[HTT15-P06] Spatiotemporal Analysis of Urban Traffic Accidents: A Case Study of Tehran City, Iran
Keywords:geographic information system (GIS), kernel density estimation (KDE), land use, rush hour, urban structure (US), urban traffic accidents (UTAs)
Traffic safety crises, death, damage, and the costs resulting from UTAs are some of the most important public health and policing challenges facing Iran and other countries. In Iran, UTA victims are typically people between 15 and 44 years of age; UTAs are the second leading cause of death after heart disease in Tehran (Aftab News Agency, March 19, 2015). UTA statistics in Tehran reveal a serious problem with a significant fatality and injury rate.
This study aims to identify the spatial pattern of UTAs in the city of Tehran, in order to identify causes and consequences, as well as the temporal and spatial variations of UTAs. The relationship between the space and time used for daily activities that generate daily urban trips and UTAs is examined using data for Tehran City between 2010 and 2011. The analysis is based on different databases, both spatial and non-spatial, which include the locations of UTAs and their dates, causes, nature, and other attributes. To identify the causes of these severe incidents and to draw effective conclusions and suggestions for reducing UTAs, this study has used data to investigate major and minor causes, various factors, and the types of UTAs in Tehran City. The study has considered different aspects of UTAs, including the urban environment, land use, population, human activities, culture, and other issues considered to be the most important pillars of the phenomenon. In order to understand drivers’ knowledge, cultural beliefs, behaviors, and attitudes toward traffic regulations, questionnaires were distributed to 1,500 drivers in the study area to gather data; of these, 1,177 were returned. The results demonstrate that the culture and knowledge of drivers have a direct effect on localized UTAs.
One of the main topics explored in this study is urban structure (US), the concentration of educational, commercial, and cultural activities that make up a large number of urban trips and urban dynamics, road usage, and time. The relationships between population, land use, and the dynamic city patterns that constitute urban structure suggest spatial considerations linking UTAs with the urban structure of Tehran City. Time is considered a crucial variable that leads people to different kinds of locations and risks. Land use and population data are combined with UTA data using geographic information system (GIS) techniques to generate relevant inputs for analysis. The methodology uses cluster analysis techniques to analyze the association between UTA numbers and land-use categories per 1,000 residents of Tehran City.
The results show that suburban zones with industrial land use and more highways are associated with higher numbers of severe accidents, involving fatalities and injuries. In comparison, the central business district (CBD) zone is the safest, as measured by the number and severity of UTAs. lower-severity UTAs occur in Tehran during rush hours, when there is traffic congestion on the transportation networks and public transportation.
Land use categories, urban structure, and population density vary across city zones and these variations create different rush hours; thus, different zones have different rush hours. In relation to population movement, urban dynamics, and urban structure, the main roads types in different zones become congested at different times of day, reflecting the activities and dynamics associated with the dominant land use category. Thus, the rush hours in different zones result in different UTA spatial patterns within the city.
In addition, this study explains the relationship between urban structures and UTAs in Tehran. It has been discovered that the locational pattern of various land uses in the urban area reflects socio-economic and ecological factors. Furthermore, the spatial and temporal analyses of relative UTA risks identify dangerous segments in different city zones and land uses, depending on the season, month, day, and time.