Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

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System using Machine Learning

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) D. U' Q: [. W" o8 V) `" xThis paper aims to develop a tool for predicting
7 s# t5 v, c8 U% o7 Saccurate and timely traffific flflow Information. Traffific Environment
involves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
) z. f% L; _  M& e% A4 H& ?% |of roads that can cause a jam. If we have prior information
, w8 h7 }  l/ W0 s, Twhich is very near approximate about all the above and many
4 e) @+ i  D+ Kmore daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
future of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
the big data concepts for transportation. Available prediction
) f  ]+ P; K) J# o# z  {4 Smethods for traffific flflow use some traffific prediction models and
8 L" s/ Z/ \6 L1 |are still unsatisfactory to handle real-world applications. This fact
5 f- h; e( _7 M& o1 Minspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
* Z- T3 ?8 r2 I0 ^2 H3 Qflflow accurately because the data available for the transportation
- m) V$ X& ~0 f/ Z: m9 asystem is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
to analyse the big-data for the transportation system with
; v4 x6 V' h$ o4 G% Jmuch-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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