Report

TRB Transportation Planning Applications 2011 | Reno, NV Destination choice model success stories Rick Donnelly & Tara Weidner | PB | [donnellyr, weidner]@pbworld.com Overview Concepts Albuquerque HBW example (urban) Maryland example (statewide) Portland (freight) Pros and cons Discussion Competing theories Gravity model: Humans spatially interact in much the same way that gravity influences physical objects. Any given destination is attractive in proportion to the mass (magnitude) of activity there, and inversely proportion to separation (distance). Destination choice model: Humans seek to maximize their utility while traveling, to include choice of destinations. A potentially large number of factors influence destination choice, to include traveler and trip characteristics, modal accessibilities, scale and type of activities at the destination, urban form, barriers, and in some cases, interactions between these factors. Quick review Gravity model formulation Analogous DC model utility function? Albuquerque HBW logsum frequencies Simple DCM formulation Daily vehicle river crossings 500 000 400 000 300 000 200 000 100 000 Observed Gravity Gravity K DCM Maryland statewide model HBWx trip length frequency distributions 0 20 40 60 80 100 0.03 0.01 0.00 0 20 travel time (min) 60 80 100 0.04 0.03 0.02 0.01 0.00 0.01 0.02 Density 0.03 0.04 HBW5 composite: n=3901 n>90=154 NA=24 mean=37.1 20 40 60 travel time (min) 80 100 0 20 40 60 travel time (min) 0 20 40 60 travel time (min) HBW4 0.00 Density 40 travel time (min) composite: n=6451 n>90=288 NA=48 mean=37.3 0 0.02 Density 0.03 0.00 0.01 0.02 Density 0.03 0.02 0.00 0.01 Density 0.04 HBW3 composite: n=5662 n>90=289 NA=67 mean=36.3 0.04 HBW2 composite: n=3798 n>90=87 NA=20 mean=30.4 0.04 HBW1 composite: n=977 n>90=29 NA=4 mean=29 80 100 80 100 Utility function structure Size term Zonal characteristics Distance term Logsum Interaction of distance and household/zonal characteristics Compensation for sampling error Estimation summary by purpose Variable(s) HBW HBS HBO NHBW NHBO Mode choice logsum S S S S S(C) Distance* -S -S -S -S -S Income | distance* S S S Intrazonal dummy S S S S CBD dummy* -S -S -S -S -S Bridge crossing dummy -S -S -S -S -S Semi-urban region dummy* -S Suburban region dummy* -S Employment exponentiated term* S S S S S S S S Households exponentiated term * Multiple variables in this category (e.g., distance includes distance, distance squared, distance cubed, and log[distance]) HBW estimation results Mode choice logsum coefficient ~0.8 (reasonable) Distance, distance cubed, and log(distance) all negative and significant Distance squared was positive (?) Income coefficients positive and significant, but not steadily increasing with higher income Intrazonal coefficient positive and significant CBD coefficients for DC and Baltimore negative and significant Bridge coefficient negative and significant Households and retail, office, and other employment used for size term HBWx model comparison Destination choice model Doubly-constrained gravity model 1.5 8 1.5 8 1.0 1.0 6 0.5 4 2 4 6 0.5 4 2 4 2 6 8 Adjusted r2 = 0.47 2 6 8 Adjusted r2 = 0.79 Another way of looking at it GM Model DC K factors Model 0.2 0.4 0.6 0.8 Portland Bootstrap Destination choice For each firm: 1. Decide whether to ship locally or export 2. Choose type of destination establishment* 3. Sample ideal distance from observed or asserted TLFD 4. Calculate utility of relevant destinations 5. Ensure utility threshold exceeded (optional) 6. Normalized list of cumulative exponentiated utilities 7. Monte Carlo selection of destination establishment * Establishment in {firms, households, exporters, trans-shippers} Utility function (b) z = a + bx2.5 + qy2, x=employment,y=Djz 100 80 Fitnes 60 s 40 20 0 −30 400 m Nu −20 300 fe ro be −10 0 pl m 200 e oy 10 es 100 20 30 Dj z Circumstantial evidence Objections Non-intuitive interactions Harder to estimate and tune Not doubly-constrained Explicit error terms ? Bottom line Matches as well as k-factors but without their liabilities Far more flexible specification than gravity models Finer segmentation in gravity models avoided Ditch k-factors = stronger explanatory power Represents heterogeneity Fits nicely in tour-based modeling and trip chaining Interpretation of ASCs more straight-forward than k-factors Flexible estimation The real proof 1.5 GM Model K factors 8 1.0 6 0.5 4 2 DC 4 Model 2 6 8 0.2 0.4 0.6 0.8 Daily vehicle river crossings 500 000 400 000 300 000 200 000 100 000 Observed Gravity Gravity K DCM <comic/> Source: “Teaching physics”, http://www.xkcd.com