5 TYPES OF IMAGERY IN THE PEDESTRIAN DRIVERS
5 TYPES OF IMAGERY IN THE PEDESTRIAN DRIVER
5 TYPES OF IMAGERY IN THE PEDESTRIAN MANUAL

While controlling for road network and area characteristics, we model the association of individual street elements with crash frequency.

5 TYPES OF IMAGERY IN THE PEDESTRIAN DRIVERS

In this paper, we develop and apply automated machine parsed imagery in conjunction with self-explaining roads theory to consider how the street space visible to drivers influences crash frequency, using data from Columbus, Ohio, USA.

5 TYPES OF IMAGERY IN THE PEDESTRIAN MANUAL

Most previous crash research using street imagery has applied manual processing methods. The availability of street view imagery and methods of scene parsing create new possibilities for understanding how features of the built environment influence crash incidence. Recent theories indicate that features of the built environment may induce unsafe driving by shaping users’ expectations and behaviors. Road crashes in metropolitan areas are challenging to prevent because they stem from the interactions of drivers and other system users in intricate built environments. This study identifies the important influencing factors of urban pedestrian safety and of the risk awareness of pedestrians and other road users and proposes solutions from the perspectives of behavior and infrastructure. Casualties are more severe at night than during the day, and school-age children and elderly pedestrians tend to suffer more. However, the severity of pedestrian casualties has strong relationships with darkness, lighting conditions, road isolation facilities and pedestrian age and behavior. The results demonstrate that there are several clusters of pedestrian crashes in urban areas, which are related to the population, road network, regional functional zoning and social and economic characteristics. The focus of the analysis is to use GIS to visualize the distribution of pedestrian crashes in cities to explore the relationships between pedestrian crashes and the population, road network, land use and social services and activities and to analyze the impacts of the building environment and road characteristics on the severity of pedestrian crashes by combining the binary logistic regression and tree-based models. The use of Baidu or Google Street View maps improves the accident reports and facilitates the detailed description of the characteristics that are related to pedestrian crashes. This study goes beyond the traditional analysis of traditional traffic accident police reports by integrating them with rich geographic information system (GIS) resources to analyze a sample of 791 pedestrian crashes that occurred in the main urban area of Changsha city, China, from 2014 to 2016. Improving the pedestrian traffic environment is important for the sustainable development of the city. Implications for road safety are consistent with national efforts to make streets safer via Complete Streets policies. 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. Older pedestrians tend to have more severe casualties. Casualties are more severe when it is dark than when it is daylight. 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.

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.

5 TYPES OF IMAGERY IN THE PEDESTRIAN DRIVER

Other controls used in the analysis include the age of the victim, ambient lighting conditions, and whether the vehicle driver was intoxicated. The analysis focuses on how these features affect the severity of a pedestrian casualty once it has occurred. 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.

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.

5 types of imagery in the pedestrian