Understanding travel and mode choice with emerging modes; a pooled SP and RP model in Greater Jakarta, Indonesia

Highlights

• We conducted a state-of-the-art RP and SP survey and presented its methodology with a total of 5,143 respondents, which covers 52,731 observations.

• The development of On-Demand Transport (ODT) is popular in many cities. Urban Air Mobility (UAM) will become a new alternative mode of transportation.

• This paper presents the travel behavior of respondents in each mode of transportation.

• We explored willingness to pay (WTP): VTTS, VTAT, elasticity, and the preference of all choice alternatives, including ODT and UAM using pooled SP and RP data sets.

Abstract

The research presented in this paper analyzed two data sets Revealed Preference (RP) and Stated Preference (SP), obtained with a new travel diary and mode choice survey. This survey, called Mobility Jakarta, combined both RP and SP parts and was conducted in the Greater Jakarta region. This is the first survey that collected responses from a substantial sample of the whole metropolitan area population. We estimated the discrete choice model pooling SP and RP data sets. We explored the Willingness To Pay, e.g., the Value of Travel Time Savings, and the elasticity for all mode choice alternatives, including On-Demand Transport and Urban Air Mobility.

Introduction

Many hours are spent in traffic every day. In Greater Jakarta, commuters are estimated to spend at least three hours daily in traffic. People do not benefit from this unproductive use of their time. The population could use this time for more important, meaningful activities rather than sitting in traffic. This situation occurs not only in Jakarta but in most metropolitan cities around the world. There are, however, a growing number of new mode of transport alternatives available in many cities to tackle this problem. Additional alternatives will surely emerge in the coming years.

One such alternatives is On-Demand Transport (ODT). ODT connects potential passengers and potential drivers through a smartphone app. Growing ODT systems have become popular in many countries. The conventional taxi industry’s passenger volume has decreased since the ODT mode of transportation became available (Lam and Liu, 2017). The lower price and the greater convenience of using a smartphone are the main advantages of ODT compared to conventional taxi service. ODT also becomes a link to connect the last miles of a trip to its final destination, especially for motorcycle (MC) ODT, that can move faster through traffic congestion and can drive on narrow roads. These systems have also reduced the number of unemployed (AngryWorkersWorld, 2019). Driver’s income was higher than the minimum wage when this system started; however, it has decreased due to the growing number of drivers (Lam and Liu, 2017). Resistance from conventional taxi drivers exists in many countries (Borowiak and Ji, 2019, Lam and Liu, 2017, Peticca-Harris et al., 2018, Rogers, 2018) for various reasons, one of which is the lack of regulations imposed on ODT (Irawan et al., 2019, Rogers, 2018).

Several studies have investigated ODT, both car-based (Dias et al., 2017, Rayle et al., 2016, Young and Farber, 2019) and MC-based (Irawan et al., 2019, Medeiros et al., 2018), but they are limited to the characteristics of ODT users and the effects of ODT on other modes. In a study by Dias et al. (2017) on the socio-demographics of respondents that use car-based ODT, it was found that ODT users tend to not only be young, well-educated, and have a higher income, but they also live in higher-density areas. In another study, Rayle et al. (2016) showed that the user characteristics, wait times, and trips served differed between car taxi and car-based ODT. There are few studies on MC-based ODT (see, e.g., Irawan et al., 2019, Medeiros et al., 2018). Irawan et al. (2019) showed that MC-based ODT had a positive effect on the use of public transport when the system becomes a feeder to public transport. They also found that MC taxis and MC ODT competed with each other (Irawan et al., 2019, Medeiros et al., 2018). Other studies found the same competition between car taxis and car ODT (Contreras and Paz, 2018, Habib, 2019).

Another alternative mode is Urban Air Mobility (UAM). There is a growing interest in solving urban transportation problems by using air mobility. Nevertheless, UAM might be suitable only for high-income users because the price is much higher than other alternative modes. UAM may eventually become a realistic alternative mode of transportation. Land transport is indeed insufficient to accommodate the demand for mobility in Greater Jakarta. Three-dimensional transport, such as UAM, may be a strategy to address such congestion. Balac et al. (2019a) noted that several studies were trying to measure the demand for UAM in urban areas (see, e.g., Balac et al., 2019b, Fu et al., 2019, Garrow et al., 2017). Balac et al., 2019b, Balac et al., 2019a attempted to simulate UAM in the urban transport environment using an agent-based modeling approach based on the potential demand of UAM. Shaheen et al. (2018) measured the potential demand of UAM in several cities in the U.S. using SP experiment and attitudinal questions. In a related study, Eker et al. (2020b) measured individuals’ perceptions regarding the potential benefits of UAM.

The developments of Information and Communications Technology (ICT), which enable the evolution of emerging transportation modes, are inevitable. Peer production concepts, also known as a sharing economy in the digital era, have been discussed by Benkler (2002) and Pepić (2018). This business models connects the service offered by a company to individuals through the internet and currently exists in the transportation industry and many other sectors, including hotels, restaurants, ticketing, and e-commerce.

There are several companies in the ODT industry, such as Uber, Lyft, Grab, and Gojek. Uber and Lyft were launched in San Francisco in 2012. Uber focused on a black-car limousine service, while Lyft focused on a long-distance intercity carpooling named Zimride in 2007 (Henao, 2017). Uber was the big ODT player in Southeast Asia before Grab took over its business and Uber began selling its shares. This has also happened in other countries (Sothy, 2019). Currently, the local big players are Grab, which started in 2012 in Malaysia, and Gojek, which began in 2010 in Indonesia. Gojek, which was started from only a MC-based ODT in Indonesia, has expanded into other Southeast Asian markets. Gojek is backed by tech giants like Google and Tencent (Russell, 2018).

Moreover, most ODT companies expanded their businesses by providing other services, including transporting goods, and buying and delivering food. In this way, they also helpedmicro and small businesses to increase their sales (Harsono, 2019). Now that ODT is established, society does not want to reduce its availability. The service is very convenient; people can easily request rides anytime and anywhere and it operates as a door-to-door service with a fixed upfront price. This system is quickly growing as it meets transportation demands when conventional urban transportation modes cannot.

In the future, alternative forms of transportation will continue to emerge, including the development of electric-based or even autonomous vehicles, and flying transportation. Several companies, including Airbus, Uber, and Lilium, have been investing in the development of UAM. Airbus tested the flight of UAM in Eastern Oregon Downing Downing (2019). This system may help the congested city and longer-distance travelers to minimize their travel time. The vertical take-off and landing (VTOL) aircraft, which can land and take-off vertically, may reduce land transportation infrastructure growth in the future. However, the UAM will continue to be more expensive than other transportation alternatives, as its operational cost will be higher.

UAM might face several challenges in the future, however. As mentioned by Ahmed et al. (2020), the sustainability of UAM involves several aspects to be considered, such as safety, training, infrastructure, environment, logistics, cybersecurity, and the human factor. Reiche et al. (2018) and Cohen et al. (2020) also discussed aspects related to the development of UAM, such as challenges, infrastructures, technology, public acceptance, and laws and regulations. Al Haddad et al. (2020) found that safety was the main concern for the adoptions of UAM. Moreover, Eker et al. (2019) found that older persons are relatively more concerned about safety issues related to UAM than younger people.

This research aims to understand and explore the factors that influence mode choice. For each mode of transportation, the study identifies the users, when the transportation is used, the purpose of the trips using that mode of transportation, the trip chains, and the speed to reach different destinations (Jakarta or its agglomeration). We also explore the demand of each alternative: the willingness to pay (WTP) or value travel time savings (VTTS), value travel time assigned to travel (VTAT), and elasticity of all choice alternatives, including ODT and UAM. We conducted a stated choice experiment to gather the data and used discrete choice models for the analysis. The measurement of WTP, VTTS, VTAT, and elasticity all together has rarely been explored by other researchers.

To achieve our objectives, we also conducted a Revealed Preference (RP) survey in Greater Jakarta. Several studies have previously conducted RP surveys to better understand travel behavior (see, for example, Axhausen, 1995, Axhausen et al., 2002, Dharmowijoyo et al., 2015). To observe the WTP from the mode choice experiment, we conducted a Stated Preference (SP) survey. We estimated the model using a pooled RP and SP data set, which allows robust estimates balancing the limitations of both data sets. This study makes several contributions, including:

• We conducted a state-of-the-art RP and SP survey and presented its methodology with a total of 5,143 respondents and 57,524 choice observations.

• We gained new insight into travel behavior in Greater Jakarta.

• We explored WTP, including VTTS, VTAT, and elasticity of all choice alternatives, including ODT and UAM, using pooled SP and RP data.

The remainder of this paper is structured as follows. The second section describes the survey design and data collection. The third section, shows the descriptive statistics of the data in general. The fourth section presents the results obtained from the RP survey. The fifth section describes the SP survey’s experimental design and the construction of non-chosen alternatives for the RP data set. The sixth section explores the WTP, the VTTS, and the direct elasticity of choice alternatives using a discrete choice model. The last section of this paper presents the conclusions of the study, and recommendations for further research.