Activity-Based Model (ABM): Approaches for sustainable cities

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Research Collection

Presentation

Activity-Based Model (ABM)

Approaches for sustainable cities

Author(s):

Ilahi, Anugrah Publication Date:

2020-12

Permanent Link:

https://doi.org/10.3929/ethz-b-000454198

Rights / License:

In Copyright - Non-Commercial Use Permitted

This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.

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Activity-Based Model (ABM)

Approaches for Sustainable Cities

Doctoral Candidate :

Examination Committee :

Chairperson:

IVT, ETH Zürich Ph.D Defense 01.12.2020

Anugrah Ilahi

Prof. Dr. Kay. W. Axhausen Prof. Abolfazl Mohamadian

Prof. Dr. Prawira Fajarindra Belgiawan

Prof. Dr. Bryan Adey

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Greater Jakarta Now

Source: https://nl.pinterest.com/pin/427067977148124500/?autologin=true

• 30 million population

• 14.464 people/km

²

High density

• 53% congestion level in 2019

• 10th congested city worldwide

• 13 cities in 3

provinces

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How to create a better plan for sustainable transportation?

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• Exploring travel behaviour in Greater Jakarta.

• Measuring the willingnes to pay (WTP) and elasticity for mode of transport available.

• Developing an agent-based simulation of Greater Jakarta

• Simulating policy scenarios for measuring the impact of

road pricing

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Activity-Based Model (ABM) Approaches for Sustainable Cities

Chapter I. Travel Behavior Exploring travel behavior in

Greater Jakarta

Chapter 2. Mode Choice Model

Measuring the willingness to pay (VTTS, Elasticity)

Chapter 3. An Agent Based Simulation Approach Population Synthesis

Chapter 4. Policy Analysis

Model Calibration

Measuring the impact of road pricing

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5

Activity-Based Model (ABM) Approaches for Sustainable Cities

Chapter I. Travel Behaviour Exploring travel behavior in

Greater Jakarta

Chapter 2. Mode Choice Model

Ilahi, Anugrah, Belgiawan, Prawira F., Balać, Milos and Kay W. Axhausen. 2019. Understanding Travel and Mode Choice with Emerging Modes: A Pooled SP and RP Model in Greater Jakarta. Arbeitsberichte Verkehrs- und Raumplanung 1448. (Under Review)

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Chapter 1. Exploring travel behaviour in Greater Jakarta

Revealed Preference Survey: Travel Diary

1432 individuals

3711 Individuals in 951 households

1 2 3

4

5

7:00 A.M

7:30 A.M 17:00 P.M 17:30 P.M

18:00 P.M

20:00 P.M

20:10 P.M

22:00 P.M 22:25 P.M

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Result 1: NMT shares is the highest for short distance trip

7 ODT :On Demand Transport/Raid hailing NMT :Non-Motorized Transport

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Result 2: Private vehicle for long duration trip

ODT :On Demand Transport/Raid hailing NMT :Non-Motorized Transport

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Result 3 : Public transport has the cheapest travel cost

9 ODT :On Demand Transport/Raid hailing BRT :Bus Rapid Transit

MC :Motorcycle Angkot :Microbus

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Result 4 : Mandatory activity chains are the most frequent

39.70 %

26.00 %

9.50 %

7.60 %

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11

Activity-Based Model (ABM) Approaches for Sustainable Cities

Greater Jakarta

Chapter 2. Mode Choice Model

• Ilahi, Anugrah, Belgiawan, Prawira F., Balać, Milos and Kay W. Axhausen. 2019. Understanding Travel and Mode Choice with Emerging Modes: A Pooled SP and RP Model in Greater Jakarta. Arbeitsberichte Verkehrs- und Raumplanung 1448. (Under Review)

• Ilahi, Anugrah, Belgiawan, Prawira F. and Kay W. Axhausen. 2020. Influence of pricing on mode choice decision integrated with latent variable: The case of Jakarta Greater Area. In Mapping the Travel Behavior Genome, edited by Goulias, Konstadinos G. and Davis, Adam W., 125-143. Amsterdam: Elsevier.

• Belgiawan, Prawira F., Ilahi, Anugrah and Kay W. Axhausen. 2019. Influence of pricing on mode choice decision in Jakarta: A random regret minimization model. Case Studies on Transport Policy 7.1: 87-95.

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Chapter 2: Mode Choice Model

Measuring value of travel time savings (VTTS) and elasticity.

•

Pooling stated preference (SP) and revealed preference survey (RP)

•

Hyphothetical experiment (SP)

•

Added non-chosen alternatives (RP)

•

Implementing discrete choice modelling using multinomial logit

model (MNL)

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Chapter 2: SP Survey Design

13

0-1.5 km 1.5-5 km 5-15 km > 25 km

Jakarta Agglomeration

Driver Non-Driver

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Chapter 2: SP Experiment

UAM :Urban Air Mobility/flying taxi

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Chapter 2: Mode shares on SP and RP dataset

15 SP : Stated Preference

RP : Revealed Prefence

ODT :On Demand Transport/Raid hailing BRT :Bus Rapid Transit

MC :Motorcycle

UAM :Urban Air Mobility/Flying taxi PT :Public Transport

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Chapter 2 : Motorcycle dominate regardless of income

ODT :On Demand Transport/Raid hailing PT :Public Transport

UAM :Urban Air Mobility/Flying taxi

1 USD : 15,000 IDR

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Chapter 2 : Motorcycle dominate independent of age

17 ODT :On Demand Transport/Raid hailing PT :Public Transport

UAM :Urban Air Mobility/Flying taxi

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Chapter 2: Equations

𝑉𝑇𝑇𝑆

_𝑖,𝑛

=

^δ𝑉^𝑖,𝑛^/δ𝑇^𝑖,𝑛

δ𝑉_𝑖,𝑛/δ𝐶_𝑖,𝑛

=

^60,000

14,000

∗

^β𝑇

β𝐶

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𝐸

_𝑖𝑞𝑋

𝑘𝑖𝑞

𝑤_𝑖

= σ

_𝑞=1^𝑄^𝑠

𝐸

_𝑖𝑞

𝑋

_𝑘𝑖𝑞 ^𝑤^𝑞^𝑃^𝑖𝑞

σ_𝑞=1^𝑄𝑠 𝑤_𝑞𝑃_𝑖𝑞

(3)

𝑈

_{𝑖,𝑛,𝑡}

= 𝐴𝑆𝐶

_𝑖

+ β

_𝑖

Χ

_{𝑖,𝑛,𝑡}

+ ℇ

_{𝑖,𝑛,𝑡}

(1)

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Result 1: The goodness of fit

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Model MNL (M1) MNL (M2)

Observations 52,731

Final-LL -57,153 -59,103

Rho-square 0.44 0.42

AIC 114,381 118,267

BIC 114,709 118,533

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Result 2: VTTS - People enjoy staying longer in car

Model Mode Fuel/Ticket

Cost

Congestion Cost

Access Cost

Model1 PT*

Bus*

BRT Train Car

Motorcycle Taxi

ODT UAM

0.86 3.56 3.23 8.21 1.80 7.06 10.52 15.38 4.98

- - - - 0.62 2.43 3.62 5.29 -

- - - - - - - - 10.70

Model2 PT

Car

Motorcycle Taxi

ODT UAM

3.07 2.55 6.85 9.88 12.92 5.47

- - - - - -

- - - - - 9.35

VTTS : Value of travel time savings

VTAT : Value of travel time assigned to travel The value in USD/hour

*Insignificant

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Result 3: Elasticity

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Model Mode Travel Time Travel Cost

Model1 Walk

BIke PT Bus BRT Train Car

Motorcycle Taxi

ODT UAM

-0.33 -0.94 -0.06*

-0.46*

-0.42 -0.87 -0.26 -0.47 -2.28 -3.68 -0.15

- - -0.63 -0.33 -0.05 -0.71 -0.28 -1.75 -1.72 -0.63 -2.07

Model2 Walk

Bike PT Car

Motorcycle Taxi

ODT UAM

-0.48 -0.99 -4.43 -0.52 -0.68 -2.17 -2.99 -0.28

- - -2.70 -1.17 -0.48 -0.96 -2.55 -2.96

*Insignificant

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Result 4: VTTS of UAM grows with income and distance

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Activity-Based Model (ABM) Approaches for Sustainable Cities

Greater Jakarta

Chapter 2. Mode Choice Model

Chapter 3. An Agent-Based Simulation Approach Population Synthesis

• Ilahi, Anugrah and Kay W. Axhausen. 2019. Integrating Bayesian network and generalized raking for population synthesis in Greater Jakarta. Regional Studies, Regional Science 6.1: 623-636.

• Ilahi, Anugrah, Balac, Milos and Kay W. Axhausen. 2019. Existing urban transportation in Greater Jakarta: Results of agent-based modelling. Arbeitsberichte Verkehrs- und Raumplanung 1478. (Under Review)

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Chapter 3: Greater Jakarta Scenario Synthesis

•

Supply

•

road network, public transport services)

•

OpenStreetMap and GTFS

•

Demand

•

synthetic population

•

HTS from JICA in 2010

•

Census 2017 and 2018

•

Matching activities

•

Mobility Jakarta Survey in 2019

•

Model Calibration using MNL model

•

Mobility Jakarta Survey in 2019

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Result 2: Validation with crow-fly distances, examples

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Result 2: Mode shares by distance band

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27

Activity-Based Model (ABM) Approaches for Sustainable Cities

Greater Jakarta

Chapter 2. Mode Choice Model

• Ilahi, Anugrah, Balac, Milos and Kay W. Axhausen. 2019. Existing urban transportation in Greater Jakarta: Results of agent-based modelling. Arbeitsberichte Verkehrs- und Raumplanung 1478. (Under Review)

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Chapter 4: Road Pricing Scenario Simulation

Scenario 1

Scenario 2

Scenario 3

1,500 IDR/Km

2,500 IDR/Km

3,500 IDR/Km

3,000 IDR/Km

4,000 IDR/Km

5,000 IDR/Km

1 USD : 15,000 IDR

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Chapter 4: Case study at eight main roads

Location:

•

Gadjah Mada (3.5 km)

•

Majapahit (1km)

•

Medan merdeka (1km)

•

Thamrin (1.7 km)

•

Sudirman (4.9 km)

•

Sisingamaharaja (1.3 km)

•

Gatot subroto (6.7 km)

•

Rasuna said (4 km)

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Result 1: Will road pricing decrease the traffic?

Scenario 1 Scenario 2

Scenario 3

-2.77%

-4.53%

-7.90%

-4.50 % -6.28 % -8.29 %

-6.05 % -7.85 % -8.64 %

Scenario 1 Scenario 2 Scenario 3

Morning peak Evening peak

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Scenario 1 Scenario 2

Scenario 3

-2.36%

-8.10%

-6.80%

+1.59 % -1.78 %

-3.68 %

+ 5.08 % -0.29 %

Scenario 1 Scenario 2

Scenario 3

Scenario 1

Scenario 2 Scenario 3

+ 5.88 %

Result 2: How about its impact on car?

31

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Result 3: How about its impact on motorcycle?

Scenario 1 Scenario 2

Scenario 3

-3.66 % -5.38 % -8.40 %

-5.58 % -8.01 % -9.71 %

-7.27 % -9.47 %

-8.64 %

Scenario 1 Scenario 2 Scenario 3

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Conclusions

•

An agent-based model can model realistic behavior and consider the activity constraint.

•

The model will be the bases for further policy scenarios

•

Understanding the travel behavior will help us to decide

which policy could significantly improve urban transportation

•

UAM (Urban Air Mobility) can be an option for long-distance trips

•

Improving public transport facilities is as important as reducing travel time

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Future Work

•

Modelling emerging transport mode in Greater Jakarta

• Micromobility, Car sharing, and Urban Air Mobility (UAM)

•

Modelling other transport demand management measures

• Parking facilities, odd and even plate policy, and emissions

•

Implementing and an Agent-Based Modelling approach for other cities in Indonesia, such as Greater Bali and Greater Bandung

•

Simulating the spread of COVID-19 in Greater Jakarta

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Acknowledgements

• Supervisor

• Prof. Dr. Kay W. Axhausen

• Examiners

• Prof. Abolfazl Mohamadian

• Prof. Dr. Prawira Fajarindra Belgiawan

• Chairperson

• Prof. Dr. Bryan Adey

• Funders (LPDP Scholarships, and Airbus Project)

• IVT Colleagues

• Milos, Basil, Kirill, Sebastian, Aoyong, Grace, Felix, and Other IVT friends

• IVT Admin and IT Staff

• Pieter, Jenny, Valérie, Elisabeth

• Mobility Jakarta Survey Surveyors (Taki, and All surveyors)

• Indonesian friends in Switzerland

• Mas Nanda, Raka, Mas Bram, Mbak Nui, Mas Wahyu and the Indonesian student association

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