•••
1

Introduction
Methodology
Data
Description
Analysis

Proposal
The WayForward2

•Toyosu PP Data 2021•

Private and/or Polluting Modes such as Cars, FreightCars, Taxis and Motorcycle contribute toapproximately 14% of Toyosu’s Traffic
Going out to eat and going for shopping contributeto approximately 26% of Toyosu’s Traffic•••

Approximately 20% of all trips for shopping andeating are caused by these polluting modes
Approximately 22% of trips by cars and 32% of trips
by motorcycles are for shopping and eating outCar

Freight Car

Taxi

Motorcycle

Going for Shopping(100%=7064)

10.28%

0.04%

0.17%

1.57%

Going out to eat(100%=1760)

9.55%

0.74%

0.85%

1.59%

Car(100%=3863)

Freight Car(100%=241)

Taxi(100%=236)

Motorcycle(100%=413)

Going for Shopping

18.79%

1.24%

5.08%

26.88%

Going out to eat

4.35%

5.39%

6.36%

6.78%

3

Car

Motorcycle

PrivateTaxiFreight Cars

SharedOtherWalk

Bike

Sharebike

Train

SubwayNon Motorised

PollutingMonorail

TramBusMotorised

Non Polluting

MODES
Eating OutShopping

Buy Commodity

SightSeeing

LeisureHospital

Strolling

Non Buy CommodityDiscretionary

WorkSchool

From Home

LessonFrom work

FromShopping

To HomeNon Discretionary
PURPOSES
4

DescriptiveAnalysis

MNL
Modelling

Trip Chain
Analysis

Studying
Alternatives

Destination• Understand theshare of shopping,eating and delivery
trips• Understand themode choice for
shopping, eating
and delivery trips• Understand thefactors influencing
mode choice
behaviour for
shopping, eating
and delivery trips byMultinomial Logit
Modeling (MNL)• Understand factors
influencing mode
choice in trip chains• Understandfeasibility ofAutomated Delivery
Systems (ADS) to
reduce Carbon
Emission and Traffic• Modelling locationof shopping, eating
and delivery
through Machine
Learning (ML)
ModelsModelling

5Descriptive Analysis

MNL Modelling

Trip Chain Analysis

Studying Alternatives

Demand Modelling

PP Data: Toyosu 2021

PP Data: Toyosu 2021

PT Data

Literature Review

PP Data: Toyosu 2021

Sample Size = 33,494

Shopping and Eating
Trips

Sample Size =
1,448,714 (trips)

Previous research onADS

Shopping and Eating
Trips

Sample Size = 2,237

Sample Size (tours) =510,222

Sample Size = 2,237

6

Variable

DescriptionSpecificto Mode

Coefficient

Std.
Error

T-statIVTT

In-vehicle travel time

Generic-6.77173***0.50046-13.53TC

Travel cost

Generic-.00088***

0.00022-3.96

ET

Egress time

Generic-11.9997***

1.59509-7.52

AT

Access time

Rail-9.55363***

1.42754-6.69

U(Rail) = - 6.77*IVTT – 0.000088*TC – 11.99*ET –9.55*AT – 0.46*FEM

FEMFemale

Rail-.46046**

0.20616-2.23

U(Bus) = - 6.77*IVTT – 0.000088*TC – 11.99 *ET -2.12

INC_C

IncomeCar.17024**

0.07489

2.27

INC_B

IncomeBike-.17962***

0.0564-3.18

CHILD

ChildrenCar.23965*

0.14399

1.66

PVOWN

Pvt. Vehicle ownership

Car3.00634***

0.15703

19.15

BIOWN

Bicycle ownershipBike

2.76261***

0.16373

16.87

U(Bike) = - 6.77*IVTT – 0.000088*TC – 11.99 *ET +2.76*BIOWN – 0.179*INC – 2.92

Peak

Peak hourcar-.31826**

0.15798-2.01

U(Walk) = - 6.77*IVTT – 0.000088*TC – 11.99 *ET – 0.23

C_BIKE

Constant

Bike-2.92325***

0.31183

-9.37

C_BUS

Constant

Bus-2.12323***

0.24904-8.53

C_CARConstantCar-4.20224***

0.34768-12.09

C_WALK

Constant

Walk-0.23228

0.25502-0.91

N=Log likelihood of constant only model =Log likelihood at convergence =Rho square =2237-2620.8-1854.7
0.30

U(Car) = - 6.77*IVTT – 0.000088*TC – 11.99 *ET +0.170*INC + 0.23*CHILD + 3*PVOWN – 0.31*PEAK – 4.2••

Females, HH with children and High Income groups
can be the potential consumers for ADS
Deliveries can be preferred and promoted in non-peakhours as peak hours have rush for work based trips7

1. It can be observed that among the trips which use cars, 33% of thetrips include at least one the purposes among shopping, eating ordelivery• Number of cars used specifically for shopping, eating or delivery
trips = 7,362,447• Number of cars used if at least one purpose in the trip chain isshopping, eating or delivery = 2,450,048

Out of the total vehicles in traffic,
approximately 10.4% are themotorcycles or cars being used forshopping, eating or delivery

3. If even 80% of these trips are reduced, approximately 42,000passenger car units of traffic can be reduced per day through ADS• PCU of Car = 1• PCU of Motorcycle = 0.5
4. Considering only cars, of 243,5 tonnes of CO2/day emission can bereduced through ADS• Carbon Emission by Cars = 140 gCO2/passenger-km (Hayashiya,2017)• Carbon Emission by Cars = 182-143 gCO2/km• Occupancy of Car = 1-1.25 (assumption)• Number of cars for shopping, eating or delivery = 2,450,048

21972659

1176712450048
Remaining vehicles
Number of motorcycles for shopping, eating or delivery
Number of cars for shopping, eating or delivery

8Three types (Figliozzi, 2020) of autonomous vehicle are:
1. Drones or unmanned aerial vehicles (UAVs)
2. Sidewalk autonomous delivery robots (SADRs)
3. Road autonomous delivery robots (RADRs)

••
•

•
•
•

ADR technology for last-mile freight deliveries is a valuable step
towards low-carbon logistics (Pani et al., 2020)
Last-mile delivery has received a great deal of attention mainly
due to the enormously growing e-commerce (Vleeshouwer etal., 2017)) (Kapser and Abdelrahman, 2020)
Approximately 61.28% population showed positive willingness
to pay responses to SADRs, and the urban residents show
positive response (Pani et al., 2020)
Investment in technologies that reduce delivery times like
SADRs is happening (Figliozzi, 2019)
The amount of time people deem acceptable for delivery times
is shortening (Figliozzi, 2019)
SADRs can also indirectly reduce the number of on-roadvehicle miles travelled by delivery vans (Figliozzi, 2019)• Approximately 17%-31% on-road van travel distance
reduction by SADR (Figliozzi, 2019)• SADRs can be faster and more cost efficient than standard
delivery vans when customer density increases• (Figliozzi, 2019)• ADRs that travel on sidewalks and roads are being tested inseveral US cities• Air and ground autonomous vehicles have high potential toreduce CO2 emissions (Figliozzi, 2020)• Customers highly value the ability to receive deliveries as per
their choice of location and time (Figliozzi, 2020)• Significant increase in e-commerce is also expected during thecurrent COVID-19 pandemic (Figliozzi, 2020)9

Shopping Trips (21.0%)

Eating Trips (5.2%)

N

Total Trips = 845
Mean (distance in km) = 10.67
Median (distance in km) = 2.48

Total Trips = 222
Mean (distance in km) = 9.50
Median (distance in km) = 2.33

10N

σ𝑛𝑗=1 𝑤𝑖,𝑗 ∗ 𝑥𝑗 − 𝑋ത σ𝑛𝑗=1 𝑤𝑖,𝑗𝑧 𝑠𝑐𝑜𝑟𝑒 = 𝐺𝑒𝑡𝑖𝑠 − 𝑂𝑟𝑑 𝐺𝑖∗ =𝑆

Shopping and Eating-out Locations

Work and School Locations𝑛 σ𝑛𝑗=1 𝑤𝑖,𝑗 − (σ𝑛𝑗=1 𝑤𝑖,𝑗 )2𝑛 −1

Home Locations

11Modelling the destination of shopping and eating trip (CBD or not). It can be elaborated to predict exact location ofsuch demands with respect to time of the day.
ANN: ACCURACY = 94.54%
Precision

Recall

F1-score

Support

0 = not CBD

0.96

0.970.961296

1 = CBD

0.90

0.89

0.89

4620.95

1758

Accuracy
Macro
Average

0.93

0.93

0.93

1758

Weighted
Average

0.95

0.95

0.95

1758

XGB: ACCURACY = 97.78%
Precision

Recall

F1-score

Support

0 = not CBD

0.98

0.99

0.99

1296

1 = CBD

0.97

0.94

0.96

462

0.98

1758

Accuracy
Macro
Average

0.98

0.97

0.97

1758

Weighted
Average

0.98

0.980.98

1758

12Feature Importance of Departure Times ofCooking and Eating-out Trips

Ten Highest Important Variables to Determine
Destination Location

0.018

0.3

0.016

0.25

0.014
0.012

0.2

0.01

0.15

0.008
0.006

0.1

0.004

0.05

0.002
0Age 50-59

Age 0-29 yrs

Rail/Train

OD DistanceCar (as mode)

Income 10-14.9 Million Yen•

We can observe that the time of the day influences whether adestination is CBD or not
Location of origin of trip, number of modes available, caravailability, OD distance, and age are a few of the importantfeatures of this model

Origin Latitude•

Number of Mode ChoicesAvailable

Time of the Day in Hours Passed since Midnight

Origin Longitude2321191715

131197531013

User Acceptance• A study to ensure user acceptance of such system should be done beforehand

National Policies• Policies related to aerial delivery vehicles must be taken care ofSafety Concerns• In the case of sidewalk delivery vehicles, pedestrian safety should be taken care ofParking Infrastructure• In case of road delivery vehicles, parking facilities should be taken care of14

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