An Experimental Procedure for Mid Block-Based Traffic Assignment on Sub-area with Detailed Road Network Tao Ye M.A.Sc Candidate University of Toronto MCRI Student Caucus Meeting September 13, 2003

Outline Background and Problem Statement Study Area and Data Resources Procedure and Methodologies Experimental Results Summary and Conclusions

Background and Problem Statement Conventional zone-based model

Background and Problem Statement Conventional zone-based model Zone-based: centroid to centroid Lack enough detail for intrazonal trips and short trips (in GTA Model, 14% intrazonal trips are not included in the traffic assignment model) Only 40% of the real road network in the GTA is included in the model Not appropriate to provide accurate Origin- Destination trip matrices for input into emerging micro-simulation models of corridors or sub- networks

Objectives Develop an experimental procedure to implement mid block-based (block: road link) traffic assignment on a detailed sub-area network Create mid block points to realistically represent trip ends Develop mid block-based trip matrix Model the detailed network including all local streets Perform mid block-based traffic assignment Compare the results from mid block-based traffic assignment and zone-based traffic assignment Data processing and network modeling--- ArcGIS8.2, Traffic assignment---EMME/2

Study Area --- Downtown Core (PD1) Features (2001): 40.6 km 2 64 traffic zones 86,900 households 164,200 residents

Data Resources From Data Management Group: 2001 Transportation Tomorrow Survey (TTS) 2001 EMME/2 GTA network model Traffic cordon counts in the study area From Data, Map & Government Information Services: 2001 Ontario detailed street map (Shapefile format) 2001 Ontario land use map (Shapefile format) 1996 Canada census: enumeration area (Shapefile format) Building heights information from Statistics Canada (Shapefile) 2002 Toronto air photos linked from Toronto Public Library (Jpeg)

Procedure and Methodology Step 1: Create a traversal matrix for the study area from the GTA model Step 2: Adjust the traversal OD matrix Step 3: Define mid block points Step 4: Estimate the production/attraction ratio for each mid block point Step 5: Redistribute zone-based trip matrix to mid block- based trip matrix Step 6: Create sub-area detailed network model Step 7: Perform mid block-based traffic assignment

Create Traversal Matrix Traversal Matrix (as used in EMME/2): An O-D matrix for a sub-area or a ramp-to-ramp matrix for a freeway facility extracted from the total demand matrix Identify and label all links entering and exiting from sub-area Run a traversal matrix traffic assignment in EMME/2 Inputs: Peak period auto-drive trip matrix retrieved from the 2001 TTS data 2001 EMME/2 GTA Network Model Outputs: A sub area network extracted from the GTA network An auto trip matrix consistent with the study area zone system, considering all the GTA traffic flows

Traversal Matrix Adjustment Run the macro DEMADJ22 (a gradient approach) to get the adjusted matrix traffic count screeline

Define mid block points On the main road Average 3-5 block points for each zone Action buffer

Distribution of mid block points

Estimate Production/Attraction Ratio Production ratio Based on the population size Census enumeration area population Attraction Ratio Based on the floor space Three land use categories: commercial, governmental and institutional, industrial and storage Assume 3-meter height as one floor layer

An example of the P/A ratio

Generate mid block-based trip matrix

Model a detailed network Mid block point Obtain coordinates from ArcGIS Base network Assume features for local street links: lane number---1, free flow speed---40, and lane capacity Turn tables DMTI format: from link to link EMME/2 format: from node to node

Experimental Results Comparison of two network model features

Experimental Results (cont’d) Mid block-based traffic assignment

Experimental Results (cont’d) Zone-based traffic assignment

Experimental Results (cont’d) Main road link volume analysis

Experimental Results (cont’d) Local street volume analysis

Experimental Results (cont’d) Running time analysis  ngap: normalized gap, which is difference between the mean trip time (or cost) at the previous iteration and the mean minimal trip time (cost) computed by assigning the demand to shortest path of the current iteration.

Conclusions and Recommendations Summary of benefits and values More realistic road network representation Suitable for data analysis of GPS-based personal travel surveys More precise results for traffic impact studies More accurate inputs for the traffic micro-simulation studies GTA model enhancement Further research Combination of long trips and short trips --- windowed model Consideration of other measures to estimate production and attraction ratio Enough traffic survey counts to conduct traversal matrix adjustment

Thank You! Open for Questions...