GENESIS Trip Generation Model using ACS, CTPP, and NHTS data - - PowerPoint PPT Presentation

GENESIS

Trip Generation Model using ACS, CTPP, and NHTS data

Kyeongsu Kim and Yohan Chang Ph.D. Connetics Transportation Group (CTG)

Presented at 2019 TRB Census for Transportation Planning Subcommittee, ABJ30(1) meeting

• n Monday, January 14, 2019 at Liberty K (M4), Marriott Marquis

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| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

• I. MOTIVATION

➢Strong desires to

➢ understand general travel patterns (e.g., # of trips by purposes) without running complicated regional travel model (often requested by stakeholders, media and decision-makers), ➢ Increase the utility of Census data products as a whole or part (via data fusion technique) for more informed decisions to address transportation issues, ➢ develop an easily-accessible visualization tool with various travel pattern-related information (from ACS, CTPP, private api data) in a centralized place.

➢Experience on decade-long Census data & big data analytics, and travel demand modeling ➢Availability of open source data analytics and visualization software (packages)

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

• II. PROBLEM

population Other socio- demographics Travel pattern

➢Different availability of trip generation-related Census data by geography levels ➢Limited NHTS samples for estimating trip generation-related factors ➢Limited public data for purpose and time of day trip imputation ➢ NHTS (not used ongoing MWCOG survey)

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

• III. SOLUTION

➢This tool offers proof-of-concept trip production estimates using ACS, CTPP, and NHTS data ➢This dashboard, GENESIS, provides information for some of these key measures that derived directly or indirectly from Census products and NHTS ➢Private location-based service (LBS) data can be used for TOD distribution (not used here)

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➢ ACS (& CTPP) travel-pattern related information ➢ Travel Time data: API-based auto & transit data

Visualizing Other travel-pattern related Census data

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

• IV. SOLUTION APPROACH

➢Trip generation for each trip purpose was estimated in multiple stages of data processing

• (can) estimate Trip length frequency distributions (TLFDs) from NHTS by trip purposes for peak

and non-peak time periods (i.e., HBW, HBO, NHB, HBSHOP, and HBSOCREC)

• Estimate auto and transit travel time using api data
• (can) develop trip O/Ds for HBW, HBO, NHB, HBSHOP, and NBSOCREC with TLFDs and average

travel time

• Estimate a number of workers (excluding workers worked at home) per HH by vehicle ownership (assign

the number of workers proportionally based on the number of HH by vehicle ownership (in ACS)

• Apply the estimated HBW trip rates (from NHTS) to the HH segment/vehicle ownership/population

density for estimating HBW trips

• Calibrate HBW trips for areas with high GQ population
• Apply relative trip rates for other purposes vs. HBW to estimate trips for other purposes
• Estimate HBW(HBW) trip rates by the average number of workers in HH/vehicle
• wnership/population density (from NHTS)
• Estimate relative trip rates for other trip purposes vs. HBW trip (from NHTS)
• Estimate a volunteer work trip rate by HH/vehicle ownership/population density (from NHTS)

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

Introduction of GENESIS

➢ Prototype dashboard for GENESIS

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

Trip Production

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

Travel Time

| GENESIS : Trip Generation Model using ACS, CTPP, and NHTS data

Thank you!

Kyeongsu Kim kkim@ctgconsult.com | Project Manager Washington DC office | Connetics Transportation Group (CTG)

2019 TRB Visualizing the Census: Innovations in Data Display, Monday, January 14

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