Survey and Model Development of DFW Commercial Vehicles

Survey and Model Development of DFW Commercial Vehicles ZHEN DING, ARASH MIRZAEI Model Development and Data Management Group NCTCOG

2 Quick Facts of NCTCOG Dallas - Fort Worth and 13 Counties 6. 9 Million Population, 4. 3 Million Employment 23. 6 Million Daily Person Trips 200 Million VMT, 5. 2 Million VHT

3 Outline 2012 Commercial Vehicle Survey Contents Observations Trip Rates 2014 Commercial Vehicle Model Methodology Evaluation

4 Define A Commercial Vehicle (CV) Trip Purposes Freight Business Purpose Vehicle Type Light Medium Heavy

5 Commercial Vehicle (CV) Classification Light CV Medium CV Heavy CV

6 Light CV Medium CV Heavy CV

7 Commercial Vehicle Survey @ Production Sites Survey @ Attraction Sites Questionnaires, Trip Diaries, Counts Questionnaires, Intercepts, Counts Statistics and Rates

8 Surveys Production Sites Business Information (1, 000) Vehicle Information (1, 400) Trip Diaries (9, 300) Vehicle Ownership Counts (14, 000) Attraction Sites Business Information (1, 300) Traveler Intercepts (11, 000) CV Intercepts (500) Vehicle Counts

9 Dimensional Variables Business Type Basic Retail Service Education Transportation & Warehouse Vehicle Type Employment Size

10 Survey Products Multi-dimensional “Section Profiles” e. g. # of heavy CV owned by a service business with 20 employees Interesting Observations Expansion 410, 000 Businesses 4. 3 Million Employment by Business Categories

11 CV Model Development Objectives To estimate commercial vehicle (CV) volume on roadway network To develop origin-destination trip table for CV

12 Development Steps Trip Generation Trip Distribution Trip Assignment

13 Trip Generation Rates Production Basic Retail Service Light 0. 30 0. 25 0. 65 Medium 0. 10 0. 02 0. 04 Heavy 0. 23 0. 01 0. 02 Attraction Basic Retail Service Light 0. 25 0. 2 0. 55 Medium 0. 09 0. 05 0. 08 Heavy 0. 18 0. 11 0. 07 Unit: trips per employee

14 Generated Trips Vehicle Type CV Production CV Attraction Light 2, 278, 915 1, 910, 878 Medium 133, 003 183, 090 Heavy 312, 805 428, 925 Total 2, 724, 723 2, 522, 893 We use the attraction trips as the balancing target. Heavy truck volume is about 2. 3 times of medium ones.

15 Trip Distribution

16 Trip Length Distribution Medium CV, R 2 = 0. 947 for Trip Length Distribution between Survey and Model 25, 0 Medium 25, 0 % of Trips 20, 0 15, 0 Med Survey 10, 0 Model Trips 20, 0 15, 0 Med % 10, 0 Line 1 Med Model 5, 0 0, 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Travel Time (minute) 0, 0 5, 0 10, 0 15, 0 Survey Trips 20, 0 25, 0

17 Trip Length Distribution Heavy CV, R 2 = 0. 848 for Trip Length Distribution between Survey and Model 18, 0 Heavy 16, 0 14, 0 12, 0 10, 0 8, 0 Hvy Survey 6, 0 Hvy Model Trips % of Trips 18, 0 10, 0 Hvy % 8, 0 Line 1 6, 0 4, 0 2, 0 0, 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Travel Time (minute) 0, 0 2, 0 4, 0 6, 0 8, 0 10, 0 Survey Trips 12, 0 14, 0 16, 0

18 Traffic Assignment Generalized Cost User Equilibrium Method (Path-based) Three Time-of-Day Assignments 2. 5 Hours, AM Peak 6: 30 am – 9 am 3. 5 Hours, PM Peak 3: 00 pm – 6: 30 pm 18 Hours, Off-peak Medium CV, and Heavy CV

19 Time-of-day Conversion We must convert the distributed trips of a day into 3 time periods (AM, PM, OP). Before converting, we’d double-check if the survey agree with the counts Heavy CV 14, 00% 12, 00% 10, 00% 8, 00% 6, 00% Med Counts Func 1 4, 00% Med 24 -hr Distribution % of Trips Medium CV 8, 00% 6, 00% Hvy Counts Func 1 4, 00% Hvy 24 -hr Distribution 2, 00% 0, 00% 0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223 0~23 Hours

20 Traffic Assignment – Freeway Counts Verify Assignment Results using 102 Freeway Counts

21 Traffic Assignment - Preliminary Evaluation Heavy Truck Assignment 1200 100 200 300 400 500 600 700 800 900 1000 1100 1200 1000 800 600 400 200 0 0 100 200 300 400 500 Counts 600 700 800 900 0 1000 0, 0 Assignment 0 Medium Truck Assignment 1600, 0 200, 0 400, 0 600, 0 800, 0 1000, 0 1200, 0 1400, 0 1600, 0 1400, 0 1200, 0 1000, 0 800, 0 600, 0 400, 0 200, 0 100, 0 200, 0 300, 0 400, 0 Counts 500, 0 600, 0 700, 0

22 ATRI Data Two Type of Data GPS Tracking Records Identify Truck Routing and Speed Identify Trucking Generators Trucks Origin-Destination Trip Table Average Trip Length of Heavy Trucks Potential Bias of Truck Samples

23 ATRI Data – GPS Tracking Locate Trucking Activity Generators

24 ATRI Data – Trip Length Bias 25, 00 Long Distance Trips ATRI: 44 minute CVS: 24 minute 20, 00 15, 00 ATRI CVS 10, 00 5, 00 0, 00 1 2 3 4 5 6 7 8 9 10 11 12 13

25 Questions?