Transportation mode detection using mobile phones and GIS information Leon Stenneth, Ouri Wolfson, Philip Yu, Bo Xu 1University of Illinois, Chicago

Problem Detecting a mobile user’s current mode of transportation based on GPS and GIS. Possible transportation modes considered are: 2University of Illinois, Chicago

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. 3University of Illinois, Chicago

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. 4University of Illinois, Chicago

Approach In addition to traditional features on speed, acceleration, and heading change. We build classification features using GPS and GIS data 5University of Illinois, Chicago

Features Traditional – Speed, acceleration, and heading change Combining GPS and GIS – Rail line closeness – Average bus closeness – Candidate bus closeness – Bus stop closeness rate 6University of Illinois, Chicago

Rail line closeness ARLC - average rail line closeness Let {p 1, p 2, p 3, p 4 …p n } be a finite the set of GPS reports submitted within a time window. ARLC = ∑ i=1 to n d i rail / n 7University of Illinois, Chicago

Average bus closeness (ABC) Let {p 1, p 2, p 3, p 4 …p n } be a finite the set of GPS reports submitted within a time window. ABC = (∑ i=1 to n d i bus ) / n 8University of Illinois, Chicago

Candidate Bus closeness (CBC) d j.t bus 1≤j≤m - Euclidian distance to each bus bus j D j - total Euclidian distance to bus j over all reports submitted in the time window D j = ∑ t=1 to n d j.t bus 1≤j≤m Given D j for all the m buses, we compute CBC as follows. CBC = min (D j ) 1≤j≤m 9University of Illinois, Chicago

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 10University of Illinois, Chicago

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 11University of Illinois, Chicago

Results Random Forest was the most effective model Precision and recall accuracy of Random forest shown below 12University of Illinois, Chicago

Feature Ranking Below we rank the features to determine the most effective. 13University of Illinois, Chicago

Results Using the top ranked features only Precision and recall accuracy is shown below 14University of Illinois, Chicago

Deployed System We can provide further information (i.e. route, bus id) on the particular bus one is riding. 15University of Illinois, Chicago

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, Chicago16

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 17University of Illinois, Chicago

18University of Illinois, Chicago