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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  k2 [5 E" l1 j, a: _: DThis paper aims to develop a tool for predicting
3 Y1 b$ L; G( N3 Taccurate and timely traffific flflow Information. Traffific Environment
' J& g4 L: f+ m7 l1 `involves everything that can affect the traffific flflowing on the
/ H: t# U1 i/ ^& L2 mroad, whether it’s traffific signals, accidents, rallies, even repairing
; n" T& {8 K' ^9 y8 [of roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
& D8 ^' R* M4 ~1 ~4 imore daily life situations which can affect traffific then, a driver
, \' O3 z' o5 ?, `6 Gor rider can make an informed decision. Also, it helps in the
" E$ h3 S9 C" Q5 G. {2 A* }- cfuture 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
$ \' k, M9 T1 d0 b2 xmethods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
6 }0 L8 \& U7 @) S5 J" `4 rinspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
system is insanely huge. In this work, we planned to use machine
& n7 R. e1 j! V# w8 k3 q( rlearning, genetic, soft computing, and deep learning algorithms
to analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
- K! E* W$ y/ G+ e. Kinvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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