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Mining Regular Route from GPS Data for Ridesharing Recommendation

Mining Regular Route from GPS Data for Ridesharing Recommendation. 2012.09.19. Author. Wen He Tsinhua University, Beijing, China and Xi'an Communication Institute, Xi'an, China Deyi Li Tsinhua University, Beijing, China and Chinese Institute of Electronic System Engineering, Beijing, China

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Mining Regular Route from GPS Data for Ridesharing Recommendation

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  1. Mining Regular Route from GPS Data for Ridesharing Recommendation 2012.09.19

  2. Author • Wen He TsinhuaUniversity, Beijing, China and Xi'an Communication Institute, Xi'an, China • DeyiLi TsinhuaUniversity, Beijing, China and Chinese Institute of Electronic System Engineering, Beijing, China • TianleiZhang TsinhuaUniversity, Beijing, China • LifengAn TsinhuaUniversity, Beijing, China • Mu GuoTsinhuaUniversity, Beijing, China • GuishengChenChinese Institute of Electronic System Engineering, Beijing, China

  3. Outline • Introduction • Related Work • Architecture • The mining of regular routing • Ridesharing recommendations • Experiment discussion • Conclusion

  4. Introduction • Why do this research • Improve traffic problem in Beijing, China. • Current ridesharing is still short supplies due to • Journey reliability. • Ridesharing less desirable • Current situation: More people are likely to record daily trajectory. • Current challenge: Regular Route(RP) is difficult recognized from frequent route.

  5. Contribution • Made a ridesharing recommendations according to a group of user’s regular routes(RP). • Propose a method to split the mixed user trajectories into each individual route. • Propose a frequency-based regular routes mining method to infer user’s RPs. • Improve the accuracy in distinguishing travel modes between public transports and private driving • Evaluate a method using a large GPS dataset which is provided by GeoLife. This dataset contains 178 realistic user GPS trajectories over a period of four years.

  6. Related Work(1/2) • Past Ridesharing Recommendation • A location-based cab-sharing service to help reduce cab fare costs and effectively utilize available cabs • Improve the quality of ridesharing by increasing the driver’s income • Multiple riders was proposed based on the Bee Colony Optimization Metaheuristicmethod

  7. Related Work(2/2) • Mining Route History • not only to driver users, but also to users who take public transport as one of their common travel modes. • Our sharable routes are not directly generated based on one day’s trajectory log. • flexible time interval • it’s difficult to find two cars that are always keep synchronized, even they started at the same time and were running on the same road. • The difference between a personal route and a regular route(RP) is that, a personal route does not consider the time factor, and is not a complete route.

  8. Architecture • Three components: • Routes Processing • Regular Routes Mining • Ridesharing Recommendation User-based components only need to be preformed once while a user submitting his/her logs to the system.

  9. Routes Processing • Stay Regions Subtracting • A stay regions is definitely not a part of a regular • Grids Mapping • combine the time information with grids, and a series of temporal grids is built. • Routes Splitting • segment the trajectory into each individual route.

  10. Regular Routes Mining • Routes Grouping • group the routes which happened at similar times of a day together. • Regular Routes Finding: • a frequency-based regular routes mining algorithm is proposed. • Travel Modes Recognizing • A feature of fixed stop rate (FSR) is used to recognize the different travel modes of an RR

  11. Ridesharing Recommendations • Grid-based Routes Table Building • For a regular route generated by public transport, we only record it at the starting and ending grids. • Routes Matching With the grid-based • Search two routes which appeared in pairs and also have similar time properties.

  12. THE MINING OF REGULAR ROUTES • Routes Processing • two cases that a sequence of GPS points should be split: • user may arrive at his destination, and when he left, a new route will begin. • GPS device was shut down or lost satellite signal over a certain time.

  13. Routes Processing Steps(1/3) • Stay Region Subtracting • Pm is just the ending point of the route which enters into a stay region • Pn+1 is the starting point when user departs from the stay region. • we do not denote this region by a single point, but by a pair of indicators (Pm ,Pn), where Pm and Pn are the beginning and ending points of the stay region.

  14. Routes Processing Steps(2/3) • Grids Mapping

  15. Routes Processing Steps(3/3) • Routes Splitting

  16. Regular Routes Mining(1/2) • An RR is a complete route where a user frequently passed through in approximately the same time of day. • The first is , which is used to decide the frequency of a route, • and the second is , which is used to decide a similar time.

  17. Regular Routes Mining(2/2) • Routes Grouping • Therefore it's difficult to extract RRs from all routes directly • But an RR should always happen at a similar time of day. • group routes not only based on the time of day but also the day of the week

  18. Regular routes Finding(1/7) • After grids mapping, the trajectories are formed as R1, R2 and R3 in Figure4 (b).

  19. Regular routes Finding(2/7) • We say a DE is a FDE if DE.num is larger than threshold of . • RR is a route which is frequently visited by a couple of complete routes, but not some parts of a route. This means we should not directly use FDEs to represent an RR

  20. Regular routes Finding(3/7) • In a set of t-Routes, FDEs may exist without an RR. • But if there is an RR, the RR will have large common parts with FDE

  21. Regular routes Finding(4/7) • A frequency-based regular route mining method • 1. Calculate frequent coefficient (FC) of each route • The frequent coefficient is defined as fc(R) =m/n, where n is the number of DEs in the route R, m is the number of FDEs in the route R. • 2. Find frequent routes • A route with fc(R)> fcthreh will be deemed as a frequent route. • 3. Calculate regular coefficient (RC) of each FDE

  22. Regular routes Finding(5/7) • A frequency-based regular route mining method • 4. Find Regular FDEs (RFDE) • 5. Use RFDEs instead of FDEs to repeat step 2 to 4.

  23. Regular routes Finding(6/7) • in Figure5 (a), both R2 and R4 passed the DE (JS->JT), but since R4 is not a frequent route, it has no contribution to an RR, DE(JS->JT) cannot be an RFDE.

  24. Regular routes Finding(7/7) • Add time property (ts, td) for each RR, where ts and td denote the start and the duration time of the route respectively where n is the number of the support routes of an RR.

  25. Travel Modes Recogning(1/3) • According to make a recommendation for ridesharing, there are two transport modes: • public transport • private driving.

  26. Travel Modes Recogning(2/3) • distinguish different transport modes • Which one is public transport • frequently at fixed regions like bus stops or subway stations • Then an RFDE with SP lower than is a stop region.

  27. Travel Modes Recogning(3/3) • Fixed Stop rate(FSR) • the number of stop regions within a certain distance

  28. RIDESHARING RECOMMENDATIONS • Grid-based Routes Table Building • if it is generatedby public transportation, it will only be recorded in its origin anddestination grids.

  29. RIDESHARING RECOMMENDATIONS • Routes Matching • Two kind of car sharing • Public transportation • Private driving • if a query route is generated by public transport, only routes by driving modes could be recommended

  30. RIDESHARING RECOMMENDATIONS • Flow chart if the process

  31. EXPERIMENTS DISCUSSION • This dataset is consisted of 178 users' realistic trips over a period of 4 years (from 2007 to 2011). • Most of the time, we see all mined RRs as different users’ RRs

  32. Experiment Result • Influence of grid size • Smaller the grid size, the larger the storage space is needed • Too larger a grid size will lost some details • 10 sec as final grid size in our experiment

  33. Experiment Result • 3 routes are support routes of RR from 9 routes in similar time. • Robust to slight disturbance Compare with ANTrip trajectory

  34. Experiment Result • the trajectories are generated by bus • Only two one RR for the user (according to three bus routes(a))

  35. Experiment Result • are too short to make a ridesharing • RR are dense in north of Beijing in Microsoft Research

  36. Experiment Result • using FSR to distinguish traffic modes between public transportation and driving. • the accuracy could reach 0.876

  37. Experiment Result • Routes matching. • (a) and (c) are public transportation • (b) and (d) are driving

  38. Experiment Result • The storage requirement of the proposed method. • The first row is the number of records • The second row is the storage ration between the numbers of the original dataset. • The storage requirement is quite lightweight

  39. Conclusion • A frequency-based regular route mining algorithm is proposed • each part of a regular route must be visited frequently. • a regular route should be frequently visited by some complete routes called support routes. • most parts of a support route must pass through the frequently visited regions • identified to distinguish travel modes between public transportation and individual driving • Valuated on a real-world GPS dataset, which is consisted of 178 users over a period of 4 years

  40. Futrue work • More flexible ridesharing strategies will be considered in our future work. • find a route which reaches at his/her nearest subway station.

  41. BACKUP Presented by Ivan Chiou

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