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.
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