USE OF STATE FLEET VEHICLE GPS DATA FOR TRAVEL TIME ANALYSIS

David P. Racca

Center for Applied Demography and Survey Research (CADSR)University of DelawareGraham Hall, Rm 284, Newark, Delaware 19716Phone: (302) 831-1698E-mail: dracca@udel.edu

State of Delaware Public Vehicle GPS Data

• About 2400 vehicles broadcasting location every two minutes.

• 2 million point measures per month• Providing 5 to 10 million travel way measures per

month• Does not include public safety, transit buses, or

road maintenance vehicles.• 40% passenger cars, 34% passenger vans, 23%

pickups and SUVs• Data as far back as year 2007

An Opportunity to Develop a StatewideTravel Time / Speed Survey

• Besides safety measures, travel time and speed throughout the day is the most important performance measure

• Addresses roads large and small• Collection costs already covered• Includes detailed trip data allowing for

analysis of turning movement statistics

State Vehicle GPS Measurements

State Vehicle GPS MeasurementsSample Trip

Northern New Castle CountyWeekday Observations2012 to 2013

Sussex CountyWeekday Observations 2012-2013

Hourly 8am Observations

Processing• Capture historical GPS data by querying Networkfleet

web services for Delaware vehicles.• Process GPS XML response data• Create GPS point databases and GIS files• Extract and associate GPS points with particular trips

taken through time• Build a trip and link based version of the GPS data

- Estimate the path taken between GPS readings- Associate speed measures with a particular road

link, direction, and tuning movement• Screen the data for errors and anomalies.

The primary issues associated with traffic data and managing it and processing it in GIS are:

• Travel flow. Most of the data is directional• Standard and effective ways of referencing traffic data

to models of the transportation network• Integrating data of different spatial types, point, line, and

polygon. Examples: speed probe data, capacity data, device counters, travel demand

• Integration of data from different time dimensions. Dealing with large volumes of data. Aggregation and disaggregation.

• Integrating across various portions of the transportation network.

Desired Features For Referencing Traffic Data• is related to an established standard• can reference the smallest portions of road as well as

the largest• is not dependent on a particular cartographic source • can relate data from various sources and measurement

schemes• can be generalized to relate information about small and

large road segments• can capture the direction of traffic flow. Traffic data for a

particular portion of road is directional• Provide a fixed identifier for use by those who cannot

work with advanced DBMS or linear referencing systems (route and mile point)

Identification illustration

To relate a measure to a particular turning movement a “S”, “L”, “R”,or “U” is appended to the LRSID, for example. Left turn fromSudlers Row LRSID = 0006160176000000L

Sample Output

Weekday Hourly

“S” Straight or Thru Shown

Also available are Right & Left

Segments statewide included

Example Detail Captured for Road Links

Routing network is segmented at every major or minor intersection

Comparison of Segment Length, VehGPS vs NPMRDS

NPMRDS Data

State Vehicle GPS Data

Comparison of Coverage, State GPS vs NPMRDS

For State GPS Weekday 2012-2013, NPMRDS in Black

Comparison of Coverage, State GPS vs NPMRDS

For State GPS Weekday 2012-2013, NPMRDS in Black

Summary of Features of the State Vehicle GPS

• Data available for up to 6 past years• Wide coverage, data for small and large roads• Captures speeds and travel times relative to turning

movement• Measures available at great detail, road link breaks at

all intersections, large and small • Delay at intersections by turning movement ,

incorporated into road link speed / travel times. Ideal for generation of time sensitive routing network impedance.

• Cost of collection covered in existing program

Aggregations

Over 100 million measurements each year availablefor very detailed road segments throughout the Statecreates a large data set that requires aggregations toexamine conditions with respect to various factors ofinterest. These factors include:

• Time of day intervals, i.e. 30 minute, 60 minute intervals• Periods of the day, AM Peak, Midday, PM Peak, Evening • Day of week• Season, i.e Summer or Non-Summer• Holiday, non-Holiday• Year

Aggregation By Road Segment

Calculation of Free Flow Speed as the 75 percentileOf Hourly Averages (just major roads shown)

Calculation of Percent Degradation at 8am, weekdaysPercent degradation = 100 * (freeflow75 – speed) / freeflow75Calculated from weekday hourly averages

Calculation of Percent Degradation at 8am, weekdaysPercent degradation = 100 * (freeflow75 – speed) / freeflow75Calculated from weekday hourly averages

Travel Time Reliability

Intersection Study

Left turns that are most effected ( > 40% degradation) by morning (8am) congestions

Other potential applications

• Establishment of a statewide routing networkpopulated with DOW, time of day, impedances

• Before and after studies, land use and facility changes• Examining delay at intersections • Estimations of capacity and studies of volume speed

relationship• Relating traffic flow to land use and travel demand• Multimodal studies• Applications of a detailed time sensitive routing

network, such as accessibility studies

Some observations• Little experience in general working with this

kind of data. Capabilities with huge amounts of traffic data are often lacking.

• the data accurate? Can we trust it? What accuracy do we need?

• How does it compare to other sources? • Number of measures• Resolution• Time of Day, Day of Week, Season• Turning movement

• Different data sets may measure differently• Value depends on intended use. Corridor performance?

congestion at intersections, effects of land use?

Blue Tooth Locations

Blue Tooth Travel Times by Hour of the Day Station 3 to Station 4, Sussex County

Blue Tooth Compared to Fleet GPS Estimates

Other CADSR Work

• Travel surveys• Development of internet mapping and data query• Population, employment, and housing projections and

allocations• Accessibility studies• Markets for transit• Environmental and land use studies• Place and address files• Network modeling and routing