Route Recommendation System based on Safety Metrics and Route Profiling
Kripa Sekaran1, Priyanka K2, Pooja R3
1Kripa Sekaran, Assistant Professor, Department of IT, St. Joseph’s College of Engineering, Chennai (Tamil Nadu), India.
2Priyanka K, Department of Information Technology, St. Joseph’s College of Engineering, Chennai (Tamil Nadu), India.
3Pooja R, Department of Information Technology, St. Joseph’s College of Engineering, Chennai (Tamil Nadu), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 259-261 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10111292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1011.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This project is based on the crime rates happening in our city and the measures taken to curb them to help in strengthening the perception of security in the minds of women and also people who are travelling alone at night. Safe route recommendation is an important part of the field of intelligent transportation, which can provide the guidance of travel mode and travel route for women as well as to travellers. The current route recommendation method has the complexity of urban transports, such as single traffic plan recommendation that often fails to meet the expected requirements. In order to solve the limitation of the one-way vehicle travel method, we propose a safe route recommendation method which includes three modes of transportation, including cars/cabs/auto rickshaws, public transport vehicles, and walking. The routes are represented in different color each denoting a different degree of safety gives user a choice to choose from. The routes/paths are categorized into high, medium and low risk areas. In GI Science, problems related to routing systems have been deeply explored an approach to provide risk score defined by crime rates for generating safe routes This obtained data is then displayed in a map with red, yellow and green patches denoting high, medium and low risk areas respectively. Thus, data are classified by the decision tree (ID3) algorithm. A geospatial repository is used to store tweets related to crime events of the city and the city’s street network is converted into graph format which will make the routing and classification mechanism easier. A forecast related to crime events that can occur in a certain place with the collected data was performed. The ID3 classifier classifies each routes into the following labels High, Medium, Low which describes the extent to which the specific route is risky. Our application presents all possible shortest and safe paths between the starting and destination point to the user.
Keywords: Classifier, Data Mining, Decision Tree Algorithm, Safe Route Recommendation.
Scope of the Article: Data Mining