is that the analysis cannot examine the probability of a pedestrian casualty. Implications for road safety are consistent with national efforts to make streets safer via Complete Streets policies.



Article Outline
1. Introduction
2. Data and methods
2.1. Case definition
2.2. Independent and control variables
2.3. Statistical analysis
2.4. Hypotheses

3. Results
3.1. Multivariate models
3.2. Killed versus incapacitated and less severe injuries
3.3. Killed or incapacitated versus less severe injuries
3.4. Case studies
3.4.1. Fatalities
3.4.2. Incapacitating injuries
3.4.3. Moderate injuries

4. Discussion
5. Conclusions
Acknowledgments
References


Figures

Fig. 1.

Screenshot of data entry screen for collecting Street View image data.

Fig. 2.

US 130, Mansfield Township, Burlington County, NJ (Lat. 40.12116 Long. −74.74731).

Fig. 3.

US 130, Cinnaminson, Burlington County, NJ (Lat. 40.00448 Long. −74.98004).

Fig. 4.

NJ 585, Pleasantville, Atlantic County, NJ (Lat. 39.39966 Long. −74.51548).

Fig. 5.

Madison Ave., Atlantic City, Atlantic County, NJ (Lat. 39.36249 Long. −74.43608).

Fig. 6.

CR 655, Maplewood, Essex County, NJ (Lat. 40.73689 Long. −74.25121).

Fig. 7.

NJ 509, Bloomfield, Essex County, NJ (Lat. 40.78707 Long. −74.18727).


Tables

Table 1. Inclusion of pedestrian crash victims by outcome, 2007–2009.

Table 2. Frequencies for independent and control variables.

Table 3. Number of lanes and presence of a median.

Table 4. Ambient light and street lighting cross tabulation.

Table 5. Sidewalks and buffers.

Table 6. Intersections, traffic control, and crosswalks.

Table 7. Binomial logit model: killed versus all other injuries.

Table 8. Binomial logit model: Killed or incapacitated versus less severe injuries.