Processing GPS Data from Travel Surveys Peter Stopher and Camden FitzGerald Institute of Transport and Logistics Studies The University of Sydney Qingjian Jiang Parsons Brinkerhoff Australia Pty Ltd.

Introduction  Considerable recent growth in applications of GPS in transport  Capability of producing huge data streams  Need for custom software to interpret and use the data  Description of 4 data processing applications  Description of 2 data analysis steps

Context  Use of a GIS base for display and analysis  Our selections is TransCAD®  Use of developer tools in TransCAD®: GISDK ®  Equipment used:  GeoStats GeoLogger (in-vehicle and wearable)  Neve StepLogger (wearable)

Application 1: Converting to Trip Records  Data must first be reformatted to match input specifications of TransCAD®  Data are usually in one of the standard NMEA streams  GeoLogger and StepLogger provide slightly different data streams

GeoLogger Data Stream

StepLogger Data Stream

Differences and Similarities  Both record latitude and longitude  GeoLogger includes hemisphere  StepLogger uses plus and minus  Both record date and time  GeoLogger is UTC  StepLogger converts to local  Both record altitude (not used), speed, and heading

Similarities and Differences  Both record HDOP and number of satellites in view  Number of satellites – 3 is minimum for 2D solution and 4 for 3D  HDOP is horizontal dilution of precision – larger values indicate poor quality of data (little dispersion of satellites)

Processing Data  Calculate local time and local date  Correct latitude and longitude and remove hemisphere designations (when necessary)  Calculate:  Distance from previous record, using latitude and longitude  Time in seconds based on zero seconds at midnight  Elapsed time from the previous record  Actual date in date format and day of week

Remove Invalid Points  All data points with too few (less than three) satellites in view and/or a value of HDOP of five or greater  Except for the first occurrence of such data points in any group of data points of this type  Any data points where no movement is recorded based on:  Speed of zero  Less than a 15 metre ( º) change in either latitude or longitude  Heading being zero or unchanged

Trip Detection  Detect a trip end when:  Ignition has been turned off (in-vehicle device only)  Ignition has not been turned off (in-vehicle device only)  Direction is reversed, but there is a very short or non-detectable stop  Detect a trip end from wearable data, where the device is always on  Determine whether a trip ended and a new one started during a period of signal loss  Determine whether there is a loss of signal at the beginning of a trip and repair the data record

Trip Detection Heuristics  The difference in successive latitude and longitude values is less than degrees AND  The heading is unchanged or is zero AND  Speed is zero AND  Elapsed time during which these conditions hold is equal to or greater than 120 seconds  If ignition is turned off for in-vehicle for seconds – probable trip end

Trip Ends for Wearable Devices  The GeoLogger works as for the in- vehicle device  The StepLogger requires different logic, because zero speeds are not recorded  Defines a Stop Point Set when there is a group of records within 30 metres of each other  When the Stop Point Set lasts at least 120 seconds, it becomes a potential trip end  When a new point falls outside this set, a new trip is assumed to have started

Completion of Trip Records  Once trip ends have been detected  All other manipulations of the data are completed  Elapsed time and distance for the trip are obtained  Each trip is given a trip number  Each point in the trip is given a new sequence number

Application 2: Correcting “Cold Start” Problems  These occur when there is a delay in position acquisition at the start of a trip  For both wearable and in-vehicle devices, this may result in a missed trip, when the trip is short  If acquisition occurs within 100 metres, this is not considered to be a problem

Identification of the Problem  Determine the distance between the start of this trip and the end of the previous one  If less than 100 metres, no action is required  Determine in which zone the trip end lies  Zone I is the Sydney CBD and surroundings  Zone II is the rest of the metro area

Correction Process  Compute the average speed of the last 10 records and the first 10 records of the new trip  Using these speeds and the look-up tables for gap speed, we determine a speed for the gap  Compute the distance for the gap  Uses straight line distance for under 150 metres  Uses minimum time path distance with end corrections for longer distances

Time Estimation  Using the speed and distance from the previous steps, determine the time taken  Trip time also adds in an average amount of terminal time for either Zone I or Zone II  This process works for car trips  A procedure for other trips is still being developed

Application 3: Signal Loss  Signal loss occurs in urban canyons, under heavy tree canopies, in tunnels, and in some public transport vehicles  Two issues:  Has a stop occurred while the signal was lost?  What path was traversed while the signal was lost?

Non-Car Data  If the gap occurs in data where the GPS is NOT in a car  Determine the location of the point of signal loss and the point of signal resumption  Determine if there is a rail, bus, or ferry path that connects the two  Determine if the connecting path is reasonable for the time between signal loss and signal resumption  If it is reasonable, then interpolate the route

Data from an Urban Canyon Sydney CBD – Urban Canyon

Car Data  Use the preceding 10 records and following 10 records to compute average speeds  Connect the last point before and first after with a minimum time path  Estimate the implied speed along the minimum time path, given distance and elapsed time  Compare this to the average speed for the last 10 and first 10 records either side of the gap  If implied speed is less than 50 percent of average speed, then assume a stop has occurred

Application 4: Displaying the Data  After completing processing, trips from one day are stored in one file  For each day, each trip within the day receives a distinct colour code for the day  Trips are converted to solid lines, which are drawn on maps offset, so that each trip on a given link can be seen separately  A table is also created giving primary information about the trip

Raw Data Display

Processed Data Display

Determining Mode  Begins with defining walk trips  Average speed maximum of 6 km/h and maximum speed of 10 km/h  Also, may not follow street system, especially where parks and open spaces occur  Next, define public transport trips  Usually follow a walk trip  May also be followed by a walk trip  Average speed in the range of km/h

Determining Mode  If trip either disappears, or follows entirely a public transport route, it is defined as public transport  Identification of rail is usually made from station boarding and disappearance of GPS signal  Identification of bus is usually made from speed and route  Remaining trips are bicycle or car  Determined principally by average speeds

Purpose  Uses address data collected at recruitment  Workplaces  Schools  Grocery stores  Requires a GIS of land use by parcel  Trip purposes are identified from known locations for work, education, and shopping  Other purposes are defined by the land uses

Processing and Data Management  All the preceding steps are accomplished in about 2 hours for one week of GPS data  A folder is defined for each device for each household  All files created for that device are stored in that folder  Files can be examined at various intermediate points for review

Conclusions  Processing steps identify about 95 percent of trip ends correctly  Data gap repair (cold starts and tunnels/canyons, etc. are found to be very successful  Mode and purpose identification is about 95 percent  Fully automated processing is still not completely possible

Future Directions  Current processing has reduced the time required for processing to about 25 percent of initial time requirements  Future potentials for better identification of cold starts/canyons, mode, and purpose from fuzzy logic