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

Some key aspects…

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GPS

sampling GPS data

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

Example…

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Fig. 7- Road map instance and the MP for r0

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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