1. Using Detailed Navigation Networks for Modeling Transit Access and Non-Motorized Modes: Application to MAG CT-RAMP ABM Roshan Kumar, Peter Vovsha, PB Petya Maneva, Vladimir Livshits, Kyunghwi Jeon, MAG 1 TPAC, Columbus, OH, May 5-9, 2013

2.  All location choices implemented at a finer level of spatial resolution in recent CT-RAMP ABMs  20,000-40,000 Micro-Analysis Zones (MAZs) -- instead of 2,000- 4,000 Traffic Analysis Zones (TAZs)  Full advantage of MAZs not taken in most ABMs because of a simplified path building procedure  straight line Euclidian distance skims (multiplied by a correction factor) used for MAZ-to-MAZ walk and MAZ-to-Stop transit access  Some distances substantially overestimated, some distances underestimated Introduction

3. Enhanced Spatial Resolution  MAZs nested in TAZs:  CT-RAMP handles all location choices at MAZ level  Assignment & skimming cannot handle MAZ-to-MAZ matrices  Virtual Path (VP) building:  Access and egress time pre-calculated for MAZ-to-station matrices using detailed navigation network (NavTeq)  Station-to-station time/cost matrices skimmed  MAZ-station-station-MAZ path calculated on the fly TPAC, Columbus, OH, May 5-9, 20133

4. Fine-Grain LOS (1=Pre-fixed VP) TPAC, Columbus, OH, May 5-9, 20134 Origin 2 TAZ/TAP Destination 1 TAZ/TAP Origin 1 TAZ/TAP Origin 3 TAZ/TAP Destination 2 TAZ/TAP Destination 3 TAZ/TAP 2 3 1 5 6 4 8 9 7 2 3 1 5 6 4 8 9 7 AccessEgressMain In-Vehicle

5. Fine-Grain LOS (2=On Fly VP/CT-RAMP) TPAC, Columbus, OH, May 5-9, 20135 Origin 2 Stop Destination 1 StopOrigin 1 Stop Origin 3 Stop Destination 2 Stop Destination 3 Stop 2 3 1 5 6 4 8 9 7 2 3 1 5 6 4 8 9 7 AccessEgressStop-to-Stop LOS

6. TPAC, Columbus, OH, May 5-9, 2013 Transit Path-Building Boarding stop requires bus transfer to rail Longer walk but no bus transfer Different Origin MAZ (same TAZ) has different walk & transit times 6

7.  Eliminate “across the board” predetermined correction factors for straight-line distance 1. Use detailed navigation networks to compute shortest path skims for walk and walk-to-transit access in built areas implementing Dijkstra’s shortest path algorithm. 2. Develop a regression model to estimate ratio of shortest path to Euclidian distance for non-built MAZs for future scenarios 3. Extract non-motorized LOS skims using a hash table Objective

8.  Objective: Find MAZ-to-MAZ Walk Paths (less than 3 miles)  Inputs : NavTeq Network, MAZ Layer  Outputs : MAZ-to-MAZ Walk Cloud (cloud[I,J] = walk dist (I,J)) MAZ to MAZ Shortest Paths

9.  Higher level facilities removed (Functional Classes 1 and 2)  Centroid connectors updated (no connectors to highways; 4 per MAZ)  Nodes close (within 0.5 miles) to highways tagged  MAZ “walkability” identified  All MAZ to MAZ shortest paths less than 3 miles found Estimating Pedestrian Shortest Paths

10. MAZ to MAZ Shortest Paths  All MAZ to MAZ shortest paths less than 3 miles found  Dijkstra’s shortest path with a heap structure implemented  Code written in Python  Network data structures modified and code parallelized:  Finding all MAZ-to-MAZ shortest paths takes only 20 minutes  Being implemented to utilize MAZ_8 IDs Compression Factor =  For 3 mile threshold, compression factor = 1.16% (6.25 million paths)  For 1 mile threshold, compression factor = 0.2% (1.08 million paths) Density/Land Use Hyperbolic Function

11. Benchmarking tests for Hash Tables  Results:  Distances checked for first 10,000 MAZs  3.7 million out of 100 million MAZ pairs within 3 miles  Space required to store 10,000 X 10,000 matrix was 780 MB  Benchmarking tests for accessing Rectangular Matrix and Hash Table Variable TypeData StructureMemory Used (GB)*Access Time (sec)** ShortRectangular Matrix2.181 FloatRectangular Matrix4.361 Java FloatHash Table0.7835 **Times reported for accessing each data structure 100 times *Total Memory = 8 GB

12. Storing MAZ Walk metrics as Nested Hash Tables Distances within 3 miles Out of Range 0 mi to 0.1 miMAZ 1 to MAZ 2 0.1 mi to 0.2 mi MAZ 1 to MAZ 3 0.2 mi to 0.3 mi MAZ 1 to MAZ 4 0.3 mi to 0.4 mi MAZ 1 to MAZ 5 00 01 02 03 MAZ-to-MAZ within 3 mi Hash Function Buckets Keys Distance MAZ-to-MAZ Distance Matrix Hash Table

13. Future Scenarios  Exact navigation network not available  Certain zones not build yet but expected to be built  Certain zones planned to change the LU substantially  In both cases, LU development plans are available  The method has to be adjusted:  Predict pedestrian conditions and walk-ability for new/changed zones  Integrate built and no-built zones in one procedure seamlessly

14. Estimating Shortest Paths  Regression model to estimate shortest path cost Hyperbolic Function Density Land Use W s = Weighted cost for every land use for every path Walk = 1 if Origin and Destination MAZs are “walkable”

15.  Procedure to calculate W s  For node j within MAZ m in path p, calculate: w jm = (c ij + c jk )/2  Weighted Path cost for land use type ‘ s ’ is:  Single family high density is assumed as base, since Estimating Shortest Paths Path p ijk c ij c jk  c ij = Cost of link ( i,j )  w sm = Share of Land use type ‘ s ’ in MAZ ‘ m ’  w jm = Weight of node j within MAZ m in path p.  W s = Weighted Path cost for land use type ‘ s ’ MAZ m

16. Regression Results Predicted Observed

17. Regression Results VariableEstimateStd. Errort-valueVariableEstimateStd. Errort-value (Intercept)0.045800.0037912.07200Industrial-0.92990.0072-129.2140 Euclidian Distance0.000370.000001443.61800Medical/Nursing Home-0.14880.0185-8.0600 Density (OD)2181.000009.63600226.37300Mixed Use2.80000.37497.4700 Walk0.171800.0045337.89200Multi Family - Apartment/Condo0.02590.00683.8310 Near Highway-0.334400.00170-197.13600Office-0.00650.0150-0.4370 Active Open Space-0.693300.01783-38.88300 Other Employment - Landfill/Proving Grounds/Sand and Gravel/etc. -1.43200.0581-24.6400 Agriculture-1.272000.01461-87.04300 Passive/Restricted Open Space/Undevelopable -1.35200.0343-39.3760 Airport-2.600000.06870-37.85200Public/Special Event/Military0.74080.014551.1660 Business Park-2.302000.11310-20.35300Religious/Institutional1.39600.025355.2810 Cemetery-1.310000.05637-23.23200 Single Family High Density - Greater than 4 du/ac - Includes Mobile Homes 0.0000-- Commercial High - Community Retail/Regional Retail -0.465100.01787-26.02800 Single Family Low Density - Less than 1 du/ac -0.67880.0122-55.6450 Commercial Low - Amusement/Movie Theatre/Specialty Retail/Neighborhood Retail 1.379000.01086127.00100 Single Family Medium Density - 1 to 4 du/ac -0.11190.0059-18.8680 Developing Employment Generating1.218000.1101011.06700 Tourist Accomodations - Motel/Hotel/Resort 0.09760.04292.2730 Developing Residential-0.636600.02819-22.58200Transportation1.52700.018283.8840 Educational/Religious-0.450300.01328-33.90700Vacant-0.49200.0116-42.5280 Golf Course-1.988000.02332-85.23100Water-4.66500.0410-113.6880 *Residual standard error: 1.82 on 6304527 degrees of freedom **Multiple R-squared: 0.2807, Adjusted R-squared: 0.2807 ***F-statistic: 8.2e+04 on 28 and 6304527 DF, p-value: < 2.2e-16

18. Regression Results (Most Walkable LU) VariableEstimateStd. Errort-valueVariableEstimateStd. Errort-value (Intercept)0.045800.0037912.07200Industrial-0.92990.0072-129.2140 Euclidian Distance0.000370.000001443.61800Medical/Nursing Home-0.14880.0185-8.0600 Density (OD)2181.000009.63600226.37300Mixed Use 2.8000 0.37497.4700 Walk0.171800.0045337.89200Multi Family - Apartment/Condo0.02590.00683.8310 Near Highway-0.334400.00170-197.13600Office-0.00650.0150-0.4370 Active Open Space-0.693300.01783-38.88300 Other Employment - Landfill/Proving Grounds/Sand and Gravel/etc. -1.43200.0581-24.6400 Agriculture-1.272000.01461-87.04300 Passive/Restricted Open Space/Undevelopable -1.35200.0343-39.3760 Airport-2.600000.06870-37.85200Public/Special Event/Military0.74080.014551.1660 Business Park-2.302000.11310-20.35300Religious/Institutional 1.3960 0.025355.2810 Cemetery-1.310000.05637-23.23200 Single Family High Density - Greater than 4 du/ac - Includes Mobile Homes 0.0000-- Commercial High - Community Retail/Regional Retail -0.465100.01787-26.02800 Single Family Low Density - Less than 1 du/ac -0.67880.0122-55.6450 Commercial Low - Amusement/Movie Theatre/Specialty Retail/Neighborhood Retail 1.37900 0.01086127.00100 Single Family Medium Density - 1 to 4 du/ac -0.11190.0059-18.8680 Developing Employment Generating 1.21800 0.1101011.06700 Tourist Accomodations - Motel/Hotel/Resort 0.09760.04292.2730 Developing Residential-0.636600.02819-22.58200Transportation 1.5270 0.018283.8840 Educational/Religious-0.450300.01328-33.90700Vacant-0.49200.0116-42.5280 Golf Course-1.988000.02332-85.23100Water-4.66500.0410-113.6880 *Residual standard error: 1.82 on 6304527 degrees of freedom **Multiple R-squared: 0.2807, Adjusted R-squared: 0.2807 ***F-statistic: 8.2e+04 on 28 and 6304527 DF, p-value: < 2.2e-16

19. Regression Results (Least Walkable LU) VariableEstimateStd. Errort-valueVariableEstimateStd. Errort-value (Intercept)0.045800.0037912.07200Industrial-0.92990.0072-129.2140 Euclidian Distance0.000370.000001443.61800Medical/Nursing Home-0.14880.0185-8.0600 Density (OD)2181.000009.63600226.37300Mixed Use 2.8000 0.37497.4700 Walk0.171800.0045337.89200Multi Family - Apartment/Condo0.02590.00683.8310 Near Highway-0.334400.00170-197.13600Office-0.00650.0150-0.4370 Active Open Space-0.693300.01783-38.88300 Other Employment - Landfill/Proving Grounds/Sand and Gravel/etc. -1.43200.0581-24.6400 Agriculture-1.272000.01461-87.04300 Passive/Restricted Open Space/Undevelopable -1.35200.0343-39.3760 Airport -2.60000 0.06870-37.85200Public/Special Event/Military0.74080.014551.1660 Business Park -2.30200 0.11310-20.35300Religious/Institutional 1.3960 0.025355.2810 Cemetery-1.310000.05637-23.23200 Single Family High Density - Greater than 4 du/ac - Includes Mobile Homes 0.0000-- Commercial High - Community Retail/Regional Retail -0.465100.01787-26.02800 Single Family Low Density - Less than 1 du/ac -0.67880.0122-55.6450 Commercial Low - Amusement/Movie Theatre/Specialty Retail/Neighborhood Retail 1.37900 0.01086127.00100 Single Family Medium Density - 1 to 4 du/ac -0.11190.0059-18.8680 Developing Employment Generating 1.21800 0.1101011.06700 Tourist Accomodations - Motel/Hotel/Resort 0.09760.04292.2730 Developing Residential-0.636600.02819-22.58200Transportation 1.5270 0.018283.8840 Educational/Religious-0.450300.01328-33.90700Vacant-0.49200.0116-42.5280 Golf Course -1.98800 0.02332-85.23100Water - 4.6650 0.0410-113.6880 *Residual standard error: 1.82 on 6304527 degrees of freedom **Multiple R-squared: 0.2807, Adjusted R-squared: 0.2807 ***F-statistic: 8.2e+04 on 28 and 6304527 DF, p-value: < 2.2e-16

20. Estimating Shortest Paths for Green Zones  Three types of paths Brown Zone Green Zone Brown Zone Green Zone Actual Shortest Path Estimate of Shortest Path Actual Shortest Path

21.  Replace simplified path building procedure with shortest path algorithm using detailed navigation networks  Algorithm implemented in Python and parallelized  6.25 million paths less than 3 miles found in 20 minutes  Applied as network processing step in CT-RAMP ABMs developed for MAG, PAG, and CMAP  Regression model that uses land use variables developed to estimate shortest path costs for future built MAZs Summary