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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9 R6 e3 a7 S% ?# Q% QThis paper aims to develop a tool for predicting
accurate 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
; a- Z2 ~4 E6 w. Wof roads that can cause a jam. If we have prior information
4 ~- u9 h: ]5 z" Z- m+ s( @which is very near approximate about all the above and many
) u$ `' i# b/ Hmore daily life situations which can affect traffific then, a driver
' L8 z' m) @) g2 kor rider can make an informed decision. Also, it helps in the
future of autonomous vehicles. In the current decades, traffific data
6 r  X& J8 W9 r2 ~, H/ u1 X5 Ehave been generating exponentially, and we have moved towards
. I8 @$ G5 b/ \/ a" O. c* Ythe big data concepts for transportation. Available prediction
2 J* [; c( e5 A: i2 hmethods for traffific flflow use some traffific prediction models and
5 c% w* @6 O) r- H3 a) L1 X, _are still unsatisfactory to handle real-world applications. This fact
- N/ |$ j: W3 X/ H3 u! e- Binspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
3 H4 O- U" ~& F0 Hflflow accurately because the data available for the transportation
3 _' `9 |2 F7 A: hsystem is insanely huge. In this work, we planned to use machine
+ J( {7 a" L: Q) Nlearning, genetic, soft computing, and deep learning algorithms
# g. s1 ~; l5 ?2 {! gto analyse the big-data for the transportation system with
+ ?" h2 j# _, B9 Y- I, a5 t9 ^much-reduced complexity. Also, Image Processing algorithms are
  s% }3 p% H+ A/ Binvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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