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An Urban Fabric Responsive Last Mile Planning

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Chidambara

Submitted: July 2nd, 2021 Reviewed: February 25th, 2022 Published: April 7th, 2022

DOI: 10.5772/intechopen.103954

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Urban Agglomeration Edited by Alessandra Battisti

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Urban Agglomeration [Working Title]

Prof. Alessandra Battisti and Dr. Serena Baiani

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Abstract

The chapter aims to cover an important and often neglected aspect of transit planning—that of last mile connectivity (LMC). Today most transit systems extend beyond the city to conurbations or metropolitan regions. However, most often LMC planning is on the hindsight or follows a “one shoe fits all” approach, without taking into cognisance the importance of the urban fabric context of the stations. Last mile solutions that do not respond to the built environment context can result in unsustainable mode choice for LMC or in reduced transit appeal. The chapter presents last mile trip characteristics for stations located in different urban fabrics in the city of Delhi and its surrounding town Noida. It explores the attributes of the built environment that impact last mile travel behaviour across the metropolitan region. Additionally, the paper discusses the level of integration, with a lens on the current last mile environment, policy and planning practice for Delhi. The chapter further makes a case for treating LMC planning as integral to transit planning and outlines last mile planning principles suitable for different urban fabrics.

Keywords

  • last mile connectivity
  • urban fabric
  • built environment and travel behaviour
  • transit access
  • walk share and urban fabric
  • Delhi metro
  • urban rail

1. Introduction

A growing number of cities across varying economies of the world today are nested within urban agglomerations or metropolitan regions. The need for or the factors resulting in co-dependence of the city with its conurbations or satellite towns are well-researched and documented in literature. Geddes in his seminal work, Cities in Evolution, nomenclated ‘city-regions’ or ‘town agglomerates’ as ‘conurbations’, identifying them as the future model of urban development; at the same time, underscoring the role of transportation in rendering redundant, the administrative boundaries between the various constituents of a city-region. Describing the absorption of the many villages and boroughs in the development of Greater London, he wrote, “…Instead of the old lines of division we have new lines of union: the very word “lines“ nowadays most readily suggesting the railways, which are the throbbing arteries, the roaring pulses of the intensely living whole;…”[1]. He further emphasised that different forms of transit systems (rail/trams/buses) will be crucial for such urban development to take shape.

The technological advancements in urban transport (both in automobile and public transport) since that period have been tremendous and we find such models of urban development prevalent economy-wide, albeit, with varying degree of penetration and role of transits. In today’s context it is common to see transits extending beyond city boundaries to conurbations or the other entities within their metropolitan regions, playing a vital role in providing several thousands of populations social, cultural, and economic opportunities. Urban rail systems (in all their variants) today have assumed greater significance than ever before, especially in Asian cities. As per a report on World Metro Figures, “at the end of 2017, there were metros in 178 cities in 56 countries, carrying on average a total of 168 million passengers per day. 75 new metros have opened since the year 2000 (+70%). This massive growth is to be credited largely to developments in a few countries in Asia” [2]. Given the pace and nature of urbanisation, the metro rails are likely to play a crucial role in the urban mobility landscape globally, owing to their higher speeds, comfort, safety in comparison to other public transport modes.

In an increasingly globalised economy, the need to connect, both in the physical and the virtual spaces cannot be negated. Travel takes a centre-stage in every urban dweller’s life. However, the way people and goods move in a city and across it impacts its socio-economic and physical environment and is one of the key measures of a city’s sustainability. Noted economist Colin Clark in his paper Transport—maker and breaker of cities, observed that transport is one of the “less tangible implements” that is necessary to create the “end-products” of what is commonly classified as man’s basic needs [3]. Developing an effective and efficient public transport thus becomes an indispensable pre-requisite for sustainable mobility and subsequently for sustainable cities. Several scholars recognise the role of transit systems in increasing economic development in cities through the creation of dense urban centres with walking and transit urban fabric [4]. Other benefits of rail transit cited in literature include higher per capita transit ridership, lower per capita traffic fatalities, lower per capita consumer transport expenditure, lower per capita motor vehicle mileage, among others [5]. Through the facilitation of easier access to opportunities, transit systems enhance the catchment and work-sheds which is not only crucial for cities to be globally competitive but also for their overall sustainability. Not surprisingly, we also find that travel patterns and urban forms, in turn, are influenced by the dominance (or absence) of transit outreach.

A substantial volume of scholarly works establishes the link between transit ridership and the surrounding built environment [4, 6, 7, 8, 9, 10, 11, 12, 13]. Density (both residential and employment) in particular, is a common indicator across several studies that is found to influence transit ridership. A study of 27 residential areas in California, having different residential densities around metro stations, concluded that higher density residential areas have higher share of transit commute trips [14]. Similarly, transit stations located in higher employment density settings are found to have greater transit shares [15, 16, 17]. It is argued that sustainable transport is possible when there is “an emphasis on urban form and density; infrastructure priorities especially the relative commitment to public transport compared to cars; and street planning especially the provision for pedestrians and cyclists”, highlighting the importance of other factors apart from densities [18]. This is reiterated through other research studies that have observed higher transit shares in transit and pedestrian-oriented neighbourhoods [10, 19].

1.1 Transit and last mile connectivity

The transit systems in their course, from the city centre to the outskirts and the conurbations traverse different built environment. Alongside, their network density and coverage drop significantly. Planning a transit network that is as dense in the peripheries/suburbs as in the city core might be an almost implausible task. Given this limitation, maintaining the attractiveness of transit, and achieving optimal ridership throughout the system is a big challenge for transit authorities too. It is increasingly accepted that in-transit and out-of-transit experience collectively account for a transit’s attractiveness. The last mile connectivity (LMC), referred to in this paper as both the first and the last mile, is an important constituent of the out-of-transit experience, and often, also one of the weakest links of the overall transit journey. The term ‘mile’ is merely representative, and it can vary from less than half a mile in central parts of the city with dense transit network to significantly over a mile in peripheral areas and conurbations with lower transit network density.

The nature of available options for LMC along with its quality can also have an impact on the catchment sheds of stations located in similar settings, and subsequently on ridership as well. It is important to understand that since the transit coverage itself varies in different parts of the city, the approach to addressing the last mile solution cannot be the same everywhere. While in some areas, it may not be necessary to stress on enlarging the catchment sheds, rather on improving the quality of infrastructure; in other areas the focus necessarily should be on enhancement of the catchment sheds, to enable more areas easier access to transit. This is especially vital for transits that serve metropolitan regions or urban agglomerations. Hence, a pragmatic approach that acknowledges and draws upon the potential and limitations of the physical built context is important to maintain transit attractiveness for higher patronage and greater user experience.

The need for a difference in the approach arises principally out of the difference in the locational context of the stations. Newman, Kosonen and Kenworthy [20] in their ‘theory of urban fabric’ show that cities are a combination and often overlapping of three distinct types of urban fabric - walking urban fabric, transit/public transport urban fabric and automobile/motor car urban fabric. The ‘urban fabric’ in this theory signifies “a particular set of spatial relationships, typology of buildings and specific land-use patterns that are based on their transport infrastructure priorities”. The three fabrics are distinguishable with respect to aspects such as distance from the city centre, densities, mix of land-use, network typology, characteristics, and quality, among others. The authors further contend that “strategic and statutory planning need to do more than land use and transport integration, and they need to have different approaches in each of the three urban fabrics”. Their theory is well applicable and relevant for LMC planning at an agglomeration scale, as well.

This paper includes the findings of a study for Delhi Metro rail (which also serves its satellite towns Noida, Gurgaon, Faridabad and Ghaziabad), which in further sections attempts to show that last mile travel characteristics vary with respect to stations located in different urban fabrics. The paper presents a case for the treatment of LMC planning differently in different urban fabrics. For cities where transits serve an entire agglomeration and/or the suburbs or the surrounding smaller satellite towns, respecting the different urban fabrics in LMC planning becomes even more crucial to maintain the attractiveness of the transit systems and subsequently, for higher transit patronage and greater user experience across all the urban fabrics.

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2. Built environment and last mile connectivity

There has been far more research examining the relationship between built environment and transit ridership than on built environment and last mile user trip behaviour. However, from the limited body of literature we can somewhat conclude that urban form surrounding a transit stop is an important decisive factor in transit users’ choice of walk, cycle, feeder bus or other forms of transport for the last mile commute. A study conducted in Bogotá examines how the built environment influence walking and cycling behaviour [21]. The authors also observe that while in the developed world, there exists substantial literature that suggest built environment are significant predictors of non-motorised travel, not much research on the same has been carried out in the developing world.

In a study of three European countries namely the Netherlands, UK, and Germany the results indicated that suburbs generate higher levels of cycling-transit users than cities [22]. It would thus be interesting to distinguish the last mile access/dispersal behaviour in city versus the satellite towns in a developing world context. The study also observed that improving the access to railway stations by public transport and non-motorised modes can limit car use. In cities in the developing world this is taken care by a variety of intermediate public transport (IPT) both motorised and non-motorised. Relatively shorter travel distances between common origins and destinations in cities as compared to suburban locations, enables higher walk share. In contrast, in transit-rich, compact cities, transit and walking are attractive alternatives to the bicycle [23]. Moreover, relatively higher densities in cities also makes possible a high-quality feeder bus service with short headways, making them more convenient [24]. The ‘Transit Choices Report’ for Santa Clara Valley Transportation Authority corroborates that the pattern of urban development largely determines how many people will be near a stop, whether they can walk to it, and whether transit can follow a path that will be useful to many customers [25]. It identifies density, walkability, diversity of land use, among others as the key indicators of built environment that governs transit ridership.

Yet other studies observe the nature of development around stations influencing non-motorised trip access to stations. Walk/bike share and trip rates were observed to be higher in transit neighbourhoods [10] and walk mode also had higher probability to be used for rail station access in a traditional neighbourhood [11]. Another study found the probability of walking to stations higher when retail uses predominate around stations [26].

Street networks are an important constituent of the built environment. Several studies associate travel behaviour, especially ‘walk’ share with transportation network. The relative association of street design: local qualities of street environment, street network configuration, spatial structure of the urban grid and land use patterns was studied with the distribution of pedestrian flows in 20 areas in Istanbul [27]. Cervero et al. [21] in their study in Bogota found that the variables that impact most are network characteristics while in developed countries diversity (of land-use) and density impact walking behaviour. They found two network characteristics variables—street density and connectivity index entered the model as significant predictors. Erstwhile, other scholars have used connectivity index [28, 29], street density [30, 31], block length [32, 28], block size [32, 33], block density [11, 34, 35] metric and directional reach and pedestrian detour factor (PDF) (also referred to as pedestrian route directness) [32, 36] as network measures. However, not all these studies have been conducted to understand the last mile travel behaviour per se and it would be interesting to explore whether network characteristics significantly influence station access/egress mode as well.

A study which directly explores this relationship is conducted by the Atlanta Regional Commission [37] which explores “how far urban density, mixed land-uses, and street network connectivity are related to transit walk-mode shares to/from stations”. It observes that “local conditions around rail stations are significantly related to riders’ choice to walk to/from transit”. In particular, the study finds street connectivity to be strongly associated with walk-mode shares when controlling for certain other built and socio-economic attributes.

There is not much conclusive evidence of the relationship between built environment and last mile travel behaviour for cities set in the developing world, which have their own set of uniqueness that set them apart from cities in the developed world. Presence of vertical mixing of land-use areas with fine-grained urban fabric, higher urban densities, poor conditions of walking and cycling infrastructure in several parts of the city, lower automobile-ownership and income levels, presence of the ubiquitous motorised and non-motorised forms of IPT available for individual hire as well as on shared basis, increasing penetration of on-demand/ride hail cabs: all of these present a very different last mile landscape in these cities and city-regions, thereby warranting studies conducted in these settings.

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3. Last mile travel behaviour of transit users in different urban fabrics: Delhi, India

The chapter focuses on the relationship of built environment and last mile trip characteristics, based out of a more comprehensive study (also covering last mile mode quality, pedestrian environment and users’ socio-economic characteristics) carried out by the author. Hence, the analysis pertaining to only built environment characteristics is presented here. The study was carried out for 10 metro stations of Delhi Metro rail network currently having a network length of 348 kms. Delhi Metro covers the National Capital Territory of Delhi (NCTD), and the surrounding towns of Noida, Gurugram, Faridabad and Ghaziabad. The average daily ridership of the Delhi metro, although not phenomenal, has gradually risen from 0.12 million in 2004–2005 to 2.2 million in 2013–2014, and 2.76 million in 2016–2017. The stations selected for the study lie on the two busiest metro lines and one on a relatively new line, representing low to high ridership levels. The stations were selected also to represent different locational contexts with respect to distance from the city centre, land-use, population and employment densities, and last mile supply/quality. Two of these stations are located in the satellite town Noida. Table 1 gives the profile of each of the ten case stations and their context.

User surveys were conducted at these 10 stations (collecting 1000 transit user samples) using revealed preference method to understand the users’ current first/last mile mode choice and other travel characteristics, their socio-economic characteristics, along with rating and ranking of criteria for mode choice decisions.

3.1 Urban fabric around stations

Each station is set in a built context that represents an urban fabric (although, some overlapping of fabrics is also evident, the dominant fabric is used) discussed in the section1. The core CBD areas which are characterised by high density, mixed land-use (primarily, vertical mixing of residential and commercial at building-use level) and narrow, dense street network, qualifies them as having a walking fabric. Transit fabric are predominantly other medium to high density areas and depending on a combination of criteria such as distance from the core, population/employment density, and contiguous development, they were further sub-classified as representing ‘inner’ or ‘outer’ transit fabric. For instance, Noida Sec-15 was classified under ‘outer’ rather than ‘inner’ since it is not part of NCT of Delhi and falls in the satellite town of Noida. Similarly, Dwarka Mor and Dwarka Sec-10, although located somewhat close to each other, were categorised differently as having ‘inner’ and ‘outer’ fabric respectively owing to the much higher densities in Dwarka Mor vis-à-vis Dwarka Sec-10 and also because Dwarka Sec-10 is still not a fully developed area. The stations qualifying under ‘automobile’ fabric are either in low density peripheral areas or terminal stations in the satellite town. Figure 1 shows the land-use and network pattern for one station representing each fabric typology.

Figure 1.

Land-use and network around stations representing each fabric. a) Chawri Bazar-Walking Fabric. b) Chhatarpur-Automobile Fabric. c) Green Park-Inner Transit Fabric. d) Dwarka Sec-10-Outer Transit Fabric.

Population and employment densities for each station were measured from population and employment data available for traffic assessment zones (TAZs) of Delhi from a transport demand forecast study [38]. The density map and locations of the case study stations are given in Figure 2.

Figure 2.

Population density and station locations on Delhi metro network.

Ranking the stations from low to high density was a challenge since there is no standard definition across globe of what qualifies as low or high densities within cities. For instance, the Master Plan of Noida has two categories of densities: greater than 500 persons per hectare (PPH) as high density and less than 500 PPH as medium density, while Santa Clara, USA considers below 11.6 PPH as low density and greater than 97 PPH as high density. Besides, there is scant literature available that specifies ranges for employment densities from low to high. As such, the study developed its own ranking methodology of low to high densities: five ranges of densities were identified to distinguish clearly the differences in mode share with varying density conditions. The density ranges and corresponding density rank was developed based on the population and employment density values observed in all the 288 TAZs of Delhi. The low, medium-low, medium, medium-high and high ranges correspond to the densities of all TAZs denoting upto 15th percentile, 15th–25th percentile, 25th–50th percentile, 50th–85th percentile and above 85th percentile respectively. Hence, the low to high densities are relative in the context of the city of Delhi.

3.2 Last mile travel and built environment characteristics of case stations

The last mile trip characteristics including mode share and average trip length (ATL) for the stations are given in Table 2. The share of walk trips has a wide variation, the highest being 82.9% while the lowest being 9.4%. The average trip length (ATL) of all modes combined point towards larger catchment-sheds for some stations compared to others. These will be discussed in the context of urban fabric.

Station nameAvg. daily ridership & line nameAdjoining land-usePopulation densityEmployment densityRepresentative urban fabric
Chawri Bazar (CB)30,798 (yellow)Mixed use
Commercial
HighHighWalking
Red Fort (RF)Low ridership
(violet)
Commercial
Mixed use
Heritage
Medium-highHighWalking
Dwarka Mor (DM)42,928 (blue)ResidentialMedium-highMediumInner transit
Green Park (GP)27,900 (yellow)Residential
Institutional Commercial
Medium-highMediumInner transit
Vishwavidyalay (VV)23,802 (yellow)Residential
Institutional
MediumMediumInner transit
Mayur Vihar-I (MV)19,413 (blue)ResidentialMediumLowInner transit
Noida Sec-15 (N15)29,220 (blue)Residential
Industrial
Institutional
MediumMedium-highOuter transit
Dwarka Sec-10 (D10)9761 (Blue)Residential
Institutional
LowMedium-lowOuter transit
Chhatarpur (CP)36,036 (yellow)ResidentialLowMedium-lowAutomobile
Noida City Centre (NCC)37,733 (blue)Residential Commercial (partially developed)MediumMediumAutomobile

Table 1.

Case stations and context profile.

Station nameMode share (in %)ATL (in kms)Predominant land-use share (in%)
WalkC.R. + E.R.A.R. + cabBusPrivateWalkAll Modes
Chawri Bazar82.917.10.00.00.00.76.7626% mixed
37% commercial
Red Fort67.332.70.00.00.00.840.898% mixed
20% commercial
Dwarka Mor51.625.39.98.84.40.701.4070% residential
Green Park46.01.151.70.01.10.761.5945% residential
14% institutional
Vishwavidyalaya41.329.118.65.85.20.731.2227% residential
30% institutional
Mayur Vihar-I41.621.230.10.96.21.181.8650% residential
Noida Sec1552.49.536.51.60.801.8521% residential
30% industrial
20% institutional
Dwarka Sec1036.119.328.910.84.80.711.7531% residential
20% institutional
Chhatarpur9.43.549.49.428.20.432.1534.8% residential
Noida City Centre15.43.574.10.56.51.263.1555% residential
11% commercial

Table 2.

Mode shares, ATL, and predominant land-use of case stations.

Note: C.R.—cycle-rickshaw; E.R.—E-rickshaw; A.R.—auto-rickshaw.

Figure 3 gives the distribution of mode share across the ten stations located in different urban fabrics. Several inferences can be drawn from the comparison given in Table 2 and Figure 3. The most evident of these is the decline in the share of walk trips from ‘walking’ to ‘transit’ and to ‘automobile’ fabric and significantly higher share of motorised IPT and private mode trips in transit and automobile fabrics.

Figure 3.

Mode shares across different urban fabrics.

Figure 4.

Huge parking spaces for private vehicles but lack of organised space for IPT and buses.

Figure 5.

Chaotic environment outside a station due to lack of physical integration.

High density mixed land-use areas (Chawri Bazar and Red Fort) have higher share of walk trips and shorter overall average trip lengths owing to maximum destinations located within 1 km range. This finding conforms with other studies where it is suggested that people are willing to use slower modes of travel, such as walking, for shorter distances, especially if many trips can be chained [7, 17].

Areas with higher share of institutional use (Vishvavidyalay, Dwarka Sec-10, Green Park, Noida Sec-15) are observed to have higher share of IPT modes. However, within this group, relatively higher activity density areas (Green Park, Noida Sec-15) also have higher share of walk trips. In areas having more than 30% residential land use, it is observed that higher density areas (Dwarka Mor, Green Park, Mayur Vihar) have higher share of walk trips compared to low density residential areas (Chhatrarpur and Dwarka Sec-10). Low to medium density stations located on the peripheries and/or terminal stations (Chhatarpur, Noida City Centre) have the highest overall average trip lengths implying a larger catchment shed. This difference in catchment sheds draws attention to the need for a differential last mile planning approach for stations across a metropolitan region.

The built environment attributes considered for the study were analysed for approximately 1 km buffer around each station. Land-use and network details of areas in 1 km radius around each of the 10 stations were obtained from the openstreetmapsand updated through site visits. Network attributes such as network density, node-link ratio (connectivity index), block size and block density were computed on ArcGIS. Another network characteristic of importance is the pedestrian route directness or pedestrian detour factor (PDF) which is the ratio of length of walking distance to the geodesic distance between its start and end points. For the detour analysis all blocks were treated and imported as zones in VISUM. Similarly metro stations were imported as a single zone but with zero area. Two skim matrices were generated: a ‘direct distance matrix’ and a ‘travel distance matrix’. Finally average of all detour factors was taken.

The values of all the network attributes discussed above for the 10 case stations are given in Table 3. Most of the stations have network attributes that are within acceptable or recommended levels. However, there is some degree of relative variation, and the models test whether network attributes significantly affect last mile mode shares.

Station nameNetwork density (kms/sq.km)Node-link ratioAverage block size (sq. m)Block density (no./sq.km.)Pedestrian detour factor
CB24.81.210,7051091.49
RF18.11.511,886621.41
VV14.61.438,952281.17
N1520.91.317,502561.71
GP17.91.612,287811.59
D1017.21.846,234191.67
DM45.71.756251551.29
MV19.91.316,684481.61
CP13127,987291.56
NCC14.91.319,068451.43

Table 3.

Network characteristics around case stations.

Handy [30] recommends a network density of 26 miles per sq. mile (16.2 km per sq. km) and Mately et al. [31] suggests 18 miles per sq. mile (11.2 km per sq. km) as minimum recommended network density. As can be seen from Table 3, almost all case stations have network densities either within these ranges or higher. The station Dwarka Mor has an extremely high network density of 46 km per sq. km. which is due to the presence of exceedingly small block sizes (5625 sqm), which in turn is on account of the area having low-income housing and very small plot sizes.

Further, the recommended and minimum block densities are 160 (62 per sqkm) and 100 per sq. mile (38.6 per sqkm) respectively [11, 34, 35]. Three stations namely Chhatarpur, Dwarka Sector-10 and Vishwavidyalaya have block densities lesser than the minimum figure given above and two stations namely Dwarka Mor and Chawri Bazar have much higher block densities. The remaining five stations have block densities within this range. The connectivity index should be preferably 1.4 or higher and minimum 1.2 [29, 39]. The minimum node-link ratio observed in the 10 case station areas is 1.0 (Chhatarpur). All other stations have connectivity index higher than 1.2. PDF should preferably be around 1.5 [32] and not more than 1.8 [36]. None of the case stations had a PDF higher than 1.8.

3.3 Impact of built environment attributes on last mile travel

Bivariate regression analysis was carried out between the dependent variables representing last mile travel characteristics and the independent variables representing built environment attributes. Multiple regression was not carried out since the dataset representing the built environment is quite small (just 10—representing 1 for each station), and because some of the network attributes also exhibit multi-collinearity. The dependent variables considered for the models were first/last mile mode share and average trip length (ATL). The specific mode share used in the models was ‘walk’ since it had the maximum share in almost all case stations except for two; at the same time, it had also wide variations across the stations as reported earlier. Besides, the built environment in 1 km radius around stations is more likely to affect ‘walk’ shares in comparison to other modes which have much larger catchment area making it unfeasible to study them in detail. The decision to select ‘walk’ was also guided by the fact that walking is the most common, affordable, and sustainable mode choice (cycling share was quite insignificant across all stations and hence not used) for LMC worldwide, and probably one that is likely to be most affected by the built environment. Hence ‘mode share (walk)’ and ‘ATL (walk)’ were selected as the dependent variables.

The independent variables include ‘population density’, ‘employment density’, ‘network density’, ‘block size’, ‘block density’ ‘link-node ratio’ and ‘pedestrian detour factor’. Curve Estimation tool under regression module in SPSS was used to check which curve fits best for each of the variable. With the entire dataset, ANOVA value for none of the regression types was observed to be significant. Hence, anomaly (1 data point) in the dataset was identified using ‘unusual cases’ tool and the models were rerun. The model results (refer Table 4) for only the significant variables are shown here.

ModelDependent variableIndependent variableR2ANOVA (P value)CoefficientsIntercept
1Mode share (walk)Population density (in PPH)0.91.0002519.8−60.8
2Mode share (walk)Network density (km/sq.km)0.72.0045.4−52.7
3Mode share (walk)Block density (no./sq.km)0.55.023.6011.87
4Mode share (walk)Block size (sq.m)0.49.034618,4388.5
5ATL (walk)Node Link Ratio
Node Link Ratio**2
0.81.0154.7
1.6
−2.7
6ATL (walk)Network Density
Network Density**2
0.52.05.06
−.001
−.07

Table 4.

Models results.

The regression analysis shows that population density, network density, block density and block size contribute significantly to ‘walk’ mode share, whereas node-link ratio and network density showed a significant relationship with ‘walk’ ATL. Population density has the highest and significant relationship with walk share. It has a logarithmic relationship with walk mode share. The finding is substantiated through claims made in other studies where density is thought to shape pedestrian activity by bringing numerous activities closer together, thus increasing their accessibility from trip origins [34, 40]. It is suggested that people are willing to use slower modes of travel, such as walking, for shorter distances, especially if many trips can be chained [7, 17]. However, the Bogota study [21] did not find density and diversity (of land-use) as significant, the reason for which the authors cite could lie on the sample selection of neighbourhoods which consisted of uniformly compact, mixed-use nature. As reported earlier, there is variation in both density and typology of land-use selection in this study and as such contradicts the Bogota study findings.

The models also indicate moderate to high relationship between ‘walk’ mode share with network density, block density and block size. Among the network attributes, network density has the highest and significant influence on walk mode share. It has a significant linear relationship with walk mode share. There is a significant linear relationship and inverse relationship of block density and block size respectively with ‘walk’ mode share. There is moderate relationship between ‘walk’ ATL and network density and link-node ratio (connectivity index). Similar results have been observed elsewhere [21] wherein street density and connectivity index were found to be significant predictors—higher connectivity index and street densities increase the likelihood of walking. The models on ‘walk’ mode share and link-node ratio (connectivity index) and PDF were not found to be significant. This may be explained by the fact that none of the stations had high PDF values. Also, the relationship between ‘walk’ ATL versus block Size, block Density, and PDF were not found to be significant and had quite low values of R square.

The study shows that there is distinctive relationship between built environment characteristics and last mile travel behaviour of transit users. Stations located in high activity density mixed land-use areas such as Chawri Bazaar and Red Fort have quite high share of last mile trips made by ‘walking’. Within each type of land-use such as those that are predominantly residential, stations located in areas with relatively higher density such as Dwarka Mor have higher ‘walk’ shares for last mile trips compared to medium to low density areas such as Mayur Vihar-I or Noida City Centre and Chhatarpur. The study also observes that last mile travel behaviour varies across different land-uses, across varying densities within a particular type of land-use and across stations located in peripheries and satellite towns. Unlike the study of European cities cited previously [23] where suburbs had higher share of cycle access to rails, stations located in outer areas in Delhi have higher shares of IPT and private mode usage. Within satellite towns, as densities increase, the share of walk increases. Networks also play a crucial role in influencing walk share for transit access and should be given due consideration in planning of new areas.

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4. Last mile mode supply and integration in Delhi

As mentioned in Section 3, Delhi metro provides services in National Capital Territory of Delhi (NCTD) and four surrounding towns of Noida, Ghaziabad, Gurugram and Faridabad, which are part of the National Capital Region (NCR) of Delhi. Although these towns share boundary with NCTD, they lie in different provincial regions (states) of the country, resulting in different administrative jurisdictions. The importance of providing metro connectivity in an integrated manner in the region was acknowledged early on and Delhi Metro Rail Corporation (DMRC) through legislative provisions was given the power for construction, maintenance, and operations of metro rail in these towns. However, the provision of last mile services by DMRC is mostly limited to the NCTD. This undermines the importance of institutional integration at a metropolitan region scale, for the provision of last mile connectivity. It is only recently that some last mile services such as cab aggregator kiosks and authorisation of e-rickshaw services have been initiated at metro stations of the satellite towns as well.

The DMRC’s official website has two sections on “passenger information” tab related to last mile connectivity: one for parking and bicycle facilities and another for feeder buses. Interestingly, an important recent addition to the website is pertaining to information on “last mile connectivity” which does not feature on the “passenger information” tab.

Delhi Metro’s feeder bus services has a fleet size of just 269 buses [41], with most of them having surpassed their life cycle and with frequency that can be clearly termed as less than satisfactory. There has been no official route rationalisation carried out for the currently operational routes with most of the routes having quite long route lengths and plying primarily on arterial and sub-arterial roads. The website gives a list of feeder buses plying from 32 metro stations [42], giving the names of the location covered under each route. This information is barely useful to commuters since it neither provides a route map of feeder buses nor contains information related to schedule and frequency of service. The physical integration at station is also quite poor. At most of the stations covered in the survey, feeder bus stops were either not clearly demarcated or not integrated with station entry/exit. There was no real time information display of feeder/city bus timings or route guide map, and some stops did not even have a basic bus shelter. While some attempt at fare integration has been recently attempted through introduction of “common mobility card”, these are available on an insignificant number of city bus routes, and feeder buses are not covered at all. The DMRC site does not give any information to users on where or how to avail this card.

However, Delhi’s metro commuters have the advantage of availability of a wide range of other IPT options for their LMC in the form of cycle-rickshaws, e-rickshaws, auto-rickshaws (for individual hire), shared-auto-rickshaws (plying on semi-fixed routes), jeeps (eg., gramin sevas), and mini-buses. These modes are largely demand-driven, and as one can relate from the study findings, also respond to the urban fabric setting. The availability of certain types of modes such as cycle-rickshaws and e-rickshaws at stations are also affected by restrictions on their plying in certain areas. Shared auto-rickshaws and gramin sevasplying on fixed or semi-fixed routes are more visible at stations on peripheral areas, thereby enhancing the catchment sheds of these stations. Ride-hailing applications (RHAs) such as Ola, Uber, Meru and other taxi and auto-rickshaw services (individual hire as well as shared) are playing significant role in urban mobility as well as for last mile commute. Besides these IPTs privately and/or company operated chartered bus services also ply to/from some stations. In the last 1 year, Delhi is also seeing some level of penetration of the electric micro-mobility, which could play a significant role in future LMC landscape.

A heartening addition on the DMRC site is the page on “last mile connectivity” which gives some information related to modal integration and/or availability of IPT modes at stations. The referred page gives information on the list of stations where one can avail DMRC-authorised e-rickshaw services, cab aggregator services, e-scooter services and cycle-sharing services. Physical spaces are provided to the operator of these services within the precinct (such as kiosks for cab booking at stations) or outside the station precinct (such as docking facilities for shared cycle services). However, only limited stations have planned spaces available for various IPT modes and the situation is worse for stations in the satellite towns where local agencies are responsible for managing these spaces outside the station precinct; a few stations have ad-hoc demarcated spaces, primarily located on service roads. The agency has not yet facilitated formal integration of metro system with semi-fixed route shared IPT modes serving the stations and its catchment and subsequently has no information related to the same. The lack of physical integration of the same sometimes results in chaotic and unsafe environment outside station premises (Figure 5).

Parking facilities are available at 105 stations with a total area of 32 Ha (for 101 stations) [42] which averages to approximately 3100 sq.m. per station as area under parking. Most stations provide surface parking facilities, and as such this land has not been put to other uses. Various studies have pointed out that the space needed for parking and access of private modes adds significantly to the cost of transit stations and attenuates environmental and traffic benefits of transit service. This negates the very objective of curtailment of automobile usage/dependence in cities like Delhi, which is key to sustainable mobility. While some stations provide huge areas for private vehicle parking, the same cannot be said about planned spaces for IPT modes (Refer Figure 4). Operator survey of IPT modes at the 10 case stations revealed 85% citing lack of adequate and designated planned spaces (and subsequently harassment by police/civic agencies) as a key issue.

The National Urban Transport Policy (NUTP), 2014 [43] for the first time explicitly covers “last mile connectivity”. The term is mentioned at four places compared to zero in the NUTP, 2006. The new policy document broadens the scope of multi-modal integration to include “private modes of transport i.e., walk, cycle, cars and 2-wheelers and para transit modes i.e., tempos, autos, minibus and cycle rickshaw to the mass rapid transit network” which was limited to “integration of buses with Metro rail” in the previous transport policy [43]. The policy also recognises the significance of improving last mile connectivity to public transport through provision of footpaths and cycle lanes, provision of feeder services, and incorporating design principle to promote safety, accessibility, reliability and affordability, among other measures.

Integration—at levels of physical, operational, fare, information and institutional—of last mile services with the transit service is crucial for enhancing the attractiveness of transit. The integration becomes more critical when transit system extends beyond city border to connect areas that fall in other provincial jurisdictions. While DMRC has taken more pro-active approach towards LMC planning in recent years, the idea of “seamless integration”, especially at a metropolitan region level is yet a far cry. There is also a need to develop a strategy or framework to cater to the last mile connectivity that responds to the locational context of the station.

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5. Conclusion and last mile planning approach

In view of the present research findings, it is reiterated that users behave differently while using stations located under different urban fabrics in the metropolitan region. A singular approach for addressing the issue of last mile connectivity is thus not appropriate. Last mile strategies and planning need to respond to the urban fabric typology of the station context. The study also reveals that the largest share of last mile commuters walk or use various IPT modes to and from metro stations and policies need to cater to their needs first. Further, the use of clean technology modes in the form of cycle-rickshaw and e-rickshaw which already have a large user base need to be promoted and thus requires appropriate regulatory framework which facilitate their operation rather than adopting a restrictive approach towards them. Policies on last mile should prioritise improving walking and cycling environment around transit stations and facilitating integration of low carbon IPTs, especially in terms of physical integration. It is evident that lack of appropriate last mile planning can result in greater dependence on private modes of transportation to access the transit system, especially in stations lying in the automobile fabric. Automobile usage for access/egress to/from stations generates large number of single occupant vehicular trips at the local level, thereby attenuating the environmental benefits of the transit. The most important policy direction that can be drawn from this study findings, is adopting a multi-pronged planning approach incorporating contextual environment to provisioning of LMC, in place of ‘one size fits all’ approach. A broad strategy could be focus on enhancing walkability in walking fabric; better physical integration and operational environment for IPT in transit fabric; and high-quality and route-rationalised feeder services and shared IPT services in the automobile fabric.

5.1 Recommended last mile planning approach

Keeping in view the fact that a large share of last mile trips across all case stations are covered by walking and since walking is the most sustainable way of last mile access, it is expected that creation of good walk infrastructure will encourage more people to walk the last mile as well as enhance users walk experience. Replogle [44] developed a transit serviceability index which included components such as ‘sidewalk conditions’, ‘biking conditions’, ‘land-use mix’, ‘building setbacks’, and transit stop amenities. He observed that zones with high transit serviceability indices not only had higher likelihood of use of transit but also had greater probability of walk access to transits. Provisioning of NMT infrastructure thus also makes economic sense.

Globally there is a lot of stress on improving both pedestrian infrastructure and environment for improving LMC. Provision of extensive network of sheltered and landscaped walkways connecting transit hubs is a pre-requisite for an enabling sustainable last mile ecosystem for Indian cities. This is of utmost significance, given the climatic conditions. However, another part of this study published earlier [45], which examined the effect of walkability on last mile travel behaviour, also suggests that it is not sufficient to merely create sidewalks and cycle lanes; other factors such as safety, aesthetics, etc. that contribute to creating the overall walking and cycling environment also determine how well these facilities are used. Hence, creating vibrant spaces along streets connecting transit hubs should be given due importance. This can be attained through paying attention to the built form design in greenfield areas and ‘placemaking’ practices in brownfield areas where there is limitation on altering the built form.

The share of bicycles for last mile connectivity as observed in this study was quite low. However, this may be on account of poor cycling environment and supporting infrastructure. Biking as the last mile mode is increasingly being given importance across the globe. The share of cycling for LMC could be enhanced through adequate safe biking and bike parking infrastructure. It is not sufficient to have public bike sharing facility only at the station precinct; there should be a network of deposit and hire facilities at several points in the catchment area (especially in institutional and commercial) for higher usage. Creation of bikeways in low-density peripheral and suburban areas can enhance their catchment sheds. It would also be beneficial in the long run as these areas grow denser in due course of time and transition from an automobile-fabric to transit and walking fabric.

A demand-driven and demand responsive system needs to be in place that caters better to connecting the users to their trip-ends. As the study highlights the vital role that IPTs play in providing LMC, it is important to acknowledge their services by integrating them in transit system planning in a concerted and organised manner. Localised loop or hub-and-spoke systems of e-rickshaws, shared auto-rickshaws, shuttle services can be operated in vicinities ranging from 1 to 5 kms (depending on the location of the station and the mode type). A good feeder service for a wider area can help in increasing the catchment sheds of each station. In this context, high frequency feeder bus services planning in peripheral/terminal stations is especially important given their larger catchment-sheds. Demarcation of planned spaces for all last mile modes at station areas and their adequate integration should be mandatory to avoid chaos and safety hazard to users.

At present there is lack of a set framework for last mile planning in the country. A toolkit containing general guidelines for last mile planning for metro stations should be developed which could guide all cities having or planning for transits. Based on the toolkit more specific area level last mile plans can be prepared for each station. These plans should cater to both station precinct level requirements and catchment area of each station. At station level the focus should be placed on seamless integration of last mile modes with the transit in terms of both spatial and non-spatial integration. Catchment area last mile planning can be more local context specific (responsive to particular urban fabric and socio-economic mix of the population in the area), with focus on making areas safer, active and vibrant for pedestrians and cyclists and facilitating services of modes that are most suited to the locality. However, some facilities should not be compromised upon and kept consistent across all stations, such as, excellent walking and cycling infrastructure and environment.

The planning approach may also be slightly altered for stations located in different urban fabrics in brownfield and greenfield areas. Brownfield stations pertain to those stations that are in areas that are already developed and as such may have limitations in altering of the built elements (except for the redevelopment TOD projects) that are known to encourage walkability. Stations in brownfield areas will be generally located in walking and transit fabrics, and last mile planning should take this into consideration. Greenfield stations pertain to stations that are in the peripheries/fringes in automobile fabric. Although these are generally low-density areas, they offer great opportunity for both station precinct planning and incorporating planning principles that create sustainable built environment and mobility systems. This potential needs to be tapped optimally while planning these areas through incorporating principles of compact development and TOD; mixed use; active frontage; and an efficient road network system that offers connectedness, directness, and permeability. In due course, they can transform to high quality walking and transit fabric.

The study draws our attention to the importance of aligning transit policies with metropolitan region planning as that would enable creating urban fabrics that support sustainable mobility. In the long run it would help in naturally attaining more sustainable last mile behaviour (having higher share of non-motorised trip access to stations) as well as higher transit patronage. Last, but not the least, the role of institutional integration is paramount to providing seamless connectivity, especially for transit systems that serve an entire agglomeration/conurbation/city-region.

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Written By

Chidambara

Submitted: July 2nd, 2021 Reviewed: February 25th, 2022 Published: April 7th, 2022