1. Traffic Prediction with Partially Observed History Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media Xinyue Liu, Xiangnan Kong and Yanhua Li Worcester Polytechnic Institute Changing the Template 2. Location-Based Social Media (LBSM) 3. Collective Inference Framework Changing the Template 4. Experiment Results fig_result2_rmse_split4.pdf 5. Acknowledgement This work is supported in part by the National Science Foundation through grant CNS-1626236. t time Prediction spatio-temporal dependencies Historical Traffic Data road network a b c e d regions without any sensor 1. Problem Studied: Traffic Prediction with Partially Observed Traffic History. 2. Traffic Prediction: Infer the traffic conditions in the future time span for a geographical area. 3. Partially Observed History: In the target area, some locations are not deployed with any sensors, their historical traffic conditions are not available. 4. The Goal: Make good predictions for every location in the target area based on the partially observed traffic history. t-1 t v i (t −1) v j (t −1) v q (t −1) v p (t −1) v i ( t ) v j ( t ) v p ( t ) v q ( t ) t-1 t v i (t −1) v j (t −1) v q (t −1) v p (t −1) v i ( t ) v j ( t ) v p ( t ) v q ( t ) t-1 t v i (t −1) v j (t −1) v q (t −1) v p (t −1) v i ( t ) v j ( t ) v p ( t ) v q ( t ) Why LBSM ? Challenge 0: How to incorporate LBSM semantics into traffic prediction? Challenge 1: Lack of Traffic History Information for Some Locations. Challenge 2: Sparsity of LBSM Information at Fine Granularities (Table 1). 1. Cover much wider range of geographic areas. 2. Provide abundant information about the road users in real-time. 3. Dictation Systems (e.g. Siri) in smart phones or smart cars allow road user to post contents in LBSM easily . 4. By mining the semantic and spatial information from LBSM, we can effectively infer the future traffic conditions for many areas, including the road segments without sensors. Target Area: Greater Los Angeles LBSM Used: Twitter Bag-of-words Representation Stemmed Words Traffic History Inter-Region History Neighboring Traffic t time Prediction Social Media Historical Traffic Data a c e a b c e d a b c e d LBSM Semantics Traffic History Neighboring Traffic Inter-Region History t-2 t-1 t-1 t-2 t t-2 t-1 0 0 0 0 Training … Bootstrap Iterative Inference 0 0 (unobserved region) (unobserved region) Response Keep updating Keep updating t-1 t t t-1 t+1 t-1 t (only observed) (only observed) (observed region) (observed region) … … 1/k regions are unobserved 1/3 Test Data 20 X 20 Grids 1/k regions are unobserved 1/k regions are unobserved 1/k regions are unobserved 1/6 Test Data 5 X 5 Grids 1/6 Test Data 20 X 20 Grids 1/4 Test Data 1/5 Test Data 1/6 Test Data 1/7 Test Data 1/3 Test Data 5 X 5 Grids J. He et al. Auto- Regression t time Prediction congestion spatio-temporal dependencies time a b c d e Local-based Social Media Historical Traffic Data Our Model
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1. Traffic Prediction with Partially Observed History
Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media
Xinyue Liu, Xiangnan Kong and Yanhua LiWorcester Polytechnic Institute
Changing the Template
2. Location-Based Social Media (LBSM)
3. Collective Inference Framework Changing the Template
4. Experiment Resultsfig_result2_rmse_split4.pdf
5. AcknowledgementThis work is supported in part by the National Science Foundation through grant CNS-1626236.
t time
Prediction
spatio-temporal dependencies
Historical Traffic Data
t time
Prediction
congestionspatio-temporal dependencies
time
abcde
Local-based Social Media
Historical Traffic Data
road network
a b
c
e d
regions without any sensor
1. Problem Studied: Traffic Prediction with Partially Observed Traffic History.
2. Traffic Prediction: Infer the traffic conditions in the future time span for a geographical area.
3. Partially Observed History: In the target area, some locations are not deployed with any sensors, their historical traffic conditions are not available.
4. The Goal: Make good predictions for every location in the target area based on the partially observed traffic history.
t-1
t
vi(t−1)
vj(t−1) vq(t−1)
vp(t−1)
vi(t )
vj(t )
vp(t )
vq(t )
t-1
t
vi(t−1)
vj(t−1) vq(t−1)
vp(t−1)
vi(t )
vj(t )
vp(t )
vq(t )
t-1
t
vi(t−1)
vj(t−1) vq(t−1)
vp(t−1)
vi(t )
vj(t )
vp(t )
vq(t )
Why LBSM ?
Challenge 0: How to incorporate LBSM semantics into traffic prediction?Challenge 1: Lack of Traffic History Information for Some Locations. Challenge 2: Sparsity of LBSM Information at Fine Granularities (Table 1).
1. Cover much wider range of geographic areas.
2. Provide abundant information about the road users in real-time.
3. Dictation Systems (e.g. Siri) in smart phones or smart cars allow road user to post contents in LBSM easily.
4. By mining the semantic and spatialinformation from LBSM, we can effectively infer the future traffic conditions for many areas, including the road segments without sensors.
Target Area: Greater Los Angeles
LBSM Used: Twitter
Bag-of-words Representation
Stemmed Words
Traffic History Inter-Region HistoryNeighboring Traffic
t time
Prediction
Social Media
Historical Traffic Data
a
ce
a b
ce d
a b
ce d
LBSMSemantics
TrafficHistory
Neighboring Traffic
Inter-RegionHistory
t-2 t-1 t-1t-2 t t-2 t-1
0
0 0 0
Training
…Bootstrap
IterativeInference
0 0
(unobserved region)
(unobserved region)
Response
Keepupdating
Keepupdating
t-1 t tt-1 t+1 t-1 t
(only observed)
(only observed)
(observed region)
(observed region)
…
…
1/k regions are unobserved
1/3 Test Data20 X 20 Grids
1/k regions are unobserved
1/k regions are unobserved
1/k regions are unobserved
1/6 Test Data5 X 5 Grids
1/6 Test Data20 X 20 Grids
1/4 Test Data 1/5 Test Data
1/6 Test Data 1/7 Test Data
1/3 Test Data5 X 5 Grids
J. He et al.
Auto-Regression
t time
Prediction
congestionspatio-temporal dependencies
time
abcde
Local-based Social Media
Historical Traffic Data
road network
a b
c
e d
regions without any sensor
Our Model
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