Mining Regular Routes from GPS Data for Ridesharing Recommendations

Mining Regular Routes from GPS Data for Ridesharing Recommendations Wen He, Deyi Li, Tianlei Zhang, Lifeng An, Mu Guo, and Guisheng Chen Tsinghua University Chinese Institute of Electronic System Engineering August 12, 2012

Regular Route • A regular route is a complete route which often happen at a similartime • Commute route • pick up children each day • …

Outline • Introduction • Architecture • Details of solution • Experiments results • Conclusion

Background • Traffic congestion has become a worldwide problem • Low vehicle occupancy • RideSharing becomes an attractive way to relieve traffic pressure

Challenges in Ridesharing • Complexity • “Stranger danger” • Reliability rider driver

Our WorkMining Regular Routes from GPS Data for Ridesharing Recommendations Common method • Complexity • “Stranger danger” • Reliability Our method • Automatic matching • Traveled regularly for a period of time • More information from GPS logs Vs.

Challenges • Uncertainty in time property • Start at different time • Complexity in traffic condition • Multiple transportation mode • Private driving • Public transportation • Uncertainty in route sequence • GPS signal drift • Obstacle in the road

Architecture User- based Database-based

Routes Processing Tthresh ta tb Stay region tc td Route1 Route2 Route3 A fragment of GPS Log

Routes Grouping One user’s routes during one month

Frequent Directed Edges (FDE) Finding DE.fre> fthreh FDE

One simple example --- FDEs finding AM->AN 2 AM->BM 1 AN->BN 3 BM->CM 2 BN->CN 3 CM->DM 2 CN->CO 1 CN->DN 2 CO->CP 3 CP->DP 3 DM->EM 2 DN->DO 2 …

Regular Routes Finding Route: If most of its DEs are FDEs  a candidate of an RR fc(R) = m/n n：number of DEs in R m：number of FDEs in R FDE: If most of its support routes are candidate routes  part of an RR (RFDE) Regular route: a link of RFDEs

Mining Travel Modes of Regular Routes • Feature of Fixed Stop Rate (FSR) • Stop rate: number of points with low velocity [Zheng,Ubicomp 2008] ( accuracy: 0.6) • Stop region: a user usually passed this region with a low velocity • Fixed stop rate: the number of stop regions in a route

Ridesharing recommendations

Experiments • Testing Data (from Geolife project*) • 178 users‘ real logs from 2007 to 2011 • 17+thousand trajectories • 48+ thousand hours • 1+ million kilometers • the majority of the data was created in Beijing, China. * http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx

Results on Regular route mining

All regular routes from Testing Data

Some results in ridesharing recommendations

Conclusion • A method for ridesharing recommendations • Finding more opportunities from GPS data • Giving more reliability of the ride • Providing more information about the riders and the routes • An algorithm for Mining regular routes • Distinguishing regular routes from frequent routes • Calculating the similarity of a group of routes • A feature for Distinguish private driving and public transportation

Thank you! Wen He he-w09@mails.tsinghua.edu.cn

Mining Regular Routes from GPS Data for Ridesharing Recommendations

Mining Regular Routes from GPS Data for Ridesharing Recommendations

Presentation Transcript

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Regression for Data Mining

Mining for ADE Data

Mining Functional Dependencies from Data

Obtaining Vessel Routes from VTOSS Data

DATA MINING Extracting Knowledge From Data

Data Mining: Discovering Information From Bio-Data

Mining Gold from Data

Mining Regular Route from GPS Data for Ridesharing Recommendation

Dendrograms for Data Mining

Exact data mining from in-exact data

Data Mining: Extracting Knowledge from Past Data

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Collaborative Location and Activity Recommendations with GPS History Data

Data Mining for Data Streams

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