Post on 23-Feb-2023
Author: PhD. Emilian Necula Faculty of Computer Science, “Al. I. Cuza” University, Iasi
PhD Fellow, SOP HRD/159/1.5/133675 Project, Romanian Academy Iasi Branch
Introduction
Motivation
Related Work
Data Collection & Description
Solution: Data Processing
Solution: Prediction Model
Solution: TraffiX Simulator
Solution: Performance Evaluation
Conclusions & Future Work
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The objective of this research is to establish a traffic flow model
On this model future traffic situation would be predictable
Predictions will be available to the drivers using navigation systems.
We need to have a large GPS
trace database
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GPS Data
Dynamic
Traffic
Prediction
Future
Traffic
Prediction
Fig.1- Smartphone enabled traffic predictions
The congestion frequency on current roads
The promising evolution of ITS
The widely spreaded GPS enabled devices
The extent use of statistical models for predicting different processes from real world
car traffic flow
The possibility to turn our predictions into effective route recommendations
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Fig. 2- BMW’s Connected Drive™ System
Easy
implementation
Cheap
computations
Weak
dependency on
historical traffic
data
Adaptive to
traffic dynamics
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Simmons [1] and Krumm [2] used Hidden Markov models to obtain short-term prediction (but static)
Wang [3] achieved a dynamic traffic prediction
Pang [4] prediction model is based on subtractive clustering for fuzzy neural network (simulated data)
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Fig.3- Wang’s flow diagram of dynamic traffic prediction model
Dougherty [5] reported a congestion prediction method using neural network
Huisken and Coffa [6] performed time series analysis for traffic congestion prediction
Chen [7] proposed a congestion prediction algorithm based on auto regressive fuzzy logic
Parametric vs. Nonparametric…
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GPS data was generated using Traffic Simulation Framework (TSF) [8]
We generated: 18716 nodes and 34150 links ◦ 7621 – major roads (average speed > 60km/h)
◦ 100 – critical links (random selected) for the main intersections
Goal: a traffic flow prediction on those links
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Fig. 4- Road network used in our research
Training data and test datasets cover 500 hours of simulation each
We are using 2 files:
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•Timestamp
•Car ID
•Instant Speed
•Latitude
•Longitude
geo location
data
•Timestamp
•ID of the second node (critical link)
•ID of the first node (critical link)
•Harmonic average of speeds in the last
6 minutes preceding timestamp
average speed
data on
critical links
Identifying mobility patterns (MP) needs first to assign a road for each GPS trace (non trivial).
We apply a map matching algorithm [9] with 95% accuracy.
Variables…
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GPS point
location
deviation
Minimum
projection
distance
Minimum
angle
deviation
Previous
reports
Mobile context
of the vehicle
After processing the raw GPS data we identify the set of all road segments
R = {r1, r2, . . .rn} We present a m segment itinerary as a road
segment sequence containing m consecutive road segments
Sm = r1r2···rm where ri ∈ R, m ≥ 1
k-segment Mobility Pattern (k ≥ 2) is a k-segment itinerary Sk where, given all Sj, 1 ≤ j < k, the probability of a vehicle to select rj+1 as the next route choice is greater than predefined threshold σ.
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We use a probabilistic suffix tree (PST) to implement a K-bounded VMM
In a K-bounded VMM a state can be determined by knowing at most K previous states in the Markov chains
The mobilitiy pattern algorithm is divided in:
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Parsing
vehicles
traces
Building
the PST MP
*K – the memory length of the model **VMM- Variable-order Markov Model
We developed this simulator to investigate the traffic flow evolution
Includes major traffic characteristics
It is developed as a microscopic traffic simulator based on Intelligent Driver Model [10]
Light and easy user interface
It can simulate 105 cars on a real road network (real time)
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Traffic:
GPS Traces
Language:
C#
Maps:
OpenStreetMap
Main user interface…
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Fig. 9 – TraffiX Simulator with part of the road network under traffic flow simulation
We compared our method with Artificial Neural Network method and Shift Model method
The performance was quantified regarding: ◦ MAE – Mean Absolute Error
◦ MAPE – Mean Absolute Percent Error
◦ RMSE – Root Mean Square Error
We will show the error level for each method trying to predict the traffic flow of a critical link.
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The prediction errors are proportional with prediction interval
ANN reduces MAE by 52%, MAPE by 36% and RMSE by 47% compared with Shift Model
Our model (VMM) reduces MAE by 30%, MAPE by 24% and RMSE by 10% compared with Shift Model
reg. MAPE: ANN performs best when prediction interval is 10s. Our model outperforms ANN when the prediction interval is >1 min.
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Fig.10 – Our method (VMM) vs. ANN and Shift Model using MAE/MAPE/RMSE
We studied the feasibility of identifying mobility patterns in a city
We used GPS traces generated from an external simulator - TSF
We proposed a VMM based method to make short-term traffic flow prediction
Experiments made withTraffiX Simulator found that:
◦ daily drivers have no constraints on route selection
◦ route selection is well behaved (MP)
◦ MP are connected with different traffic conditions
Performance tests showed that our VMM model reduces prediction error up to 52% compared to the Shift Model and up to 22% compared to the ANN model.
FW: fine tuning of the MP generation algorithm (clusterization, information from adjacent links..), extend traffic flow prediction.
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[1] R. Simmons, B. Browning, Y. Zhang, and V. Sadekar, “Learning to Predict Driver Route and Destination Intent,” ITS Conference, 2006, pp. 127–132
[2] J. Krumm, “A Markov Model for Driver Turn Prediction,” SAE World Congress, 2008
[3] Y. Wang, Y. Chen, M. Qin, and Y. Zhu, “Dynamic Traffic Prediction Based on Traffic Flow Mining,” 6th World Congress on Intelligent Control and Automation, pp. 6078–6081, June 2006
[4] M. Pang, X. Zhao, “Traffic Flow Prediction of Chaos Time Series by Using Subtractive Clustering for Fuzzy Neural Network Modeling,” in Intelligent Information Technology Applications, 2007, pp. 23–27
[5] M. S. Dougherty, H. R. Kirby, and R. D. Boyle, “The use of neural networks to recognize and predict traffic congestion,” Traffic Engineering and Control, vol. 34, no.6, 1993, pp. 311–314
[6] G. Huisken and A. Coffa, “Short term congestion prediction: comparing time series with neural networks,” IEE conference on road transport information and control, 2000, pp. 66–69
[7] B. Chen, S. Peng, and K. Wang, “Traffic Modeling, Prediction, and Congestion Control for High-Speed Networks: A Fuzzy AR Approach,” IEEE On Fuzzy Systems, 2000, pp. 491–508
[8] P. Gora, “Traffic Simulation Framework - a cellular automaton based tool for simulating and investigating real city traffic,” Recent Advances in Intelligent Information Systems, Warsaw, 2009, pp. 642–653
[9] E. Necula, “Mining GPS Data to learn driver’s route patterns,” (under review for SYNASC 2014).
[10] S. Zhang, W. Deng, Q. Zhao, H. Sun, and B. Litkouhi. “An Intelligent Driver Model with Trajectory Planning,” 2012 IEEE ITS Society Conference Management System,pp. 876–881, Sept. 2012,USA
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