15.  the University of Tokyo A Aoi Watanabe, Bingchang Shen, Hikaru Tsukida, Rena Koseki, Takuro Arai Introduction Difficulty of Data Acquisition for Route Choice Model Classical Data Image Data
RGB Value Near infrared radiation Location of shopping street and the number of shops
Difficulty of Data Acquisition for Route Choice Model
Estimation method based on aerial photos / satellite images ▷ Easy and uniform estimation of various places / years ▷ Compensate the disadvantage of the classical data Aerial photo Satellite image (Near infrared) Data ● Grid Network grid size 900m 1200m Matsuyama @ 2007 ● Grid Size (variable) 100m, 75m, 60m, 50m, 30m ● PP data Car: 733 trips Walk: 643 trips
β-scaled Recursive Logit β: time discount rate v(a|k): instantaneous utility from grid k to grid a ∀k,a ∈ G : set of grids V (k) = E {v(a|k) +  βV(a) + ε(k)}] Grid Network Explanatory variables: ① RGB ② RGB + other Bands ③ Category value 7
β-scaled Recursive Logit v(a|k): instantaneous utility from grid k to grid a ∀k,a ∈ G : set of grids Grid Network Explanatory variables: ① RGB Red Green Blue transition cost θ 1 AR α  : Ave of Red values in grid a AG a  : Ave of Green values in grid a AB α  : Ave of Blue values in grid a DRa :S.D. of Red values in grid a DGa : S.D. of Green values in grid a DBa :S.D. of Blue values in grid a
β-scaled Recursive Logit v(a|k): instantaneous utility from grid k to grid a ∀k,a ∈ G : set of grids Grid Network Explanatory variables: ② RGB + other Bands B11 transition cost All : Ave of B08 values in grid a AX² : Ave of B11 values in grid a D λ 1 : S.D. of B08 values in grid a D λ² : S.D. of B11 values in grid a B08: near infra-red, Resolution 10m <Plants> B11: SWIR, Resolution 20m <Water>
β-scaled Recursive Logit v(a|k): instantaneous utility from grid k to grid a ∀k,a ∈ G : set of grids Grid Network Explanatory variables: ③ Category value (N-categories) v(a|k) = θ³· RC 1 + θ³· RC 2 + … + θ³· 1 · RC N − 1 − 1 transition cost Category Values θ³ : parameters RC i   : Ratio of Category in grid a (i = 1,2,...,N-1)
mode = car, β = 0.1, grid = 100m ** 5% significant
① RGB ② RGB + other Bands ③ Category value it: RGB + other Bands > RGB > Category value
mode = car, β = 0.1, grid = 100m ** 5% significant

classical data
image data
combination
ation > classical data ÷ image data
β = 0.1 ▲ RGB-car    ● RGB-walk ▲ RGB+otherBand-car    ● RGB+otherBand-walk •Decreasing the grid size reduces accuracy •Walk fits better than car on a small grid 13
① Transition of Hanazonomachi Street road width: 20m(before war)  寧◊  40m(after war) 寧◊ redistribution of road space (2017) classical data: we have “number of lanes” as explanatory variable ② Greenization of parking lots the parking lots scattered in the center of the area becomes green  space classical data: we don’t have “parking lot distribution” as explanatory variable
① Transition of Hanazonomachi Street Aerial Image taken in 2007
① Transition of Hanazonomachi Street Aerial Image with narrowed Hanaoznomachi Street
① Transition of Hanazonomachi Street * Assignment using Combinational Method The instantaneous utility (Car) of Aerial Image taken in 2007 2007
① Transition of Hanazonomachi Street * Assignment using Combinational Method The instantaneous utility (Car) of Aerial Image Increase in the utility outside of the Hanazonomachi Street
② Greenization of parking lots The Parking Lots Distribution
② Greenization of parking lots Aerial Image taken in 2007
② Greenization of parking lots Image after greenizing parking lots
② Greenization of parking lots * Assignment using Combinational Method The instantaneous utility (Car) of Aerial Image taken in 2007
② Greenization of parking lots * Assignment using Combinational Method The instantaneous utility (Car) of image after Greenization Changes in the instantaneous utility in various areas
● Estimation basing on Aerial Image and Satellite Image is proposed ● The proposed method has the almost same accuracy as the classical method ● with the combination of classical data and image data, improved fitness and interpretability coexist in the limited data
mode = car, β = 0.1, method = ① RGB
mode = walk, β = 0.1, method = ① RGB
Comparison of the Time Discount Rate β mode = car, grid = 100m, method = ③ RGB E
Comparison of the Time Discount Rate β mode = car, grid = 50m, method = ③ RGB