Prediction of Traffic Density for Congestion Analysis under Indian Traffic Conditions Proceedings of...

Prediction of Traffic Density for Congestion Analysis under Indian

Traffic Conditions

Proceedings of the 12th International IEEE Conferenceon Intelligent Transportation Systems, St. Louis, MO,

USA, October 3-7, 2009Ameena Padiath, Lelitha Vanajakshi, Shankar C.

Subramanian, and Harishreddy Manda

R E P O RT E R : W E I HS U

2014/ 10/ 23

1

2

Introduction The available infrastructure is not enough to meet the

demand. Solution: flyovers, road widened

External events have effect on traffic congestion. Solution: Intelligent Transportation System (ITS)

3

Introduction (cont.) ITS control strategies take many forms

Metering flow onto roadways

Dynamically retiming traffic signals

Managing traffic incidents

Providing travelers with information

4

Introduction (cont.) Traffic data were collected using videographic. (Manual)

Using loop detectors for collecting traffic data.

(Automatic)

Comparison of the performance Historic method

Artificial Neural Networks (ANN)

Model based approach

5

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion

6

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion

7

Schematic Representation of The Study Site

8

Data Collection Data extraction was carried out using video image

processing software.

Video data were collected

Initial number of vehicles inside the section

Number of vehicles entering and leaving the section

9

Calculating Traffic Density

is traffic density

is average vehicle length

is detection zone length

is percentage occupancy time

Total occupancy time in time period T

Time period of observations (hours)

10

Calculating Traffic Density (cont.)

11

Calculating Traffic Density (cont.)Density(vehicles / lane-mile) %occ Flow Conditions

0 - 12 0 - 5 Free-flow operations

Uncongested flow conditions

12 - 20 5 - 8 Reasonable free-flow operations

20 - 30 8 - 12 Stable operations

30 - 42 12 - 17 Borders on unstable operations

42 - 67 17 - 28 Extremely unstable flow operations

Near-capacity flow conditions

67 - 100 28 - 42 Forced or breakdown operations Congested flow

conditions> 100 > 42 Incident situation

operations

12

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion

13

Prediction Techniques Three different techniques can predict the traffic density

Historic technique

Artificial Neural Network (ANN) technique

Model based approach

14

Historic Technique It is the most popularly adopted methods for short term

prediction

Historical average value will be used for prediction

Using average of the density data from four days’ data to

predict density for fifth day

15

Artificial Neural Network(ANN) Technique ANNs can be trained to learn a complex relationship in a

data set

Previous time steps values were used as input and future

time steps values were obtained as output

Attempting to identify a pattern, and assuming that it will

continue in the future

16

Artificial Neural Network(ANN) Technique (cont.) Four days’ data were used for training the network and

fifth day’s data were used for testing

During training, network takes the first 5 one minute

interval density value for computing the next one

17

Model Based Approach

is the number of vehicles in the section at any instant of time

The time rate at which vehicles enter and exit the section respectively

The net time rate at which vehicles enter the section from the ramp

Aggregate speed of the vehicles in the section at any instant of time

Length of the section under study

18

Model Based Approach (cont.) Discretizing the above equations using time step h

is cumulative number of vehicles in the section till kth

interval of time

19

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion

20

Comparison of The Techniques (cont.)

is number of samples

is predicted number of vehicles

is measured number of vehicles

Mean Absolute Percentage Error

21

Comparison of The Techniques (cont.)

Using model based technique

MAPE = 40.83%

22

Comparison of The Techniques (cont.)

Using ANN based technique

MAPE = 32.47%

23

Comparison of The Techniques (cont.)

Using historic technique

MAPE = 38.57%

24

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion

25

Conclusion Implementing this application under a heterogeneous,

less lane disciplined traffic condition is more challenging.

Accuracy of the model based approach may improve if

collecting data for longer time period.

Accuracy of data driven techniques may improve if

collecting more video data.

26

Thank you for listening