Abstract
Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.
Highlights
The population and economic growth of cities are the main causes of the increase in the number of vehicles [1]
The model selected is available in Spacy (Available on https://spacy.io/, accessed on 20 November 2021) [44], a state-of-the-art library used in Python for Natural Language Processing tasks in several languages
The city of Bogotá has a total area of 1775 km2 divided into 20 administrative divisions or localities, of which 307 km2 and 19 localities correspond to the urban area; the city has a population of 7 million inhabitants
Summary
The population and economic growth of cities are the main causes of the increase in the number of vehicles [1]. These devices are required to be fixed on major road segments, reducing coverage; other common problems that reduce traffic forecasting are maintenance costs and accuracy errors due to weather conditions This has motivated researchers to study the effectiveness of other available secondary sources of information—such as social media—for the detection of traffic incidents, expanding the coverage in the city, involving all road safety stakeholders [9,10] and offering the possibility of analyzing other factors such as mass events and road conditions [11]. These techniques allow monitoring traffic incidents using the resources wisely, so that local governments and researchers can gain control in these types of accidents
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