Research Collection Working Paper Existing urban transportation in Greater Jakarta Results of agent-based modelling Author(s): Ilahi, Anugrah; Balać, Miloš; Axhausen, Kay W. Publication Date: 2019-12 Permanent Link: https://doi.org/10.3929/ethz-b-000394347 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. ETH Library 1 Existing urban transportation in Greater Jakarta: Results of 2 agent-based modelling a,1,∗ a a 3 Anugrah Ilahi , Milos Balac , Kay W. Axhausen a 4 ETH Zurich, Institute for Transport Planning and Systems, Stefano-Franscini-Platz 5, 8093 Zurich, 5 Switzerland 6 Abstract 7 Agent-based models have become popular over the years as more traditional modelling 8 approaches were not suitable for studying emerging transportation modes and transport 9 policies. However, creating a detailed scenario, which is the basis of the agent-based 10 model that represents the supply and demand for a study area, is not trivial and requires 11 substantial effort. This is even more the case when the study area is large and contains 12 many people living and performing many different daily activities. This is exactly the 13 case for the region of Greater Jakarta, the subject of this research. Greater Jakarta has 2 14 an area of approximately 8000 km and is home to approximately 30 million people. 15 Here we present the synthesis of the Greater Jakarta commuting scenario to help future 16 researchers generate large-scale scenarios in similar regions where the data is Limited or 17 difficult to gather. First runs, using an agent-based model MATSim, integrated with a 18 mode-choice model are presented and can be used as a backbone for future improvements 19 of the Greater Jakarta scenario and investigations of different transport related questions. 20 Keywords: MATSim; Agent-Based Model; Mode-Choice Model; Greater Jakarta 21 1. Introduction 22 Travel activities are affected by many different factors, including the mode of trans- 23 portation, the trip purpose, and social interactions with other people (Kitamura, 1988; 24 Axhausen and Grling, 1992). Bradley and Vovsha (2005) studied how intra-household 0 25 interactions changed trip decisions. For example, a family s morning travel activities 26 could be influenced by the needs of a child in the household. If a parent needed to drop 27 off the child at school before he or she went to the office, that decision impacted the way ∗Corresponding author Email address: [email protected] (Anugrah Ilahi) 28 the schedule was planned. Borgers et al. (2001); Gliebe and Koppelman (2005); Simma 29 and Axhausen (2001) noted that different time allocations for different activities greatly 30 impacted trip planning. In addition, Axhausen et al. (2002) discovered that existing 31 routines, weather, and the purpose of each trip made mapping transportation efforts a 32 challenging undertaking. Arentze and Timmermans (2004) found several types of con- 33 straints on travel such as household constraints, spatial constraints, time constraints, and 34 spatial-temporal constraints. An example of time constraints was the opening and closing 35 times of different stores or shopping places. In addition, the distance is a constraint on 36 travel. The farther away sometime was, the longer it would take to travel there from a 37 home location. Trip activities can be influenced by bigger group interactions; however, 38 people are more greatly influenced by their personal needs, occupations, ethnicity, na- 39 tionality, or interests. One of the greatest influences on a persons trip decisions was their 40 family or other people with whom they spent most of their time. For this purpose, we 41 developed an agent-based model that was able to simulate complex interactions based on 42 previous studies. There are several agent-based models, such as ORIENT/RV (Axhausen, 43 1989), TRANSIMS (Smith et al., 1995), SimMobility (Adnan et al., 2016), SimTRAVEL 44 (Pendyala et al., 2012), Multi-Agent Transport Simulation MATSim (MATSim) (Balmer 45 et al., 2006; Horni et al., 2016), and GEMSim (Saprykin et al., 2019). However, we make 46 use of MATSim in this research which has been shown to be suitable to model large-scale 47 cities in Singapore (Erath et al., 2012), was able to include microbuses in simulation 48 (Neumann et al., 2015), and utilized joint activities between household and household 49 members as can be seen in Dubernet and Axhausen (2015). In addition, it could also 50 simulate the impact of emerging transportation options and policies, such as the impact 51 of car-sharing (Balac et al., 2019), Urban Air Mobility (Balac et al., 2018), bike-sharing, 52 congestion pricing, automated vehicles or equity effects, which are hard to investigate on 53 a suitable level using the more traditional modeling techniques (Horni et al., 2016). 54 Our contributions in this paper were threefold: first, to the best of the authors knowl- 55 edge, this paper will be the first to make use of an agent-based model that incorporates all 56 of mode transport available including microbuses, called angkots, as a form of transporta- 2 57 tion and that simulates the daily behaviors of the people performing their daily activities 58 in Greater Jakarta. There are previous studies that had been conducted in Jakarta. Yagi 59 and Mohammadian (2010) simulated mode and destination choices based on discrete 60 choice modelling, and Dharmowijoyo et al. (2016) measured variability of travel patterns 61 in Greater Jakarta; however, those studies do not take in to account Greater Jakarta as 62 a whole object of study using agent-based modelling. Second, our model used a novel 63 approach that integrated the mode-choice model in MATsim simulation that allowed it 64 to filter unnecessary plans. As shown in H¨orlet al. (2018, 2019), this integration can 65 give a faster convergence speed of simulations than previous scoring based models which 66 estimated all the plan options available (Balmer et al., 2006; Horni et al., 2016). Third, 67 this paper will also add to the growing literature on modelling large-scale cities, especially 68 when the data is scarce or difficult to obtain. Finally, different from a conventional four 69 step model, each individual is simulated as an agent, with each of them having their own 70 attributes, such as sociodemographic features, activity locations, and modes of trans- 71 portation. The attributes of each agent are used as an input plan, and by using iterative 72 processing, we found the best plan that maximized utility. 73 The remainder of this paper is structured as follows: The following section describes 74 the case studies of Greater Jakarta. The third section explains the MATSim framework. 75 The fourth section presents mode-choice in MATSim. The fifth section presents the first 76 results obtained for the commuting population of the Greater Jakarta. Finally, the last 77 section presents conclusions, limitations, and further recommendations. 78 2. Case studies of Greater Jakarta 79 Greater Jakarta is comprised of nine cities and four regencies. The province of Jakarta 80 itself has five cities. The other four cities are Bogor, Depok, Tangerang, and Bekasi. The 81 four regencies are Tengerang, South Tangerang, Bogor, and Bekasi. Greater Jakarta, 82 known as Jabodetabek, has a population of approximately 30 million inhabitants. Greater 83 Jakarta has a significant role in the national economy in Indonesia producing more than 84 IDR 1,500 trillion (USD 113.51 Billion ). In 2012, Jakarta was the primary contributor 3 85 to the Jabodetabek GDP, with a share of 72.44%; therefore, the economic activities and 86 employment opportunities of Jabodetabek are concentrated in the city of Jakarta. At the 87 national level, Jabodetabek contributes 18.48% of Indonesias GDP. Around 3.6 million 88 people commute within, into, and out of Jakarta (BPS-Statistics, 2014). Therefore, traffic 89 congestion is generated by daily activities of commuters coming to Jakarta. The sample 90 of the data that we used is from a JICA study (JICA, 2009), as can be seen in Table 1. 91 The total number of respondents were 334,973. 69.91% of respondents were male, and 92 47.58% of respondents who worked had university degrees. Most of respondents, 71%, 93 were in the agglomeration area (the area outside of Jakarta), and 28.74% were living in 94 Jakarta. 4 Table 1: Sample summary statistics Categorical variables N % Categorical variables N % Share Share Person is male ** 234,181 69.91 Car ownership University degree*** 96,542 47.58 No car 164'364 82.95 Employed** 202,924 60.58 1 12,703 7.10 Age categories ** 2 1,486 0.83 < 6years old 9,212 2.75 3 270 0.15 6 - 12 years old 62,086 18.53 > 3 130 0.07 12 - 18 years old 53,286 15.91 Motorcycle ownership 18 - 24 years old 34,627 10.34 No Motorcycle 53'009 29.62 24 -32 years old 51,435 15.35 1 92,668 51.78 32 - 42 years old 61,023 18.22 2 26,274 14.68 42 - 60 years old 57,565 17.18 3 5,635 3.15 > 60 years old 5,739 1.71 > 3 1,367 0.76 Income (in IDR per NMT ownership month)* No NMT 146,724 81.99 No answer 1,445 0.81 1 24,786 13.85 < 1 M 28,024 15.66 2 5,722 3.20 1 M - 3 M 116,461 65.08 3 1,192 0.67 3 M - 5 M 23,369 13.06 > 3 529 0.30 5 M - 8 M 6,746 3.77 Driving license** 8 M - 15 M 2,216 1.24 Motorcycle 98,854 29.56 > 15 M 692 0.39 Private car 7,410 2.22 Total expenditures (in Passenger vehicle 2,512 0.75 IDR per month)* Motorcycle and car 13,195 3.95 No answer 1,818 1.02 Motorcycle