1 / 21

Roshan Kumar, Peter Vovsha, PB Petya Maneva, Vladimir Livshits, Kyunghwi Jeon, MAG

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 . Introduction.

adeola
Download Presentation

Roshan Kumar, Peter Vovsha, PB Petya Maneva, Vladimir Livshits, Kyunghwi Jeon, MAG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 TPAC, Columbus, OH, May 5-9, 2013

  2. Introduction • 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

  3. TPAC, Columbus, OH, May 5-9, 2013 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

  4. TPAC, Columbus, OH, May 5-9, 2013 Fine-Grain LOS (1=Pre-fixed VP) Access Main In-Vehicle Egress 1 1 Origin 1 TAZ/TAP Destination 1 TAZ/TAP 2 2 3 3 4 4 Origin 2 TAZ/TAP Destination 2 TAZ/TAP 5 5 6 6 7 7 Origin 3 TAZ/TAP Destination 3 TAZ/TAP 8 8 9 9

  5. TPAC, Columbus, OH, May 5-9, 2013 Fine-Grain LOS (2=On Fly VP/CT-RAMP) Access Stop-to-Stop LOS Egress 1 1 Origin 1 Stop Destination 1 Stop 2 2 3 3 4 4 Origin 2 Stop Destination 2 Stop 5 5 6 6 7 7 Origin 3 Stop Destination 3 Stop 8 8 9 9

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

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

  8. MAZ to MAZ Shortest Paths • 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))

  9. Estimating Pedestrian Shortest Paths • 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

  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) Hyperbolic Function Density/Land Use

  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

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

  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 Density Land Use Hyperbolic Function Ws = Weighted cost for every land use for every path Walk = 1 if Origin and Destination MAZs are “walkable”

  15. Estimating Shortest Paths • cij=Cost of link (i,j) • wsm = Share of Land use type ‘s’ in MAZ ‘m’ • wjm=Weight of node j within MAZ m in path p. • Ws= Weighted Path cost for land use type ‘s’ • Procedure to calculate Ws • For nodejwithin MAZmin path p, calculate: wjm = (cij+cjk)/2 • Weighted Path cost for land use type ‘s’ is: • Single family high density is assumed as base, since cij cjk Pathp i j k MAZ m

  16. Regression Results Predicted Observed

  17. Regression Results

  18. Regression Results (Most Walkable LU)

  19. Regression Results (Least Walkable LU)

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

  21. Summary • 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

More Related