CONTEXT AWARE ROAD TRAFFIC SPEECH INFORMATION

SYSTEM FROM SOCIAL MEDIA

This project focuses on developing a

mobile application that transmits real-time

traffic state to motorcyclists. The traffic

data is collected from Twitter. The data

collected is subjected to various processes

like Named Entity Recognition, Sentiment

Analysis and Statistical Analysis and the

derived traffic state will be transmitted to

the user’s mobile application. A Bluetooth

enabled helmet of a motorcyclist will then

playback the traffic state to the user

according to their location.

Lim Cheng Yang, Ian K.T. Tan, Bhawani Selvaretnam, Poo Kuan Hoong (Multimedia University)

Ewe Kok Howg, Lau Heng Kar (Intel)

Overview

1) To derive traffic state from various

traffic condition reports.

2) To provide traffic state information

based on user’s coordinates.

3) To develop a mobile application for

vocal transmission of traffic state to

users.

Objectives

The dynamics that occur on daily

road traffic conditions can be taxing and

disruptive for many motorists. Having prior

knowledge of the traffic conditions ahead

can alleviate some of the stress. Recently,

approaches using crowd sourced

information has been successful in

applications such as Waze. However,

these applications generally serve

motorists in enclosed vehicles that have

the luxury of on-board information

systems. Meanwhile, motorcyclist aren’t

able to use these applications, making

them vulnerable to the sudden changes of

traffic conditions.

Twitter contains real-time information

that could be useful to the road users. The

traffic tweets can be classified into formal

and informal sources. These data could be

extracted and processed into useful traffic

information that could help out the road

user, and in this case, motorists.

Traditional traffic applications used

Global Positioning systems to determine

the user’s position and this method is used

in this project. The GPS coordinates will

determine the users location and the

application will use it to grab the respective

data from the database.

Using Bluetooth helmet as a medium

to transmit the data to the user is a must

as motorcyclist are not encouraged to use

enclosed headphones.

Background Study

Development Requirements

1) Android Studio 1.0 or higher

2) PyCharm

3) Spring Tool Suite

4) MongoDB

Hardware Requirements

1) Android phone (4.3 & above)

2) Bluetooth speaker integrated helmet

Requirements

Figure 1: Request for traffic condition ahead

Figure 2: Detection of congestion ahead

Figure 3: Report to the user

Idea

In this project, the traffic tweets

will be collected through Twitter API

and the location and traffic

conditions will be extracted through

NER. The tweets will then go though

semantic analysis to determine the

polarity of the traffic state. An

Android application will request the

traffic state according to the location

of the user. The traffic state will then

be reported to the user through the

Bluetooth helmet. With the usage of

this Android application,

motorcyclists could get traffic

information based on their locations.

Conclusion

Twitter

Named

Entity

Recognition

Phone Application

raw traffictweet

User location (GPS)

Traffic state ahead

Implementation

1. Li, Chenliang, et al. "Twiner: named

entity recognition in targeted twitter

stream." Proceedings of the 35th

international ACM SIGIR conference on

Research and development in

information retrieval. ACM, 2012.APA

2. Java, Akshay, et al. "Why we twitter:

understanding microblogging usage and

communities." Proceedings of the 9th

WebKDD and 1st SNA-KDD 2007

workshop on Web mining and social

network analysis. ACM, 2007.

3. Agarwal, Apoorv, et al. "Sentiment

analysis of twitter data." Proceedings of

the workshop on languages in social

media. Association for Computational

Linguistics, 2011.

4. Ritter, Alan, Sam Clark, and Oren Etzioni.

"Named entity recognition in tweets: an

experimental study." Proceedings of the

Conference on Empirical Methods in

Natural Language Processing.

Association for Computational

Linguistics, 2011.

References

Sentiment

Analysis

Statistical

Analysis Database

traffictweet

Location and state

Location, state

Request location state

Location traffic state

Request Web service:

Sending GPS coordinates

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