• Keine Ergebnisse gefunden

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

N/A
N/A
Protected

Academic year: 2021

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

Copied!
37
0
0

Wird geladen.... (Jetzt Volltext ansehen)

Volltext

(1)

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.

(2)

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

(3)

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

(4)

How to create a better plan for sustainable transportation?

3

• 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

(5)

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

(6)

5

Activity-Based Model (ABM) Approaches for Sustainable Cities

Chapter I. Travel Behaviour 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

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)

(7)

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

(8)

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

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

(9)

Result 2: Private vehicle for long duration trip

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

(10)

Result 3 : Public transport has the cheapest travel cost

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

MC :Motorcycle Angkot :Microbus

(11)

Result 4 : Mandatory activity chains are the most frequent

39.70 %

26.00 %

9.50 %

7.60 %

(12)

11

Activity-Based Model (ABM) Approaches for Sustainable Cities

Chapter I. Travel Behaviour 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

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.

(13)

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)

(14)

Chapter 2: SP Survey Design

13

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

Jakarta Agglomeration

Driver Non-Driver

(15)

Chapter 2: SP Experiment

UAM :Urban Air Mobility/flying taxi

(16)

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

(17)

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

(18)

Chapter 2 : Motorcycle dominate independent of age

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

UAM :Urban Air Mobility/Flying taxi

(19)

Chapter 2: Equations

𝑉𝑇𝑇𝑆

𝑖,𝑛

=

δ𝑉𝑖,𝑛/δ𝑇𝑖,𝑛

δ𝑉𝑖,𝑛/δ𝐶𝑖,𝑛

=

60,000

14,000

β𝑇

β𝐶

(2)

𝐸

𝑖𝑞𝑋

𝑘𝑖𝑞

𝑤𝑖

= σ

𝑞=1𝑄𝑠

𝐸

𝑖𝑞

𝑋

𝑘𝑖𝑞 𝑤𝑞𝑃𝑖𝑞

σ𝑞=1𝑄𝑠 𝑤𝑞𝑃𝑖𝑞

(3)

𝑈

𝑖,𝑛,𝑡

= 𝐴𝑆𝐶

𝑖

+ β

𝑖

Χ

𝑖,𝑛,𝑡

+ ℇ

𝑖,𝑛,𝑡

(1)

(20)

Result 1: The goodness of fit

19

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

(21)

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

(22)

Result 3: Elasticity

21

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

(23)

Result 4: VTTS of UAM grows with income and distance

(24)

23

Activity-Based Model (ABM) Approaches for Sustainable Cities

Chapter I. Travel Behaviour 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

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)

(25)

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

(26)

Result 2: Validation with crow-fly distances, examples

25

(27)

Result 2: Mode shares by distance band

(28)

27

Activity-Based Model (ABM) Approaches for Sustainable Cities

Chapter I. Travel Behaviour 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

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)

(29)

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

(30)

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)

29

(31)

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

Scenario 1 Scenario 2 Scenario 3

Morning peak Evening peak

(32)

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

Morning peak Evening peak

(33)

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

Scenario 1 Scenario 2 Scenario 3

Morning peak Evening peak

(34)

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

33

(35)

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

(36)

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

35

(37)

THANK YOU !

Questions & Answers

Referenzen

ÄHNLICHE DOKUMENTE

This behavior relates to the process de- scribed by Braybrooke (1978), where he points out that issues are frequently trans- formed over time. Round B is a good

The MAMP model is a natural extension of the burgeoning literature on the key role that limited time, attention and information processing capabilities play in

For example, if there are m := 1000 numbers available to play the lottery, and one is going to play 50 times, the probability that one will never win is 0.9512; if the number of

In the proposed project a simulation model of urban land use, mobility and environment developed at the Institute of Spatial Planning of the University of

The most important feature of ParTeG is the ability to satisfy combined coverage criteria: For instance, control-flow-based coverage criteria like MC/DC or transition-based

Within the framework of the funding initiative ‘Energy Efficient Cities’ (‘Eneff:Stadt’) of the Federal Ministry of Economics and Technology (BMWi) the research

With their participation, residents contribute to the sustainable energy transition, not only by shifting to a renewable and locally produced source of energy but also by

Gender, perceived health status, level of concern for air quality, level of concern for climate change, and the desire for greater access to information regarding air quality