Dhaka Metro Rail

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Shohel Amin	--	2406	2023-08-02 22:35:30	\|
2	update references and layout	Rita Xu	Meta information modification	2406	2023-08-03 04:18:30	\| \|
3	update description	Rita Xu	-128 word(s)	2278	2023-08-04 08:21:45	\|

This entry is adapted from the peer-reviewed paper 10.3390/su151411270

The Dhaka Metro Rail (DMR) has been constructed as part of the Dhaka Transport Coordination Authority’s 20-year Strategic Transport Plan to reduce traffic congestion in Dhaka, the capital city of Bangladesh. The DMR is the first urban rail transit system in Bangladesh and has the potential to change the existing modal share. Commuters have mixed responses about the daily commuting on the DMR and mode choice behavior.

MRT theory of planned behavior structural equation modeling commuter's perception modal shift

1. Introduction

A transportation system that includes effective and service-friendly public transit ensures accessibility and improves the living standards of a community. Cities in developing nations are implementing rail-based transit systems as a solution to the challenges of urban traffic congestion and rapidly expanding travel demands ^[1]. Following the lead of initiatives in Hong Kong and Singapore, Dhaka, the capital city of Bangladesh, has constructed the Dhaka Metro Rail (DMR) to accommodate the region’s growing transport demands ^[2].

Dhaka is one of the world’s most densely inhabited cities and has a high population growth rate ^[3]. The city is struggling with traffic congestion due to the unregulated growth of private vehicles, inadequate traffic signaling systems, the road infrastructure, and drivers’ propensity for risk-taking behavior. Long-term traffic congestion significantly reduces available working hours and has a detrimental impact on the economy. The traffic congestion also worsens the city’s general environmental predicament causing air and noise pollution ^[4]. A mass rapid transit (MRT) system is a sustainable solution to minimize traffic congestion, improve vehicle mobility efficiency, and reduce air pollution. The MRT system, a low-noise and -vibration electric mechanical system, runs on renewable energy, making it an environmentally benign, sustainable, and efficient transportation system.

The DMR was constructed as part of the Dhaka Transport Coordination Authority’s 20-year Strategic Transport Plan to reduce traffic congestion in the city center of Dhaka, where six rail lines are planned for the total network ^[5]. Line 6 was recently inaugurated to operate from Uttara to Agargaon. The MRT system is crucial to securing long-term advancements and stabilizing the modal share of commuters for maintaining an affordable transport system within a transit-friendly city ^[6]. The MRT system is new for the residents of Dhaka, and there are mixed responses about the daily ridership and mode choice behavior. In addition, insignificant attention was given to studying the modal preferences, the willingness to switch to the MRT for daily commuting, and the supportive circumstances for the potential users ^[7]. The voice of the daily commuters and their travel preferences about a new transport system are often neglected. The perception of commuters portrays the quality of transportation services that support the service providers to set up the development goals and priority areas required for improvement within the budget constraints. The improvement of the MRT system improves its performance and services resulting in higher MRT ridership. Extensive research is necessary to properly apprehend the travel behavior of the modal shift for daily commuting.

2. Background on Dhaka MRT Line 6

Dhaka Metro Rail is a public transport project carried out by the Dhaka Mass Transit Company Limited (DMTCL) under the Department of Road Transport and Highway division. It is built by the state-owned DMTCL and funded by JICA (Japan International Cooperation Agency, Tokyo, Japan). The design was approved under a fast-tracked procedure at the Executive Commission of the National Economic Council (ECNEC) on 18 December 2012, and the construction was initiated on 26 June 2016. The DMR network will have a total of 104 stations, including 53 underground stations and 51 elevated stations. The Metro Rail carriages are outfitted with top-of-the-line amenities such as information displays, enhanced seating arrangements, wheelchair availability, and air exertion. The vibration and noise are reduced using cutting-edge technology. Due to the reduced travel time, it is expected to carry around 1.6 million passengers per day after the completion of the entire project. This will significantly reduce traffic congestion, air pollution, and journey times in Dhaka City.

A total of six routes were defined for the project, and in the first phase of the design, two metro lines will be constructed: Line 6 (Uttara North–South Line) and Line 1 (Airport–Gulistan Line). The first route, called “MRT Line 6”, is now operational. MRT Line 6 is 20.1 km long with 16 stations and runs from Uttara in the north to Kamalapur in the south, with a 3.5 km long underground section and a 16.6 km long elevated section. However, in the present timeline, the first route to commence is the Uttara-to-Agargaon section. Its length is 21.26 km. MRT Line 6 can carry 60,000 passengers per hour. A six-auto train each way can carry up to 2308 passengers. The study is primarily focused on the effect of the introduction of the first route of MRT Line 6.

3. Studies on Mode Shift to Metro

Although metro systems are becoming popular worldwide as an efficient resource to alleviate congestion and pollution ^[8], the willingness of prospective consumers to switch to the metro as their primary mode of transport is frequently ignored in feasibility assessments for new MRT projects ^[7]. This may cause the demand for metro services to be overestimated. A study ^[9] of possible metro customers in the vicinity of a proposed metro line was carried out in the Red Line project in Thailand using a sample of 667 respondents (staff and students situated at a nearby university campus). A total of 90% of the respondents were willing to switch to the metro for an upcoming 20 km trip between Salaya and Bangkok.

Some studies were conducted on the pre-launch views about MRT services in both industrialized ^[10] and underdeveloped ^[1]^[9] nations. Fraszczyk and Mulley ^[10] studied Sydney’s new autonomous metro trains and how the public felt about this innovative form of transportation. A study in Copenhagen ^[11] on the transport effects of a harbor corridor metro was based on traffic counts, panel discussions, and model projections. According to the traffic data in the Copenhagen study, bus riders accounted for 70–72% of the modal shift to the metro, whereas automobile users made up just 8–14%. The Copenhagen passenger travel demand model is a state-of-the-art model that incorporates business, commuter, educational, and leisure groups. The metro’s appeal as a brand-new form of transportation is estimated using stated preference (SP) data and a tour-based model. It was proposed that the planned metro infrastructure development might be utilized as a tool for policy to assure ideal traffic conditions in and out of the city.

Yuanqing Wang et al. ^[12] examined the mode-shifting behavior in response to the opening of the metro system in Xian City, China. An SP study was conducted along the metro route before starting the metro service. The SP model and the revealed preference (RP) survey, which was carried out after the metro’s launch, were contrasted. A logistic regression model for both work-related and leisure travel was created. The lack of modal incorporation into various cities resulted in an 8 to 19% decline in the number of travelers choosing the metro. Besides this, Ling Ding et al. ^[13] investigated the impact of multiple transit priority methods on passengers’ modal change in China. A comparison of the impact of single and multiple strategies on modal shifts was conducted by applying the logit model to SP and RP datasets.

To determine the likelihood of non-metro commuters switching to the Delhi metro, Chauhan ^[14] evaluated the effectiveness of a multivariate statistical modeling strategy and also examined the causes of this departure from buses and private motor vehicles (PMVs). A survey database of 500 commuters on different metro lines was used by Chauhan ^[14]. Binary logistic regression was applied to determine the proportion of modal shift among bus passengers and PMV riders towards the Delhi Metro. Chauhan ^[14] identified that 57% of metro customers switched from buses, and 28.8% switched from PMVs. Since most metro commuters no longer utilize buses, there has not been much growth in the public transportation sector. By conducting two surveys—an RP survey (actual behavior) with a sample of 153 respondents and an SP survey (hypothetical situations) with a sample of 169 respondents—Sohoni et al. ^[1] studied the mode change behavior of commuters in Mumbai. According to the RP survey’s findings, most participants (almost 80%) had previously used public transportation before switching to a new metro line. Additionally, more than half of the respondents (about 60%) who commuted by private vehicle in the SP questionnaire indicated a readiness to switch to the planned metro line. Selvakumar et al. ^[15] studied the increasing competition across PT modalities due to Chennai’s recently installed metro rail system. Express bus passengers were the subjects of an SP survey to investigate the impact of metro rail on the bus mode. A modal shift model was created using the SP data to anticipate the likely transition from bus to metro rail. The findings showed that fare difference, age, and wealth have a significant impact on shift behavior. Selvakumar et al. ^[16] investigated the ridership of the proposed metro extension corridor in Chennai, India, by analyzing the inter-rail modal shift behavior of suburban train passengers. Using a stated preference questionnaire, a sample of 272 suburban rail customers traveling for employment, school, and other purposes was questioned for this inter-rail competition study. Six scenarios were considered for the assessments, including the time saved by taking the metro and the cost difference between commuting by metro and bus. The survey revealed that suburban rail travelers were less interested in journey time savings and instead prioritized travel costs regardless of the objective of their trips. In the context of India, this demonstrates the distinctive metro choice behavior of suburban train travelers.

The pre-launch impressions of a new metro service in Jakarta, which was to be the first metro system in Indonesia, were investigated by Dahlan et al. ^[9]. They divided the 516 respondents into two groups based on where they lived: those who lived along the new metro passageway and those who lived outside of it in other parts of the city. They found a few notable differences between the two groups. Fewer people who owned private cars belonged to the metro passageway category than the other group, and they were more inclined to use the metro as a substitute for other forms of transportation.

4. Conceptual Background

The TPB offers a key conceptual framework for addressing the intricacies of human social behavior. According to the TPB, the personal judgment of a behavior (attitude), socially anticipated style of behavior (subjective norm), and self-efficacy concerning an activity (perceived behavioral control) are the prime variables in social and behavioral research ^[17]. A positive attitude and a norm that is supportive of the action offer the desire to engage in it, but a definite intention to do so is only created when perceived control over the conduct is strong enough.

Attitude, the first component of the model, is considered to be a consequence of easily available beliefs, particularly behavioral beliefs, which are an individual’s subjective likelihood that engaging in an activity would result in a certain experience or outcome. These behavioral beliefs collectively result in either a favorable or unfavorable attitude toward the behavior ^[18]. Although the strength of the relationship between attitude and behavior has occasionally been disappointing, the results have typically confirmed the hypothesized relationship between salient beliefs and attitudes ^[17].

The second component of the model, normative belief, comprises two categories: injunctive and descriptive ^[19]. An anticipation or subjective probability that a particular referent individual or group approves or disapproves of engaging in the action in question is known as an injunctive normative view. On the other hand, descriptive normative beliefs are opinions about whether significant others engage in the behavior. The perceived total social pressure to follow the behavior or subjective standard is influenced by both sorts of views ^[18]. The chance that significant referent persons or groups approve or disapprove of engaging in a certain activity is what normative views are concerned with ^[17].

The third component of the model, perceived behavioral control, is predicated on accessible control beliefs, just as attitudes are predicated on accessible behavioral beliefs, and subjective norms on accessible normative beliefs ^[18]. According to TPB, a set of beliefs that ultimately decide intention and action has to do with whether or not necessary resources and opportunities are available. These control beliefs may be influenced by prior experience with the behavior, but they will typically also be influenced by hearsay about the behavior, interactions with acquaintances and friends, and other factors that either increase or decrease the perceived difficulty of engaging in the behavior in question ^[17]. A person’s subjective likelihood that a certain facilitating or inhibiting factor would exist in the circumstance of interest is referred to as a control belief. Each control belief interacts with the factor’s perceived ability to help or hinder the performance of the activity to influence perceived behavioral control. In the TPB, it is anticipated that real behavioral control would reduce the impact of intention on behavior as well as the influence of attitude and subjective norms on intention ^[18].

A vast body of research on the theory of planned conduct has convincingly proved that attitudes toward the conduct, subjective norms about the behavior, and perceived control over the behavior are all proven to accurately predict behavioral intentions. However, the hypothesis makes no mention of how these beliefs came to be ^[19]^[20]; it just draws attention to a wide range of potential background elements, including exposure to media and other sources of information, aspects of a personal nature like personality and general life values, and demographic factors like education, age, gender, and income. These factors are anticipated to have an impact on intentions and behavior through their impact on the theory’s more immediate determinants. Most empirical studies only evaluate a few demographic factors when they are used as controls. Nonetheless, other studies concentrate on one or more background variables that are thought to be pertinent to the behavior being studied for intuitive or theoretical reasons ^[19].

References

Sohoni, A.V.; Thomas, M.; Rao, K.K. Mode shift behavior of commuters due to the introduction of new rail transit mode. Transp. Res. Procedia 2017, 25, 2603–2618.
Alam, M.S. Factors in deciding Metro Rail in Developing Countries: A study on the proposed Metro Rail system for Dhaka. J. Bangladesh Inst. Plan. 2010, 2075, 9363.
Paul, T.; Chakraborty, R.; Ratri, S.A.; Debnath, M. Impact of COVID-19 on mode choice behavior: A case study for Dhaka, Bangladesh. Transp. Res. Interdiscip. Perspect. 2022, 15, 100665.
Mahmud, K.; Gope, K.; Chowdhury, S.M.R. Possible Causes & Solutions of Traffic Jam and Their Impact on the Economy of Dhaka City. J. Manag. Sustain. 2012, 2, 112.
Akhter, S. Six MRT Lines to Crisscross Bangladesh Capital by 2030. New Age, 28 December 2022.
Rahman, M.S. Future Mass Rapid Transit in Dhaka City: Options, Issues and Realities. Jahangirnagar Plan. Rev. 2008, 6, 69–81. Available online: https://ssrn.com/abstract=1293804 (accessed on 13 June 2023).
Fraszczyk, A.; Weerawat, W.; Kirawanich, P. Commuters’ Willingness to Shift to Metro: A Case Study of Salaya, Thailand. Urban Rail Transit 2019, 5, 240–253.
Otsu, H. Thailand MRTA Initial System Project (Blue Line) I–V. 2007. Available online: https://www.jica.go.jp/Resource/english/our_work/evaluation/oda_loan/post/2008/pdf/e_project09_full.pdf (accessed on 13 June 2023).
Dahlan, A.F.; Fraszczyk, A. Public Perceptions of a New MRT Service: A Pre-launch Study in Jakarta. Urban Rail Transit 2019, 5, 278–288.
Fraszczyk, A.; Mulley, C. Public Perception of and Attitude to Driverless Train: A Case Study of Sydney, Australia. Urban Rail Transit 2017, 3, 100–111.
Vuk, G. Transport impacts of the Copenhagen Metro. J. Transp. Geogr. 2005, 13, 223–233.
Wang, Y.; Li, L.; Wang, Z.; Lv, T.; Wang, L. Mode shift behavior impacts from the introduction of metro service: Case study of Xi’an China. J. Urban Plan. Dev. 2013, 139, 216–225.
Ding, L.; Zhang, N. Estimating modal shift by introducing transit priority strategies under congested traffic using the multinomial logit model. KSCE J. Civ. Eng. 2017, 21, 2384–2392.
Chauhan, V.; Suman, H.K.; Bolia, N.B. Binary Logistic Model for Estimation of Mode Shift into Delhi Metro. Open Transp. J. 2016, 10, 124–136.
Selvakumar, M.; Reddy, M.A.; Sathish, V.; Venkatesh, R. Potential Influence of Metro on Bus: A Case Study. J. Inst. Eng. Ser. A 2018, 99, 379–384.
Selvakumar, M.; Ramulu, D.S.; Sankar, K. A Unique Metro Choice Behaviour of Suburban Rail Passengers in India. Urban Rail Transit 2023, 9, 31–41.
Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211.
Ajzen, I. The theory of planned behavior: Frequently asked questions. Hum. Behav. Emerg. Technol. 2020, 2, 314–324.
Fishbein, M.; Ajzen, I. Predicting and Changing Behavior: The Reasoned Action Approach, 1st ed.; Psychology Press: New York, NY, USA, 2011; p. 538.
Conner, M.; Armitage, C.J. Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. J. Appl. Soc. Psychol. 1998, 28, 1429–1464.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Transportation; Urban Studies

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Silvia Saleh

Anusree Saha Tithi

Nazmus Sakib

Tonmoy Paul

Nafis Anwari

Shohel Amin

View Times: 972

Update Date: 04 Aug 2023

Table of Contents

Video Upload Options

Confirm