Cycling Volume Estimation Methods for Safety Analysis XI ICTCT extra Workshop in Vancouver, Canada Session: Methods and Simulation Date: March, 01 The Highway Safety Manual (HSM) documents many safety performance functions (SPFs) for motor-vehicles (AASHTO, 0) The HSM suggests a method for predicting bicycle-motorist collisions by multiplying the total predicted motorist crashes by a factor The factor is based on speed and road type and it does not account for cyclist volume Mohamed Elesawey, Ph.D., P.Eng. Clark Lim, P.Eng. /9/01 Some Challenges: Insufficient collision data Collisions are rare for motor-vehicles, low bicyclist mode shares make bicyclist-motorist collisions even rarer A considerable range of facility types, and Inaccurate cyclist volume data The focus is on how to estimate Annual Average Daily Bicycles (AADB) 3 /9/01 /9/01 Motivation: What Does The Cycling Picture Look Like? The Annual Average Daily Bicycle (AADB) The equivalent measure for bicycle traffic Requires the availability of all-around-the-year data of Daily Bicycle Volumes (DBV) at a particular location Research done as a part of the development of Vancouver Cycling Data Model (VCDM) Two Key Problems Lots of bicycle count data Many puzzle pieces Need a way to put the pieces together Not enough bicycle count data Many pieces short of a puzzle Need a way to fill in the missing pieces 5 /9/01 /9/01
Cycling Data Model: Objectives Development of a Cycling Data Model for the City of Vancouver: 1. Guiding the development of the Active Transportation Master Plan. Monitoring the success of the plan The model is intended as an estimation and visualization tool Estimation: the annual average daily bicycle traffic (AADB) Visualization: displays the estimated AADBs and quality indices on a road digital map 7 /9/01 Data Description Bicycle Network Data Total length: 70 lane-kilometers of on-street and off street cycling facilities Facility types: separated bicycle lanes, local street bikeways, arterial street bike lanes and off-street pathways Bicycle Volume Data Amount: more than,000 hours of bicycle volume data Period:005-011 Weather Data Mean Temperature Total Precipitation (mm) Total Snow (cm) Snow on the Ground (cm) /9/01 Cycling Data Model Process and Modules Data Description: Bicycle Network Data 9 /9/01 /9/01 Data Description: Bicycle Network Data Data Description: Cycling Volume Data Automatic Bicycle Counts Permanent inductive loop counters Temporary (portable) pneumatic hose counters Manual Intersection Bicycle Counts Cycling counts as part of vehicle intersection counts *Numbers in parenthesis represent the total number of bicycle links Counts carried out solely for bicycles Data stored in various spreadsheets with liberal formatting 11 /9/01 Key is processing and centralizing disparate datasets into a relational database (i.e. data model) Allows for consistent data in a structure that can be accessed by algorithms 1 /9/01
,50 3 31 1,57 3 7,9 1,17 1,3 0000 3 1,5 1,7,919 9,9 7 93 79 5,50 9 1,75 3,937 1,71 1,3 17,9 1,93 30 957 1,0 1,9 9 1 1,39 939 957 7 1,01 73 1 1,05 30, 7,9, 1, 973 3 7 0 11,3 1,013 7 70,13 1 955 91 Number of Hours 11 939 9, 1 79 (Mid-block count) 1,05,0,000 to 30,300 (35) 51 1,500 to,000 () 3 000 1,3 1,39 1,319 79 1,0 1,000 to 1,500 (17) 0000 0000 500 to 1,000 () 1,00 95 0 to 500 () 7 1,139 0 519 3 11,0 1,371 kilometers 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1.17 1 7.5 1 1 1 1 1 1 1 0 15 1 0 3 1 0 0 0 0 1 1 7.17 1 1 1.17 1 1 1 1 1 1 1 5.5 1 1.7 1 1 1 1 1 0 1 0 1 1 1 1 1 1 9 1.5 5.5 5.5 1 0 1.751 1 1 1 0.5 1 1 1 1 1 1 11.17 1 5.5 1 5 1 1 1.5.5 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 5.5 Number of Hours 1 1 (Intersection count) 1 to 99 ().17 1 to 1 (7) to 1 (1) to (331) 1 1 0 to (3) 1 1 1 11.5 1 1 0 1 1 1 0 1 1 0 1 kilometers Data Description: Cycling Volume Data Data Description: Cycling Volume Data Mid-Block Data Year Total no. of hourly data sessions % of Total no. of hourly data sessions 19 unique locations 05,70 count hours 005,53 0.% 00 3,01 0.% 007 1,9 0.% # 0 1 00 7,1 3.% Intersection Data 1 unique locations 51,7 count hours # 13 /9/01 009,0.7% 0 7,79 33.9% 011 1,50 50.9% Sum,3 0.0% 1 /9/01 Methodology Methodology The estimation model components: Daily bicycle volume (DBV) Monthly average of daily bicycle volume (MADB) Annual average daily bicycle traffic volume (AADB) Quality indices Actual if data is available for an entire day Estimated if data is available for few hours Heuristic Algorithm of Search Methods: Match target data to similar data with hr profile Six search methods 15 /9/01 1 /9/01 Methodology: Daily Bicycle Volume (DBV) Methodology: Daily Bicycle Volume (DBV) Outliers are removed to reduce the variance of PCF jt values Outliers percentage: user-specified between 1% and % The minimum, mean, and maximum values of PCF jt are computed If the DD ratio less than 0.5 for one link, the direction withthe smaller volume is considered suspicious The ratio 0.5 was arbitrary and was set as variable 17 /9/01 1 /9/01
/9/01 Methodology: Monthly Average Daily Bicycle Volume Methodology: Annual Average Daily Bicycle Volume 19 0 /9/01 Methodology: Quality Indices Results: Visualization QI of DBV QI of MADB QI of AADB Year No. of AADB Volumes 005 1 00 007 173 00 5 009 71 0 3 011 3 Total 3 1 /9/01 /9/01 Results: Accuracy of Daily Adjustment Factors Results: Daily Adjustment Factors Factors for each day of the week provided similar estimation errors to grouping the factors for weekdays and weekends. Weather-specific daily factors provided the best estimation results. Developing factors by road classes did not improve the estimation accuracy. Group ID Development Criteria Calculation Method 1 Day of the week I***, II**** Weekdays/Weekends I, II 3 Day of the week, weather* I, II Weekdays/Weekends, weather I, II 5 Day of the week, road class** I, II Weekdays/Weekends, road class I, II 3 /9/01 Normal weekdays (i.e. Mondays to Thursdays) provided the lowest estimation errors especially with the three months of the summer (i.e. June-August). /9/01
Results: Accuracy of Monthly Adjustment Factors Results: Error Decomposition Magnitude of error attributable to the use of daily and monthly adjustment factors 5 The MAPE of the estimated AADB by daily and monthly factors was a minimum; 1%, when using daily cycling volume of weekdays (i.e. Tuesdays to Fridays) in August. Summary The VDCM model estimates an annual average of daily bicycle volume The estimation model components: Daily bicycle volumes (DBV): actual or computed Monthly average of daily bicycle volumes (MADB): actual or computed using daily expansion factors Annual average daily bicycle traffic (AADB): actual or computed using monthly expansion factors Quality indices for each daily, monthly, and annual average daily bicycle volume Summary The model resulted in an estimation percentage of more than 70% of the entire bicycle network using 5% of the actual data needed Different elements of the model were validated Future work needed to answer some of the questions related to model development and parameters 7 /9/01 /9/01 Practical Advantages Improved models using less data Few days of data can be used to obtain the annual averages. Optimum utilization of data collection resources (i.e. specification of the best days for collecting data). Calculation of estimation accuracy Decomposition of error between daily and monthly adjustment factors Other Cycling Volume Estimation Methods Adjustment Factors Design Hour Factors (K-factor) ( % error in AADB) Temporal Models Log-linear Count Models ( % error in imputing missing DBV) Historical Average Spatial Models ( 39% error in imputing missing DBV) Neighbor Regression Models ( 0% error in imputing missing DBV) Spatial/Temporal Models Deep Learning Autoencoder ( % error in imputing missing DBV) MCMC MI ( 13% error in imputing missing DBV) 30 /9/01
Estimation of AADB Traffic With Adjustment Factors Thank You - Questions? 31 /9/01 3 /9/01 Estimation of AADB Traffic With Adjustment Factors /9/01