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Real-time spatio-temporal analysis and prediction of traffic using social media

IP.com Disclosure Number: IPCOM000244512D
Publication Date: 2015-Dec-17
Document File: 9 page(s) / 312K

Publishing Venue

The IP.com Prior Art Database

Abstract

A low cost solution for traffic prediction using the data available on social media and other sources like weather and news reports. The social media posts are one of the useful information providers describing the sentiments of the user about a particular topic (here, traffic problems at certain location). Traffic problem prediction for a location over real time is the need of the hour for efficient management of traffic. By using social media data which is equivalent to crowdsourcing of information, real time update from people for people is provided. The administrators of the city can manage their resources efficiently leveraging over the prediction made by the proposed system along with the insights of problems and causes identified. Real-time data analysis from social media includes analysis of social media information and viral event detection which impact the traffic of a city. Influence and sentiment analysis of the posts identify the severity of these events and seed location of problem. Co-occurring problems and their root causes are identified using frequent item set mining techniques.

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Real-time spatio-temporal analysis and prediction of traffic using social media

Deepali Semwal, Sonal Patil, Sainyam Galhotra and Narayanan U. Edakunni

February 7, 2015

1 Introduction

Transportation is one of the key factors which contribute towards the sustain- able economic growth of any country. The roads form a whopping 40% of total transportation across entire India. The percentages are more or less similar in other countries as well. Given this huge percentage, the number of problems pertaining to accidents, traffic etc. have been a major concern. The revolu- tion in the automobile industry and liberalised economy has led to tremendous increase in the vehicle ownership levels. This has resulted in changing traffic characteristics on road network which demands a promising improvement over traditional methods. With the advent of social media, reporting and bringing up these problems have now become easier than before. In this disclosure, we present a novel invention that provides a low cost system to do a spatio-temporal analysis of the problems faced, detect spike in problems at a certain time and provide popular suggestions recommended by the local posters for tackling the same. Along with this, we devise a model to predict major problems which could occur at particular locations in the near future. The real-time data is used to keep the model updated on any change in popular notion of problems, solutions and causes among the masses posting about the traffic.

  In this invention, we are trying to provide low cost solution for traffic pre- diction using the data available on social media and other sources like weather and news reports. The social media posts are one of the useful information providers describing the sentiments of the user about a particular topic (here, traffic problems at certain location). Traffic problem prediction for a location over real time is the need of the hour for efficient management of traffic. Since we are using social media data which is equivalent to crowd sourcing of infor- mation, providing real time update from people for people. The administrators of the city can use this invention to manage their resources efficiently leveraging over the prediction made by the proposed system along with the insights of problems and causes identified. This invention has the following advantages,


A holistic tool to analyse problems and recommend crowd sourced sug- gestions for the same.

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Root cause analysis of the problems.


Temporal aspect included in the prediction model along with weather information and news data


1.1 Novelty

The key contributions of the invention are,


1. Real-time data analysis from social media:


Analysis of social media information and Viral event detection which impact the traffic of a city.


InfLuence and sentiment analysis of the posts to identify the severity of these events and seed location of problem.


Identify co-occurring problems and their ro...