Prediction of Traffic Density for Congestion Analysis under Indian Traffic Conditions Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA, October 3-7, 2009 Ameena Padiath, Lelitha Vanajakshi, Shankar C. Subramanian, and Harishreddy Manda REPORTER: WEI HSU 2014/10/23 1

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) 2

Introduction (cont.) ITS control strategies take many forms Metering flow onto roadways Dynamically retiming traffic signals Managing traffic incidents Providing travelers with information 3

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 4

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion 5

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion 6

Schematic Representation of The Study Site 7

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 8

Calculating Traffic Density is traffic density is average vehicle length is detection zone length is percentage occupancy time 9 Total occupancy time in time period T Time period of observations (hours)

Calculating Traffic Density (cont.) 10

Calculating Traffic Density (cont.) Density(vehicles / lane-mile) %occFlow Conditions Free-flow operations Uncongested flow conditions Reasonable free-flow operations Stable operations Borders on unstable operations Extremely unstable flow operations Near-capacity flow conditions Forced or breakdown operations Congested flow conditions > 100> 42 Incident situation operations 11

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion 12

Prediction Techniques Three different techniques can predict the traffic density Historic technique Artificial Neural Network (ANN) technique Model based approach 13

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 14

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 15

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 16

Model Based Approach 17 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

Model Based Approach (cont.) Discretizing the above equations using time step h is cumulative number of vehicles in the section till k th interval of time 18

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion 19

Comparison of The Techniques (cont.) is number of samples is predicted number of vehicles is measured number of vehicles 20 Mean Absolute Percentage Error

Comparison of The Techniques (cont.) 21 Using model based technique MAPE = 40.83%

Comparison of The Techniques (cont.) 22 Using ANN based technique MAPE = 32.47%

Comparison of The Techniques (cont.) 23 Using historic technique MAPE = 38.57%

Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion 24

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. 25

Thank you for listening 26