University of Illinois, Chicago 1

Transportation mode detection using mobile phones and GIS

information

Leon Stenneth, Ouri Wolfson, Philip Yu, Bo Xu

University of Illinois, Chicago 2

Problem

• Detecting a mobile user’s current mode of transportation based on GPS and GIS.

• Possible transportation modes considered are:

University of Illinois, Chicago 3

Technique

• A supervised machine learning model

• New classification features derived by combining GPS with GIS

• Trained multiple models with these extracted features and labeled data.

University of Illinois, Chicago 4

Motivation

• Value added services to context detection systems

• More customized advertisements can be sent

• Providing more accurate travel demand surveys instead of people manually recording trips and transfers

• Determining a traveler’s carbon footprint.

University of Illinois, Chicago 5

Approach

• In addition to traditional features on speed, acceleration, and heading change. We build classification features using GPS and GIS data

Mobile Phone’s GPS sensor report

Bus stop spatial data

Rail line spatial data

Real time bus locations

Training example

University of Illinois, Chicago 6

Features

• Traditional – Speed, acceleration, and heading change

• Combining GPS and GIS– Rail line closeness– Average bus closeness– Candidate bus closeness– Bus stop closeness rate

University of Illinois, Chicago 7

Rail line closeness

• ARLC - average rail line closeness• Let {p1, p2, p3, p4…pn} be a finite the set of GPS

reports submitted within a time window. ARLC = ∑i=1 to n di

rail / n

University of Illinois, Chicago 8

Average bus closeness (ABC)

• Let {p1, p2, p3, p4…pn} be a finite the set of GPS reports submitted within a time window.

ABC = (∑i=1 to n dibus) / n

University of Illinois, Chicago 9

Candidate Bus closeness (CBC)

• dj.tbus 1≤j≤m - Euclidian distance to each bus busj

• Dj - total Euclidian distance to bus j over all reports submitted in the time window

Dj = ∑t=1 to n dj.tbus 1≤j≤m

• Given Dj for all the m buses, we compute CBC as follows.

CBC = min (Dj) 1≤j≤m

University of Illinois, Chicago 10

Bus stop closeness rate (BSCR)

• | PS | is the number of GPS reports who's Euclidian distance to the closest bus stop is less than the threshold

BSCR = | PS | / window size

0 50 100 150 200 250 300 350 400 450 5000

20

40

60

80

100

120

140

GPS sensor report number

Eucl

ilidi

an d

ista

nce

from

clo

sest

bus

stop

(m

)

University of Illinois, Chicago 11

Machine learning models

• We compared five different models then choose the most effective– Random Forest (RF)– Decision trees (DT)– Neural networks (MLP)– Naïve Bayes (NB)– Bayesian Network (BN)

• WEKA machine learning toolkit

University of Illinois, Chicago 12

Results

• Random Forest was the most effective model• Precision and recall accuracy of Random

forest shown below

train bus

stationary walk car bike

avera

ge0

10

20

30

40

50

60

70

80

90

100

Traditional features onlyTraditional and GIS fea-tures

mode

prec

ision

train bus

stationary walk car bike

avera

ge0

10

20

30

40

50

60

70

80

90

100

Traditional features onlyTraditional and GIS fea-tures

mode

reca

ll

University of Illinois, Chicago 13

Feature Ranking

• Below we rank the features to determine the most effective.

University of Illinois, Chicago 14

Results

• Using the top ranked features only• Precision and recall accuracy is shown below

train bus

stationary walk car bike

avera

ge0

10

20

30

40

50

60

70

80

90

100

Top ranked features only

mode

prec

ision

train bus

stationary walk car bike

avera

ge0

10

20

30

40

50

60

70

80

90

100

Top ranked features only

mode

reca

ll

University of Illinois, Chicago 15

Deployed System

• We can provide further information (i.e. route, bus id) on the particular bus one is riding.

University of Illinois, Chicago 16

Related work with GPS

• Liao et. al (2004) – consider the user’s history such as where one parked.

• Zheng et. al (2008) – Robust set of features and a change point segmentation method.

• Reddy et. al (2010) – Combined accelerometer and GPS to achieve high accuracy.

University of Illinois, Chicago 17

Conclusion

• Using GIS data improves transportation mode detection accuracy.

• This improvement is more noticeable for motorized transportation modes.

• Only a subset of our initial set of features are needed.

• Random forest is the most effective model• We can provide further information about the

bus that a user is riding

University of Illinois, Chicago 18