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Geo-spatial Analysis in
Transit Demand Estimation utilizing ITS Applications
Peter Bang, Ph.D., AEI
GIS in Transit ConferenceOctober 16, 2013Washington, DC
Introduction of This Study
Current Transit Demand Estimation
Conventional Travel Demand Model Simple Linear Est. based on Land Use, Route LOS, Historic Data Sketchy Route Assignments by Experts, Surveys Try and Error Adjustment based on Experts’ Expertise Multiple non-linear Regression Est. with AVL, APC, & Parcel-Level
Land Use Data
RTC’s Current ITS Application
Automatic Vehicle Location (AVL) on approximately 147 fixed-route, paratransit and supervisor vehicles
Automatic Passenger Counters(APC) Transit Signal Priority (TSP) installed on at least 56 fixed-route
vehicles Computer-Aided Dispatch Real-Time Traveler Information Web based Trip Planning tool
Example of Raw Data from ITS
AADAY AATRIPHR AAROUTE AAQSTOP AASTOP AANAMSTP AAADRCTN AAON AAOFF SON SOFF STOTAL AALOAD AALAT AALONG NNSMPLES
1 3 101 30 0 MEADOWOOD MALL I 4.75 .00 5 0 5 4.75 39.472090 -119.783830 1
1 3 101 45 8 VIRGINIA/ I 4.67 .00 5 0 5 12.67 39.497743 -119.799130 1
1 3 101 46 9 VIRGINIA/GROVE I .00 .00 0 0 0 12.67 39.501857 -119.801183 1
1 3 101 47 10 VIRGINIA/ I .00 .33 0 0 0 12.33 39.503177 -119.801847 1
1 3 101 48 11 VIRGINIA/WELLS I .33 .00 0 0 0 12.67 39.507447 -119.803957 1
1 3 101 49 12 VIRGINIA/HOLCOMB I .00 .00 0 0 0 12.67 39.509290 -119.804810 1
1 3 101 50 13 VIRGINIA/ARROYO I .00 .00 0 0 0 12.67 39.511830 -119.805967 1
1 3 101 51 14 VIRGINIA/VASSAR I .33 .00 0 0 0 13.00 39.513160 -119.806577 1
1 3 101 52 15 VIRGINIA/CENTER I .33 .00 0 0 0 13.33 39.515240 -119.807530 1
1 3 101 53 16 VIRGINIA/TAYLOR I .33 .00 0 0 0 13.67 39.516563 -119.808140 1
1 3 101 56 19 VIRGINIA/LIBERTY I .00 1.00 0 1 1 12.67 39.520697 -119.810027 1
1 3 101 57 20 CENTER/RYLAND I .00 .00 0 0 0 12.67 39.522863 -119.810473 1
1 3 101 58 21 CENTER/STATE I .00 1.67 0 2 2 11.00 39.524357 -119.810807 1
1 3 101 59 22 CENTER/1ST TO 2ND I .00 1.67 0 2 2 9.33 39.526590 -119.811507 1
1 3 101 99 3 VIRGINIA/KIETZKE I .00 .00 0 0 0 6.33 39.480710 -119.789207 1
1 3 101 506 1 VIRGINIA/MEADWOOD CIR I .00 .00 0 0 0 4.75 39.473540 -119.784863 1
1 3 101 507 4 VIRGINIA/ I .00 .00 0 0 0 6.33 39.485227 -119.792403 1
1 3 101 508 5 VIRGINIA/PECKHAM I .33 .00 0 0 0 6.67 39.487947 -119.794387 1
1 3 101 509 6 VIRGINIA/MOANA I 1.00 .00 1 0 1 7.67 39.490247 -119.795497 1
1 3 101 510 7 VIRGINIA/GENTRY I .33 .00 0 0 0 8.00 39.493720 -119.797217 1
1 3 101 1411 2 VIRGINIA/MEADOWOOD MALL WAY I .00 .00 0 0 0 6.33 39.475560 -119.785550 1
1 3 101 1850 17 VIRGINIA/THOMA I .00 .00 0 0 0 13.67 39.518867 -119.809207 1
1 3 101 1977 23 BAY Q 4SS - EOL I .00 10.67 0 11 11 .33 39.530117 -119.811540 1
1 3 101 9999 999 Not Identified - Cal I .00 .00 0 0 0 .00 39.475863 -119.786853 1
1 4 2 98 10 YORK/PYRAMID I .00 .00 0 0 0 2.00 39.550660 -119.754020 1
1 4 2 100 11 YORK/11TH I .00 .00 0 0 0 2.00 39.550815 -119.758150 1
1 4 2 101 12 YORK/ROCK I .00 .00 0 0 0 2.00 39.550850 -119.760905 1
1 4 2 102 13 ROCK/VANCE I 1.00 .00 1 0 1 3.00 39.550690 -119.761905 1
1 4 2 103 14 ROCK/TYLER I .00 .00 0 0 0 3.00 39.549865 -119.762375 1
1 4 2 104 15 ROCK/GREENBRAE I 1.67 .00 2 0 2 3.67 39.547550 -119.762083 1
1 4 2 105 16 GREENBRAE/LORENA I .00 .00 0 0 0 3.25 39.547933 -119.767908 1
1 4 2 106 17 GREENBRAE/SULLIVAN I .00 .00 0 0 0 3.25 39.547755 -119.771693 1
1 4 2 107 18 GREENBRAE/EL RANCHO I .75 .25 1 0 1 3.75 39.547578 -119.775625 1
1 4 2 108 22 SILVERADA/FANTASTIC I .50 .00 1 0 1 5.75 39.546533 -119.782233 1
1 4 2 109 23 SILVERADA/PARADISE I .00 .00 0 0 0 5.75 39.544798 -119.782950 1
1 4 2 110 24 SILVERADA/ORCHID I .00 .00 0 0 0 5.75 39.543070 -119.783670 1
Demand Estimation I
• All 575 Demand Points are IN;
= (.000)* (.000)*
= 0.965 = in .05 level significance
0 2 4 6 8 10 12 14 16 18 200
5000
10000
15000
20000
25000
30000
# of Routes
Tota
l Dem
and
Demand Estimation I
0 2 4 6 8 10 12 14 16 18 200
5000
10000
15000
20000
25000
30000
# of Routes
Tota
l Dem
and
Demand Estimation I
y = 88.631 + 4.8737·(# of Routes)3 R² = 0.9652
0 2 4 6 8 10 12 14 16 18 200
5000
10000
15000
20000
25000
30000
# of Routes
Tota
l Dem
and
Demand Estimation I
y = 88.631 + 4.8737·(# of Routes)3 R² = 0.9652
4th St. Station, 18 routes
0 2 4 6 8 10 12 14 16 18 200
5000
10000
15000
20000
25000
30000
# of Routes
Tota
l Dem
and
Demand Estimation I
y = 88.631 + 4.8737·(# of Routes)3 R² = 0.9652
4th St. Station, 18 routes
Meadowood Mall, 8 routes
0 2 4 6 8 10 12 14 16 18 200
5000
10000
15000
20000
25000
30000
# of Routes
Tota
l Dem
and
Demand Estimation I
y = 88.631 + 4.8737·(# of Routes)3 R² = 0.9652
4th St. Station, 18 routes
Meadowood Mall, 8 routes
Centennial Plaza, 6 routes
Demand Estimation II
= -51.615Ret8th (.001)* (.000)* (.000)*
(.000)* (.000)* (.000)*
(.008)* (.022)*= 0.592 = in .05 level significance
Conditions ; SELECT IF (rank >= 4).SELECT IF (Num_Routes >= 2 ).SELECT IF (DU_8 > median ).SELECT IF (SUM_TOT > 0 ).
Demand Estimation II
= -0.4 (.098)* (.000)* (.000)*
Acre84 (.000)* (.004)*
(.026)*= 0.632 = in .05 level significance
Conditions ; SELECT IF (rank >= 4).SELECT IF (Num_Routes >= 2 ).SELECT IF (Emp_8 > median ).SELECT IF (SUM_TOT > 0 ).
Demand Estimation II
= -11.5 (.066)* (.000)* (.000)*
Acre84 (.003)* (.019)*
= 0.623 = in .05 level significance
Conditions ; SELECT IF (rank >= 4).SELECT IF (Num_Routes >= 2 ).SELECT IF (Emp_4 > median ).SELECT IF (SUM_TOT > 0 ).
Demand Estimation II
Selection Conditions Major Explainable Factors # of Demand Points
Predictable Demand
Predictable Demand w/ Top3
Top 3 D.P. Regional Transit Hubs # of Routes 3 0.5% 37,350 39.2%
Eq. 1 Higher Pop. Density, Busy Routes
# of Routes, Near-by Employment, Populations 81 14.1% 11,426 12.0% 48,776 51.2%
Eq. 2Higher Emp. Density,
Busy Routes
# of Stops, Near-by Employment, Populations 88 15.3% 16,678 17.5% 54,028 56.7%
Eq. 3 # of Stops, Near-by Employment, Populations 85 14.8% 16,164 17.0% 53,514 56.2%
Total Demand 575 100.0% 95,236 100.0% 95,236 100.0%
Model Output Validation
# of Cleaned
AVL & APC data per
Stop
# of Demand
PointApplied Estimate
Tool# of
Sample size
Major Explainable Factors
Est. Demand
per Demand Point in
2011
Est. Demand
per Route in 2011
Comparison of Market Share per
Route
Est. Demand
per Route in 2035
32,766 samples
575 Demand
Points
Regression Eq. 0 3 # of Routes 0.996
575 Demand Points 26 Routes = 0.57 37 Routes
Regression Eq. 1
88 Demand
Points
# of Routes, Near-by Employment, Populations 0.592
Regression Eq. 2 # of Stops, Near-by Employment, Populations 0.632
Regression Eq. 3 # of Stops, Near-by Employment, Populations 0.623
Linear Eq. 4
484 Demand
Points
Population in ⅛-mile-buffer / Thiessen line
0.30 - 0.40Linear Eq. 5 Employment in ⅛-mile-buffer / Thiessen line
Linear Eq. 6 Population in ¼-mile-buffer / Thiessen line
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
f(x) = 0.967947026123667 x + 0.00118741321393836R² = 0.549958896391909
Series1 Linear (Series1)
Actual Ridership Market Share per Route in 2011
Est.
Rid
ersh
ip M
arke
t Sh
are
per
Rout
e
Model Output Validation
Model Output Validation
Buffer Area 2011 est. 2011 est. 2035% increase
In 25 years
Yearly % Increase
Population
⅛-mile-buffer 98,476 152,125 54.48% # 1.83%
¼-mile-buffer 176,356 295,267 67.43% # 2.17%
½-mile-buffer 235,505 399,078 69.46% # 2.22%
Employment
⅛-mile-buffer 123,112 220,570 79.16% # 2.46%
¼-mile-buffer 170,643 326,346 91.24% # 2.74%
½-mile-buffer 188,865 334,402 77.06% # 2.41%
Transit DemandGPS data 47,731 51,752 70,915 48.57% # 1.66%
All 7,973,480 11,846,299 48.57% # 1.66%
CONTENTS
RouteDemand Per Route 2011 Demand Per Route 2035
AVL Sample Data (Partial) Est. Est. % Demand Increase
2 2,857 6.0% 486,773 6.1% 564,718 4.8% 16.0%3 1,394 2.9% 298,215 3.7% 365,388 3.1% 22.5%4 1,603 3.4% 390,506 4.9% 453,231 3.8% 16.1%5 2,982 6.2% 339,594 4.3% 387,302 3.3% 14.0%6 2,299 4.8% 558,512 7.0% 733,625 6.2% 31.4%7 3,095 6.5% 325,913 4.1% 431,198 3.6% 32.3%8 1,707 3.6% 322,554 4.0% 393,645 3.3% 22.0%9 2,999 6.3% 480,747 6.0% 580,952 4.9% 20.8%
11 2,077 4.4% 272,416 3.4% 331,277 2.8% 21.6%12 2,013 4.2% 335,856 4.2% 404,949 3.4% 20.6%13 1,723 3.6% 375,804 4.7% 439,760 3.7% 17.0%14 2,440 5.1% 296,625 3.7% 404,850 3.4% 36.5%15 2,488 5.2% 297,530 3.7% 380,225 3.2% 27.8%17 1,042 2.2% 182,106 2.3% 299,655 2.5% 64.5%18 1,865 3.9% 274,685 3.4% 310,053 2.6% 12.9%19 1,194 2.5% 287,027 3.6% 358,007 3.0% 24.7%25 692 1.4% 178,280 2.2% 206,903 1.7% 16.1%26 596 1.2% 161,306 2.0% 207,915 1.8% 28.9%28 437 0.9% 158,211 2.0% 176,980 1.5% 11.9%54 1,056 2.2% 236,279 3.0% 318,796 2.7% 34.9%56 913 1.9% 358,386 4.5% 467,410 3.9% 30.4%57 644 1.3% 185,843 2.3% 294,161 2.5% 58.3%
100 2,458 5.1% 279,053 3.5% 386,061 3.3% 38.3%101 3,773 7.9% 472,408 5.9% 681,922 5.8% 44.4%201 24,845 0.2%
202 290,009 2.4%204 180,121 1.5%205 430,011 3.6%206 66,974 0.6%207 91,881 0.8%209 55,916 0.5%211 99,165 0.8%212 47,025 0.4%213 294,651 2.5%214 3,817 0.0%395 1,311 2.7% 211,229 2.6% 263,328 2.2% 24.7%777 2,074 4.3% 207,620 2.6% 419,576 3.5% 102.1%
9999 - 0.0% - 0.0% - 0.0% 47,731 100.0% 7,973,480 100.0% 11,846,299 100.0% 48.6%
Future Routes
Existing Routes
Further Study
Needs More Understanding on Data Income, Captive Riders, Alternative Modes New Mobility Indexes of Each Routes Refined Accessibility Indexes of Each Stops Transit LOSs ; Total Service Area, Fare System, Headways, etc.