Use of Spatial Interpolation to Estimate Interurban Traffic Flows from Traffic Counts

Transcription

1 Use of Spatial Interpolation to Estimate Interurban Traffic Flows from Traffic Counts NATMEC: Improving Traffic Data Collection, Analysis, and Use June 29 July 2, 2014 Swissôtel Chicago Chicago, Illinois

2 Motivation 1. SCT (Mexico s FHWA) is developing a national transportation planning model with emphasis in highways Base inputs 1. Traffic counts 2. Origin - destination roadside surveys 2. Model to be used for evaluation and decision making 3. Project screening level 4. National infrastructure plan Identified needs a. Data to validate model b. Estimate traffic where no field data is available

3 Highway network Federal network 85,000 miles Toll roads: 4,000 miles 2-lane highways: 89% 4-lane highways: 11%

4 Origin destination data Roadside surveys scattered across the country for specific projects applied by different contractors and different specifications Relevant demand data collected Major and minor corridors covered More than 10 years of data

5 Traffic count data More than 20 years of traffic data collected on federal network Last year, almost 7,000 temporary traffic count stations were installed One week worth of data for each site Data is public and made available through SCT s website and includes location information

6 Traffic count data

7 Previous research on AADT and Kriging interpolation Eom, Park, Huntsinger analyzed Wake County, NC traffic count data (2006) Wang, Kockelman studied Texas data (2009) Shelby, Kockelman compared differences between Euclidean and network distances (2011) Shamo, Asa, Membah analyzed Washington state data (2013) Wang expanded her research applying technique to NYC subway data (2013)

8 Kriging interpolation Geostatistical technique for estimating unknown data based on their location among existing information Technique was developed by mining industry for gold exploration Kriging interpolation relies on the notion that data is autocorrelated over space and the levels of autocorrelation decline with distance Three types of Kriging: Simple: global mean is known Ordinary: global mean unknown Universal: global mean assumption cannot be made and trends are used for estimations GIS software include Kriging interpolation utilities

9 Kriging interpolation Location of a point Traffic counts esri.com hunter.cuny.edu

10 Traffic count data

11 Kriging interpolation application Randomly selected a traffic counts subset for validation purposes. Used ArcGIS for Simple Kriging and Matlab for Universal and Ordinary Kriging Distances were based in network layer in GIS and exported for Universal and Ordinary Kriging to be used in Matlab Traffic data summary (AADT) Parameter Automobiles Buses Trucks Total Average 7, ,132 9,879 Standard Deviation 8, ,053 10,165 Range Minimum Maximum 89,497 4,800 15,778 99,997

12 Results Traffic count subset Parameter Automobiles Buses Trucks Total Average 7, ,561 10,946 Standard Deviation 11, ,182 10,306 Range Minimum Maximum 116,713 5,791 21, ,169 After interpolation Parameter Automobiles Buses Trucks Total Average 9, ,223 12,806 Standard Deviation 10, ,377 12,076 Range Minimum Maximum 113,399 5,307 16, ,606 Difference Parameter Automobiles Buses Trucks Total Average 30% 5% -13% 17% Standard Deviation -13% 10% 9% 17% Range Minimum 25% 44% -3% Maximum -3% -8% -23% -6%

13 Traffic count data

14 Free roads - After Kriging interpolation

15 Toll roads - After Kriging interpolation

16 After Kriging interpolation

17 3d view of Traffic counts

18 3d view of Kriging interpolation

19 3d view of population

20 3d view of traffic interpolation and population

21 Conclusion and next steps Conclusions As concluded by others, Kriging interpolation is a better technique than other techniques for estimating spatial unavailable data. Universal Kriging is better technique among three approaches Better results in roads closer to urban areas A continuous traffic count layer can be obtained through this methodology Next steps Evaluation of use of travel time or other variables as distance in interpolation Use of socioeconomic data in interpolation Coding software for use by SCT in day to day operations