Association Between Built Environment and Pedestrian Fatal Crash Risk in Delhi, India
01 Jan 2015-Transportation Research Record (Transportation Research Board of the National Academies)-Vol. 2519, Iss: 2519, pp 61-66
TL;DR: In this paper, the authors present an analysis of the built environment factors that influenced the safety of pedestrians in Delhi, India, from 2006 to 2009, using a geographic information system.
Abstract: An analysis of fatal crashes involving pedestrians in Delhi, India, from 2006 to 2009 that used a geographic information system showed clusters of crashes at certain locations. An evaluation of the characteristics of the built environment around the high crash location clusters was necessary. This paper presents an analysis of the built environment factors that influenced the safety of pedestrians. Locations surveyed included roads around the fatal crash clusters of pedestrians. Factors that influenced the number of fatal crashes of pedestrians were analyzed with negative binomial regression. Types of locations were categorized into locations with a flyover and without a flyover. Results showed that an increase in traffic volume by 1% would increase pedestrian fatal crashes by 1.6% at locations with a flyover and by 0.9% at locations without a flyover. Arterial roads with more traffic volume, more road lanes, and higher speed tended to have more fatal crashes. Locations where medians were fenced or raised...
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TL;DR: The study finding indicates that while improvement in the design of the built environment and signal settings to increase pedestrians’ convenience and confidence are must, there is a crucial need to integrate innovative safety management practices to address spatial and temporal needs of an intersection.
49 citations
TL;DR: This study proposes an artificial neural network (ANN) technique to develop a pedestrian fatal crash frequency model at the intersection level and finds that the ‘approaching speed’ of the motorized vehicle has the most significant influence on the fatal pedestrian crashes.
Abstract: Urban intersections in India constitute a significant share of pedestrian fatalities. However, model-based prediction of pedestrian fatalities is still in a nascent stage in India. This study proposes an artificial neural network (ANN) technique to develop a pedestrian fatal crash frequency model at the intersection level. In this study, three activation functions are used along with four different learning algorithms to build different combinations of ANN models. In each of these combinations, the number of neurons in the hidden layer is varied by trial and error method, and the best results are considered. In this way, 12 sets of pedestrian fatal crash predictive models are developed. Out of these, Bayesian Regularization Neural Network consisting of 13 neurons in the hidden layer with 'hyperbolic tangent-sigmoid' activation function is found to be the best-fit model. Finally, based on sensitivity analysis, it is found that the 'approaching speed' of the motorized vehicle has the most significant influence on the fatal pedestrian crashes. 'Logarithm of average daily traffic' (ADT) volume is found to be the second most sensitive variable. Pedestrian-vehicular interaction concerning 'pedestrian-vehicular volume ratio' and lack of 'accessibility of pedestrian cross-walk' are found to be approximately as sensible as 'logarithm of ADT'.
48 citations
TL;DR: Analyzing pedestrian behavior and perceptions across twenty-four signalized intersections in Kolkata city, India, indicates that pedestrians' signal violations behavior and dissatisfaction are statistically significantly higher at locations with recorded fatal pedestrian crashes.
46 citations
01 Oct 2019
TL;DR: In this article, the binary logistic regression models were developed to identify the risk factors associated with road infrastructure, land use, and traffic operational characteristics for pedestrian safety in urban India.
Abstract: Pedestrian safety in urban India is an issue of growing concern. While an extensive body of literature is available on the identification of elements related to the pedestrian’s risk, the studies are mainly conducted in urban areas of developed countries, where the condition of infrastructure, built environment, road user’s behavior and perception are very different from Indian cities. As a result, findings from these studies may only be partially applicable to the cities of developing countries such as India. The present study makes an attempt to fill the gap by analyzing historical crash records obtained from Kolkata police and identifies the hazardous corridors and network locations posing the highest risk to the pedestrian. Subsequently, the binary logistic regression models are developed to identify the risk factors associated with road infrastructure, land use, and traffic operational characteristics. Based on the statistical assessment carried out in this study, there is evidence that factors such as road width, land use type, the absence of designated bus stop facility, inadequate sight distance, average daily traffic and pedestrian volume, and pedestrian–vehicular interaction considerably affect pedestrian safety at the intersections. On the other hand, road width, land use type, inadequate sight distance, the absence of designated bus stop facilities significantly influence pedestrian safety at midblock road segments. The knowledge of the risk factors specific to a location on the road network is helpful for incorporation and/or modification of the particular planning and design elements to reduce risk.
41 citations
TL;DR: Using Bayesian hierarchical approach with a Poisson-lognormal regression model, spatial analysis of road fatalities of VRUs with wards as areal units finds that fatality risk has a negative association with socio-economic status, population density, and number of roundabouts, and has a positive association with percentage of population as workers, number of bus stops,Number of flyovers, and vehicle kilometers travelled.
32 citations
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