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Mapping public transport accessibility levels (PTAL) in India and its applications: A case study of Surat
Case Studies on Transport Policy 7 (2019) 293–300
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Mapping public transport accessibility levels (PTAL) in India and its applications: A case study of Surat
T
Bhargav Adhvaryua, , Abhay Chopdeb, Lalit Dashorac ⁎
a
Faculty of Technology, CEPT University, Ahmedabad, India Transport Infrastructure Consultant, India c Urban Planning & Resilience Consultant, India b
ABSTRACT
Rapid urbanisation is a key factor in making public urban infrastructure systems in developing countries overstressed. A public transport system of a city is one of its vital urban infrastructure systems. Good public transport enhances connectivity and mobility, especially for lower income groups making them better participate in the labour market, eventually fostering economic growth and social equity. Therefore, it is important in cities of developing countries to ensure the best possible public transport system given the limited availability of resources. One of the ways to enhance a public transport system is to improve its accessibility with regard to population distribution. The first step is to measure the level of accessibility offered by the current system. Public Transport Accessibility Level (PTAL) is a tool to measure accessibility at various location in a city and spatially visualise it. A case study of Surat, India is used to implement the PTAL. Surat PTAL maps are created for a base year (2016) and future year (2021), superimposed with population density, and compared with Ahmedabad city. PTAL maps could be useful in several ways. Key applications and lessons for planners include guiding future public transport investments, enhancing the urban plan-making process by integrating transport and land use decisions, better informing the parking policy, improving residential location choice, optimising supply locations of affordable and low-cost housing, and better understanding the mobility needs of the urban poor.
1. Introduction Accessibility has various meanings depending on the context. On the one hand, at a micro level, it could mean the ease of accessing facilities by different groups of people (such as physical ability, age, gender, etc). Micro-accessibility is about barrier-free designs of public transport infrastructure components. On the other hand, at a macro level, it could mean the potential of opportunities of interaction (as defined by Hansen, 1959). Both these definitions can be extended to public transport and are of course interrelated. In this paper, we use accessibility in its latter sense i.e., as a measure of access to the public transport system potentially available at various spatially segregated locations. An inclusive public transport system represents a fair society. Citizens not being able to access public transport, due to both monetary reasons and/or physical challenges, are excluded from participation in economic and social activities in a society. One of the scenarios wherein social exclusion can happen is when the public transport system offers poor accessibility. Most public transport systems in developing countries are bus-based and therefore usually use the same road space as private vehicles. A robust road network does not necessarily translate to a robust bus-based public transport system if the bus routes are sparse and have limited frequencies. It is therefore important for cities to have ⁎
a baseline assessment of accessibility offered by their current public transport systems, and if needed, the same can be used to make more informed future public transport investment decisions. In addition, measuring and mapping accessibility also has valuable links with land use planning. In this paper, we first present a brief introduction of tools to measure public transport accessibility (Section 2) and justify the chosen tool—PTAL. We implement PTAL using a case study of Surat, India (introduced in Section 3) to measure accessibility at various location in a city and spatially visualise it. In Section 4, we briefly introduce London’s PTAL methodology, adaptation to the Indian context, provide an overview of PTAL calculations, generate PTAL maps for year 2016 and 2021 to show improvements, overlaying with population density, and compare Surat PTAL mapping with Ahmedabad. In the last section, we discuss PTAL application and lessons for planners, with discussion on five key applications such as guiding future public transport investments; enhancing the urban plan-making process by integrating transport and land use decisions; better informing the parking policy; improving residential location choice and optimising supply locations of affordable and low-cost housing; and better understanding the mobility needs of the urban poor.
Corresponding author. E-mail address: [email protected] (B. Adhvaryu).
https://doi.org/10.1016/j.cstp.2019.03.004 Received 25 July 2017; Received in revised form 19 February 2019; Accepted 13 March 2019 Available online 14 March 2019 2213-624X/ © 2019 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
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2. A brief introduction to tools for measuring public transport accessibility
Rapid Transit System (BRTS). As in 2016, majority of the public transport services are being operated within SMC area only.
One of the ways to enhance a public transport system is to improve its accessibility with regard to population distribution. Therefore, it is important to measure the level of accessibility offered by the current system. Accessibility being offered by a public transport system can be measured in several ways. Some of the well-published tools are TTSAT, LUPTAI, and PTAL, which are briefly described below in terms of the data required and comments on computational requirements. A useful summary of review of studies on public transport accessibility from various aspects such as social exclusion, perceived accessibility, employment rate, sustainability, public health, spatial and temporal efficiency, and mobility is presented in Saif et al (2018, p. 6) and therefore is not repeated here. Time-Based Transit Service Area Tool (TTSAT) (Cheng and Agrawal, 2010) measures accessibility of a point of interest with respect to the number of destinations which can be accessed within given maximum travel time/cost (including transit access and egress and in-transit time). Key data required are stop locations, route frequency, road network, location of all probable destinations, maximum preferable walk distance/time, and in-transit travel time. Intense computations are required as accessibility index is to be calculated for each permutationcombination of origin–destination within given maximum first mile, intransit, and last mile travel time. Land Use & Public Transport Accessibility Index (LUPTAI) (Pitot et al, 2006) measures accessibility of a destination which can be accessed from various origins within given maximum travel time/cost. The approach is similar to TTSAT, but the difference is that LUPTAI is a destination-based approach i.e., calculations are from destination to origin. Key data requirement is same as TTSAT. As compared to TTSAT, LUPTAI is computationally less demanding. Public Transport Accessibility Levels (PTAL) (Transport for London, 2010) measures accessibility of a point of interest in terms of availability of public transport service at nearest service access point (e.g., public transport stop) within given maximum access time, irrespective of destination. The PTAL methodology was originally developed by the London Borough of Hammersmith and Fulham in 1992 and was later adopted by Transport for London as a standard method for calculation of public transport accessibility in London (Transport for London, 2010). An adaptation of London PTAL to Greater Manchester, known as Greater Manchester Accessibility Levels (GMAL) was also done (Transport for Greater Manchester, 2016). Yet another extension to PTAL was done in Melbourne Australia (Saghapour et al., 2016). Public transport accessibility ratings have been used by other countries such as the US, the Netherlands, Australia, and New Zealand (Joyce and Dunn, 2010). In the Indian context, Shah and Adhvaryu (2016) demonstrated the first, city-wide application of the London methodology to Ahmedabad. The key data required for PTAL are stop locations, route frequency, and road network, which is much lesser than TTSTAT and LUPTAI and is computationally much simpler and faster than both. In addition, the other advantage of PTAL is that it is easy to understand (Wu and Hine, 2003). In summary, the main justification of using PTAL in this study is requirement of minimal data (usually available in developing countries), its computational simplicity, and lucid visualisations.
4. Mapping public transport accessibility levels (PTAL) 4.1. Introduction to London methodology London’s PTAL calculation methodology entails defining a point of interest (POI) and service access points (SAP). POI is a point for which the accessibility level is to be measured with reference to an SAP (e.g., bus stop, metro station, etc). SAPs have a pre-defined catchment area measured as 8-minute walk for buses and 12-minute walk for rail-based services. Total access times are calculated using walk times and average waiting times for each SAP and for each of the public transport service available at that SAP. A reliability factor (measured in minutes) is added to the average waiting time to account for the delay in a public transport service. These total access times are then converted to equivalent doorstep frequency, which are used to calculate the accessibility index (AI) for each POI. The overall AI for a POI is the sum of AI for all available public transport modes. The map of London PTAL is available in Transport for London (2015, p. 10). 4.2. Adaptation to the Indian context Shah and Adhvaryu (2016) demonstrated the first, city-wide application of the London methodology in the Indian context for Ahmedabad. However, the London methodology was adapted to Ahmedabad as summarised below:
• Grid approach: in London methodology, POIs were considered by • • •
•
3. Introduction to Surat Surat, with 4.5 million people (Census of India, 2011), is the second most populated city of Gujarat and the largest city in South Gujarat region. It has two urban local bodies, Surat Municipal Corporation (SMC) and Surat Urban Development Authority (SUDA) (see Fig. 1). In 2016, SMC had an area of 327 km2 and SUDA had an area of 1357 km2 (including the extended boundary), which was reduced to 986 km2 in 2016. Surat has two public transport systems: the City Bus and the Bus
built development. However, given the lack of availability of building footprint data, the study area was divided into grids (1 km2) with the centroid of each grid cell representing the POI for measurement of AI for each cell. Peak hour: in London methodology, the peak hour used for PTAL mapping is 8:15 AM–9:15 AM. However, the peak hour in the city of Ahmedabad based on several studies was identified as 9:30 AM–10:30 AM. Walk speed: in London methodology, the walk speed is 4.8 km/h. However, it was reduced in Ahmedabad to 3.6 km/h (based on empirical observations). The reduction accounts for lack of footpaths and in case where available are mostly occupied by street vendors and parking. Reliability factor (K): in London methodology, 0.75 (minute) as reliability factor is used for metrorail and 2 for buses. Following the same principle, in Ahmedabad methodology a reliability factor of 0.75 for metrorail, 1 for BRTS, and 2.5 for AMTS (buses in mixed traffic). The rationale being that in Ahmedabad, even though BRTS runs on dedicated corridors (segregated by railings), some delays are still inevitable at intersections due to mixed traffic movement, whereas AMTS buses ply in mixed traffic having more traffic delays. Public transport frequency: in London methodology, for bi-directional routes i.e., same numbered routes moving in opposite directions, only the highest frequency route is considered. In Ahmedabad, each SAP is considered as one point for PTAL calculations even though it is a pair of two complimentary stops (as they are on the opposite side of the road but only a few metres apart). Therefore, the frequency used in PTAL calculations is a summation of the frequencies of the routes passing through the pair of public transport stops.
4.3. Adaptation to Surat In Surat, like Ahmedabad, building footprint data was not available, and therefore a 1 km2 grid approach was adopted with the following justification. A comparison of PTAL mapping for 1 km, 500 m, 100 m, 294
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Fig. 1. Surat location, and SMC and SUDA boundaries. Source SMC-SUDA: www.sudaonline.org.
Fig. 2. PTAL map for various grids sizes.
and 30 m square grids (Fig. 2) shows that as the grid size decreases the visual grain of the PTAL map becomes finer, but it also increases the number of POIs (i.e., halving the grid increases the number of POIs by four times). This increases the requirement of walk times input data from POI to SAP. For generating PTAL maps aimed at supporting citylevel strategic public transport improvement decisions, creating smaller grids is not likely to justify the additional cost (i.e, walk time data, and computing time and power). PTAL maps can be generated with a much finer grid, once specific smaller areas are chosen for improvements, based on the strategic 1 km2 PTAL map. Of course, if the local authorities have enough resources the PTAL maps can be generated for the desired grid size from the start. Overall, the 1 km2 grid appears to be a reasonable starting point with finer grid PTAL maps for specific areas. Owing to several similarities of street infrastructure in Ahmedabad
and Surat, the same average pedestrian speed was adopted for Surat. Primary surveys were conducted in Surat (during Aug–Sep 2016), which recorded an average of 4 minutes of delay in the City Bus services. Therefore, a reliability factor of 4 has been used for the City Bus service and for BRTS it was assumed as 1 (same as Ahmedabad). Also, a sample survey (which was not carried out in Ahmedabad) showed the willingness to walk as 10 and 15 min for City Bus and BRTS, respectively. A comparison of key parameter assumptions used in AI calculations are shown in Table 1. To implement the PTAL methodology, the data and its sources are: SMC boundary GIS shape file from SMC; Surat city base map from Google satellite image; BRTS routes, frequencies, and stops from Surat BRTS Cell; City Bus routes and stops from SMC. At the time of this study, except at individual bus stops, the scheduled timetable for City Bus service was not published as a single document or 295
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Table 1 Key parameter assumptions: London, Ahmedabad, and Surat. Parameter
London
Ahmedabad
Surat
Peak hour Walk speed
8:15 AM–9:15 AM 4.8 km/h 80 m/min
9:30 AM–10:30 AM 3.6 km/h 60 m/min
9:00 AM–10:00 AM 3.6 km/h 60 m/min
Reliability (K) Max. walk time Max. walk distance
Bus 2.0 min 8 min 640 m
AMTS BRTS 2.5 min 1.0 min The maximum distance of an SAP from POI was 993 m (which is about 16 min walk) so no threshold was considered.
City Bus 4.0 min 10 min 600 m
Reliability (K) Max. walk time Max. walk distance
Underground, tram, DLR, rail 0.75 min 12 min 960 m
Metrorail (expected 2019) 0.75 min The maximum distance of an SAP from POI was 993 m (which is about 16 min walk) so no threshold was considered.
– – –
on a website. Therefore, based on primary surveys, an average peakhour frequency of 10 min was considered.
AWT = 0.5 ×
4.4. Overview of PTAL calculations
BRTS 1.0 min 15 min 900 m
60 +K f
(1)
Step 4: Calculate total access time (TAT) for each valid route at each SAP: This is done as shown in Eq. (2) by adding times obtained in steps 2 and 3.
The final output of PTAL calculations is the AI values for all POIs, which are obtained through the following six steps (adapted from Transport for London, 2010):
(2)
TAT = WT + AWT
Step 5: Convert TAT into equivalent doorstep frequency (EDF): This is obtained as 30 divided by TAT (see Eq. (3)). The principle is to treat access time as a notional average waiting time as though the route was available at the ‘doorstep’ of the selected POI.
Step 1: Define points of interest (POI) and service access points (SAP): POI is defined as a point for which the accessibility level is to be measured with reference to a public transport stop (such as bus stop, metro station, etc), referred to as SAP. The POI in this study is taken as the centroid of a grid of 1 km2 and SAPs are superimposed along with the grid on a satellite image. Step 2: Calculate walk access time from POI to SAP: The actual road network distance from POI to SAP is measured on the satellite image, and assuming a walk speed of 3.6 km/h, the walk time (WT) is calculated. The maximum walk times for City Bus and BRTS are taken as 10 and 15 min, respectively. Any SAPs beyond these distances are not considered for that particular POI. Step 3: Identify valid routes at each SAP and calculate average waiting time (AWT): Routes of all services for the peak hour and the frequency of services on all these routes during this hour is used in the calculation of AWT, which is defined as the period from when a passenger arrives at an SAP to the arrival of the desired service. In the calculation, the hourly frequency (f) is doubled because the scheduled waiting time (SWT) is estimated as half the headway. For example, a 10-minute service frequency (f = 6 buses per hour) would give an SWT of 5 min. In addition, to make the calculations more realistic, a ‘reliability factor’ (K) is added to the SWT depending on to transport mode. For Surat, these were 1 min for BRTS and 4 min for City Bus (see Eq. (1)).
EDF =
1 60 30 × = 2 TAT TAT
(3)
The reason for dividing 30 by TAT is to re-apply half-the-headway rule because the values have different meanings. In step 3, frequency is converted into AWT and in the step 5, TAT is converted back into a frequency (EDF). What the calculation does is that it first calculates the TAT i.e., the time it takes to leave a POI and get on a service, which includes three elements: walk time + average waiting time (assumed to be half the headway) + reliability factor. TAT is now converted into a number that is comparable to service frequency that considers the additional walk time taken to reach the stop along with reliability of the service. Step 6: Calculate accessibility index (AI) for each POI: In this step, the most dominant route i.e., the route with highest frequency is assigned a weighting factor of 1.0, and for all the other routes, a weighting factor of 0.5 is assigned. Thus, for a transport mode (m) the AIm is calculated as shown in Eq. (4). The accessibility index for a POI (AIPOI) is then calculated as shown in Eq. (5). A typical
Table 2 Typical accessibility index calculation. POI ID#
Mode
156
BRTS
SAP name
Route No.
A 1 B 2 C 3 D 4 City Bus A 1 B 2 Total (AI for a POI ID#156)
Distance (m) of SAP from POI
Frequency (per hour)
Reliability factor (k)
WT (min)
AWT (min)
TAT (min)
Weight
EDF (per hour)
AI
293 366 694 868 418 548
4 4 4 4 6 6
1 1 1 1 4 4
4.9 6.1 11.6 14.5 7.0 9.1
8.5 8.5 8.5 8.5 9.0 9.0
13.4 14.6 20.1 23.0 16.0 18.1
1 1 1 1 1 1
2.2 2.1 1.5 1.3 1.9 1.7
2.2 2.1 1.5 1.3 1.9 1.7 10.6
Key: POI – point of interest; SAP – service access point; WT – walk time; AWT – average waiting time; TAT – total access time; EDF – equivalent doorstep frequency; AI – accessibility index. Notes: [1] Calculations are as per Section 4.4 [2] All routes are assigned a weight of 1 as there is only one route at that SAP.
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Fig. 3. Surat PTAL 2016 (City Bus and City Bus + BRTS).
calculation for a POI is shown in Table 2.
AIm = EDFmax + 0.5
EDF allotherroutes
AIPOI =
AIm m
Secondly, comparing before and after the implementation of BRTS (which started in 2014), we see that it had improved the accessibility considerably. It could be argued that city’s investment in the new and better public transport system seems well justified. PTAL mapping could be used in two important ways. The first use is by overlaying the population density map on the existing PTAL map (Fig. 4). As shown in this figure, Surat has areas with poor to moderate PTAL but high population density. Such areas should certainly be top priority for more focused public transport improvements. As discussed before, specific areas can be selected for detailed improvements using finer grid local PTAL maps. Also, there are some reverse cases i.e., high PTAL but low population density, in which case the urban local bodies could re-route resources (e.g., fleet and staff) to other PT deficient areas with high population, although in practice this might be difficult to achieve. The second use would be to create future PTAL maps based on alternative scenarios. In this case, we took the committed SMC’s BRTS expansion plan expected to be completed by 2021. While doing so, we also improved the City Bus services (with SAP spacing assumed as the current average) such that it acts as complementary to the BRTS. From Fig. 5, which shows the comparison of 2016 and 2021 PTAL maps, we see that PTAL would improve in the area immediately east of the city centre and in the south east where new BRT lines have been proposed. Given a budget constraint, the local authorities could test the efficacy of alternative public transport routes (and station spacing) at a city-wide network level.
(4) (5)
4.5. Surat PTAL mapping Once the AIs for all POIs are calculated, the next step is to create a graphical representation of the AIs to spatially interpret its implications. We use ArcGIS to map the AI values which offers four mapping methods. Of those, the quantile break is the most suitable as it distributes all AI values approximately equally in the identified bands. This allows a better visualisation of areas with high, medium, and low accessibility (Shah and Adhvaryu, 2016). PTAL for 2016 is created using the AI values calculated for each POI as per the sample calculation shown in Table 2. The map has accessibility levels from 1 to 10, where level 1 (blue) represents poor accessibility and level 10 (red) represents excellent accessibility. The number of categories is selected based on visual judgement. On trial mapping runs, increasing the number of categories made the colour indistinguishable and reducing made the visual representation coarser. Fig. 3 shows PTAL 2016 maps for both the City Bus service and City Bus with BRTS. Firstly, from the combined map we see that the accessibility to public transport is as expected—relatively very high in the city centre and some of the other well-developed areas of the city. Also, there are few scattered, leap-frogged areas with excellent PTAL partly surrounded by medium PTAL. These areas are the newer commercial development with a high level of road connectivity (also used by public transport). The accessibility pattern also coincides well with the radial growth of the city. The eastern area has medium to poor PTAL because it is dominated by shared auto-rickshaws (a paratransit mode).
4.6. Comparing Surat and Ahmedabad PTAL If the first application of PTAL to an Indian city of Ahmedabad in 2014 (Shah & Adhvaryu, 2016) is used as a benchmark i.e, if the same colour bands used in Ahmedabad PTAL are applied to Surat then the comparison produces a different result (see Fig. 6). Since the PTAL map shows the relative accessibility, Surat map by itself may appear to be reasonably acceptable. However, when compared with Ahmedabad, 297
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Fig. 4. Surat PTAL 2016 overlaid with population density.
Fig. 5. Surat PTAL 2016 v. 2021.
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Fig. 6. Ahmedabad v. Surat PTAL.
most of the higher PT accessibly area disappears except for a few central areas in Surat. This would be useful for urban local bodies to ascertain how they fare compared to similar cities.
project population density maps, wherein the future population could be obtained from an external source (e.g., planning authority projections). Although a land use—transport model (for an application to the Indian context see Adhvaryu, 2010) with built-in feedback loop would be desirable, in absence of that, such alternative “what-if” transport scenarios could be created within an iterative loop in which public transport supply and population density could be adjusted to get optimum results. Since DPs are made for about a 20-year horizon, overlaying PTAL with population density can also be used to anticipate future dynamics. Such overlays for base and future years could also help make changes in the DP (e.g., higher FSI, change in land use zoning, etc) to attract more development along public transport routes (i.e., transit-oriented development (TOD)). This helps integrate land use zoning (which is part of the DP) with public transport accessibility—an aspect usually ignored by Indian planners (Balchandran et al., 2005).
5. PTAL applications and lessons for planners, and conclusions 5.1. Applications and lessons 5.1.1. Prioritising public transport and NMT investments Areas with poor accessibility can be identified on a base (current) year PTAL map. Such a map would show the spatial distribution of accessibility (with regard to population, e.g. see Fig. 4) and would serve as a useful guide in making strategic city-level decisions allowing planners to prioritise investments in public transport and supporting non-motorised transport (NMT) facilities. Of course, once local areas are prioritised, more detailed micro-PTAL maps (with smaller grid spacing) at local area level can be created to facilitate micro-level decisions (e.g., changing public transport stops spacing, re-routing existing routes, or adding new routes). At this spatial scale, the NMT infrastructure must also be addressed in tandem, as NMT is key to the first and last mile connectivity. NMT improvements translate into better walk times, both of which increase the AI.
5.1.3. Informing parking policy In general, we propose the principle that areas better served with public transport would be identified for lesser parking supply (both on and off street). On developing city-wide parking policy, ITDP (2013) suggests that series of steps, one of which includes delineating “parking” and “no parking zones”. Using example of Zurich, Switzerland in which a PTAL map and off-street parking requirements map are compared (ITDP, 2013, Fig. 4, p. 7), parking requirements are reduced, closer a development is to a public transport station. A similar strategy is also used in London (Transport for London, 2015). Planners could consider outlying areas with poor to medium PTAL for strategic location of park and ride facilities. Usually on-street parking requirements vary across the city and are best dealt at a local area planning level. Parking policies can be made more specific using micro-PTAL map (as discussed in Section 5.1.1).
5.1.2. Integrating transport in the development/master plan (DP) One of the key jobs of planners is to make a statutory DP of the city, which usually is for a 20-year horizon. The transport system and its impact on land use and vice versa is an important element that needs to be dealt in the development plan. To be able to do this effectively, it is important for planners to assess the efficiency of the existing public transport infrastructure using PTAL maps (as discussed in Section 5.1.1) – which would be the first step. The second step would be to create future PTAL maps with “what-if” scenarios. Future transport inputs could be based on committed future plans by the city authorities (like the one used in Fig. 5) or it could be hypothetical scenarios. Similar to the existing situation, the future PTAL maps can be superimposed with
5.1.4. Improving residential location choice and optimising supply of affordable housing Captive public transport households can use the PTAL map for a 299
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more informed residential location choice. Surat has a huge migrant population which has been increasing significantly over the years, most of which are dependent on public transport. Real-estate developers can use PTAL maps (both existing and future) for locating affordable housing projects. Cities promoting housing infill development and urban regenration can also use PTAL maps for priotitising development (see Adhvaryu and Rathod, 2019). Lastly, and most importantly, government agencies can use PTAL maps to address the social inclusion agenda by prioritising site locations for government-supplied low-income housing.
Acknowledgments
5.1.5. Understanding the mobility needs of the urban poor A study on public transport inclusivity in Ahmedabad (Adhvaryu and Patel, 2019) argues that living in high PTAL areas may not translate to high accessibility to destinations specifically for those urban poor whose job locations vary by month and season (e.g., construction works, casual labourers, street vendors, etc). It therefore follows that planners can superimpose housing locations of the urban poor on a PTAL map (e.g., see Adhvaryu and Patel, 2019, Fig. 1) to identify specific areas to enhance their mobility needs.
References
The authors thank the following for their valuable inputs: the 100 Resilient Cities Surat city project team (including Surat Municipal Corporation, Surat Climate Change Trust, and TARU Leading Edge Pvt Ltd); Mr. Jay Shah, Transport Planning Consultant; students of MTech in Infrastructure Engineering Design (2015–17) batch at CEPT University; and Mr Mustafa Sonasath, Assistant Manager (Operations), Surat Sitilink Ltd (a wholly-owned subsidiary of SMC that manages the City Bus services and BRTS in Surat).
Adhvaryu, B., 2010. Enhancing urban planning using simplified models: SIMPLAN for Ahmedabad, India. Progress Planning 73 (3), 113–207. https://doi.org/10.1016/j. progress.2009.11.001. Adhvaryu, B., Patel, M., 2019. Is public transport in Ahmedabad inclusive? Econ. Political Weekl 54 (8), 17–20. Adhvaryu, B., Rathod, V., 2019. Estimating housing infill potential: developing a case for floorspace pooling in Ahmedabad, India. Plann. Pract. Res. https://doi.org/10.1080/ 02697459.2019.1590772. (forthcoming). Balchandran, B.R., Adhvaryu, B., Lokre, A., 2005. Urban transport in India: problems, responses and strategies. EPC Working Paper 1–1. https://doi.org/10.13140/RG.2.1. 1798.0327. Census of India, 2011. District Census Handbook, Surat. Available from: http://www. censusindia.gov.in/2011census/dchb/2425_PART_B_DCHB_SURAT.pdf. Cheng, C., Agrawal, A.W., 2010. TTSAT: a new approach to mapping transit accessibility. J. Public Transp. 13 (1), 55–72. https://doi.org/10.5038/2375-0901.13.1.4. Hansen, W.G., 1959. How accessibility shapes land use. J. Am. Plann. Assoc. 25 (2), 73–76. https://doi.org/10.1080/01944365908978307. ITDP, 2013. Common Sense Parking Reforms for Indian Cities. Institute for Transportation and Development Policy. Available from: https://go.itdp.org/ download/attachments/46137384/Guindance%20on%20Parking%20Reform%20in %20Indian%20cities%20-%20ITDP%20-%20130815.pdf?api=v2. Joyce, M., Dunn, R., 2010. A methodology for measuring public transport accessibility to employment: a case study. Auckland, CDB, NZ. Transp. Res. Rec. 5–6. Pitot, M., Yigitcanlar, T., Sipe, N., Evans, R., 2006. Land Use & Public Transport Accessibility Index (LUPTAI) Tool – The development and pilot application of LUPTAI for the Gold Coast. Available from: https://research-repository.griffith.edu. au/bitstream/handle/10072/11547/ATRF_Full_Paper_2006_V2.pdf?sequence=1& isAllowed=y. Saghapour, T., Moridpour, S., Thompson, R.G., 2016. Modeling access to public transport in urban areas. Journal of Advanced Transportation 50, 1785–1801. https://doi.org/ 10.3311/PPtr. 12072. Saif, M., Zefreh, M., Torok, A., 2018. Public transport accessibility: a literature review. Periodica Polytechnica Transp. Eng. https://doi.org/10.3311/PPtr. 12072. Shah, J., Adhvaryu, B., 2016. Public transport accessibility levels for Ahmedabad, India. J. Public Transp. 19 (3), 19–35. https://doi.org/10.5038/2375-0901.19.3.2. Transport for Greater Manchester, 2016. Greater Manchester Accessibility Levels (GMAL) Model. Available from: http://odata.tfgm.com/opendata/downloads/GMAL/GMAL %20Calculation%20Guide.pdf. Transport for London, 2010. Measuring Public Transport Accessibility Levels - PTALs summary. Available from: https://files.datapress.com/london/dataset/publictransport-accessibility-levels/PTAL-methodology.pdf. Transport for London, 2015. Accessing transport connectivity in London. Available from: https://data.london.gov.uk/download/public-transport-accessibility-levels/ 86bbffe1-8af1-49ba-ac9b-b3eacaf68137/connectivity-assessment-guide.pdf. Wu, B.M., Hine, J.P., 2003. A PTAL approach to measuring changes in bus service accessibility. Transp. Policy 10, 307–320. https://doi.org/10.1016/S0967-070X(03) 00053-2.
5.2. Conclusions This study discusses development of public transport accessibility mapping in India, using Surat as a case study city. We demonstrate that the well-developed PTAL methodology can be easily applied to cities with constrained budgets and limited data availability. We argue that PTAL maps could provide a strong visual tool to assess the level of accessibility offered by existing transport systems, which could be very useful in guiding strategic, city-level public transport improvements. This study goes beyond the first city-wide application in the Indian context to Ahmedabad (Shah and Adhvaryu, 2016), first, by correlating PTAL with population densities, and second, by demonstrating the use of PTAL for evaluating future transport investments. Future PTAL scenarios could also be mapped in the context of future population distribution scenarios, which becomes a strong “what-if” decision-support tool for the long-term master/development plan of the city. PTAL maps with periodic updating if put in the public domain1 is likely to improve the ‘image’ of the public transport system and could contribute to a modal shift in its favour. Given the simplicity of PTAL mapping as a tool it could be upscaled for more detailed micro-accessibility assessment (depending on need and availability of resources) at local area levels with smaller grid size that can be linked with station accessibility plans and TOD policies. Lastly, such tools can then be used as building blocks for more sophisticated models to measure, map, and apply public transport accessibility to enhance planning. We believe that PTAL mapping should be an integral part of the standard tool-kit for planners to guide them enhance transport infrastructure investments and inform urban and transport planning policy decisions in developing countries.
1 For example see https://tfl.gov.uk/info-for/urban-planning-andconstruction/planning-with-webcat/webcat.
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Mapping public transport accessibility levels (PTAL) in India and its applications: A case study of Surat
T
Bhargav Adhvaryua, , Abhay Chopdeb, Lalit Dashorac ⁎
a
Faculty of Technology, CEPT University, Ahmedabad, India Transport Infrastructure Consultant, India c Urban Planning & Resilience Consultant, India b
ABSTRACT
Rapid urbanisation is a key factor in making public urban infrastructure systems in developing countries overstressed. A public transport system of a city is one of its vital urban infrastructure systems. Good public transport enhances connectivity and mobility, especially for lower income groups making them better participate in the labour market, eventually fostering economic growth and social equity. Therefore, it is important in cities of developing countries to ensure the best possible public transport system given the limited availability of resources. One of the ways to enhance a public transport system is to improve its accessibility with regard to population distribution. The first step is to measure the level of accessibility offered by the current system. Public Transport Accessibility Level (PTAL) is a tool to measure accessibility at various location in a city and spatially visualise it. A case study of Surat, India is used to implement the PTAL. Surat PTAL maps are created for a base year (2016) and future year (2021), superimposed with population density, and compared with Ahmedabad city. PTAL maps could be useful in several ways. Key applications and lessons for planners include guiding future public transport investments, enhancing the urban plan-making process by integrating transport and land use decisions, better informing the parking policy, improving residential location choice, optimising supply locations of affordable and low-cost housing, and better understanding the mobility needs of the urban poor.
1. Introduction Accessibility has various meanings depending on the context. On the one hand, at a micro level, it could mean the ease of accessing facilities by different groups of people (such as physical ability, age, gender, etc). Micro-accessibility is about barrier-free designs of public transport infrastructure components. On the other hand, at a macro level, it could mean the potential of opportunities of interaction (as defined by Hansen, 1959). Both these definitions can be extended to public transport and are of course interrelated. In this paper, we use accessibility in its latter sense i.e., as a measure of access to the public transport system potentially available at various spatially segregated locations. An inclusive public transport system represents a fair society. Citizens not being able to access public transport, due to both monetary reasons and/or physical challenges, are excluded from participation in economic and social activities in a society. One of the scenarios wherein social exclusion can happen is when the public transport system offers poor accessibility. Most public transport systems in developing countries are bus-based and therefore usually use the same road space as private vehicles. A robust road network does not necessarily translate to a robust bus-based public transport system if the bus routes are sparse and have limited frequencies. It is therefore important for cities to have ⁎
a baseline assessment of accessibility offered by their current public transport systems, and if needed, the same can be used to make more informed future public transport investment decisions. In addition, measuring and mapping accessibility also has valuable links with land use planning. In this paper, we first present a brief introduction of tools to measure public transport accessibility (Section 2) and justify the chosen tool—PTAL. We implement PTAL using a case study of Surat, India (introduced in Section 3) to measure accessibility at various location in a city and spatially visualise it. In Section 4, we briefly introduce London’s PTAL methodology, adaptation to the Indian context, provide an overview of PTAL calculations, generate PTAL maps for year 2016 and 2021 to show improvements, overlaying with population density, and compare Surat PTAL mapping with Ahmedabad. In the last section, we discuss PTAL application and lessons for planners, with discussion on five key applications such as guiding future public transport investments; enhancing the urban plan-making process by integrating transport and land use decisions; better informing the parking policy; improving residential location choice and optimising supply locations of affordable and low-cost housing; and better understanding the mobility needs of the urban poor.
Corresponding author. E-mail address: [email protected] (B. Adhvaryu).
https://doi.org/10.1016/j.cstp.2019.03.004 Received 25 July 2017; Received in revised form 19 February 2019; Accepted 13 March 2019 Available online 14 March 2019 2213-624X/ © 2019 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
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2. A brief introduction to tools for measuring public transport accessibility
Rapid Transit System (BRTS). As in 2016, majority of the public transport services are being operated within SMC area only.
One of the ways to enhance a public transport system is to improve its accessibility with regard to population distribution. Therefore, it is important to measure the level of accessibility offered by the current system. Accessibility being offered by a public transport system can be measured in several ways. Some of the well-published tools are TTSAT, LUPTAI, and PTAL, which are briefly described below in terms of the data required and comments on computational requirements. A useful summary of review of studies on public transport accessibility from various aspects such as social exclusion, perceived accessibility, employment rate, sustainability, public health, spatial and temporal efficiency, and mobility is presented in Saif et al (2018, p. 6) and therefore is not repeated here. Time-Based Transit Service Area Tool (TTSAT) (Cheng and Agrawal, 2010) measures accessibility of a point of interest with respect to the number of destinations which can be accessed within given maximum travel time/cost (including transit access and egress and in-transit time). Key data required are stop locations, route frequency, road network, location of all probable destinations, maximum preferable walk distance/time, and in-transit travel time. Intense computations are required as accessibility index is to be calculated for each permutationcombination of origin–destination within given maximum first mile, intransit, and last mile travel time. Land Use & Public Transport Accessibility Index (LUPTAI) (Pitot et al, 2006) measures accessibility of a destination which can be accessed from various origins within given maximum travel time/cost. The approach is similar to TTSAT, but the difference is that LUPTAI is a destination-based approach i.e., calculations are from destination to origin. Key data requirement is same as TTSAT. As compared to TTSAT, LUPTAI is computationally less demanding. Public Transport Accessibility Levels (PTAL) (Transport for London, 2010) measures accessibility of a point of interest in terms of availability of public transport service at nearest service access point (e.g., public transport stop) within given maximum access time, irrespective of destination. The PTAL methodology was originally developed by the London Borough of Hammersmith and Fulham in 1992 and was later adopted by Transport for London as a standard method for calculation of public transport accessibility in London (Transport for London, 2010). An adaptation of London PTAL to Greater Manchester, known as Greater Manchester Accessibility Levels (GMAL) was also done (Transport for Greater Manchester, 2016). Yet another extension to PTAL was done in Melbourne Australia (Saghapour et al., 2016). Public transport accessibility ratings have been used by other countries such as the US, the Netherlands, Australia, and New Zealand (Joyce and Dunn, 2010). In the Indian context, Shah and Adhvaryu (2016) demonstrated the first, city-wide application of the London methodology to Ahmedabad. The key data required for PTAL are stop locations, route frequency, and road network, which is much lesser than TTSTAT and LUPTAI and is computationally much simpler and faster than both. In addition, the other advantage of PTAL is that it is easy to understand (Wu and Hine, 2003). In summary, the main justification of using PTAL in this study is requirement of minimal data (usually available in developing countries), its computational simplicity, and lucid visualisations.
4. Mapping public transport accessibility levels (PTAL) 4.1. Introduction to London methodology London’s PTAL calculation methodology entails defining a point of interest (POI) and service access points (SAP). POI is a point for which the accessibility level is to be measured with reference to an SAP (e.g., bus stop, metro station, etc). SAPs have a pre-defined catchment area measured as 8-minute walk for buses and 12-minute walk for rail-based services. Total access times are calculated using walk times and average waiting times for each SAP and for each of the public transport service available at that SAP. A reliability factor (measured in minutes) is added to the average waiting time to account for the delay in a public transport service. These total access times are then converted to equivalent doorstep frequency, which are used to calculate the accessibility index (AI) for each POI. The overall AI for a POI is the sum of AI for all available public transport modes. The map of London PTAL is available in Transport for London (2015, p. 10). 4.2. Adaptation to the Indian context Shah and Adhvaryu (2016) demonstrated the first, city-wide application of the London methodology in the Indian context for Ahmedabad. However, the London methodology was adapted to Ahmedabad as summarised below:
• Grid approach: in London methodology, POIs were considered by • • •
•
3. Introduction to Surat Surat, with 4.5 million people (Census of India, 2011), is the second most populated city of Gujarat and the largest city in South Gujarat region. It has two urban local bodies, Surat Municipal Corporation (SMC) and Surat Urban Development Authority (SUDA) (see Fig. 1). In 2016, SMC had an area of 327 km2 and SUDA had an area of 1357 km2 (including the extended boundary), which was reduced to 986 km2 in 2016. Surat has two public transport systems: the City Bus and the Bus
built development. However, given the lack of availability of building footprint data, the study area was divided into grids (1 km2) with the centroid of each grid cell representing the POI for measurement of AI for each cell. Peak hour: in London methodology, the peak hour used for PTAL mapping is 8:15 AM–9:15 AM. However, the peak hour in the city of Ahmedabad based on several studies was identified as 9:30 AM–10:30 AM. Walk speed: in London methodology, the walk speed is 4.8 km/h. However, it was reduced in Ahmedabad to 3.6 km/h (based on empirical observations). The reduction accounts for lack of footpaths and in case where available are mostly occupied by street vendors and parking. Reliability factor (K): in London methodology, 0.75 (minute) as reliability factor is used for metrorail and 2 for buses. Following the same principle, in Ahmedabad methodology a reliability factor of 0.75 for metrorail, 1 for BRTS, and 2.5 for AMTS (buses in mixed traffic). The rationale being that in Ahmedabad, even though BRTS runs on dedicated corridors (segregated by railings), some delays are still inevitable at intersections due to mixed traffic movement, whereas AMTS buses ply in mixed traffic having more traffic delays. Public transport frequency: in London methodology, for bi-directional routes i.e., same numbered routes moving in opposite directions, only the highest frequency route is considered. In Ahmedabad, each SAP is considered as one point for PTAL calculations even though it is a pair of two complimentary stops (as they are on the opposite side of the road but only a few metres apart). Therefore, the frequency used in PTAL calculations is a summation of the frequencies of the routes passing through the pair of public transport stops.
4.3. Adaptation to Surat In Surat, like Ahmedabad, building footprint data was not available, and therefore a 1 km2 grid approach was adopted with the following justification. A comparison of PTAL mapping for 1 km, 500 m, 100 m, 294
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Fig. 1. Surat location, and SMC and SUDA boundaries. Source SMC-SUDA: www.sudaonline.org.
Fig. 2. PTAL map for various grids sizes.
and 30 m square grids (Fig. 2) shows that as the grid size decreases the visual grain of the PTAL map becomes finer, but it also increases the number of POIs (i.e., halving the grid increases the number of POIs by four times). This increases the requirement of walk times input data from POI to SAP. For generating PTAL maps aimed at supporting citylevel strategic public transport improvement decisions, creating smaller grids is not likely to justify the additional cost (i.e, walk time data, and computing time and power). PTAL maps can be generated with a much finer grid, once specific smaller areas are chosen for improvements, based on the strategic 1 km2 PTAL map. Of course, if the local authorities have enough resources the PTAL maps can be generated for the desired grid size from the start. Overall, the 1 km2 grid appears to be a reasonable starting point with finer grid PTAL maps for specific areas. Owing to several similarities of street infrastructure in Ahmedabad
and Surat, the same average pedestrian speed was adopted for Surat. Primary surveys were conducted in Surat (during Aug–Sep 2016), which recorded an average of 4 minutes of delay in the City Bus services. Therefore, a reliability factor of 4 has been used for the City Bus service and for BRTS it was assumed as 1 (same as Ahmedabad). Also, a sample survey (which was not carried out in Ahmedabad) showed the willingness to walk as 10 and 15 min for City Bus and BRTS, respectively. A comparison of key parameter assumptions used in AI calculations are shown in Table 1. To implement the PTAL methodology, the data and its sources are: SMC boundary GIS shape file from SMC; Surat city base map from Google satellite image; BRTS routes, frequencies, and stops from Surat BRTS Cell; City Bus routes and stops from SMC. At the time of this study, except at individual bus stops, the scheduled timetable for City Bus service was not published as a single document or 295
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Table 1 Key parameter assumptions: London, Ahmedabad, and Surat. Parameter
London
Ahmedabad
Surat
Peak hour Walk speed
8:15 AM–9:15 AM 4.8 km/h 80 m/min
9:30 AM–10:30 AM 3.6 km/h 60 m/min
9:00 AM–10:00 AM 3.6 km/h 60 m/min
Reliability (K) Max. walk time Max. walk distance
Bus 2.0 min 8 min 640 m
AMTS BRTS 2.5 min 1.0 min The maximum distance of an SAP from POI was 993 m (which is about 16 min walk) so no threshold was considered.
City Bus 4.0 min 10 min 600 m
Reliability (K) Max. walk time Max. walk distance
Underground, tram, DLR, rail 0.75 min 12 min 960 m
Metrorail (expected 2019) 0.75 min The maximum distance of an SAP from POI was 993 m (which is about 16 min walk) so no threshold was considered.
– – –
on a website. Therefore, based on primary surveys, an average peakhour frequency of 10 min was considered.
AWT = 0.5 ×
4.4. Overview of PTAL calculations
BRTS 1.0 min 15 min 900 m
60 +K f
(1)
Step 4: Calculate total access time (TAT) for each valid route at each SAP: This is done as shown in Eq. (2) by adding times obtained in steps 2 and 3.
The final output of PTAL calculations is the AI values for all POIs, which are obtained through the following six steps (adapted from Transport for London, 2010):
(2)
TAT = WT + AWT
Step 5: Convert TAT into equivalent doorstep frequency (EDF): This is obtained as 30 divided by TAT (see Eq. (3)). The principle is to treat access time as a notional average waiting time as though the route was available at the ‘doorstep’ of the selected POI.
Step 1: Define points of interest (POI) and service access points (SAP): POI is defined as a point for which the accessibility level is to be measured with reference to a public transport stop (such as bus stop, metro station, etc), referred to as SAP. The POI in this study is taken as the centroid of a grid of 1 km2 and SAPs are superimposed along with the grid on a satellite image. Step 2: Calculate walk access time from POI to SAP: The actual road network distance from POI to SAP is measured on the satellite image, and assuming a walk speed of 3.6 km/h, the walk time (WT) is calculated. The maximum walk times for City Bus and BRTS are taken as 10 and 15 min, respectively. Any SAPs beyond these distances are not considered for that particular POI. Step 3: Identify valid routes at each SAP and calculate average waiting time (AWT): Routes of all services for the peak hour and the frequency of services on all these routes during this hour is used in the calculation of AWT, which is defined as the period from when a passenger arrives at an SAP to the arrival of the desired service. In the calculation, the hourly frequency (f) is doubled because the scheduled waiting time (SWT) is estimated as half the headway. For example, a 10-minute service frequency (f = 6 buses per hour) would give an SWT of 5 min. In addition, to make the calculations more realistic, a ‘reliability factor’ (K) is added to the SWT depending on to transport mode. For Surat, these were 1 min for BRTS and 4 min for City Bus (see Eq. (1)).
EDF =
1 60 30 × = 2 TAT TAT
(3)
The reason for dividing 30 by TAT is to re-apply half-the-headway rule because the values have different meanings. In step 3, frequency is converted into AWT and in the step 5, TAT is converted back into a frequency (EDF). What the calculation does is that it first calculates the TAT i.e., the time it takes to leave a POI and get on a service, which includes three elements: walk time + average waiting time (assumed to be half the headway) + reliability factor. TAT is now converted into a number that is comparable to service frequency that considers the additional walk time taken to reach the stop along with reliability of the service. Step 6: Calculate accessibility index (AI) for each POI: In this step, the most dominant route i.e., the route with highest frequency is assigned a weighting factor of 1.0, and for all the other routes, a weighting factor of 0.5 is assigned. Thus, for a transport mode (m) the AIm is calculated as shown in Eq. (4). The accessibility index for a POI (AIPOI) is then calculated as shown in Eq. (5). A typical
Table 2 Typical accessibility index calculation. POI ID#
Mode
156
BRTS
SAP name
Route No.
A 1 B 2 C 3 D 4 City Bus A 1 B 2 Total (AI for a POI ID#156)
Distance (m) of SAP from POI
Frequency (per hour)
Reliability factor (k)
WT (min)
AWT (min)
TAT (min)
Weight
EDF (per hour)
AI
293 366 694 868 418 548
4 4 4 4 6 6
1 1 1 1 4 4
4.9 6.1 11.6 14.5 7.0 9.1
8.5 8.5 8.5 8.5 9.0 9.0
13.4 14.6 20.1 23.0 16.0 18.1
1 1 1 1 1 1
2.2 2.1 1.5 1.3 1.9 1.7
2.2 2.1 1.5 1.3 1.9 1.7 10.6
Key: POI – point of interest; SAP – service access point; WT – walk time; AWT – average waiting time; TAT – total access time; EDF – equivalent doorstep frequency; AI – accessibility index. Notes: [1] Calculations are as per Section 4.4 [2] All routes are assigned a weight of 1 as there is only one route at that SAP.
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Fig. 3. Surat PTAL 2016 (City Bus and City Bus + BRTS).
calculation for a POI is shown in Table 2.
AIm = EDFmax + 0.5
EDF allotherroutes
AIPOI =
AIm m
Secondly, comparing before and after the implementation of BRTS (which started in 2014), we see that it had improved the accessibility considerably. It could be argued that city’s investment in the new and better public transport system seems well justified. PTAL mapping could be used in two important ways. The first use is by overlaying the population density map on the existing PTAL map (Fig. 4). As shown in this figure, Surat has areas with poor to moderate PTAL but high population density. Such areas should certainly be top priority for more focused public transport improvements. As discussed before, specific areas can be selected for detailed improvements using finer grid local PTAL maps. Also, there are some reverse cases i.e., high PTAL but low population density, in which case the urban local bodies could re-route resources (e.g., fleet and staff) to other PT deficient areas with high population, although in practice this might be difficult to achieve. The second use would be to create future PTAL maps based on alternative scenarios. In this case, we took the committed SMC’s BRTS expansion plan expected to be completed by 2021. While doing so, we also improved the City Bus services (with SAP spacing assumed as the current average) such that it acts as complementary to the BRTS. From Fig. 5, which shows the comparison of 2016 and 2021 PTAL maps, we see that PTAL would improve in the area immediately east of the city centre and in the south east where new BRT lines have been proposed. Given a budget constraint, the local authorities could test the efficacy of alternative public transport routes (and station spacing) at a city-wide network level.
(4) (5)
4.5. Surat PTAL mapping Once the AIs for all POIs are calculated, the next step is to create a graphical representation of the AIs to spatially interpret its implications. We use ArcGIS to map the AI values which offers four mapping methods. Of those, the quantile break is the most suitable as it distributes all AI values approximately equally in the identified bands. This allows a better visualisation of areas with high, medium, and low accessibility (Shah and Adhvaryu, 2016). PTAL for 2016 is created using the AI values calculated for each POI as per the sample calculation shown in Table 2. The map has accessibility levels from 1 to 10, where level 1 (blue) represents poor accessibility and level 10 (red) represents excellent accessibility. The number of categories is selected based on visual judgement. On trial mapping runs, increasing the number of categories made the colour indistinguishable and reducing made the visual representation coarser. Fig. 3 shows PTAL 2016 maps for both the City Bus service and City Bus with BRTS. Firstly, from the combined map we see that the accessibility to public transport is as expected—relatively very high in the city centre and some of the other well-developed areas of the city. Also, there are few scattered, leap-frogged areas with excellent PTAL partly surrounded by medium PTAL. These areas are the newer commercial development with a high level of road connectivity (also used by public transport). The accessibility pattern also coincides well with the radial growth of the city. The eastern area has medium to poor PTAL because it is dominated by shared auto-rickshaws (a paratransit mode).
4.6. Comparing Surat and Ahmedabad PTAL If the first application of PTAL to an Indian city of Ahmedabad in 2014 (Shah & Adhvaryu, 2016) is used as a benchmark i.e, if the same colour bands used in Ahmedabad PTAL are applied to Surat then the comparison produces a different result (see Fig. 6). Since the PTAL map shows the relative accessibility, Surat map by itself may appear to be reasonably acceptable. However, when compared with Ahmedabad, 297
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Fig. 4. Surat PTAL 2016 overlaid with population density.
Fig. 5. Surat PTAL 2016 v. 2021.
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Fig. 6. Ahmedabad v. Surat PTAL.
most of the higher PT accessibly area disappears except for a few central areas in Surat. This would be useful for urban local bodies to ascertain how they fare compared to similar cities.
project population density maps, wherein the future population could be obtained from an external source (e.g., planning authority projections). Although a land use—transport model (for an application to the Indian context see Adhvaryu, 2010) with built-in feedback loop would be desirable, in absence of that, such alternative “what-if” transport scenarios could be created within an iterative loop in which public transport supply and population density could be adjusted to get optimum results. Since DPs are made for about a 20-year horizon, overlaying PTAL with population density can also be used to anticipate future dynamics. Such overlays for base and future years could also help make changes in the DP (e.g., higher FSI, change in land use zoning, etc) to attract more development along public transport routes (i.e., transit-oriented development (TOD)). This helps integrate land use zoning (which is part of the DP) with public transport accessibility—an aspect usually ignored by Indian planners (Balchandran et al., 2005).
5. PTAL applications and lessons for planners, and conclusions 5.1. Applications and lessons 5.1.1. Prioritising public transport and NMT investments Areas with poor accessibility can be identified on a base (current) year PTAL map. Such a map would show the spatial distribution of accessibility (with regard to population, e.g. see Fig. 4) and would serve as a useful guide in making strategic city-level decisions allowing planners to prioritise investments in public transport and supporting non-motorised transport (NMT) facilities. Of course, once local areas are prioritised, more detailed micro-PTAL maps (with smaller grid spacing) at local area level can be created to facilitate micro-level decisions (e.g., changing public transport stops spacing, re-routing existing routes, or adding new routes). At this spatial scale, the NMT infrastructure must also be addressed in tandem, as NMT is key to the first and last mile connectivity. NMT improvements translate into better walk times, both of which increase the AI.
5.1.3. Informing parking policy In general, we propose the principle that areas better served with public transport would be identified for lesser parking supply (both on and off street). On developing city-wide parking policy, ITDP (2013) suggests that series of steps, one of which includes delineating “parking” and “no parking zones”. Using example of Zurich, Switzerland in which a PTAL map and off-street parking requirements map are compared (ITDP, 2013, Fig. 4, p. 7), parking requirements are reduced, closer a development is to a public transport station. A similar strategy is also used in London (Transport for London, 2015). Planners could consider outlying areas with poor to medium PTAL for strategic location of park and ride facilities. Usually on-street parking requirements vary across the city and are best dealt at a local area planning level. Parking policies can be made more specific using micro-PTAL map (as discussed in Section 5.1.1).
5.1.2. Integrating transport in the development/master plan (DP) One of the key jobs of planners is to make a statutory DP of the city, which usually is for a 20-year horizon. The transport system and its impact on land use and vice versa is an important element that needs to be dealt in the development plan. To be able to do this effectively, it is important for planners to assess the efficiency of the existing public transport infrastructure using PTAL maps (as discussed in Section 5.1.1) – which would be the first step. The second step would be to create future PTAL maps with “what-if” scenarios. Future transport inputs could be based on committed future plans by the city authorities (like the one used in Fig. 5) or it could be hypothetical scenarios. Similar to the existing situation, the future PTAL maps can be superimposed with
5.1.4. Improving residential location choice and optimising supply of affordable housing Captive public transport households can use the PTAL map for a 299
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more informed residential location choice. Surat has a huge migrant population which has been increasing significantly over the years, most of which are dependent on public transport. Real-estate developers can use PTAL maps (both existing and future) for locating affordable housing projects. Cities promoting housing infill development and urban regenration can also use PTAL maps for priotitising development (see Adhvaryu and Rathod, 2019). Lastly, and most importantly, government agencies can use PTAL maps to address the social inclusion agenda by prioritising site locations for government-supplied low-income housing.
Acknowledgments
5.1.5. Understanding the mobility needs of the urban poor A study on public transport inclusivity in Ahmedabad (Adhvaryu and Patel, 2019) argues that living in high PTAL areas may not translate to high accessibility to destinations specifically for those urban poor whose job locations vary by month and season (e.g., construction works, casual labourers, street vendors, etc). It therefore follows that planners can superimpose housing locations of the urban poor on a PTAL map (e.g., see Adhvaryu and Patel, 2019, Fig. 1) to identify specific areas to enhance their mobility needs.
References
The authors thank the following for their valuable inputs: the 100 Resilient Cities Surat city project team (including Surat Municipal Corporation, Surat Climate Change Trust, and TARU Leading Edge Pvt Ltd); Mr. Jay Shah, Transport Planning Consultant; students of MTech in Infrastructure Engineering Design (2015–17) batch at CEPT University; and Mr Mustafa Sonasath, Assistant Manager (Operations), Surat Sitilink Ltd (a wholly-owned subsidiary of SMC that manages the City Bus services and BRTS in Surat).
Adhvaryu, B., 2010. Enhancing urban planning using simplified models: SIMPLAN for Ahmedabad, India. Progress Planning 73 (3), 113–207. https://doi.org/10.1016/j. progress.2009.11.001. Adhvaryu, B., Patel, M., 2019. Is public transport in Ahmedabad inclusive? Econ. Political Weekl 54 (8), 17–20. Adhvaryu, B., Rathod, V., 2019. Estimating housing infill potential: developing a case for floorspace pooling in Ahmedabad, India. Plann. Pract. Res. https://doi.org/10.1080/ 02697459.2019.1590772. (forthcoming). Balchandran, B.R., Adhvaryu, B., Lokre, A., 2005. Urban transport in India: problems, responses and strategies. EPC Working Paper 1–1. https://doi.org/10.13140/RG.2.1. 1798.0327. Census of India, 2011. District Census Handbook, Surat. Available from: http://www. censusindia.gov.in/2011census/dchb/2425_PART_B_DCHB_SURAT.pdf. Cheng, C., Agrawal, A.W., 2010. TTSAT: a new approach to mapping transit accessibility. J. Public Transp. 13 (1), 55–72. https://doi.org/10.5038/2375-0901.13.1.4. Hansen, W.G., 1959. How accessibility shapes land use. J. Am. Plann. Assoc. 25 (2), 73–76. https://doi.org/10.1080/01944365908978307. ITDP, 2013. Common Sense Parking Reforms for Indian Cities. Institute for Transportation and Development Policy. Available from: https://go.itdp.org/ download/attachments/46137384/Guindance%20on%20Parking%20Reform%20in %20Indian%20cities%20-%20ITDP%20-%20130815.pdf?api=v2. Joyce, M., Dunn, R., 2010. A methodology for measuring public transport accessibility to employment: a case study. Auckland, CDB, NZ. Transp. Res. Rec. 5–6. Pitot, M., Yigitcanlar, T., Sipe, N., Evans, R., 2006. Land Use & Public Transport Accessibility Index (LUPTAI) Tool – The development and pilot application of LUPTAI for the Gold Coast. Available from: https://research-repository.griffith.edu. au/bitstream/handle/10072/11547/ATRF_Full_Paper_2006_V2.pdf?sequence=1& isAllowed=y. Saghapour, T., Moridpour, S., Thompson, R.G., 2016. Modeling access to public transport in urban areas. Journal of Advanced Transportation 50, 1785–1801. https://doi.org/ 10.3311/PPtr. 12072. Saif, M., Zefreh, M., Torok, A., 2018. Public transport accessibility: a literature review. Periodica Polytechnica Transp. Eng. https://doi.org/10.3311/PPtr. 12072. Shah, J., Adhvaryu, B., 2016. Public transport accessibility levels for Ahmedabad, India. J. Public Transp. 19 (3), 19–35. https://doi.org/10.5038/2375-0901.19.3.2. Transport for Greater Manchester, 2016. Greater Manchester Accessibility Levels (GMAL) Model. Available from: http://odata.tfgm.com/opendata/downloads/GMAL/GMAL %20Calculation%20Guide.pdf. Transport for London, 2010. Measuring Public Transport Accessibility Levels - PTALs summary. Available from: https://files.datapress.com/london/dataset/publictransport-accessibility-levels/PTAL-methodology.pdf. Transport for London, 2015. Accessing transport connectivity in London. Available from: https://data.london.gov.uk/download/public-transport-accessibility-levels/ 86bbffe1-8af1-49ba-ac9b-b3eacaf68137/connectivity-assessment-guide.pdf. Wu, B.M., Hine, J.P., 2003. A PTAL approach to measuring changes in bus service accessibility. Transp. Policy 10, 307–320. https://doi.org/10.1016/S0967-070X(03) 00053-2.
5.2. Conclusions This study discusses development of public transport accessibility mapping in India, using Surat as a case study city. We demonstrate that the well-developed PTAL methodology can be easily applied to cities with constrained budgets and limited data availability. We argue that PTAL maps could provide a strong visual tool to assess the level of accessibility offered by existing transport systems, which could be very useful in guiding strategic, city-level public transport improvements. This study goes beyond the first city-wide application in the Indian context to Ahmedabad (Shah and Adhvaryu, 2016), first, by correlating PTAL with population densities, and second, by demonstrating the use of PTAL for evaluating future transport investments. Future PTAL scenarios could also be mapped in the context of future population distribution scenarios, which becomes a strong “what-if” decision-support tool for the long-term master/development plan of the city. PTAL maps with periodic updating if put in the public domain1 is likely to improve the ‘image’ of the public transport system and could contribute to a modal shift in its favour. Given the simplicity of PTAL mapping as a tool it could be upscaled for more detailed micro-accessibility assessment (depending on need and availability of resources) at local area levels with smaller grid size that can be linked with station accessibility plans and TOD policies. Lastly, such tools can then be used as building blocks for more sophisticated models to measure, map, and apply public transport accessibility to enhance planning. We believe that PTAL mapping should be an integral part of the standard tool-kit for planners to guide them enhance transport infrastructure investments and inform urban and transport planning policy decisions in developing countries.
1 For example see https://tfl.gov.uk/info-for/urban-planning-andconstruction/planning-with-webcat/webcat.
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