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II. Methodology
• Source from Microsoft Asia
– T‐Drive trajectory data sample
– GPS trajectories of 10,357 taxis in a week
• Date of data
• Data format‐ taxi ID, data, time, longitude, latitude
Table. Date of Sample Data (February 2008)
Date 2nd 3rd 4th 5th 6th 7th 8th
Week Sat Sun Mon Tue Wed Thu Friday
Useful x x √ √ √ x x
Chinese New Year; √ Useful the data between morning peak 5am to 11am
Question 1:
Data was collected
just before the
Chinese New Year.
Does it matter?
2.1 Beijing Taxi GPS Data
5
II. Methodology
• Sampling Time of Raw Data
Date Counts of Records Sample Time /s
(5am‐11am) Total Useful Mean Median
4th Feb 2008 560,000 532,433 206.10 221
5th Feb 2008 510,000 483,151 219.61 251
6th Feb 2008 420,000 398,766 231.97 300
Total 1,490,000 1,414,350 219.23 seconds
2.1 Beijing Taxi GPS Data
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II. Methodology 2.2 Road Network
9
Icon description
Expressway
Ring Road
1st Level Road
2nd Level Road
3rd Level Road
4th Level Road
II. Methodology
• If travel speed = 20 km/h (very typical in Beijing)
• If travel time of 219.23 seconds
• Then travel distance = 1217.9 metres >1103.1!
Q2:
How to allocate taxi GPS point on
to the modelled links?
Q3:
How to tell the correct route from
point 998 to 999?
Q4:
How to tell the speed on each link
of the route?
2.3 Questions?
Q1:
Data was collected just before the
Chinese New Year. Problem?
10
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II. Methodology
• Filtering Process
• Filtering Results
• Definition of Buffer Tolerance
– Tolerance for each direction:
» Lane width x Lane Number +
» Separate band Width x Number +
» Utilities Width
Link Type Buffer / m
Expressway 20.8
Ring Road 20.8
Type 1 20.1
Type 2 18.1
Type 3 10.2
Type 4 / infill 8.2
2.4 Data Process
>= 100 m
<= 10km
> 0kph
<= 120kph
LENGTH
In 5th Ring Road
Within Buffer
P2P SPEED LOCATION
Date 4th Feb 2008 5th Feb 2008 6th Feb 2008
All trajectories 563,000 510,000 423,000
Within length tolerance 325,000 ( 58%) 276,000 (54%) 191,000 (45%)
Within speed tolerance 322,000 (57%) 274,000 (53%) 188,000 (44%)
Within spatial tolerance 114,000 (20%) 90,000 (18%) 56,000 (13%)
II. Methodology 2.5 Produce Detailed Network
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III. Analyses 3.5 Min‐Path Test
19
Original
Beijing Language and Culture
University
Ma Jia Bao (nearby Beijing
South Railway Station) Wang Jing Residential Area
Destination Jin Ze Building, Xi Dan Beijing Olympic Forrest Park Guo Mao CBD
Estimation
Google Map
(21/10/13) Estimation
Google Map
(21/10/2013) Estimation
Google Map
(21/10/2013)
Distance
(km) 13.2 10.2 27.8 22.7 16.1 14.3
% Error of Dist 29.4% 22.5% 12.6%
Travel time
(min)
28.1
(Mon 4/2/08)
25.0
(7:30am)
52.4
(Mon 4/2/08)
47.0
(7:30am)
24.4
(Mon 4/2/08)
24.0
(7:30am)
25.5
(Tue 5/2/08)
25.0
(8:00am)
50.3
(Tue 5/2/08)
48.0
(8:00am)
22.4
(Tue 5/2/08)
24.0
(8:00am)
22.6
(Wed 6/2/08)
25.0
(8:30am)
44.9
(Wed 6/2/08)
48.0
(8:30am)
21.4
(Wed 6/2/08)
24.0
(8:30am)
25.0
(9:00am)
47.0
(9:00am)
26.0
(9:00am)
Mean (min) 25.4 25 49.2 47.5 22.7 24.5
% Error of Time 1.6% 3.6% 7.3%
20
ModelGoogle
Q1:
Data was collected just before the
Chinese New Year. Problem?
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IV. Further Work
• To Improve the method
– If we can have more GPS data, we could enhance the accuracy of
outputs
– Compare only high sampling frequency taxi traces with this method,
to assess the added value of our new tool
– Second iteration of shortest path model using speeds calculated in
iteration 1
– Evaluation of zero speed observations (parking or congestion?)
• Further applications
– To apply the method to other cities
– Investigate the traffic patterns for different link type
• To enhance the strategic transport model
– To find ways to automate the network generation
– To infill the recent road network, and add the railway and subway
network to the model – multiple modal
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V. Conclusion
• Built up a new tool to estimate link speed using low‐
frequency taxi GPS data on GIS platform
– Efficient, accurate and reliable: Half of the results have a RSD below
34.9%, 34.2% and 32.3% from Monday to Wednesday
– To create the detailed network for the transport model, and help
test the accessibility of network as well
• Set up a multi‐modal strategic transport model for Beijing
• Developed a visible speed monitor, to help learn the speed
pattern
– The patterns of the estimated speed match the reality
• Tests of the transport model
– The travel time based minimum‐path test of 3 typical routes
– Error of travel time is less than 7.5% (compare with Google Map)
– The modelled routes are more realistic
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I. Introduction
3
1.1 Purpose of Study
Strategic
Transport Model
Demand Matrices
Survey Data
Traffic Flows
Transport Network
Cost / Tariffs
Used to be
very costly
To develop a
set of vehicle
speed data for
modal
calibration /
validation
I. Introduction
Taxi GPS
Traces
City Dynamics
Traffic Flow
Patterns
Social/Economic
Realities
GIS
Platform
Spatial Analyses
Large Scale Data
User Friendly
4
• 2012 population 20,693,000[1,2]
‐ Political, cultural, educational centre
‐ Headquarters of state‐owned companies
‐ Transport hub
• Beijing Capital International Airport
‐ World 2nd busiest airport 2010‐2013
(by passenger traffic ) [3]
‐ 73.95 million passengers in 2010
• Car ownership
‐ 5 million estimated registered cars in 2011
‐ 6 millions cars predicted by 2016
1.2 Beijing](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)