Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

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This paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
/ v  ~! B( F: |# D( a2 linvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
" d; |, ]0 M2 \of roads that can cause a jam. If we have prior information
% ^8 z  {3 h0 e! J: M, xwhich is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
* [/ H; ]( v) H% [2 afuture of autonomous vehicles. In the current decades, traffific data
# i9 a. `( N( Q/ P" Rhave been generating exponentially, and we have moved towards
& ?4 ^  h- `# r) Q5 q' U+ D! dthe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
9 U6 b; d, }  ^( D1 I/ aare still unsatisfactory to handle real-world applications. This fact
1 C0 y; y3 f% k( o) k4 Y: n) tinspired us to work on the traffific flflow forecast problem build on
7 F  k+ M2 O& _/ b1 f3 fthe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
1 Z* H0 K; x% o+ y- x1 `# lsystem is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
3 Q2 H; [' r7 A  L" T# h# i4 \$ c4 D: Yto analyse the big-data for the transportation system with
0 r' k" r) J% A' U0 L# zmuch-reduced complexity. Also, Image Processing algorithms are
  P9 b5 G+ T9 r2 I, D* I' r; f# H5 n. Tinvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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