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Integration of GIS and Orthophoto to Enhance Road-Network Screening – A 3GR Approach
Mohamed Abdalla
Chartered Member of the Royal Institution of Charter Surveyors (RICS) UK And Americas
Department of Civil Engineering, Ryerson University, Toronto, Ontario, Canada
777 Bay St, P.O Box 46047, Toronto, On., Canada, M5G 2P6
msaad2000@msn.com
Abstract
This paper presents a new road safety analysis technique for verifying and enhancing road-network screening database. The technique is based on the integration of data obtained from a Geographic Information System (GIS), orthophotos and a road-networks database. The integration of the GIS with different road-network database was used to identify illuminated road segments. A cross correlation technique was used to extract the streetlight location and position. The integration of GIS and orthophotos was used to check and update the road-network database. A semi-automatic method was developed to recognize illuminated and un-illuminated road segments by using a specific group of filters and the cross correlation technique. Validation of the procedure showed that the new technique improved the light database, and the semi-automatic method successfully identified street segment types and extracted the streets poles’ positions. Rural and semi-urban areas were targeted in this study. The limitations of the new technique are discussed and future research into the integration of geomatics tools with road safety is highlighted.
Introduction
Fatal collisions are more likely to occur on rural highways than on any other road types. Traffic crashes are a major cause of death and injury in the United States. In 2002, there were 42,815 fatalities and over 2.9 million injuries on the nation’s highways. Crashes on rural roads (roads in areas with populations of less than 5,000) account for over 60 percent of the deaths nationwide, or about 70 deaths each day and the rural fatality rate per vehicle mile traveled on rural roads was over twice the urban fatality rate (General Accounting Office, 2004). Good visibility is essential to the safe operation of motor vehicles. Driving at night can be more challenging than daytime driving, as the distance that a driver can see clearly is reduced at night.
Collisions and lighting conditions are generally classified into eight categories: (1) collisions in daylight, (2) collisions in daylight with artificial light, (3) collisions at dawn, (4) collisions at dawn with artificial light, (5) collisions at dusk, (6) collisions at dusk with artificial light, (7) collisions in darkness, and (8) collisions in darkness with artificial light. There is a clear need to improve the analysis for nighttime collisions and to identify the lighting conditions accurately, but little effort has been done in this area because the collection process is costly and time consuming.
1.1 Scope
The focus of this paper is identifying illuminated and un-illuminated rural highways segments to check the existing data; complete missing records and solves data conflict. This will improve the quality of the data, road safety analysis, and network screening. Since collecting detailed information on street poles is costly and time-consuming, fast and inexpensive methods must be explored.
1.2 Objectives
The purpose of this thesis is to develop a semi-automatic method to identify illuminated and un-illuminated road segments by integrating geomatics tools and road network databases. The specific objectives of this research are:
- To integrate Geographic Information System (GIS) and collisions data.
- To integrate GIS and road network data other than collisions data.
- To integrate GIS and orthophoto images to identify illuminated road segments and to extract the location of street light pole locations and positions from the images.
- To validate the proposed method by using actual data.
- To examine the implication of the proposal methodology for the safety performance function.
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