A Simulation Study on Automated

Transport Mode Detection in Near-Real Time using a Neural Network

Rahul Deb Das

Stephan Winter, Nicole Ronald

Department of Infrastructure Engineering

Locate’ 15 | Brisbane

Transport Modes

Motivation: I

• Estimating travel demand/patronage over a specific

mode/ specific network (say Train/Tram)

Motivation: II

• Providing

context-aware

location-based

services

1 km

Not to scale: for demonstration purpose only

GPS Trajectory

Trajectory : Set of time ordered

spatio-temporal points, where

Pi=(Xi,Yi, [Zi], ti)

P1: (X1,Y1,t1)

(X2,Y2,t2)

P6: (X6,Y6,t6)

R2

Y

X

State-of-the-Art and Problems: I

• Offline (historical trajectories)

based on walking-based segmentation

– Difficult to set walking distance threshold

– Cannot provide just-in-time information

• Schuessler and Axhausen, 2009; Zheng et al., 2010; Stenneth et al., 2011; Biljecki et

al., 2012; Hemminki et al., 2013;

State-of-the-Art and Problems: II

• Online (real time)

– Existing approaches do not use spatial information

– Smart-phone based mode detection in real time is not well explored

– Longer response time for near real time

• Byon et al., 2009;

Contribution and Hypothesis

• Trajectories from Smart-phones and GPS

loggers

• Exploring possibilities of using spatial

information

• Developing a neural network based online

predictive model

• Exploring accuracy measures for different

temporal window for mode detection

Hypothesis: A neural network can detect a

transport mode in near real time with

reasonable accuracy over a reasonable time

window

Real Time Detection

Home

Office P1: (X1,Y1,t1)

(X2,Y2,t2)

P6: (X6,Y6,t6)

Near Real Time Detection

Home

Office P1: (X1,Y1,t1)

(X2,Y2,t2)

P6: (X6,Y6,t6)

Methodology: Simulation Design

Segmentation

Modal Temporal

Pre-processing of trajectories

Signal loss Unreasonable speed

GPS Trajectories

Smart-phones GPS loggers

Methodology: Multi-modal GPS Trajectory

Methodology: Mode Segmentation

Methodology: Temporal Segmentation

POI relevance

t1

t2

dt

Temporal segments of ‘dt’ length

T

s

t3= t2+δ

t4

Temporal Segmentation: Clustering based POI relevance

POI

relevance

N-1

Train

station

{POIc+1= POIc+ s*(n/N) | POIc+1, POIc ε TWi }

n= number of points falling in the vicinity of a given

POI

N= Total number of points in the cluster

s= scaling factor

Methodology: Temporal Segmentation

POI relevance

t1

t2

dt

Temporal segments of ‘dt’ length

T

s

t3= t2+δ

t4

Kinematics:

•Max (velocity, acceleration)

•Min (velocity, acceleration)

•Avg (velocity, acceleration)

•Var (velocity, acceleration)

Non-Kinematics:

•Avg prox (road netwk, train netwk)

•Var prox (road netwk, train netwk)

•POI relevance (bus stop, train

stop, traffic light, car wash and

parking lot)

Mode Prediction: Training and Testing

Kinematics:

•Max (velocity, acceleration)

•Min (velocity, acceleration)

•Avg (velocity, acceleration)

•Var (velocity, acceleration)

Non-Kinematics:

•Avg prox (road netwk, train

netwk)

•Var prox (road netwk, train

netwk)

•POI relevance (bus stop,

train stop, traffic light, car

wash and parking lot)

Predictive

model

(Neural

Network

based)

mode

Model Architecture: Simulation Design

Neural Network Model (16-10-5 MLP)

Dataset: Beijing and surrounding suburbs

Microsoft Geolife data: for more information see- Zheng, Y., Liu, L.,Wang, L., & Xie, X (2008). Learning Transportation Modes from Raw GPS Data for Geographic Application on

the Web, Proceedings of International conference on World Wide Web (WWW 2008), Beijing, China. ACM Press: 247-256

264 GPS

Trajectories

Networks used:

road, train

POI:

Bus stop, train

stop, car wash &

parking lot, traffic

light

Sampling

interval: 2-5 sec

Separate

trajectory and

annotation files

Experimental Design

Exp 1: With Filtered

walking speed (if

walking speed >2.5

m/s then assigned as

2.5 m/s)

Exp 2: Without

filtered walking

speed

IDW kernel smoothing

GPS trajectories

Temporal Windows

(120 sec, 180 sec, 240

sec, 300 sec, 480 sec,

600 sec)

Experiment and Results: 1

Car Walk Train

Bus Bike

Accuracy 16-10-5 MLP 8-6-5 MLP

Temporal

Window (sec) with spatial

information (%)

without

spatial

information

(%)

120 81.05 75.31

180 81.98 77.17

240 85.12 77.81

300 86.11 78.42

480 90.64 82.09

600 92.43 82.27

N-fold cross validation (N=10)

81.05 81.98

85.12 86.11

90.64 92.43

75.31

77.17 77.81 78.42

82.09 82.27

65

70

75

80

85

90

95

0 100 200 300 400 500 600 700

Accu

rac

y (

%)

Temporal window (sec)

Accuracy Measure (Walk, Car, Bus, Train, Bike)

with spatial information

without spatial information

Without Filtered Walking Speed

Temporal Window (sec)

16-10-5 MLP

with spatial information (%) 8-6-5 MPL

without spatial information (%)

120 74.08 71.26

180 79.65 73.5

240 79.34 74.15

300 82.55 73.52

480 86.63 77.38

600 87.05 77.29

Experiment and Results: 2

CarWalkTrainBusBike

CarWalkTrain With spatial information, accuracy reached up to 96% (over 600 sec time window)

Experiment and Results: 3

Car Walk Train Bus

Bike

Time window (sec)

Accuracy

16-10-5 MLP

With spatial

information (%)

8-6-5 MLP

Without

spatial

information

(%)

120 73.71 71.37

180 78.69 71.8

240 81.38 74.19

300 85.21 74.18

480 85.32 72.38

600 85.94 77.53

Hold-back type: 85:15

50

55

60

65

70

75

80

85

90

95

100

0 200 400 600 800

Accu

rac

y %

Time window

Mode Detectinon Accuracy

with spatial info

without spatial info

Time window

(sec) SVM (%) MLP (NN) (%) Naïve Bayes (%) RBFNetwork (%)

120 59.10 74.08 30.28 58.77

180 60.65 79.65 32.05 60.37

240 64.15 79.34 32.6 64.15

300 61.78 82.55 31.68 66.89

480 66.84 86.63 24.78 68.95

600 68.35 87.05 25.06 66.87

Experiment and Results: 4

Accuracy Measures:

Discussions and Conclusions

• Selecting optimal temporal window is critical

and context dependent

• Detecting composite modes in a same

temporal window is challenging and need

more sensor information

• The simulated model is limited by a single

mode (similar mode) over a time window

• Needs to integrate more sensor information

Future Works

• Evaluating on a Melbourne data set

• Needs more rigorous evaluation

• Testing sensitivity of different spatial and non-spatial

parameters during modeling phase

• Incorporating more spatial information (bus network)

• Integrating more sensors (accelerometer, gyroscope

along with GPS) in order to decompose composite

modes

Selected References

• Biljecki, F., Ledoux, H., & Oosterom, P. V. (2012). Transportation mode-

based segmentation and classification of movement trajectories.

International Journal of Geographical Information Science, 17(2), 385-

407.

• Byon, Y., Abdulhai, B., & Shalaby, A. (2009). Real-time transportation

mode detection via tracking Global Positioning System mobile devices.

Journal of Intelligent Transportation Systems: Technology, Planning, and

Operations,, 13(4), 161-170.

• Minetti, A. (2000). The three modes of terrestrial locomotion.

Biomechanics and Biology of Movement B. M. Nigg, B. R. MacIntosh, and

J. Mester, Eds., ed: Human Kinetics, pp. 67–78.

• Zheng, Y., Chen, Y., Li, Q., Xie, X., & Ma, W.-Y. (2010). Understanding

transportation modes based on GPS Data for web applications ACM

Transactions on The Web, New York, USA.

• Zheng, Y., Li, Q., Chen, Y., & Xie, X. (2008). Understanding mobility

based on GPS Data. In Proceedings of ACM conference on Ubiquitous

Computing (UbiComp 2008), Seoul, Korea.

THANK YOU

rahuld@student.unimelb.edu.au

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