1. Traffic flow prediction and minimization of traffic congestion using Adaboost Algorithm with Random Forest as a weak Learner
ניבוי מצבי גודש ומזעור לחצי התנועה באמצעות מערכת לומדת (אלגוריתי Adaboost עם Random Forest כלומד חלש) המשמשת לניבוי, וזיהוי של בעיות תעבורה באזור אורבאני by
Guy Leshem
Joint work with Prof. Ya’acov Ritov
Department of Statistics, The Hebrew University of Jerusalem, Israel
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2. Outline Introduction Motivation Methods Results Conclusion Future work
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3. Part I
Introduction
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4. Traffic in urban road network
Road transportation is a critical link between all the other modes of transportation and their proper functioning.
Signalized intersections, is a critical element of an urban road transportation system.
Traffic congestion in urban areas is causing vehicular delays (which increases the total travel time through an urban road network), thus resulting in a reduction in speed, reliability, air and sound pollution and cost-effectiveness of the transportation system.
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5. Part II
Motivation
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6. Our Motivation
The first motivation for this research is to check if from traffic related data, we can predict heavy traffic with high accurate at the urban area.
The main goal of this research is to plan and build a learning system for “traffic flow manage control” of urban area for prediction of traffic flow problem, and to use with this prediction to minimized traffic congestion.
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7. Part III
Methods
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8. Definition of Machine learning
Machine learning is concerned with the development of algorithms and techniques that allow computers to "learn".
Inductive machine learning methods create computer programs by extracting rules and patterns out of massive data sets.
Every discrete sample come with a label, and the goal of the machine learning is to predict labels of a new sample that the algorithm doesn’t meet during the learning process.
The learning algorithm will receive partial feedback on his performance during the learning process, and “he” needs to conclude which decision bring to him success and which bring failure.
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9. Example to train / test files (medical data)
Train File:
Test File:
Patients name
E.K.G
Blood
test
Temperature .
Guy
Yossi
Yaacov
Classifier by doctor
1+(healthy)
1- (Sick) 1- 1+
Patients name
E.K.G
Blood
test
Temperature .
Amir
Dov
Moshe
Classifier by the algorithm ?
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10. Part III - A
System Part
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11. Scheme of intersection
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12. Data Collection by Magnetic loop detectors
Traffic management and information systems (TMIS) must rely on a system of sensors for estimating traffic parameters in real-time. Currently, the dominant technology for this purpose is use of magnetic loop detectors, which are buried underneath at almost every urban intersections and which measure traffic parameters of vehicles passing over them.
Example of the data set (training and testing set) collected by TMIS and used by our system (table below). The last parameter Y is the hypothesis class (-1=congestion, 1=under congestion) which determined by Traffic Flow Evaluation model.
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13. Data Collection by Real-time computer vision system for measuring traffic parameters
The technology of video monitoring systems is alternative and / or addition to the magnetic loop detectors.
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14. Traffic Flow Evaluation model: determination of hypothesis class (LOS) for train file (Example 1 – under congestion)
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15. Traffic Flow Evaluation model: determination of hypothesis class for train file (Example 2 – congestion)
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16. Part III - B
Algorithmic part
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17. Boosting Algorithm - What is Boosting?
A method for improving classifier accuracy
Basic idea:
Perform iterative search to locate the regions/ examples that are more difficult to predict.
Thorough each iteration reward accurate predictions on those regions.
Combines the rules from each iteration.
Only requires that the underlying learning algorithm be better than guessing.
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18. Boosting - Sample space X with M samples: XM = {(x1,y1),…, (xm,ym)}
- Distribution D, e.g. D= 1/M.
- Labels Y, Y : X → {-1,+1}
- Weak Learner with two parameters g, l
(x1,y1),(x2,y2)…(xm,ym)
Weak Learn
Hypothesis h
errorD(h)=Px:D(h(x) ¹Y(x))<1/2-g
Will occur at least in probability l
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19. Boosting (cont.) (x1,y1),(x2,y2)…(xm,ym) Weak Learn Hypothesis 1h
Over D1
(x1,y1),(x2,y2)…(xm,ym)
Weak Learn
Hypothesis h2
Over D2
(x1,y1),(x2,y2)…(xm,ym)
Weak Learn
Hypothesis hk
Over Dk
Final Hypothesis : hM=F(h1,h2,…,hk)
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20. Example of a Good Classifier+ - + + - + - - + -
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21. + Round 1 of 3 - + - + + - + - - + - O D2 h1 e1 = 0.300 a1=0.424
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22. + - + + + Round 2 of 3 - - + - - + - O D2 h2 e2 = 0.196 a2=0.704
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23. - - - + + + Round 3 of 3 STOP - + - + O h3 e3 = 0.344 a2=0.323
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24. Final Hypothesis
0.42
+ 0.70
+ 0.32
Hfinal = sign[ 0.42(h1? 1|-1) (h2? 1|-1) (h3? 1|-1) ] + -
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25. AdaBoost on our Example
Train data
Round 1
Initialization
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26. Accuracy Change per Round
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27. Random Forest Model What is a Random Vector ?
Combination of Tree predictors i.e. a large number of trees is generated to create a Random Forest.
Each Tree created depends on the value of a Random vector .
is generated independently using an identical distribution for all trees
What is a Random Vector ?
Represents a random columns chosen from Training Data.
Data Samples (columns) are usually selected with replacement
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28. How Random Forest algorithm working ?
Random vector are
chosen from train file
Using any decision Tree
Algorithm to build
classifier tree
Large number of trees is
generated to create a
Random Forest
Each variable pass-
through the Random
Forest and gets
classification (e.g 10
will classify as 1, -10 as
1 and -1 as -1).
Each new variable will
gets forest of results
because each Tree casts
1 vote, the class at X is
predicted by selecting
the Class with maximum
Votes
Random column
Label column 1 3 2- 1- 3- 4- 8
< 0 1
=< 3- 1-
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29. Random Forest Pseudo Code
Initially select the number of trees to be generated e.g. K.
At Step k (1 < k < K):
A Vector is generated, represents Samples (data selected for creating Tree)
Construct Tree – h(x, )
Using any Decision Tree Algorithm
Each Tree casts 1 vote for the most popular class at X
The class at X is predicted by selecting the Class with maximum Votes
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30. A New Approach: the Adaboost-Random Forests algorithm
The first is "boost in forest", where an Adaboost classifier is built for each random vector (i.e., a collection of variables), and to get by that sequences of ``simple" Adaboost classifiers, each with small number of variables.
The second approach is to use Random Forests algorithms as a weak learners.
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31. Adaboost with Random Forest as Weak Learner Pseudo Code
Input:
N examples SN = {(x1,y1),…, (xN,yN)}
a weak base learner h = h(D,S)
Initialize:
equal example weights wl= 1/N for all n = 1..N
Iterate for l = 1..L:
1. computed normalized weights Pl(i)=wl(i) / Σ wl(i)
2. weak learner: ht := Learn( S; Pl ) call Random Forest:
Initially select the number K of trees to be generated,
For k = 1 to K
 A Vector is generated from the chosen columns (e.g include columns 2 4 5).
 Construct Tree h(x, ) Using any Decision Tree Algorithm.
 Each Tree casts 1 vote multiple by Pl t for the most popular class at X , The class at X is predicted by selecting the class with maximum Votes
 Return a hypothesis hl
End For
2. compute hypothesis error per tree εl,
3. compute hypothesis weight βl
4. update example weights for next iteration wl + 1(i),
Output: final hypothesis as a linear combination of ht
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32. Prediction of traffic congestion using machine learning
The algorithm is trained on this train file (X is the samples of traffic data, with matrices size m x n which were produced from specific date d and specific time t with class labels m* which were produced from same date d but from different time t+Δt), and the test samples ( which produce from latterly date d* and specific time t) were classified by the trained classifier.
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33. Traffic Signal Timing Optimization
There is number of models / Software for Traffic Signal Timing Optimization (e.g. PASSER ).
Base on the existing data, and with the software for “Traffic Signal Timing Optimization”, we can to plan many programs of stop light for every possible congestion scenario.
The suitable stop light program to the given prediction can be found, and the current stop light program will change with this optimize program before the congestion situation will occurred.
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34. Microscopic traffic simulator
In order to check the quality of the traffic prediction and the quality of “Traffic Signal Timing Optimization”, we also used the microscopic traffic simulator.
We found an error rate of approximately 7%. In comparison, the naive predictor (consisting in the estimation of the future class labels by its current value) gives an error rate of 16%
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35. Part IV
Results
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36. Experiment 1: Error curve of Adaboost with threshold, and with Random Forest as weak learners
Error curve for Adaboost algorithm with classical weak-learner (threshold), and with Random Forest as weak learners, on the same dataset (satimage).
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37. Error curve for Adaboost algorithm with Random Forest as
Experiment 2: Error curve of Adaboost with Random Forest as weak learners, with or without missing data.
Error curve for Adaboost algorithm with Random Forest as
weak learners, with / without missing data, on the same
dataset (satimage).
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38. Experiment 3: prediction results when one week predict one week later
First prediction results on Jerusalem data set: one week predict one week later
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39. Experiment 4: prediction results when one week predict one day later
First prediction results on Jerusalem
data set: one week predict one day later
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40. Experiment 5: Using with Traffic Signal Timing Optimization (Preliminary test)
Number of lanes with congestion situation lengthwise Yermiyahu road – 4.
Number of lanes with congestion situation lengthwise Yermiyahu road with Optimization program – 1.
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41. Part V
Conclusions
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42. Advantage of the new approach
The final classifier of this approach (Adaboost algorithm with random forest as weak learning) is very accurate with minor misclassification and strong predication ability (it is excelled in accuracy among current algorithms).
It has an effective method for dealing with missing data and maintains accuracy when a large proportion of the data are missing.
It has an effective method for predication of multi-class classification problems.
It has an effective method for unbalanced data (for instance: one label (e.g “1”- congestion) in the train file is very poor (e.g 10%) and in the test file is a much larger (e.g 35%)).
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43. Conclusions from the predication results
This preliminary test showed very good results and the methodology used to predict traffic congestion for t +
t=07:30 gave an error rate of approximately 2% in the first experiment ("one week predicts the following week"), and approximately 3% in the second experiment ("one week predicts a following day").
The possibility of prediction of traffic congestion half an hour before its happening enable to talk about future traffic flow policies.
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44. Part VI
Future Work
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45. future traffic flow policies
System methodology
Data set collection by Traffic management and information systems and preprocessing of traffic data (producing train and test files).
Prediction of traffic flow congestion by the machine learning.
Dynamic change of traffic lights program according to traffic lights optimization which achieved according to given predictions
future traffic flow policies
Reducing of average time delay
Prediction of
traffic flow by
machine learning
Dynamic change
of traffic lights
program
Real-time data
collection
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46. The suggested system
Data set collection by Traffic management and information systems
optimization of traffic lights according to given predictions
Prediction of traffic flow congestion by the machine learning
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47. That is all, folks… Thank you for your patience!
Guy Leshem
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