Analytics for Smart Transport - LTA Academy - Land Transport ...

Intelligent Transportation Systems Initiative
Analytics for Smart Transport
Traffic Informatics
Dr. Wanli Min
IBM Singapore
Urban Sustainability R&D
Congress, Singapore
June 28, 2011

Outline
Intelligent Transportation Systems Initiative
Introduction
Traffic Data Model and Applications
Analytics for Traffic Data Collection
Conclusion

Intelligent Transportation Systems Initiative
Commonality Across Multiple Fields
新概念
数据流形:交通流,公交车网络,水流,物流,手机塔台,社交网络互动

Intelligent Transportation Systems Initiative
Commonality Across Multiple Fields
• Common challenges on data analytics
• Techniques developed for traffic data have interdisciplinary reference value
• Key elements: network structure, dynamic characteristics

Intelligent Transportation Systems Initiative
Introduction
ITS: intelligence vs. information
Traffic Informatics
– It is all about data
Technology development facilitates data collection
– Video camera
– Loop detector
– On board unit (OBU)

Intelligent Transportation Systems Initiative
• How do we make sense out of massive traffic data ?
• Innovation should not stop with real-time traffic information
Data collection
– Where
– When
– What kind
Data Fusion
– Quality assurance
– Data imputation
– Data expansion
Data Model
– Tracking/Incident detection
– Prediction
Application

Intelligent Transportation Systems Initiative
Innovation Concepts – Transport Informatics
► Issue: strained
infrastructure
More transport capacity is
needed, but construction of
new physical infrastructure
is cost prohibitive, if even
possible
vanpool
3rd train
bus
weather service
vanpool air
taxi probe fleets
historical data incident
loop detectors
floating cell 3 party
video analytics
parking
toll gantry
rd train
bus
weather service
air
taxi probe fleets
historical data incident
loop detectors
floating cell party
video analytics
parking
toll gantry
► Issue: navigating
mass transit
Transit is part of the
solution, but it must be
easier for travelers to find
their way and weigh
options
Data Integration
& Analytics
Focus
► Required Innovation: foundation of data
integration & analytics
• Multiple data sources across transport modes
• Integrated to single foundation of information
• Leveraged for multiple uses
• Based on open standards
• Integrated systems approach, not point solutions
Traveler
Advice
Network
Response
real-time advice
route planning
personalization
signal timing
transit routing
incident response
performance
measurement

Intelligent Transportation Systems Initiative
Model-based Data Applications
– Optimal Path
– Future Travel Time
– Seamless Bus Transfer
– Prediction

Optimal Path Given Future Network Traffic
• Take Speed and Volume as example
• With distributional information of Speed and Volume over network during
next D time period starting from current moment
• The optimal path from O to D ? Assuming the trip starts after T0
?
• The optimal path starting time (less cost) to reach D before time target ?
• Introduce link-wise cost, may introduce other Value-at-Risk type of criteria
O
{ ( Vol, Sp) it , | i = 1, LN
, t ∈ ( T0, T0 + D)
}
D

Driver location
Future Travel Time Prediction
Travel time A to B
display board
A B
what will be this driver’s actual travel time if he
starts the journey AB in 3 minutes ?
• The observed travel time is collected AFTER a car has arrived at B.
• The observed travel time of other drivers is past information.
• Need a predictive view of future travel time when start from A in 10 minutes.

Predictive Bus Travel Planner
Future bus arrival time, trip duration
When traveling by buses, very often transfer among different service routes are
needed, even so is there might be different connecting bus service routes and
multiple choice of connection bus stops. Passengers need recommendation on bus
travel plan from departure to destination.
Bus A
departure
Knowing predicted arrival of Bus A & B to overlapping stops,
with confidence interval, which stop should the passenger
switch from B to A, with minimum risk (probability of missing
connecting bus) ?
Bus B
Bus B
destination
Bus A

Intelligent Transportation Systems Initiative
Traffic Prediction
Pilot: Singapore’s Land Transport Authority
Current Status - Volume
Forecast Attribute
Forecast Period

Prediction vs. Broadcasting
Traffic information up to now
6:50
6:55
7:00 7:05 7:10 7:15
Current time
Future traffic information
7:20
A
B
C
D

Intelligent Transportation Systems Initiative
Pilot: Singapore’s Land Transport Authority
Current Status - Volume
Forecast Attribute
Forecast Period

Accuracy
0.89
0.885
0.88
0.875
0.87
0.865
0.86
0.855
July 22 2009
Cat
CATA
CATB
CATC
CATD
CATE
SLIP_ROAD
New testing links in CBD Network: Speed Prediction
Accuracy July 19 ~ 25, 2009
10-min 15-min 30-min 45-min 60-min
0.939
0.887
0.876
0.846
0.831
0.919
CATB CATC SLIP_ROAD
Average speed prediction accuracy (zone 1)
10min 15min 30min 45min 60min
0.931
0.875
0.864
0.834
0.82
0.908
0.918
0.874
0.864
0.833
0.82
0.891
0.917
0.874
0.864
0.833
0.821
0.869
0.906
0.873
0.864
0.833
0.821
0.821

Intelligent Transportation Systems Initiative
Innovation starts with Data Quality
Data collection
– Where
– When
– What kind
Data Fusion
– Quality assurance
– Data imputation
– Data expansion

Intelligent Transportation Systems Initiative
Bad Data is Even Worse !
0 50 100 150
0 50 100 150
0 50 100 150
ID 9900020207
0 50 100 150 200 250
ID 9900073805
0 50 100 150 200 250
ID 9903000807
0 50 100 150 200 250
0 50 100 150
0 50 100 150
0 50 100 150
ID 9900020601
0 50 100 150 200 250
ID 9900120301
0 50 100 150 200 250
ID 9903001105
0 50 100 150 200 250
0 50 100 150 200 250
3/10/2007 actual speed (black) vs. historical average speed (blue)
0 50 100 150
0 50 100 150
0 50 100 150
ID 9900063107
ID 9903000201
0 50 100 150 200 250
ID 9903002001
0 50 100 150 200 250

Where to Collect Data ?
13
1
4
2
3
7 8
1
∂ log( V )
max [ W W − W IM( s, T + t)
]
d d
i, T0+ t
∑∑∑ s, t s', t α∑∑
s, t
0
A, d, W | A| d s∈A s'∈ A t= 1 ∂ log( Vs,
T)
s∈ A t=
1
0
IM ( s, T ) is the minimum and sufficient subnetwork representation around link s.
0
A is a set of links, W = [ W ] is a matrix, d is an integer parameter.
st ,
i, T0+ t i, T0+ t T

maximum gain of effective coverage with fixed number of new sensors
smallest number of new sensors to be installed given coverage target
(1, 5)
(2, 5)
OD (5, 12)
Index
1 2 3 4 5 6
Link Index
7 8 9 10 11 12 13
⎡ 1 0 1 0 1 0 0 0 0 0 0 0 0⎤
⎢
0 1 1 0 1 0 0 0 0 0 0 0 0
⎥
⎢ ⎥
⎢ 0 0 0 0 1 0 0 0 0 1 0 1 0⎥
⎢ ⎥
⎣ 1 0 1 1 1 0 1 1 1 0 1 1 0⎦
Main idea:
Construct OD vs link incidence matrix (above), compute Its
reduced row Echelon form (RREF). Replacing entries of “1” by observed traffic
volume, construct a statistically sufficient and irreducible RREF.
Reference Paper: Wanli Min and Ruey Tsay, Statistica Sinica, 303-323, 2005

Intelligent Transportation Systems Initiative
Commonality Across Multiple Fields
• Common challenges on data analytics
• Techniques developed for traffic data have interdisciplinary reference value
• Key elements: network structure, dynamic characteristics

Optimal Control Actions
Scenario
- Water pipeline overflow
- Contamination in water system
- Traffic incident on expressway
Optimal Control Actions on demand
- See demo on Google earth
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© 2007 IBM Corporation

Summary
Analytics can help make sense of collected data
- Directly help controllers’ operations (decision support)
- Benefit individual driver / passenger
- More return on the investment of ITS infrastructure
Analytics can help determine where to collect data
- Placement of sensors
- Minimum network of sensors to ensure continuity of data-dependent
applications
Analytics can help ensure quality of collected data
- Data fusion, anomaly detection
- Missing data imputation
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© 2007 IBM Corporation

Reference
Nie, Y, M. H Zhang and D H Lee, "Models and algorithms for the traffic assignment problem with link
capacity constraints" ,
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 38, no. 4 (2004): 285-312
Wanli Min and Ruey Tsay, “On Canonical Analysis of Multivariate Time Series”,
Statistica Sinica, 303-323, 2005
Lee, D H, W Zheng and Q X Shi, "Short-term freeway traffic flow prediction using combined
neural network model“,
JOURNAL OF TRANSPORTATION ENGINEERING-ASCE, (2006): 114-121
Weibiao Wu and Wanli Min, “On Linear Processes with Dependent Innovations”,
Stochastic Process and Their Application, 939-958, 2005
Ben Haaland, Wanli Min, Peter Qian and Yasuo Amemiya. “Statistical Approach to Thermal
Management of Large Data Centers under Steady State and System Perturbations”,
Journal of American Statistical Association (JASA), 1030-1041, Vol 105, 2010
Wanli Min, and Laura Wynter. “Road Trac Prediction with Spatial-Temporal Correlations”,
Transportation Research Part C: Emerging Technology, 606-616, 2011
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© 2007 IBM Corporation

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Thanks
© 2007 IBM Corporation