Trip Reconstruction Tool for GPS- Based Personal Travel Surveys Eui-Hwan Chung

Introduction Transportation planning model Forecast and evaluate transportation scenarios Require good-quality travel survey data Conventional self-reporting survey method lack of reporting of short trips and actual routes traveled poor data quality of travel start and end times, total trip times and, location of destination the amount of detail that it is feasible to ask individuals and households to report is well below that needed for the activity-based micro-simulation models

Introduction Application of GPS for travel surveys As the GPS receiver is given to respondents, improve the quality of the collected data serve the convenience of both respondents and survey operators The benefit of GPS [Wolf, 2000] trip origin, destination, and route data are automatically collected without burden on the respondent routes are recorded for all trips allowing for the post- processing recovery of unreported or misreported trips accurate trip start and end times are automatically determined, as well as trip lengths the GPS data can be used to verify self-reported data.

Introduction Approaches for GPS Application Electronic Travel Diary (ETD) with GPS For each trip a respondent records the following information to ETD (Just replace paper) trip mode, vehicle identification, driver identification, passenger identification, driver and passenger trip purposes, trip start time, finish time (or duration), origin location, destination location,and distance traveled. In addition to these traditional elements, from GPS Route choice and travel speed can be captured

Introduction Approaches for GPS Application Passive in-vehicle GPS systems The intent of passive in-vehicle GPS systems To conduct a passive audit of in-vehicle travel The GPS data will be used in a post-processing step to the recorded travel diary of the respondent to validate the reported data and/or to determine trip under-reporting rates. The data can be useful in telephone interview (by refreshing respondent’s memory).

Introduction Approaches for GPS Application Total replacement of the travel diary with GPS. Use GPS data logger to completely replace, rather than supplement, traditional travel diaries All essential trip elements are derived through a computerized process of all GPS data both respondent burden and telephone interview time could be reduced significantly. Previous research [Wolf, 2000] tried the following Trip detection (the number of trips) To find Land uses and addresses for trip destinations Trip Purpose Derivation Trip Distance

Purpose Develop algorithms to reconstruct trips of a traveler holding GPS-logger To automatically identify network links and modes used

Illustration of Overall Concept Passenger Car Walking Mode Change Used links

Used Data Sets for Base Map Data sources for map of this thesis Geographic features of the map DMTI CanMap® Streetfiles Version 6.2 Transportation property of the map 2001 EMME/2 road network V1.0 Transit stop and timetable information from the TTC (Toronto Transit Commission) Spatial Scope : Downtown Toronto Used Tool - ArcGIS Version 8.2 Popular and powerful GIS S/W Provides programming interface ArcObject – Component Object Model (COM)

Used Device – GPS(1/2) Global Positioning System(GPS) Provides 3D coordinates of current position on Earth using artificial satellites 3D coordinates – at least 4 satellites 2D coordinates – at least 3 satellites As distribution of the satellites is wider and the num. of the satellites that a GPS receiver gets signal from simultaneously is larger, the accuracy of the estimated coordinate improves Gets speed and azimuth of movement using the Doppler effect

Used Device – GPS(2/2) GPS used in this research Wearable With a logger – recorder of GPS points which are collected by a GPS receiver

Identifying Used Road Segment GPS data pre-processing Main matching process Post matching processing GPS logger GIS map Reformat data Delete invalid data Match the GPS point with the link Arrange the results of the matching process

Pre-processing Reformat GPS data CVS file format (text file)  DBF format UTC time  Local time Change format of longitude/latitude Add additional fields for the next process Eliminate invalid data Number of satellites To get 2D coordinates, a GPS receiver should get signals from at least 3 GPS satellites simultaneously if #Sat < 3 then delete the row HDOP Dispersion of satellites from which a GPS receiver receives signal The wider the dispersion of satellites, the better accuracy of the measured coordinates if HDOP > 5 then delete the row

Main matching process The purpose of this process Match the GPS point with the link Identify the traveled links based on the respondent’s GPS data Find matched a link based on distance and azimuth of moving direction Road Network GPS points Which link should be matched with each GPS point ?

Main matching process Matching Algorithm Considering both distance between the GPS point and the link and an azimuth of GPS point movements Use topological information – make use of the geometry of the arcs as well as the connectivity of the arcs almost perpendicular

Post Matching Process The aim of the process Make a list of used links Link OID: 101 Link OID: 103Link OID: 102 Link OID: Start (Point_OID = 1 ) (11) (10) (12) (13) (14)

Identification of Used Modes Available clues for estimation of used modes List of Used Links Availability of transit Property of used link – e.g.) freeway, one-way road GIS Map Location of transit stops Travel Speed From GPS

Limitations of GPS Limitations Effect of “Cold/warm Start”

Effect of Urban Canyon Limitations of GPS Limitations

Limitations of GPS Limitations Difficulty of Getting GPS Signal in a bus

Identification of Used Modes Basically, to estimate modes, good level of quality of GPS data is required. No GPS Data  No Result Good quality of data  Elaborate rule Just depending on GPS Data, it is not easy to estimate all kinds of mode configurations. Assumptions A trip is one purpose trip. A mode configuration pattern of a trip is one of the following Walk only, Walk  Bicycle  Walk, Walk  Passenger Car  Walk, and Walk  Transit  Walk. Both trip ends are not in urban canyon area. There is no cold start of GPS receiver.

Identification of Used Modes Process for mode identification Two important clues Location of mode change points (ending point of the first walking, and the starting point of the last walking) Maximum speed from GPS data Process Step 1) Find two mode change points, the ending point of the first walking and the starting point of the last walking segments. Step 2) If two mode change points exist, go to step 3. Otherwise, go to step 4 Step 3) If both points are in a buffer area of bus stops, the used mode is a bus. The size of the buffer is set at a radius of 40m from a stop. Step 4) If maximum speed is faster than 32 km/h, the used mode is a passenger car. Step 5) If maximum speed is faster than 10 km/h, the used mode is a bicycle. Otherwise, the used mode is a walk

Evaluation Test suggested methodology using real GPS data Made 60 trips with GPS receiver Reproduce the 60 trips based on existing TTS data Randomly sample each trip from the data O/D and Mode Route of a trip was decided by a respondent Both O and D are in downtown Toronto area To satisfy assumptions Mode Configuration 10 Bus 24 Passenger Car 23 Walk 3 Bicycle

Evaluation Identification of used links % of correctly identified links: 78.5 % % of un-detected links: 21.5 % % of incorrectly identified links: 0 % Identification of used modes % of correctly identified modes: 91.7(55/60)