Estimating Activity-Travel Patterns from Cellular Network Data

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Presentation transcript:

Estimating Activity-Travel Patterns from Cellular Network Data 15th TRB Transportation Planning Applications Conference Estimating Activity-Travel Patterns from Cellular Network Data Luning Zhang and Jing Dong, Iowa State University Xuesong Zhou, Arizona State University May 18, 2015, Atlantic City, NJ

Objective Estimate the most likely activity patterns on the transportation network using cellphone data home activity duration major office/work activity duration frequently-visited other locations rough estimate of departure and arrival times

Data Description Cellphone use data Travel diaries Phone locations are based on cell towers Travel diaries

Challenges - Inaccuracy Signal strength varies: Network Coverage Network Congestion Obstruction Hardware and software Signal fluctuation happens may occur due to several factors: Radio interference Reflective/absorbent surfaces Ref: http://wrongfulconvictionsblog.org/2012/06/01/cell-tower-triangulation-how-it-works/

Challenges - Multiple mapping Cell location area may include multiple territorial function commercial area residential area working area

Data Preparation Travel diary data are recorded at one-hour increment The travel day is segmented in 24 hours

Methodology – HMM Hidden Markov model x — states (real activity) y — observations (cell location) a — state transition probabilities b — emission probabilities Ref: http://en.wikipedia.org/wiki/Hidden_Markov_model#/media/File:HiddenMarkovModel.svg

Methodology – Baum-Welch Alg. Given a new cell location sequence, Baum-Welch algorithm can adjust model parameters to maximize the probability of the sequence given the model Ref: http://www.shokhirev.com/nikolai/abc/alg/hmm/hmm.html

Methodology – Viterbi Algorithm Given a new sequence of cell location, Viterbi algorithm can find the most likely sequence of activities Ref: http://spectrum.ieee.org/geek-life/profiles/2010-medal-of-honor-winner-andrew-j-viterbi

Result Example of transition matrix 7:00~8:00 and 17:00~18:00 17:00 -> 18:00 Home Office Lab Gym Other 0.5 1 0.2 0.4 7:00 -> 8:00 Home Lab Other 0.78 0.22 0.00 1.00 0.50 Example of transition matrix 7:00~8:00 and 17:00~18:00

Conclusion Cellphone data can be used to supplement traditional travel survey data Cell location inaccuracy and multiple mapping issues are addressed using Hidden Markov model Activity patterns can be estimated using Baum-Welch algorithm