Mobility and transport infrastructure in Mumbai Metropolitan Region: growth, exclusion and modal choices

ABSTRACT

Social and physical infrastructure development needs to catch up to population growth in cities of the Global South. The infrastructures are emerging in piecemeal ways following demand, be it housing, water supply, electricity, financial, educational, health or transport services. Indian cities are typical examples of this and can be called archetypes of the problem. This paper finds that transport infrastructure development in Mumbai Metropolitan Region has not kept pace with the demand. Notwithstanding recent developments, urban mobility in the region is characterised by traffic jams, delays and a lack of integrated and inclusionary transport systems, adversely affecting the economic efficiency of the region through loss of labour time, delays in deliveries of goods and services, environmental pollution and health costs.

KEYWORDS:

• Urban mobility
• disorderly development
• transport modal choice
• traffic congestion
• exclusion

1. Introduction

The Global South is undergoing an urban revolution (Datta & Shaban, 2016). However, this urban revolution is characterised by what Albert O. Hirschman (1958, p. 89) called ‘disorderly’ and ‘compulsive’ development as the amassing of the population in cities of the Global South is not accompanied by infrastructural development. Rather, it is following demand in gradual and piecemeal ways, be it housing, water supply, educational or health infrastructure. The unplanned urban growth, lack of resources and sustainable urbanisation policies have also compromised the urban transport infrastructure affecting the accessibility and mobilities in cities. In many cities of the Global South, more than half of their residents experience restricted access, leading to either high travel burdens or exclusion from opportunities (Venter et al., 2019). Lack of affordable and efficient transport infrastructure in the cities also leads to high pollution, rising health costs and exclusion of economically weaker sections, women, the elderly, children and differently abled persons from the benefits of living in a city. Indian cities are typical examples of this disorderliness and can be called archetypes of this problem (Shaban et al., 2022). With more inclusive accessibility, better mobility for all and sustainable transport infrastructure, the cities can tackle the issues of deteriorating environmental quality, economic competitiveness and inequality. Hence, it is necessary to study various aspects of urban transport and examine as to what extent it is affecting the liveability and the economic efficiency of cities, especially the megacity like Mumbai which is the commercial capital of India. This is important because, ‘while air, water, drainage, and waste management are important parameters affecting the liveability of a city, it is the physical infrastructure elements, such as transportation and connectivity that decide the future of a high growth city like Mumbai’ (V. Rathi et al., 2020, p. 53).

The present paper attempts to understand urban mobility in MMR and the related infrastructure development. Specifically, it (a) examines the development of transport infrastructure in Mumbai Metropolitan Region (MMR), (b) investigates the people’s mobility pattern, exclusion, mode preferences and their determinants and (c) discusses the evolving policies about transport infrastructure with present and future of the mobility in the region. The rest of the paper is organised as follows: Section 2 reviews the relevant literature on urban mobility, while Section 3 discusses the data and methodology employed in this paper. Discussion on the development of transport infrastructure in the region is presented in Section 4. Section 5 examines people’s mobility patterns, travel costs, experience and determinants of modal choices. Section 6 concludes the paper.

2. Review of literature

Urban mobility in the context of cities of the Global South has been studied quite widely. Most of the literature have highlighted the immense pressure on transport infrastructure due to the growing population and a steep rise in the number of vehicles (Runji, 2015; V. Rathi et al., 2020), and how they adversely affect the cities’ economic vitality and quality of life. These studies often recommend that urban transport planning systems and promoting more sustainable urban transport can help build healthy cities (Pojani & Stead, 2018; Thondoo et al., 2020; Venter et al., 2019). It has also been emphasised that a citizen-centred approach provides a unique opportunity to reshape and re-orient policies on urban transport planning for more healthy and equitable cities (Thondoo et al., 2020).

Most of the literature demonstrates that the mobility pattern and modal choice are determined by various factors like socio-economic conditions of the urban residents, availability of public transport infrastructure, user’s perception about the means of transport, etc. Several studies have analysed modal choices and identified the factors determining the modal preferences. Cheba and Saniuk (2016) modelling transport preferences and the nature of changes in transport behaviour in Western Europe and Central and Eastern Europe have thrown light on the factors affecting the residents’ mobility behaviour. Jalón et al. (2019) studied users’ perception of different categories of transport modes, and how that leads to individual’s preferences while selecting one particular means of transport for daily travel in the city of Madrid. They find that users’ perceptions of the underground metro are better than the buses in the city. Islam and Hoque (2020) using a multinomial logistic model find that the major preference of the people in Bangladesh is railways.

In the context of India, several studies have discussed how cities are grappling with problems arising out of rapid urbanisation and inadequate transport infrastructure, affecting the city’s mobility, economy and environment. Reddy and Balachandra (2010) based on their analysis of the modal split of passenger vehicles, energy use and energy intensity of different transport modes for the million-plus cities in India concluded that the absence of a proper public transportation system is the main detrimental factor affecting the mobility and accessibility in Indian cities. They argue, ‘the combination of public transport, cycling and walking and their integration into a single overall transport system, makes a city more liveable than one that almost exclusively depends on private automobiles’ (Reddy & Balachandra, 2010, pp. 30–31). Ahmad and de Oliveira (2016) find that densification promotes public transportation in Indian cities, but the rise in income is the main reason for the rise in private transportation.

Kumar et al. (2016) find that in the absence of adequate government-operated public transport, privately operated public transport modes like autorickshaws, taxis, minibuses, etc. serve the mobility needs of the commuters and ‘bridge a large transport supply gap and play an important role in Indian cities’. Sarna and Bhatia (1991) show that mobility levels of low-income households are quite low compared to high-income households in Indian cities, and non-motorized transportation is present mainly in lower-order or smaller cities. Reviews of urban transport trends, problems, and potential solutions (S. Rathi, 2017) and various plans, and policy initiatives for the development of transport infrastructure in Indian cities have also been carried out in various studies. Nallathiga (2008, 2006) evaluates various projects undertaken to provide mass urban transport systems in Mumbai, Delhi and Ahmedabad and finds that for Mumbai the mass rapid transport system based on rails and buses continues to be the major option in comparison to water transport, as the water transport is yet to develop in the city. Patil et al. (2020) argue that for efficient traffic and transport planning, there is a need to understand the modal choice behaviour of travellers in Indian cities.

The issues of unequal and restricted mobilities are found in many cities of the Global South. Venter et al. (2019) from their studies on Johannesburg and Mexico City find that more than half of the city dwellers, comprising the low- and medium-income groups, experience limited access, leading to increased travel burdens or exclusion from opportunities. Through a large household survey, Baker et al. (2005) show that the poor in Mumbai work closer to their residence because of higher travel costs. This implies that the poorer households in the city, because of higher travel costs, cannot negotiate spaces and work in areas offering better wages. The poor earners spend 19% of their income on transport if they travel by bus, and 17% on sub-urban trains. Further, the time cost for the poor is also higher as a majority of the poor on average travel 20 min to avail railways. Baker et al. (2005) argue that lowering transport costs may improve the accessibility of the poor to better wages and housing. The higher transport costs in relation to wages of the poor, unskilled workers create a vicious circle of being spatially locked in areas of low wages, which leads to residential/housing immobility. Baker et al. (2005) found low residential mobilities in Mumbai, as 75% of households stayed in their current houses for the last 10 or more years. Living in highly dense and socially disorganised slums leads to low educational attainment and skills in the younger generation and, in turn, works as a barrier to higher wages and economic mobility (Shaban, 2013). Other studies have also shown the relations between transport access, availability and inequality (Mahendra et al., 2020) and how transport inequalities impact ‘people’s quality of life and their ability to participate in everyday activities’ (Banister, 2018). The disparity in transport access also emerges because of digital inequalities, as not all are able to benefit from digitalisation in transport services (Durand et al., 2022).

Bandagi (2021) and Alam and Dappe (2021) argue the need for gender-sensitive public transport system and gender-responsive mobility planning to address gender inequality which affects women’s economic independence and empowerment. Gilder (2020) based on their research in Mumbai argues for making transport more inclusive by addressing the special requirements of people with disabilities. Sharma and Patil (2016) show that transport networks and costs also determine Mumbai’s healthcare system’s access and utilisation. Among others, the poor households in the city are not able to effectively avail of public healthcare services because of the higher transport costs. In sum, the studies show a kind of exclusionary and intensifying mobility crisis in cities of the Global South and more so in the case of Indian cities.

3. Data and method

We have acquired the data needed for this study from primary and secondary sources. The secondary data on various infrastructures and their growth and development are obtained from different government departments and related reports. For instance, data on motor vehicles are obtained from the Department of Motor Vehicles, Government of Maharashtra, Mumbai; data on city bus fleets are taken from The Brihanmumbai Electric Supply & Transport Undertaking (BEST), Government of Maharashtra, Mumbai; population data are obtained from Census of India, Ministry of Home Affairs, Government of India, New Delhi. For examining the individual behaviour related to transport mobility and travel costs incurred within the MMR, a survey was conducted during October–December 2022 with the commuters at different nodes, Vashi, Navi Mumbai, Thane, Kurla, Bandra, Borivali, Colaba and Bhiwandi. A total of 163 respondents were interviewed. The respondents were requested to provide details of their usual modal choice, travel expenditure, problems they faced and their socio-economic situation. Besides having questions related to individual socioeconomic characteristics, like education and income, the urban mobility interview schedule also had questions of polytomous responses. For instance, it was attempted to find out whether a person is using multi-mode of travelling such as sub-urban railways, metro rails, monorail, private car, bus, taxis, autorickshaw, cycle and walk, and what is the distance one covers with each mode and time and money one spends on each mode of travel. This helped us to understand not only the modal choices of individuals but also the expenses incurred for each mode.

Polynomial logistic regression has been used to identify and understand the determinants of modal preference by individuals. Polynomial regression is useful for understanding the impact of independent variables on a polytomous response or dependent variables. The modal choice we have taken in MMR regions are (a) train, bus, metro rails and monorails (public transport), Y_b, (b) personal car, Y_c, (c) taxi and autorickshaw (hired), Y_t and (d) motorbikes, Y_m. The use of cycles, including hired cycles, is still very limited in the region; therefore, we have not included the same in our model. The independent variables used in the model are age in years (Age), gender, where male = 0 and female = 1 (Gender), and monthly family income in INR (Income). We have used monthly family income as some of the travellers, like students, did not have earnings and were dependent on family income. Their behaviour and modes of travel are shaped by family income status.

We have used the public transport mode as the base outcome, and in that reference, factors determining the other modal preferences are examined. This required estimating k − 1 equation, where k is the number of modes of travel. The three multinomial equations estimated with Y_b as a base response are as follows:

(1) $ln\left(\frac{Y_c}{Y_b}\right)={\alpha }_{11}+{\beta }_{11}Income+{\beta }_{12}Age+{\beta }_{13}Gender+{\epsilon }_{11}$(1)

(2) $ln\left(\frac{Y_t}{Y_b}\right)={\alpha }_{21}+{\beta }_{21}Income+{\beta }_{22}Age+{\beta }_{23}Gender+{\epsilon }_{21}$(2)

(3) $ln\left(\frac{Y_m}{Y_b}\right)={\alpha }_{31}+{\beta }_{31}Income+{\beta }_{32}Age+{\beta }_{33}Gender+{\epsilon }_{31}$(3)

where ${\alpha }_s$ are intercepts, ${\beta }_s$ are the regression coefficients and ${\epsilon }_s$ are the error terms.

4. Development of urban transport infrastructure in MMR

The availability of adequate and well-managed public transport modes is vital for providing equitable access to transport infrastructure to all which will further enhance the access to jobs, services, all that is necessary for the city’s economic vitality and improved quality of life. An increase in the number of personalised vehicles adds to traffic congestion, rise in vehicular pollution and increased fuel consumption, thus, cumulatively adversely impacting the environment. Poor and unequal mobility due to restricted access to urban infrastructure affects mainly the low-income communities, further increasing the city’s socio-economic inequalities. The availability of choices of different transport modes can address this problem. ‘For an urban transportation system to function efficiently and equitably, it should be capable of meeting the transport needs of the diverse users’ (Mitra et al., 2013, p. 1).

MMR is one of the fastest-growing metropolises in India with a population of 23.6 million (Census of India, 2011), covering an area of 6328 sq. km (MMRDA, 2021a). Regarding local governing bodies, MMR consists of nine Municipal Corporations, nine Municipal Councils and one Nagar Panchayat. Greater Mumbai is the hub of the MMR and is one of the nine Municipal Corporations with a population of 12.44 million, spreading over an area of 437 sq. km (MMRDA, 2021a). It occupies 9.1% of the MMR area. The growths of other cities within MMR have historically depended on the economic vitality of Greater Mumbai.

Greater Mumbai being the financial capital of India attracts a large number of migrants from different states of India and also from Maharashtra state itself, in which the city is located. The city is connected by roadways having a cumulative length of 2000 km and 450 km of the rail network (Figure 1). Every day during the morning peak hour about 700,000 persons enter Greater Mumbai from other neighbouring cities/municipal corporations and municipalities for work and other purposes (LEA Associates, 2016). This creates a huge pressure on the existing transport infrastructure leading to traffic congestion, and overcrowded trains and buses. Commuters struggle in long queues, delays and discomforts during their daily travel. Though MMR had a good transport base for a long time, which includes suburban railways and bus services, the growth of these two public transportation modes in the city ‘has proven to be insufficient to match with the growth of population in this hub of business’ (Varshney, 2018).

Figure 1. Major transport networks in MMR, 2022.

Source: Prepared by the authors based on ArcGIS base map and MMRDA (2016).

Suburban rail service which runs across 450-km networks, along the Central, Western and Harbour Lines, is the lifeline of MMR. The mobility in the MMR is very much connected to the suburban rail services. Historically, it was here that the first train service of India was flagged off from ‘Bori Bunder’ (Bombay) to Thane in 1853, and the first suburban rail operation was initiated between Virar located in the northern suburbs and Backbay (the present Churchgate Terminus) in 1867. By suburban trains, in 2012–13, the numbers of passenger trips carried were 7.34 million passengers daily (LEA Associates, 2016). With the laying of the Eastern Freeway connecting the eastern suburban district with the Central Business District (CBD) and the widening of the Sion-Panvel Expressway, which is another important road running through the city, there has been a reduction in the number of passenger trips on suburban trains, specifically on Central and Harbour Lines, as many commuters to escape the crowded conditions of the suburban rails have changed their travelling modes.

Each of the municipal corporations in the region has its own owned buses services. In Greater Mumbai, the bus service is operated by Brihanmumbai Electric Supply and Transport (BEST) Undertaking which has 3380 buses operating on 335 routes (BEST, 2023). BEST is the largest bus service provider in the region. BEST provides service within Greater Mumbai and to some important destinations within MMR. There had been a dip in the number of daily passengers riding BEST buses from 4.2 million in 2011–12 to 2.3 million in 2018. In 2019, it marginally rose to 2.5 million, but again declined to 1.9 million during COVID-19. Post lockdown period there has been a sharp increase in ridership, to 4.5 million in 2022. The main reason for the decline is high travel time, mainly due to traffic congestion. However, 2011–12 to 2018–19 was also the period when the BEST started increasing ticket prices. Therefore, some passengers shifted to suburban trains, which hardly changed ticket prices (Sen, 2019, 2020). In June 2021 (during the COVID-19 period), it had 3,369 buses, and a total of 1.9 million passengers travelled daily (Ahmed, 2021). The increase in the use of intermediate public transport modes on share bases, by which passengers can travel faster, flexibly and comfortably to their destinations, has also been a reason for this decline (Sen, 2019). BEST has presently introduced new routes to provide last-mile connectivity to commuters who use Metro services and then for further travel avail other public transport like auto/taxi/buses to reach their final destinations (Adimulam, 2023). BEST also provides a ferry service between Malad and Manori located in the northern suburbs. Electric bus services running on lithium-ion batteries have been added recently.

Other than rail and bus services, there are metro and monorail services, which are modes of rapid transit systems. The metro rail system is a viable option to provide fast, safe and comfortable mobility in MMR. ‘The government has thus developed a plan for 12 metro lines covering 276 km’ (Saxena, 2019). Among the operational lines are Line 1 running between Versova and Ghatkopar, Line 2A and Line 7 connecting some densely populated areas from Western Suburb, while the other lines are under various stages of construction (Figure 2). The monorail service operating along 19.5-km Chembur-Wadala-Jacob circle corridor did not have a substantive impact on the city’s transport mobility as it does not connect important mobility nodes, and also because ‘monorail rakes have low carrying capacity’ (Kamble, 2019). Also, there are share-an-auto services, taxis and app-driven private taxis and auto services. Cycles and bikes on rent also have been introduced as an attempt towards an environment-friendly transportation system at the micro-transit level. However, their use remains almost negligible in the city because of the lack of dedicated corridors for operating them.

Figure 2. Major transport infrastructure under construction in MMR, 2019.

Source: Created by authors based on ArcGIS base map & MMRDA (2016).

The MMR is multi-modal in terms of transport infrastructure having both rail and bus services as modes of public transport, taxis and autorickshaws as Intermediate Public Transports (IPT), and individual transportation modes like personal cars and two-wheelers. To meet the rising demand, the vehicular population has increased sharply in MMR (Table 1 and Figure 3) and ‘has far exceeded the carrying capacity of roads in Mumbai’ (V. Rathi et al., 2020) leading to frequent occurrences of traffic congestion and long commute time. Also, over the years from 2001 to 2019, there has been a considerable increase in vehicular density. As per the Traffic Index 2018, Mumbai is having the worst traffic congestion. It ranks first amongst 403 cities globally regarding peak-hour traffic congestion, with a congestion level of 65% (Rao, 2019). On a regular day during peak hours, it takes 2.5 h to travel from one end to the other, especially west to east (10–15 km) of Greater Mumbai itself (V. Rathi et al., 2020, p. 42). Additionally, in MMR, the traffic movement becomes slower and gets affected due to flooding at many places during the Monsoon season. Parked vehicles along the roadside adds to the problem of traffic congestion.

Figure 3. Annual growth rate (%) of vehicles in MMR and its major sub-regions vis-a-vis Maharashtra state, 2001–2019.

Source: Computed using data from the Government of Maharashtra (2017, 2020).

Table 1. Vehicular population in MMR, 2001–2019.

Year	No. of vehicles (in million)					The density of vehicles/sq. km
	Greater Mumbai	Thane Region	Panvel Region (Panvel+Pen)	MMR	Maharashtra	MMR (area 6328 sq. km)	Greater Mumbai (area 437 sq. km)
2001	1.03	0.67	0.14	1.84	6.76	291.24	2355.98
2005	1.29	1.11	0.23	2.63	9.94	415.95	2963.25
2010	1.77	1.86	0.41	4.04	15.77	638.93	4045.30
2015	2.57	2.89	0.56	6.02	25.59	951.22	5883.76
2019	3.79	4.36	0.92	9.07	36.91	1432.96	8664.64

Source: Computed using data from Government of Maharashtra (2017, 2020).

Aggregation of motor vehicle data and its analysis shows that given households’ low-income level and lack of parking spaces, two-wheelers or motorbikes remain very popular in the MMR region (Table 2). The share of two-wheelers in the total registered vehicles in the region is 73%, followed by cars, 12.3% (Government of Maharashtra, 2020). Among the para-transit modes (for-hire flexible passenger transport), autorickshaw has the largest share of 2.8% followed by luxury/tourist taxis and meter-fitted taxis (0.8%). Autorickshaws and taxis known as intermediate public transport (IPT) modes are found to be effective for the last mile connectivity in Mumbai. They act as feeder services to mass transport modes.

Table 2. Types of registered vehicles by regions in MMR.

Category	Number of vehicles (in ‘000’)				Share (%) of type of vehicle
	Gr. Mumbai	Thane Region	Panvel Region	Maharashtra	Gr. Mumbai	Thane Region	Panvel Region	Maharashtra
Motorcycles	1463.1	2111.3	476.2	19974.7	40.5	50.8	54.5	56.4
Scooters	634.9	450.5	55.6	4263.2	17.6	10.8	6.4	12.0
Moped	31.5	24.6	1.8	1728.9	0.9	0.6	0.2	4.9
Total 2-wheelers	2129.5	2586.3	533.8	25966.8	58.9	62.2	61.1	73.4
Cars	1031.0	781.5	153.1	4347.1	28.5	18.8	17.5	12.3
Jeeps	29.5	57.3	13.3	568.7	0.8	1.4	1.5	1.6
Stn. Wagons	3.7	6.3	0.2	18.9	0.1	0.2	0.0	0.1
Taxis meter fitted	44.4	5.8	0.9	65.6	1.2	0.1	0.1	0.2
Luxury/Tourist Cabs	75.6	68.0	18.3	284.2	2.1	1.6	2.1	0.8
Auto-rikshaws	206.8	233.3	41.0	974.3	5.7	5.6	4.7	2.8
Stage carriages	5.2	2.5	1.5	37.6	0.1	0.1	0.2	0.1
Contract carriages/Mini Bus	7.0	21.5	1.1	65.1	0.2	0.5	0.1	0.2
School Buses	2.9	3.1	2.2	29.5	0.1	0.1	0.3	0.1
Private Service Vehicles	1.1	2.8	0.5	12.7	0.0	0.1	0.1	0.0
Ambulances	1.4	2.9	0.7	16.0	0.0	0.1	0.1	0.0
Articulated/Multi-Axled Vehicles	0.0	16.2	25.1	63.0	0.0	0.4	2.9	0.2
Trucks & Lorries	9.2	122.6	41.2	521.4	0.3	2.9	4.7	1.5
Tanker	0.6	21.1	3.5	50.5	0.0	0.5	0.4	0.1
Delivery Van (4 wheelers)	40.3	107.6	20.7	665.6	1.1	2.6	2.4	1.9
Delivery Van (3 wheelers)	24.8	99.4	7.3	450.3	0.7	2.4	0.8	1.3
Tractors	0.2	3.2	1.9	771.0	0.0	0.1	0.2	2.2
Trailers	0.1	13.0	2.4	413.9	0.0	0.3	0.3	1.2
Others	2.0	5.8	5.3	70.0	0.1	0.1	0.6	0.2
Total	3615.2	4160.0	873.8	35,392.2	100.0	100.0	100.0	100.0

Source: Computed using data from the Government of Maharashtra (2020).

5. Mobility patterns and determinants of modal choice

MMR has multiple modes of transport, and among these, as the survey shows, public transport (buses, suburban trains, Metrorail & Monorail) is a dominant primary mode of travel. About 64.4% of commuters use them. About 9.8% of commuters use a personal car, 6.7% use motorbikes and 13.5% use taxis and autorickshaws as the primary mode of travel. In the MMR region, a commuter’s average daily distance travelled is 37.37 km. Out of the total 163 respondents interviewed, 16% reported that they travel all 7 days a week, while 30.7% and 22.7%, respectively, reported that they travel 6 and 5 days a week. Thus, about 70% of the commuters travel five or more days a week in the MMR. The maximum distance travelled is by those using suburban trains (32.4 Kms/person/day), followed by those using personal cars (27.6 Km). The average time per commuter spent daily for travel is 2 h 20 min and the longest time spent on travelling is by those using a personal car. Those using personal cars incur maximum expenditure for travel (Table 3). Among the various reasons for time wasted during commuting, the maximum time is lost due to traffic congestion. Thirty-seven per cent of the respondents reported this as the major problem faced in their travel. On average, 36 min are lost due to traffic congestion (Table 4).

Table 3. Share of respondents using different modes of travel, average distance, time of travel and expenses in MMR.

Mode of travel	Users (%)	Average distance travelled per day (km)	Average time spent per day (min)	Average expenses per day (INR)
Bus	37.42	11.0	29.3	18.11
Trains	43.56	32.4	33.8	16.91
Taxi	19.02	13.2	50.5	238.64
Autorickshaw	49.08	7.2	39.1	107.81
Monorail	3.68	7.7	53.2	17.66
Metro Rail	8.59	5.6	22.1	14.14
Private car	22.70	27.6	92.9	358.72
Motorbike/scooter	24.54	16.2	49.4	66.65
Cycle	8.59	4.9	112.0	–
Walk	50.31	4.0	32.7	–
Total	–	37.37	140.15	236.40

Source: Based on data from field survey.

Table 4. Reasons for time loss in waiting.

Reasons	Average waiting time (in min)
Waiting for rides (taxi, bus, autorickshaw, trains)	26
Traffic congestion (non-toll plaza)	36
Congestions at toll plazas	22
All reasons	66

Source: Based on data from field survey.

The study by Baker et al. (2005) has shown that the poor in Mumbai spent 17% and 19% of their monthly income, respectively, even if they travel in suburban trains or city buses. However, our study shows that currently on average individuals spend 12.59% of their income on transport. However, the brunt is faced by the poorer sections. Those with income below INR 50,000 (approximately US $600 at the current exchange rate), spent about 26.42% of their monthly income on transport. Those in the monthly income band of INR 50,001-INR 100,000 on an average spend 9.73% of their income on transport while for higher classes it is lower. It is 4.74% for those with a monthly income of INR 100,001–INR 200,000, and 3.58% for those with a monthly income of INR 200,001 or more. The difference in the expenditure between our study and that by Baker et al. (2005) is mainly because the latter took into account only suburban trains and buses which are relatively low-cost modes of travel, and our study includes all the modes of travel used by individuals. Further, over the years, we have seen a rise in the fare especially for taxis and autorickshaws because of the rising fuel costs and wages. The ticket prices of suburban trains and city buses have also increased over the years (Sen, 2019). The higher transport cost in the MMR also implies that people may not have enough saving for investing in housing and even not been able to move to different parts of the city to avail better wages. Even their access to public health care gets compromised due to the high transport costs (Sharma & Patil, 2016).

We employ multinomial logistic regression to examine the impact of family monthly income, age of commuters and gender of a commuter on modal choice. The results of the estimations are produced in Table 5. We have used public transport mode as the base outcome factor while estimating the other modal preferences of commuters. The likelihood ratio (LR) chi-squared of the model is statistically significant, and this shows that in the model not all the coefficients of the slope are zero. The pseudo-R-squared also shows a relatively good fit of the equation (McFadden, 1974).

Table 5. Multinomial regression representing determinants of modal selection.

Dominant mode of travel		Coefficients (β)	p-values	Exp(β)/RRR
Base outcome = Public mode of travel (Bus, Train, Metro, Monorail)
Personal car	Intercept	−7.510	0.004	–
	Age	0.035	0.549	1.035
	Monthly income (INR)	0.000	0.000	1.000
	[gender=Female]	−1.978	0.149	0.138
Taxi and autorickshaw	Intercept	−2.091	0.005	–
	Age	0.002	0.940	1.002
	Monthly income (INR)	0.000	0.213	1.000
	[gender=Female]	0.230	0.629	1.259
Motorbike	Intercept	−0.062	0.954	–
	Age	−0.043	0.322	0.958
	Monthly income (INR)	0.000	0.327	1.000
	[gender=Female]	−1.313	0.108	0.269
Model Fitting Information: Likelihood ratio test (chi-square)		92.510		0.000
Pseudo R-square – Cox and Snell		0.452
Pseudo R-square – Nagelkerke		0.529
Pseudo R-square – McFadden		0.312

Source: Computed using data from field survey.

The monthly income is statistically significant in the personal car category of travel. This means that with an increase in income, there is more likelihood of using of car and less probability of travelling in trains and buses. However, the relative risk ratio (RRR) of the model being near 1.00, and the coefficient of the slope being very small, shows that the slope is very flat and as such the impact of income is not very high on modal change from public transport to private car. There is also hardly any impact of income on the probability of using taxis and autorickshaws, customised transport. The lower impact of income on using a personal car for travel and also almost no impact on the likelihood of using hired taxis and autorickshaws are mainly because of the traffic congestion and fear of loss of time. The public transport services, especially sub-urban trains, and metro rails, though overcrowded, are the first choice of commuters as they save their travel time and are less costly.

The gender coefficient, though statistically significant at a higher level, shows that women have a lower probability than males of using a personal car for travel and higher probabilities of using public transport (train and bus). In other words, the regression results importantly point out that in Mumbai females have higher probabilities to use public transport in comparison to male counterparts and the use of personal cars is less by women. The RRR of females indicates that the probability of females using personal cars is 0.138 times less than their male counterparts. Probably because they find traveling in public transport and intermediate public transport more convenient and cost-effective. Additionally, driving a vehicle has been socially more approved for men in India’s middle class. This may be another reason for women’s lower use of personal vehicles like a car. Motorbike use does not relate to income, age, or gender.

Poor frequency, long waiting time and traffic congestion have been expressed as the main problem by commuters followed by the poor condition of vehicles, and the threat of crime (Table 6). To women commuters, the main concerns are over-crowded coaches (trains and buses), inadequate and lack of clean public washrooms at stations, and an insufficient number of seats allotted for women in trains and buses. Further, train stations are not designed for the elderly, differently abled and child-friendly. Most of the commuters find the stairs of train stations difficult to negotiate. Therefore, a considerable share of commuters find travelling by public transport uncomfortable but have no other easy options. Presently, several corridors of metro rails and flyovers/overbridges are being constructed in MMR. These construction works are also one of the reasons for traffic congestion. Only about one-fifth of the commuters are of the view that completion of these workers will have any remedial impact on ongoing traffic jams or will make their travel smooth. This shows that at this level of infrastructure development, congestion, traffic jams, loss of time and higher spending on mobility are imagined to be long-term issue in the MMR regions. Further, 37.4% of commuters feel that women face sexual harassments while travelling in different modes of transports in the city. Though these are issues related to women, but we asked the same from all the respondents as most of them have travelled with their families and/or have experience/seen the inconvenience.

Table 6. Share of respondents with the major problem of mobility faced.

S. No.	Major problem identified	% of respondents
By all commuters
1	Threat of crime (chain snatching, pickpockets, etc.)	12.3
2	Poor condition of the transport vehicles/old vehicles	29.5
3	Poor frequency/long waiting time	44.8
4	Traffic jams	37.4
By women commuters (both male and female respondents)
5	Sexual harassment	37.4
6	Inadequate separate seating	47.9
7	Inadequate washrooms	51.5
8	Overcrowded coaches	64.4

Source: Based on data from field survey.

6. Conclusions

MMR is characterised by multi-modal transport infrastructure, yet the existing transport infrastructure is falling short of the rising demand and expectations for fast, safe and comfortable travel. Though most of the MRR population relies on the bus and suburban rail services, these two modes of public transport are proving insufficient to match the growing population, especially in Greater Mumbai. The suburban trains and buses remain packed during peak hours and even the narrow platforms of suburban rails at many stations, including Thane, Dadar and Kurla, become so full of commuters that it becomes a struggle to stand and wait for suburban trains. Boarding and alighting from suburban trains during peak hours (9 am–11 am; and 4 pm−8 pm) remains another struggle. The aged, children, women and differently abled (the latter two have reserved compartments for them in suburban trains) especially face more difficulties in boarding or alighting during these hours.

Due to overcrowding and deteriorating condition of the public bus and sub-urban train services, many are opting for intermediate public transport modes like app-driven private taxis, and autorickshaws for travel, which add some comfort and flexibility to travel. This has led to the rising vehicular population in the city though the road length and width have not changed significantly over time. It leads to heavy traffic congestion, which is the main detrimental factor for fast and smooth mobility. Studies have also found that in Mumbai ‘the main reasons for people to buy private vehicles are inefficient public transport and easy availability of car loans for buying vehicles’ (Varshney, 2018).

Various plans and policies have been designed to address these issues, and several infrastructure projects have been launched over the years (Table 7). However, to date, they have failed to mitigate the problems effectively. The major shortcomings are that they have focused on the supply-side interventions and improvements without considering the demand aspects. Many of the policies are myopic and not future-oriented. For example, toll plazas at each entry point of the island city of Mumbai (at Vashi, Mulund, Thane and Dahisar) cause delays of 22 min with heavy traffic congestion. This in turn not only leads to loss of labour time but also the burning of fossil fuels creating atmospheric pollution adversely affecting residents’ health and more of the poor. These, in turn, compromise India’s commitment to the Conference of the Parties (COP26) to the United Nations Framework Convention on Climate Change (UNFCCC) to reduce the country’s emissions intensity of its GDP by 45% by 2030 (Government of India, 2022).

Table 7. Major transport development plans for Mumbai Metropolitan Regions (MMR).

Year	Plans	Major recommendations	Remarks
1962	Wilbur Smith Plan (Balakrishnan, 2000; Nallathiga, 2007)	Proposed a network of freeways in Greater Mumbai which included Worli Bandra Sea Link Road, the East Island Freeway, The Western Expressway, The Eastern Expressway, the Mahim Creek Connector and the Cross Island Connector	It served as a base document for planning the major road networks in Mumbai. Most of the suggested work began decades later
1973	The Regional Plan by the Bombay Metropolitan Region Development Authority (BMRDA, now MMRDA)	Inter-regional, intra-regional and intra-city transport plans, new regional corridors and rail linkages. The plan was driven by socialist ideology aimed to limit population growth in Greater Mumbai, the core of the region, to 7 million	Implemented in the next three decades in a delayed manner (MMRDA, n.d.). The population growth of Greater Mumbai City could not be controlled and grew to 9.8 million in 1991
1977	Bombay Urban Transport Project (1977–84) (Balakrishnan, 2000)	Road improvement, increase in bus fleets, bus depots, termini, workshops, flyovers, pedestrian bridges and tunnels	Implemented with support and investments from the World Bank, Indian Railways, BEST and Municipal Corporation of Greater Mumbai (MCGM)
1983	Plan by the Central Road Research Institute, New Delhi (Balakrishnan, 2000)	Development of freeways, link roads, expressways and major arterial roads	This was a sequel to the Wilbur Smith Plan and re-recommended the freeways
1983	Development of Waterways around Bombay Harbour for Commuter Traffic (Balakrishnan, 2000)	Recommended water transport for commuters along West Coast; Along East Coast, Along Harbour and Thane Creek	It is also known as Datta Committee Report. It is hardly implemented till date.
1983	The Traffic System Management Plan (Nallathiga, 2007)	Short-term measures for construction and improvement of arterial and link roads, footbridges and rail-crossings, and traffic management systems at major crossings	Traffic management at some major junctions have been major achievements
1994	Comprehensive Transport Strategy for MMR (Balakrishnan, 2000; Nallathiga, 2007)	Coherent transport sector strategy for MMR linked with a rational land use plan	Mainly east–west linkages in the city have been implemented through MUTP-I and MUTP-II
2002	Mumbai Urban Transport Project (MUTP)-I	Enhance public transport with demand management, through capacity optimisation of railways and roadways and organising resettlement and rehabilitation of project-affected households	Received major financial support from the World Bank (57%), and the remaining (43%) from Indian Railways, Municipal Corporation of Greater Mumbai (MCGM), BEST and the Government of Maharashtra
2005	Jawaharlal Nehru Urban Renewal Mission (JNNURM)	Urban renewal and redevelopment; improving the roads and bus services	Major emphasis on road widening and bridges and introduction of a fleet of buses
2010	MUTP-II	Upgrade the existing infrastructure, especially the widening of roads (MMRDA, 2021b; World Bank, 2015)	In collaboration with the Government of India and the World Bank
2015	Smart City Mission	Upgrade the existing infrastructure and create provisions for sustainable and environmentally friendly transport infrastructure for providing a decent quality of life to the citizens through the application of smart solutions (Express News Service, 2015; MMRDA, 2021b)	Among others, the implementation ongoing of 36 km 8 lane coastal road from Nariman Point to Kandivali; 34 km Colaba-Bandra-SEEPZ Metro; LED-based Passenger Information System (PIS) systems at bus stops
2015	Atal Mission for Rejuvenation and Urban Transformation (AMRUT)	Complements Smart City Mission and includes Mass Rapid Transport System (MRTS); environmentally friendly transport; electric buses; infrastructure upgrade the existing infrastructure (Express News Service, 2015; Hindustan Times, 2021; MMRDA, 2021b; Srivastava, 2021; V. Rathi et al., 2020)	Several projects are under implementation and include 12 Mumbai Metro Lines covering a total length of 223 km, Navi Mumbai Metro line (Phase I, II and III) stretching across 23.4 km, Coastal Roads of 29.2 km length, Mumbai Trans-Harbour link (MTHL) with a length of 22 km, 17.2 km long Bandra–Versova Sea Link and Navi Mumbai International Airport.
2020	MUTP-III (Economics Times, 2020	To improve the network capacity, service quality and safety of the suburban railway system in the MMR (Express News Service, 2015)	With assistance from the Asian Infrastructure Investment Bank (AIIB)

Source: Compiled by authors from sources mentioned against each plan.

Lack of coordination, proper financing mechanism, timely implementation and completion of the projects are also the major challenges. Moreover, it is found that the present development in the transport system has been more favourable to the upper class (except recent development of metro rails) as more car-oriented transport corridors, such as Eastern Free Way and Chembur-Bandra Link Road, have been created.

The mobility crisis in MMR is enormous and almost affects each mode, which gets intensified at nodes and places where the population concentration is higher, as in the BMC area, Bhiwandi, Thane, Kalyan and Dombivli. The region requires considerable investments in developing mobility infrastructures which are more accessible, equitable and affordable such as mass public transport systems of suburban railways, metro rails and buses.

From the piecemeal approach to the development of transport infrastructure in the past, we have seen grand planning approach of metro-rails and bridges in the city under different policies in recent years (Table 7). However, on the freeways only cars and taxis are allowed creating exclusion of the poor who often travel in buses or autorickshaws. Metro rail corridors being constructed today can provide good connectivity in some places, largely in the BMC region, but several nodes and region remain unconnected from the same. For instance, the areas with a large emerging concentration of population in Navi Mumbai, Kalyan-Shill Road, Dombivli East, Uran, etc., need effective mass transport systems connected to the regional CBD of South Mumbai. Additionally, the higher ticket prices of metro rails may add to the exclusion of the poor, as even suburban air-conditioned trains are not used by the poorer sections because of the higher ticket costs. The accessibility to the metro rails to the differently abled and elderly remains low. On an average, 30–50 ft of stairs climbing to reach the metro stations may not be possible for them as only a few lifts are being installed at odd places. Their smooth accessibility to the buses and suburban trains already remains compromised because of the lack of automation and ramps. The last-mile connectivity and walkability (because of the lack of walkable spaces and footpaths) remain poor. As a result, the vehicular load may not decline in the region. The congestion on major arterial roads in the region may remain higher because of toll tax booths, the major reasons for congestion and traffic jams at the entry points to the island city. This may in turn compromise the climate change mitigation mechanism. As such, it seems it may take decades to have a regional inclusive and hassle-free transport or urban mobility in the MMR.

To conclude, a robust public transport service can solve several social and economic problems. It can be significant in making MMR more liveable and inclusive. Well-planned transport services, especially efficient public transport systems, such as suburban railways, metro rails and buses, which are faster and affordable will improve accessibility for all, and may reduce transportation costs, time loss and environmental pollution. These can increase economic productivity and help in achieving SDGs 11 (sustainable cities and communities), SDG 13 (climate action) and SDG 3 (good health and well-being).

The present paper provided a larger picture of the mobility crisis in this strategic urban region of India. More research needs to be undertaken on each mode of transport to understand the deeper causes which have impacted them over time and the transport policy which has been demand-following rather than futuristic.

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

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