Details

Autor(en) / Beteiligte

• Pulugurtha, Srinivas S.
• Sambhara, Venkata R.

Titel

Pedestrian crash estimation models for signalized intersections

Ist Teil von

• Accident analysis and prevention, 2011, Vol.43 (1), p.439-446

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2011

Quelle

MEDLINE

Beschreibungen/Notizen

• ▶ Population, transit-stops, # legs and pedestrians exponentially increase pedestrian crashes. ▶ Single family, urban residential commercial and neighborhood service lower pedestrian crashes. ▶ Demographic, socio-economic, land use and road data are better predictors than traffic data. 0.5 mile buffers to extract data would yield better estimates for all and low activity intersections. ▶ 1 mile buffers to extract data would yield better estimates for high activity intersections. The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables. The Pearson correlation coefficient was used to eliminate predictor variables that were correlated to each other. Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25 mile, 0.5 mile and 1 mile buffer widths to extract data and develop models was also evaluated.