Mobile Area Transportation Study Urban Area and Planning Boundary
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1 Mobile Origin- Destination Study
2 Mobile Origin- Destination Study Trip Distribution Calibration WHY? Some background on Mobile Long Range Transportation Plan Crash course in travel demand forecasting HOW? AirSage data collected from cell phones Fi Friction Factors Trip Length Frequencies
3 Long Range Transportation Plan The Plan outlines the projects within a MPO study area that t need to be completed to fulfill the projected demands by the 25 year forecast date. The Plan is done every 5 years and is balanced against projected revenue over the next 25 years. The Plan is important because it is how we forecast what tranpsortation projects need to be widened within the next 25 years.
4 Long Range Transportation Plan The figuring out what roads need to be widened uses a methodology called travel demand forecasting and is based on Productions and Attractions (P s and A s) As) per Traffic Analysis Zone (TAZ) The Socio-economic data, or P s and A s, are input into a travel demand forecasting model, and the output is volume, or estimated traffic. The estimated traffic is validated to current traffic counts, then the socio-economic data is forecasted 25 years, model is re-run, run, and df future volumes are produced.
5 Mobile Area Transportation Study Urban Area and Planning Boundary
6 Mobile, AL Traffic Zones
7 2007 Traffic Volumes Created by Model, replicating real traffic counts
8 Travel Demand Forecasting Model Red is projected road capacity deficiency Green is projected capacity sufficiency
9 Highway Element of 2035 Long Range Plan
10 Travel Model Steps INPUT DATA MODEL STEP OUTPUT DATA Socioeconomic Data Trip Rates Trip Generation P s and A s by Zone P s and A s by Zone Travel Times Trip Distribution Trip Tables by Purp. Trip Tables by Purp. Mode Characteristics Mode Choice Trip Tables by Mode Trip Tables by Mode Highway Network Traffic Assignment Loaded Network
11 From Wikipedia: Trip distribution (or destination choice or zonal interchange analysis), is the second component (after trip pgeneration, but before mode choice and route assignment) in the traditional four-step transportation forecasting model. This step matches tripmakers origins and destinations to develop a trip table, a matrix that displays the number of trips going from each origin to each destination. Historically, this component has been the least developed d component of fthe transportation t ti planning model.
12
13 Methodology for Mobile OD Study Using Cellular l Technologies Task 1 Develop Sample Origin Destination Matrices from cell phone data Task 2 Expand Sample O D matrices into internal TAZ trip tables Task 3 Develop External Trip Tables Task 4 Travel Time Data and Impedance Matrix Development Task 5 Trip Distribution Model Calibration
14 Let s Talk about the Data What are the traditional data sources used in Calibration? Census Data Household Travel Surveys Workplace Surveys Commercial Vehicle Surveys External Station / Cordon Line Surveys License Plate Surveys Special Generator surveys Travel Time Surveys
15 Comparison of Datasets HH Travel Survey Travel Diary one-day snapshot Survey sample is usually limited and almost always less than 5% (~1500 +/- HH) Self Reporting bias Able to obtain more detailed information on HH characteristics Cell Phone Data Large Sample (40,000 +/- HH) Multiple Observations Extended period of travel (months) Reported electronically Some HH characteristics must be imputed
16 Cell Phone Data AirSage processes and archives a location each time a mobile device interacts with the network Start of Call End of Call Text Message Data Transfers
17 BIG DATA AirSage data centers within the carriers are now processing location information from more than 100 MILLION devices in real-time. 15 BILLION locations everyday are analyzed and then archived to provide an unprecedented understanding of population movements. AirSage is pioneering Big Data technologies to give its customers access to real-time and historical information and trends.
18 Very Large Sample Size Snap shot of a day, October, 2010: Devices Sampled 33,160 Devices Sampled Avg 136 locations (pings) recorded per device 119,860 Trips analyzed
19 Desire Lines from sample day
20 Recorded Trips by O-D Class (H-Home, Home, W-Work, Work, O-Other) Other) Count Percent Trip Type 119, % HW 5, % WH 4, % HO 29, % OO 35, % OH 30, % 5% WO 5, % OW 4, %
21 Trip Purposes Home Based Work (HBW) Home Based Other (HBO) Non-Home Based (NHB) External-Internal (E-I) External-External (E-E) E)
22 Calibrating Trip Distribution Match Modeled to Observed for: Ti Trip Length thf Frequency Distribution tib ti Average Trip Length by Trip Purpose Area to Area flows of trips
23 Calibrating Trip Distribution The basic idea is to develop a set of factors which will allow the model to match the observed trip length distribution. ib ti Probability Density HBW Smoothed Observed Income Group 1 Probability Density HBW Smoothed Observed Income Group Trip Length (min) Trip Length (min) Proba ability Density HBW Smoothed Observed Income Group 3 Proba ability Density HBW Smoothed Observed Income Group Trip Length (min) Trip Length (min)
24 Calibrating Trip Distribution Primarily the calibration of customized friction factors There is, in the model, a unique F factor for each unit of impedance (typically one per minute). There is almost no rules about what an F factor should be except that they should be reasonably smooth and not jump around. By purpose and income group Iteratively ti adjusted d
25 Friction Factor Frict tion Facto or Time
26 Match Trip Length Frequency HBW Survey vs. Gamma TLFD HBW TLFD Average Trip Length = minutes 18.00% 16.00% 18.00% 14.00% 16.00% 12.00% 14.00% % Trips 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Minutes Percent 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Minutes Survey Gamma Gamma Modeled HBS Survey vs. Gamma TLFD HBS TLFD Average Trip Length = 11.7 minutes 18.00% 16.00% 18.00% 14.00% 16.00% 12.00% 14.00% % Trips 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Minutes Percent 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Minutes Survey Gamma Gamma Modeled Alliance Transportation Group, Inc.
27 Match Average Trip Length Compare the modeled average trip lengths and dth the observed average trip ti lengths (3% usually reflects a reasonable match) Example Trip Length Distribution by Trip Purpose Trip Purpose Modeled Average Trip Length (min) Observed Average Trip Length (min) % Trip Length Difference HBW % HBS % HBO % NHB % 1
28 Checking Trip Distribution Results Average Trip Length Trip length frequency distribution coincidence ratio 0.30 Coincidence count min count T, count count T T Total max, count count Coincidence Coincidence e Ratio Total count count T F(X) Where: 3 count +T = value of estimated distribution at Time T 2 count + = total count of estimated distribution count T = value of observed distribution at time T 1 count = total count of observed distribution X Source: Travel Model Validation Reasonability Checking Manual 2 nd Ed. rcent of Trips Per 5 4 Income Group Travel Time (in Minutes)
29 Other Calibration Efforts Cross Classification Variables Household Size, Income, Auto Availability Number of Workers HH Disaggregation Model Household variables stratified on calibrated curves Compute Generation Variables Household size Calculation of Time of Day Factors Peak and off-peak
30 Other Calibration Efforts Fitting the F factors is the initial phase of the calibration. The proof of the pudding though is the comparison of the geographical pattern of the observed and estimated t trips. This is usually done by checking Screenlines District to District movements Checking intra-zonal trips
31 Mr. Kevin Harrison, P.T.P. Director, Transportation Planning South Alabama Regional Planning Comm. org (251)
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