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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
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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)
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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
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Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion
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Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion
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Schematic Representation of The Study Site
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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
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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)
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Calculating Traffic Density (cont.)
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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
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Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion
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Prediction Techniques Three different techniques can predict the traffic density
Historic technique
Artificial Neural Network (ANN) technique
Model based approach
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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
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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
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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
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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
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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
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Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion
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Comparison of The Techniques (cont.)
is number of samples
is predicted number of vehicles
is measured number of vehicles
Mean Absolute Percentage Error
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Comparison of The Techniques (cont.)
Using model based technique
MAPE = 40.83%
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Comparison of The Techniques (cont.)
Using ANN based technique
MAPE = 32.47%
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Comparison of The Techniques (cont.)
Using historic technique
MAPE = 38.57%
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Outline Data Collection Prediction Techniques Comparison of The Techniques Conclusion
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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.
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