Public transport accessibility level


The public transport accessibility level is a method sometimes used in United Kingdom transport planning to assess the access level of geographical areas to public transport.
PTAL is a simple, easily calculated approach that hinges on the distance from any point to the nearest public transport stop, and service frequency at those stops. The result is a grade from 1–6, where a PTAL of 1a indicates extremely poor access to the location by public transport, and a PTAL of 6b indicates excellent access by public transport.

Background

The PTAL calculation was originally developed by the Borough of Hammersmith and Fulham in 1992, and was later adopted by Transport for London in 2004 as the standard method for calculation of public transport access in London. It is not commonly used outside Greater London or the south east of England.

Method

The first stage in PTAL calculation is to calculate the walking distance from the site to the nearest bus stops and rail stations. These stops and stations are known as service access points. Only SAPs within a certain distance of the POI are included.
The next stage is to determine the service level during the morning peak for each route serving a SAP. Where service levels differ in each direction on a route, the highest frequency is taken. On railways, a route is generally defined as a service with a particular calling pattern – for example, services on the Piccadilly line from Hammersmith could be divided into two "routes": Cockfosters to Heathrow and Cockfosters to Uxbridge.
A total access time for each route is then calculated by adding together the walking time from the POI to the SAP and the average waiting time for services on the route. This is converted to an equivalent doorstep frequency by dividing 30 by the total access time, which is intended to convert total access time to a "notional average waiting time, as though the route were available at the doorstep of the POI".
A weighting is applied to each route to simulate the enhanced reliability and attractiveness of a route with a higher frequency over other routes. For each mode, the route with the highest frequency is given a weighting of 1.0, with all other routes in that mode weighted at 0.5.
Finally, the EDF and the weighting are multiplied to produce an accessibility index for each route, and the accessibility indices for all routes are summed to produce an overall accessibility index for the POI.
This accessibility index can then be converted to a PTAL grade through a banding system.
TfL introduced the WebCAT automatic calculator in 2015.

Uses

The PTAL is used as a development planning tool in London, to determine both permitted parking standards and development densities. Large site developments must follow planning guidelines that allow more parking in areas with low PTALs and vice versa—and that also relate the allowed density of development to PTAL.
TfL also have software to calculate PTALs across wide areas using GIS and timetable data, the typical result being a map with coloured bands relating to PTAL grades.

Application to other countries

Ahmedabad, India

The London PTAL method was first applied in the Indian context to Ahmedabad in 2014 by Bhargav Adhvaryu and Jay Shah. PTAL mapping for Surat, Pune and Bangalore are under development. In the London method, points of interest were considered by the actual development. However, in Ahmedabad, given the lack of availability of building footprint data at the time of the study, the method deviated by construing POIs as centroids of a 1 km2 grid. Given that the purpose of the study was to explore implications of PTAL at a macro-scale and the data constraints, the grid-cell approach seemed justified. In addition, it made the computations much faster.
The others adaptation of London method to Ahmedabad included revisiting walk speed and public transport service reliability assumptions. Most of the roads in Ahmedabad do not have footpaths and, if any, are usually occupied by street vendors and parking. Therefore, people are forced to walk on the road, which creates unsafe and potentially hazardous situations, such that walking is avoided as much as possible, even for short trips. To account for this discomfort walk speed was decreased to 60m/min as against 80m/min used in London. The reliability factor added in case of London were 2 minutes and 0.75 minutes for buses and rail services, respectively. In Ahmedabad, this was changed to 2.5 minutes for city buses and 1 minute for BRTS, and 0.75 minutes was not changed for the proposed metrorail - Ahmedabad Metro. Lastly, in London 8 minutes and 12 minutes were used as the threshold walk distances to bus and rail SAPs, respectively; SAPs beyond these distances are rejected. However, in Ahmedabad, surveys to determine willingness to walk for public transport were not carried out. Therefore, the farthest SAP from a POI was measured, which turned out to be 993m. At 60m/min, this give as willingness to walk at about 16 minutes, which seemed reasonable.
The Ahmedabad study discussed several uses of PTAL mapping:
  1. Improving existing public transport systems by recognizing areas with poor accessibility, thereby enabling decision makers to prioritize investments in public transport systems and support non-motorized transport infrastructure.
  2. Formulating parking policies, e.g. park-and-ride facilities could be provided to supplement areas with low and medium PTAL and parking may be restricted or charged at a higher rate in areas with high PTAL.
  3. Integrating land use zoning with public transport accessibility. By allowing future transport improvements to be incorporated into PTAL calculations, a future PTAL map becomes an important tool in supporting land use and zoning decisions for local authorities, including introduce transit-oriented development, as PTALs already incorporate walkability criteria from POIs – an important D in the 6Ds of TOD. It can also be useful in testing “what if” scenarios using land use - transport interaction models.
  4. PTAL maps could be used by households to inform their residential location choices, especially low-income households that are captive public transport users. Real estate developers could use PTAL maps for locating potential projects, while government agencies could use PTAL maps to locate social housing projects.

    Surat, India

Building from the Ahmedabad case study, PTAL was applied for Surat by Bhargav Adhvaryu, Abhay Chopde, and Lalit Dashora. This application goes beyond the Ahmedabad study in two ways. First, it overlays population density map on PTAL maps demonstrating a better way to use PTAL maps to inform public transport investment decisions. Second, it demonstrates the use of PTAL for evaluating future transport investment options. PTAL maps for year 2021 were generated based on information on future proposals to demonstrate PTAL’s strategic use to create “what-if” scenarios. The Surat study also explicitly justified the use of 1 sq km grid for PTAL mapping in data and resource constrained situation by showing changes in PTAL map resolutions for grid sizes for comparison. Of course, smaller grid micro-PTAL maps can be prepared for specific areas of the city which could be used to fine-tuning public transport infrastructure provision at local area level.
The Surat study discussed several uses of PTAL mapping such as: prioritising public transport investments Integrating transport in development/master plan informing the parking policy improving residential location choice and optimizing supply of affordable housing, and understanding the mobility needs of the urban poor. The last application is based on another study in Ahmedabad which argues that living in high PTAL areas may not necessarily translate to high accessibility to destination by public transport, especially those urban poor with variable job destination by month and season. Superimposing housing location of the urban poor on the PTAL map allows identifying specific areas for enhancing the mobility.

Advantages & disadvantages

Whilst PTAL is a simple calculation that offers an obvious indication of the density of public transport provision in an area, it suffers two key problems:
Accessibility modelling has been proposed as a solution to these problems. It uses GIS to calculate door-to-door travel times by public transport to a grid of points around the point of interest, resulting in a set of isochrone maps – journey time contours – within which the number of workplaces, households or residents can be calculated using census data. This method takes into account many more factors than PTAL, but is much more time-consuming and requires a level of expertise with GIS software and methodologies.

Similar methods