1. An Enhanced Framework for Link and Mode Identifications for GPS-Based Personal Travel Surveys Amy Tsui and Amer Shalaby University of Toronto June 13, 2005 For PROCESSUS COLLOQUIUM

2. Presentation Outline Background Objectives Tools and Methods Structure of Analysis System Version 1: GPS-Alone System Version 2: GPS/GIS Integrated System Results of Test Summary and Conclusion

3. Background Transportation planning models Forecast and evaluate transportation scenarios Require good quality of travel survey data Conventional self-reporting survey method The most popular survey method Lack of reporting short trips and actual routes traveled Poor data quality due to depending on participant’s report The amount of detail that it is feasible to ask individuals and households to report is restricted

4. Background A Vision for New Technology in Travel Survey GPS technology: Accuracy in collecting travel survey information Less burden on survey participants Large amount of GPS data requires processing tools & analysis methods to maximize benefits

5. Application scenario of the GPS-based Survey Analysis Tools automatically extract trip data of interest from GPS trace data, such as traveled link, used mode and activity However, there are limitations to identify all trip data only from GPS trace We need additional prompted recall survey. Trip data is collected by participants using GPS GPS data is processed by analysis tools Prompted recall survey to verify results from analysis tools GPS data is sent to central server at the end of day

6. Objectives Develop an integrated GPS-GIS analysis tool to automate GPS-based personal travel survey. The developed tool can automatically identify traveled road links and used modes based on GPS data. Two versions GPS-alone framework GPS/GIS integrated framework Trip data is collected by participants using GPS GPS data is processed by analysis tools Prompted recall survey to verify results from analysis tools GPS data is sent to central server at the end of day

7. Tools and Methods GPS unit: GeoStats Wearable Geologger Stores one-day GPS trace data of a survey participant Software: Visual Basics 6.0 ArcGIS: ArcMap, ArcObject NEFCLASS-J: a neuro-fuzzy classifying software Methodological Approach: Fuzzy Logic [GeoStats, 2003]

8. Structure of Analysis System GPS data filtering Activity/Trip Identification Mode Segment Identification Fuzzy Logic Mode Identification One day continuous GPS travel data Ver. 1: GPS-Alone System Module 1 Module 2 Module 4 Module 3 GIS Mode ID Treatment of Link Matching Failure Underground/Indoor Travel Detection Off-road Travel Detection Ver. 2: GPS-GIS Integrated System Module 1 Module 2 Module 4 Module 3 Treatment of Warm/Cold Starting Problems Module 5 Link Matching Algorithm Output Of Ver-1

9. Structure of Analysis System GPS data filtering Activity/Trip Identification Mode Segment Identification Fuzzy Logic Mode Identification One day continuous GPS travel data Ver. 1: GPS-Alone System Module 1 Module 2 Module 4 Module 3 GIS Mode ID Treatment of Link Matching Failure Underground/Indoor Travel Detection Off-road Travel Detection Ver. 2: GPS-GIS Integrated System Module 1 Module 2 Module 4 Module 3 Treatment of Warm/Cold Starting Problems Module 5 Link Matching Algorithm Version-1: GPS-Alone System - It only uses one-day GPS data as input. - It is independent of GIS S/W and map (expensive). That is, it is low-cost analysis system. - Results are identifications of activities and used modes from one-day GPS travel data. Version-1: GPS-Alone System - It only uses one-day GPS data as input. - It is independent of GIS S/W and map (expensive). That is, it is low-cost analysis system. - Results are identifications of activities and used modes from one-day GPS travel data. Output Of Ver-1

10. Structure of Analysis System GPS data filtering Activity/Trip Identification Mode Segment Identification Fuzzy Logic Mode Identification One day continuous GPS travel data Ver. 1: GPS-Alone System Module 1 Module 2 Module 4 Module 3 GIS Mode ID Treatment of Link Matching Failure Underground/Indoor Travel Detection Off-road Travel Detection Ver. 2: GPS-GIS Integrated System Module 1 Module 2 Module 4 Module 3 Treatment of Warm/Cold Starting Problems Module 5 Link Matching Algorithm Output Of Ver-1 Ver.2: GPS-GIS integrated system - Results of Ver-1 are used as input of Ver-2. - Ver-2 refines the results of Ver-1, and additionally identifies traveled link utilizing GIS Map data. - Results are: * refined identifications of activities and used modes, and * identification of traveled links from one-day GPS trace data. Ver.2: GPS-GIS integrated system - Results of Ver-1 are used as input of Ver-2. - Ver-2 refines the results of Ver-1, and additionally identifies traveled link utilizing GIS Map data. - Results are: * refined identifications of activities and used modes, and * identification of traveled links from one-day GPS trace data.

11. Version 1 – Flow Chart GPS data filtering Activity/Trip Identification Mode Segment Identification Fuzzy Logic Mode Identification One-day raw GPS travel data Valid GPS data Each trip is separated into mode segments Individual mode segment with the likelihood of being a certain mode Version 1: GPS-alone System GPS data is separated in activities and trips OUT IN Module 1Module 2 Module 4Module 3

12. Module 1: Data Filtering Eliminate invalid GPS data Based on GPS data filters provided by Laval University (project partner of this research) Additional filtering rules to eliminate possible noised GPS points, such as: Low no. of Satellites (<= 3) High HDOP (> 5) Sudden jump points (Typical noised output of GPS data in urban canyon area)

13. Module 2: Activity Identification By identifying activities of a GPS trace data, we can separate the GPS data into trip and activity segments. Definition of activity Activity is basically defined by dwell time (120 sec). Two categories of activities according to GPS signal availability: Outdoor activity and Indoor activity Outdoor Activity (No signal loss during Activity) If Zero speed GPS trace (dwell time) > 120sec, the set of points are considered as activities. * The rule may also capture “waiting bus” or “delay by traffic congestion” as outdoor activities.

14. Module 2: Activity Identification Definition of activity Outdoor activity and Indoor activity Indoor Activity (Signal loss during Activity) Indoor activity is sub-divided into: Short duration indoor activity Underground/indoor activity** Long duration indoor activity These three sub-activities are re-categorized by the duration time of the signal loss (dwell time), and distance of the gap by the signal loss. ** Feature of Underground/indoor activity is very similar to that of subway trip. Underground/indoor activities can include subway trip. Possible errors to detect activities: the activities are defined based on movement patterns of GPS trace

15. Module 3: Mode Segment Identification Multi-modal trip combines properties of different travel modes in one GPS trip segment Goal: Divide a trip segment into mode segment such that each segment travel on one mode Mode segments will be separated by points called “Mode Transfer Point” (MTP) Two-Step Procedure to detect MTP (Rule based) Step 1) Search All Potential MTP’s Step 2) MTP Selection MTP GPS data in Trip Segment GPS data in Mode Segment Trip seg. Mode seg.

16. Module 4: Fuzzy Logic Mode Identification Fuzzy expert system: Expert system + Fuzzy Expert system solves a given problem using stored knowledge of a expert (knowledge-base, set of rules for inference) Fuzzy provides a way to express the linguistic variable in numerical way using membership function (defining possibility) Ex) Height of a man = 1.85m. His height is medium or tall?? Fuzzy gives flexibility to traditional expert system. ShortMediumTall 1.91.51.7 Height (m) Membership 1.0 0 h = 1.85m Tall: 80% possibility Medium:20%

17. Module 4: Fuzzy Logic Mode Identification Estimate used mode for each mode segment using fuzzy logic Input variables: 95 percentile value of GPS speed data Mean value of GPS speed data Median value of acceleration profile from GPS speed data Data quality (Total Valid Records / Total Records) Mode classifications (output) walk, cycle, bus, auto Membership functions (fuzzifying the input variables) Triangular membership functions Calibrate the membership functions using NEFCLASS-J S/W Inference Rule: 14 decision rules

18. Module 4: (Cont…) Membership Functions: LowMediumHigh 28.09.020.0 Average Speed (kph) Mem 1.0 0.0 6.020.030.0 25.0 LowMediumHigh 30.17.710.0 95 Percentile Speed (kph) Mem 1.0 0.0 7.023.634.9 25.6 Note: the parameters (shapes) of membership functions are calibrated by the NEFCLASS-J S/W using collected data sets for calibration.

19. Module 4: (Cont…) Membership Functions: LowMediumHigh 1.20.20.4 Median Acceleration (m/s 2 ) Mem 1.0 0.0 0.20.4 1.3 0.7 Bad Good 0.4 Data Quality (Total Valid Records / Total Records ) Mem 1.0 0.0 0.40.6

20. Module 4: (Cont…) Used Rules for the Fuzzy Expert System: Total 14 Rule #1) If 95 percentile speed is low, and median acceleration is low, then used mode is Walk. … Rule #6) If 95 percentile speed is medium, average speed is medium and median acceleration is medium, then used mode is Bus. … Rule #12) If 95 percentile speed is high, average speed is high and median acceleration is high, then used mode is Auto. …

21. Version 2 GPS/GIS Integrated System Ver. 1: GPS-alone System Link Matching Algorithm Interactive Link Matching-Mode Identification Subsystem Ver. 2: GPS/GIS Int. System GIS Mode ID Treatment of Link Matching Failure Underground/Indoor Travel Detection Off-road Travel Detection Module 1 Module 2 Module 4 Module 3 Treatment of Warm/Cold Starting Problems Module 5 * List of traveled links based on GPS data GIS Map * GPS Trace data * Results of Ver.1

22. Version 2 Link Matching Algorithm Find List of Traveled Road Links (on GIS road network layer) from GPS trace data collected by a survey participant. GIS road network layer GPS trace data Find List of Traveled Road Links

23. Version 2 Link Matching Algorithm Find List of Traveled Road Links (on GIS road network layer) from GPS trace data collected by a survey participant. Developed by Chung in 2003 at U of T It strictly depends on GPS trace data. Good Quality of GPS data  Good Results Poor Quality of GPS data  No results It will produce a lots of gaps for poor quality GPS data, because it does not have estimation logic for the gaps. The Module2 of the Version 2 will provide treatments to fill the gaps.

24. Version 2 GPS/GIS Integrated System Ver. 1: GPS-alone System Link Matching Algorithm Interactive Link Matching-Mode Identification Subsystem Ver. 2: GPS/GIS Int. System GIS Mode ID Treatment of Link Matching Failure Underground/Indoor Travel Detection Off-road Travel Detection Module 1 Module 2 Module 4 Module 3 Treatment of Warm/Cold Starting Problems Module 5 * List of traveled links based on GPS data GIS Map * GPS Trace data * Results of Ver.1 Refine results of Ver-1 and Link Matching Algorithm Refine results of Ver-1 and Link Matching Algorithm

25. Module 1: GIS Mode Identification Revise the identified mode of version-1 using transit route information on GIS data Differentiate transit modes from others based on availability of transit routes Rule-based (total 4 rules) Example of one rule used in the module Result of Ver-1 for a mode segment: “Used mode = Bus” “Module 1” check transit route availability, and If route is found, keep the result of V1. Used Mode=Bus If route is not found, revise the result of V1. Used Mode=Auto

26. Module 2: Treatment of Link Matching Failure Results of map matching algorithm is incomplete. Matching Failure for poor GPS signal, and Gaps b/w identified links. Beginning of Gap End of Gap Gap ???

27. Module 2: Treatment of Link Matching Failure Find (estimate or guess) set of links connecting beginning and end of gap which were most likely traveled by a survey participant. Beginning of Gap End of Gap

28. Module 2: Treatment of Link Matching Failure Find (estimate or guess) set of links connecting beginning and end of gap which were most likely traveled by a survey participant. Define various rules to fill the gaps i.e., The rules define how to fill the gap according to pre-defined gap patterns. This treatment may not estimate perfectly correct set of links connecting gap. We don’t have perfect GPS data for the gaps. This treatment is estimation, even though it tries to utilize the all given possible clues for better estimation.

29. Module 3: Underground/Indoors Travel Detection Underground/indoor activity in activity identification of Version-1 Although the underground/indoor activity is defined as activity in Ver-1, it can be subway trip. This Module of Version-2 tries to check if the activity is subway trip. This module is applied to segments categorized as undergrd/indoor activity in Ver-1. How?: check if there are subway stations around starting and ending points of this activity Yes  Update this activity to Subway trip

30. Module 4: Off-road Travel Detection Walk and cycle mode can have off-road travel Tag walk and cycle mode segments not having matched links as “off-road travel” Off-road travel

31. Module 5: Treatment of Warm/Cold Starting Problems GPS unit needs some time of satellite acquisition. No GPS data during the satellite acquisition Treatment by the Gap filling rules of Module 2 (Treatment of Link Matching Failure)

32. Test and Results Collected Data: Total 58 one-day trips in Toronto Area For Version1: 28 trips are used to calibrate the Fuzzy model Activity identification (Version1) All activities are detected. Results show some overestimation of activities for traffic congestion, long traffic signal… Mode identification (Version1 & Version2) Good detection rates Version1 (91%) and Version2 (94%): 3% Improvement Good detection rates for Auto and Walk Lowest detection rate for Bus Link identification (Version2) Good detection rate (94%) for link identification

33. Summary and Conclusion Develop two versions of GPS data analysis tools Version 1: Low cost, simple, location non-specified For user w/o GIS software & network Version 2: Visualization (e.g. travel routes on GIS map) For user w/ GIS software & transportation networks ALL activities were detected Over 90% detection rates for the two versions of mode identification 94% average detection rate for link identification

34. Summary and Conclusion Delivers a package for GPS multi-modal travel survey data analysis Provides usage flexibility and consistent results when combining the two versions of system Benefits Cost, time reduction Detailed and accurate travel info

35. ~ Thank You ~ Questions & Comments