Inferring Gas Consumption and Pollution Emission of Vehicles throughout a City

Dr. Yu Zheng yuzheng@microsoft.com

Lead Researcher, Microsoft Research Chair Professor at Shanghai Jiao Tong University

Presented by: Xinhe Gao

Urbanization’s big issue

Traffic congestion Energy consumption

Pollution

Solution?

Urban computing• Urban computing aims to tackle these issues by

using the data that has been generated in cities • e.g. traffic flow, human mobility, and geographical

data. • The big data implies rich knowledge about a city and

can help tackle these challenges when used properly.

Questions

How many liters of gas have been consumed by the vehicles, in the entire city, in the past one hour?

What is the volume of PM2.5 that has been generated accordingly?

Download data here

Jingbo Shang, Yu Zheng, et al. Inferring Gas Consumption and Pollution Emission of Vehicles throughout a City. KDD 2014.

Why is the answer important?

• Save energy • Suggest cost-efficient driving routes

• Identify road segment at any time

• Protect the environment• Enable pollution alerts

• Help diagnose the root cause of air pollution.

Goals• Estimate the gas consumption and vehicle emissions

– on arbitrary road segment

– at any time intervals

– using GPS trajectories of a sample of vehicles

0

150

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National Holiday

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Jingbo Shang, Yu Zheng, et al. Inferring Gas Consumption and Pollution Emission of Vehicles throughout a City. KDD 2014.

An Idea Solution

• Install sensors in each vehicle– Sensor monitoring gas consumption

– Sensor monitoring pollution emission

– GPS logger tracking the location

– Wireless communication module

• Mission impossible– Costly and obtrusive

– Privacy

impossible

A Three-Step Approach

(1) Travel Speed Estimate (TSE) on each road segment

(2) Traffic Volume Inference (TVI) for each road segment

(3) Calculate the gas consumption and emission of vehicles

Jingbo Shang, Yu Zheng, et al. Inferring Gas Consumption and Pollution Emission of Vehicles throughout a City. KDD 2014.

Sparse Trajectories Travel Speed

Gas Consumption & Vehicle Emission

(1)

TSE

Traffic Volume

(2)

TVI

GPS-equipped taxis are mobile sensors

System Overview

Trajectory mapping

System Overview

Context extraction

Including the road length, road level (highway or street), road direction (one way or

bi-direction), the number of lanes, the number of connections, and a tortuosity 𝜏

Schools, Companies & Offices, Banks & ATMs, Malls &

Shopping(1), Restaurants(1), Gas stations & Vehicle

services, Parking(1), Hotels, Residences, Transportation(2),

and Entertainment & Living Services.

(0,0,1,1,0,1,0,0,2,0)

Indicates which part of the city a road segment falls in.

Context extraction

fine-grained traffic pattern coarse-grained traffic pattern

r1

d1 r2

r3

r4

r6

r5

r1

r2

rn

f1 f2 fk

Z

fr fp fg

Z

• X: denotes fine-grained traffic conditions

• Y: denotes coarse-grained traffic conditions

• Z: geographical contexts of road segments

𝐿 𝑇, 𝑅, 𝐺, 𝐹 =1

2|𝑌 − 𝑇 𝐺; 𝐺 𝑇| 2 +

𝜆1

2|𝑋 − 𝑇 𝑅; 𝑅 𝑇| 2 +

𝜆2

2|𝑍 − 𝑅𝐹𝑇| 2 +

𝜆3

2( |𝑇 |2 + |𝑅| 2 + |𝐺 |2 + |𝐹 |2),

Travel Speed Estimation (TSE)

𝑌 ≈ 𝑇 × (𝐺; 𝐺)𝑇; 𝑋 ≈ 𝑇 × 𝑅;𝑅 𝑇; 𝑍 ≈ 𝑅 × 𝐹𝑇XY

r2

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Tr2 Tr3

r1: (v1, v2, v4) r2: (v4, v5, v6) r3: (v5, v7) r4: (v2, v3)

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System Overview

Traffic Volume Inference (TVI)

• Objective– Given the travel speed of a road segment and other observations

– Infer the volume of traffic on the road segment

• Traffic volume depends on– the current travel speeds and density of vehicles

– the length, shape, and capacity of a road

– weather conditions

• Unsupervised learning approach– Not enough training data

– Biased distribution between taxis and other vehicles

fp

fg

t Nt

ɵ Na

v

dv

fr

w

Np α

Traffic Volume Inference (TVI)

• 𝑁𝑎: the number of vehicles per minute per lane

– The volume of observed sample vehicles 𝑁𝑡– Weather conditions 𝑤

– Time of day 𝑡

• ҧ𝑣 the average travel speed

• 𝑑𝑣 The variance of the speed

• The type of the road 𝜃

– Road network features 𝑓𝑟

– Global position feature 𝑓𝑔

• surrounding POIs 𝛼

– Distribution of POIs 𝑓𝑝

– total number of POIs 𝑁𝑝

fp

fg

t Nt

ɵ Na

v

dv

fr

w

Np α

Gray node: hidden variable White nodes: observations

Traffic Volume Inference (TVI)

• EM algorithm

• Discretization of traffic volume– Recorded 358 video clips on 50 road segments at different time of day

– Manually count the traffic volume

– Fit a curve that reveals the distribution of the traffic volumes (for different levels)

– find a group of splitting points 𝑚0, 𝑚1, ⋯ ,𝑚5 that divide the traffic volume into five categories (𝑐1,𝑐2,…,𝑐5),

Discretization

0 10 20 30 400

5

10

# o

bse

rvatio

ns

#veh/min/lane

True Traffic Volumes

Normal Distributionµ = 21.61

σ= 6.38

0 10 20 30 400

10

20

30

40

# o

bse

rvatio

ns

#veh/min/lane

True Traffic Volumes

Normal Distribution

µ = 5.44

σ= 3.13

0 10 20 30 400

5

10

15

# o

bse

rvatio

ns

#veh/min/lane

True Traffic Volumes

Normal Distribution

µ = 2.95

σ= 2.38

A) Level 0-1 (highways)

B) Level 2 (main roads)

m1 m2 m3 m4m5 m 0

C) Level 3 (small streets)

Normal distribution:

Inferred category:

Volume distribution:

Inferred probability:

The total traffic volume on a road segment equals

System Overview

Energy and Emission Calculation

a b c d eCO 71.7 35.4 11.4 -0.248 0

Hydrocarbon5.57×10−2

3.65×10−2

-1.1× 10−3-1.88×10−4

1.25×10−5

Nox9.29×10−2

-1.22×10−2

-1.49×10−3

3.97× 10−56.53×10−6

Fuel Consumption

217 9.6× 10−2 0.253-4.21×10−4

9.65×10−3

𝐸𝐹 = (𝑎 + 𝑐𝑣 + 𝑒𝑣2)/(1 + 𝑏𝑣 + 𝑑𝑣2).

𝐸 = 𝐸𝐹 × 𝑟. 𝑁𝑎 × 𝑟. 𝑛 × 𝑟. 𝑙𝑒𝑛

Average vehicle capacity: 1.4-2.0L

Adopt environmental theories: European-3 standards

Experiments• Road networks:

– 148,110 nodes and 196,307 edges

– total length of road segments: 21,895km

• POIs:

– 273,165 POIs

– classified into 195 tier two categories

– choose the top 10 most frequent categories nearby road segments

• GPS Trajectories:

– generated by 33,000 Beijing taxis over a period of 47 days

– 673,469,757 GPS points

– total length is over 26,218,407km

– average sampling rate is 96 seconds per point

Baselines

• KNN• Use average speed and traffic volume of nearest 3

neighbors to represent a road segment’s values.

• Kriging• Spatial prediction method

• With best linear unbiased estimator

Baseline and Ground truth

• Ground Truth• When evaluating TSE, randomly remove 30% of the non-zero entries from

M’r’s last row

• Predict the values using different methods.

•

Time 7:00 ~ 10:00 10:00~16:00 16:00~20:00 after 20:00 total

Level 0,1 2 3 0,1 2 3 0,1 2 3 0,1 2 3Holiday 0 0 0 6 14 4 6 8 1 4 6 0 49

Workday 7 28 8 29 74 9 28 92 7 6 17 4 309Total 43 136 142 37 358

The number of taxi of different time slot in different day

Evaluation of TSE’s effectiveness

Experiments

Methods RMSE of ഥ𝒗 RMSE of 𝒅𝒗 Time (sec)

MF(𝑀′𝑟) 2.172 1.833 2.2

MF(𝑀′𝑟 + 𝑍) 1.939 1.385 18.2

MF(𝑀′𝑟 +𝑀′𝑔 + 𝑍) 1.908 1.314 20.2

TSE 1.369 1.035 22.2

Kriging 2.340 1.300 1,000

KNN 3.360 1.590 0.14

• Evaluation on TSE

RMSE level 0-1 level 2 level 3 level >=4Speed 2.575 1.189 0.977 1.612

Variance 1.526 1.128 0.628 1.234

Download data here

Experiments

Methods MAE MRE Inference time (us/road)TVI 3.01 29% 7.27 TVI w/o 𝑑𝑣 3.19 31% 7.18TVI w/o 𝑤 3.15 29% 7.10LR 3.06 27% 0.15FD-SC 3.9 42% 0.13

• Evaluation on TVI

level 0-1 level 2 Weekday WeekendMAE 5.55 2.23 2.97 3.28

Download data here

Time 7:00 ~ 10:00 10:00~16:00 16:00~20:00 after 20:00 total

Level 0,1 2 3 0,1 2 3 0,1 2 3 0,1 2 3Holiday 0 0 0 6 14 4 6 8 1 4 6 0 49

Workday 7 28 8 29 74 9 28 92 7 6 17 4 309Total 43 136 142 37 358

Experiments

Online components Time Offline components Time

Map-matching 4.94min Geo-feature extraction 149s

TSE 22.2s Historical pattern extraction 240s

TVI (inference) 0.84s TVI learning 89s

Total 5.32min Total 478s

• Efficiency

6 8 10 12 14 16 18 20 22 24

3700K

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Gas consumption NOx emission

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Conclusion

• We can infer the citywide gas consumption and emission– Using a sample of vehicles

– in 8 minutes on a single machine

– With 70% accurate.

• Deal with data sparsity and data bias– Speed can be propagated while the volume cannot

– Unsupervised learning

– From a little labeled data distributions discretization

Q & A