Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

9 {8 n  z$ O: p4 R

! D2 J3 ~& a( l0 XThis paper aims to develop a tool for predicting
( E9 c" I8 p0 Jaccurate 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
of roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
7 r+ n; G* q3 o2 s( x# imore daily life situations which can affect traffific then, a driver
0 q* D& m# \' _2 ^  A- cor rider can make an informed decision. Also, it helps in the
future of autonomous vehicles. In the current decades, traffific data
2 {3 p. U- [% C) K( l5 m: ]" u: Zhave been generating exponentially, and we have moved towards
the big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
# k# ~- [; @* O0 q  j% M6 pare still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
& ^8 Y7 P/ ?( N2 M$ o3 [4 {/ Wthe traffific data and models.It is cumbersome to forecast the traffific
( i4 [/ |+ u, V$ M7 Mflflow accurately because the data available for the transportation
system is insanely huge. In this work, we planned to use machine
* F! H' I4 ]* Q+ |! @6 olearning, genetic, soft computing, and deep learning algorithms
1 `- x+ \! U8 M- Eto analyse the big-data for the transportation system with
$ r/ j8 {& T3 a% u# smuch-reduced complexity. Also, Image Processing algorithms are
# k; _. y+ a6 L7 R8 K; c5 Hinvolved in traffific sign recognition, which eventually helps for the
5 }$ G( i$ ]& H! K8 d7 ~right training of autonomous vehicles.
/ c; W$ s$ G, m$ t+ w