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Title of Research: The application of Accessibility Planning techniques and

Accession software to a North American City, with comparison results of Greater

Manchester.

Written by:

Keith Drew, Halcrow Group Manchester

Drewk@halcrow.com

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Abstract:

Accessibility planning is a process that aims to promote social inclusion by helping

people from disadvantaged groups or areas access jobs and essential services

(Department for Transport, 2005). This paper looks specifically at the mapping of

accessibility and the application of UK based software to a region in North America

(Twin City region, Minnesota) and compares the results to equivalent calculations in a

UK metropolitan area (Greater Manchester).

This paper has been driven by three objectives that will:

1. assess current UK guidance and the suitability of them to other regions;

2. test for suitability and apply using UK accessibility software (Accession)

outside of the UK (i.e. in North America); and

3. calculate consistent indicators of accessibility to both Twin City region and

Greater Manchester, and measure the difference between them.

The aspiration of this paper is to raise awareness and the application of mapping

accessibility outside of the UK; and as a result introducing UK accessibility planning

to a wider audience.

Accessibility indicators in this paper are calculated through two separate indicators,

termed network and local accessibility. These are defined as:

• Network: the measurement of access based on door-to-door travel time from

home to destination (such as education, employment, health, major centres and

places for shopping), and;

• Local: the measurement of accessibility in terms of providing access to the bus

network (stop) to the nearby population.

The research review in North America shows that there is a desire to conduct the

work discussed in this paper, with equivalent studies looking at access to education,

employment and ‘green’ spaces. However nothing as sophisticated or detailed as

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Accession has been used to complete these studies, suggesting there is a market

(potentially world wide) for its use and application.

The overall conclusion of this paper is that, at a macro scale, it is possible to apply

UK accessibility software outside of the United Kingdom and to do so at low cost

(dependent on the availability of data). Subsequently the paper shows successful

application of accessibility indicators to both the Twin City region and Greater

Manchester.

The results of this research suggests that bus network planning in the Twin City

region is primarily set to provide access to the core centres, while in Greater

Manchester networks are more consistent to serve the entire county area. In particular

buses in Greater Manchester would appear to serve urban areas with more

penetration, i.e. going into housing estates, while in the Twin City region the network

(particularly in the outer areas) is designed to run along side major roads, and not

divert within suburban areas.

In both regions, the bus networks are proved to serve sections of society who could be

argued to be more reliant on a bus services, and the indicators could be used

(especially in the Twin City example) as measures to improve and monitor

accessibility within this group. Thus could be used to improve levels of accessibility

to this group, i.e. “promote social inclusion by helping people from disadvantaged

groups or areas access jobs and essential services”.

Overall the indicators, suggested in this paper, do work as a means of measuring

accessibility, and could provide future monitoring of access. However, it is suggested

that the indicators, especially the local accessibility variant, are adapted to suit the bus

network in that area.

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1 INTRODUCTION

In any part of the world there are sections of society who, compared to the general

population around them, are considered disadvantaged whether through social or

economic factors; and to ease the level of inequality there is a need for the adoption of

adequate policies that aim to reduce them. For example, in the United States of

America current (October 2009) reform is being sought in health care provision so

that those who are unable to afford medical insurance are able to access health care

services.

In the United Kingdom research (by the Social Exclusion Unit, and discussed later in

this paper) has shown there is a key link between reducing inequality in our society

through the provision of public transport services and the location of jobs and

services. The work done by the Unit had led to the adoption of accessibility planning

in UK policy, which aims to promote social inclusion by helping people from

disadvantaged groups or areas access jobs and essential services (Department for

Transport, 2005).

The aim of the research is to take the concept of accessibility planning, including UK

software, to a North American urban area and compares the results to a UK

metropolitan area to test how transferable the concept and software could be.

Objectives and aspirations

This paper has been driven by three objectives that will:

• assess current UK guidance and the suitability of them to other regions;

• test for suitability and apply using UK accessibility software (Accession)

outside of the UK (i.e. in North America); and;

• calculate consistent indicators of accessibility to both a North American and

UK region, and measure the difference between them.

The aspiration of this research is to raise awareness and the application of mapping

accessibility outside of the UK; and, as a result, promote the principle of accessibility

planning, and the benefits in its application, to a wider audience. The results and the

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presentation of results are given as examples and should be considered secondary to

this aspiration.

Accession, discussed in more detail later in section 2, was commissioned by the

Department for Transport in 2003. It is considered a ground-breaking piece of

software, in that, unlike other software packages, it is capable of using a road

network, a full passenger transport timetable and calculates total journey times from

door-to-door.

The study areas

The city/region chosen to represent North America is the Minneapolis-St. Paul

metropolitan area (also known as the Twin City region), while in the UK the city/

region chosen is Greater Manchester (a metropolitan county).

Both have similar geographic characteristics (in terms of population size1 and area

covered) and for both areas data applicable to the research is readily available The

data used, that relates to the Twin City region, including bus network data, can be

found on the internet (at www.metrogis.org)2. Knowledge from previous work has

also shown that Greater Manchester has high levels of accessibility and thus provides

a comparative ‘benchmark’ for the Twin City specific results.

Indicators of accessibility

Accessibility indictors in this paper are calculated through two separate indicators,

termed network and local accessibility. These are defined as:

• Network: the measurement of access based on door-to-door travel time from

home to destination (such as education, employment, health, major centres and

places for shopping), and;

• Local: the measurement of accessibility in terms of providing access to the bus

network (i.e. stop) to the nearby population.

1 Population being 2.1 million people in the Twin City region and 2.5 million in Greater Manchester.

2 Data for Greater Manchester has been collected from the National Public Transport Data Repository

(www.nptdr.org.uk, April 2009).

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The detailed processes employed to calculate the network and local results are

discussed in more detail in the methodology section (section 4) of this paper.

Note, this paper is based on objectives that test the ability and potential merits of

applying accessibility planning to another city (outside of the UK); as such this paper

does not go into any detail regarding planning policy or the characteristics of the local

population.

The remaining part of the introduction discusses the concept and history of

accessibility planning.

History of accessibility planning

Accessibility Planning is not a recent concept to the Transport Planner, as maps

currently on display at the John Ryland Library (University of Manchester)

demonstrate (figure 1)3.

Figure 1: ‘Accessibility planning’ in (circa) 1900 Manchester

However, only in the last decade has it re-emerged as an important decision making

tool in UK policy that assists in bus network design through encouraging improved

the links between transport, people and destinations.

3 The map shows an early transport, circa 1900, hand drawn, isochrone map of travel time from City

Centre, Manchester

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The driver behind recent advancements was the 2003 report by the Social Exclusion

Unit (SEU) ‘Making the Connections’; the SEU, set up in 1997 by the Prime Minister,

is an intra-governmental group working across the traditional Central Government

departments to tackle social exclusion.

Why use Accessibility Planning

The key message from the SEU report was that Accessibility Planning is needed to

make ensure access needs of excluded groups, particularly people on low incomes,

people without access to car, the elderly, the disabled, the young, are met.

Accessibility Planning, among other things, can be used to:

• make it easier for people to get to work;

• help reduce health inequalities; and;

• help to increase participation and attainment in education (Solomon, 2004).

Benchmarking

The subsequent guidance (section 2) for Accessibility Planning is based around a

number of indictors/standards to measure accessibility. By using these indicators it

provides:

• baseline standards against which policymakers can work against;

• transparent means of judging what needs to be done or not done;

• practical ways of measurement; and;

• ways of judging success (taken from Solomon, 2004).

Principles applied to other countries

Outside of the UK, the underlying principles of Accessibility Planning still apply with

world demands for sustainable transport solutions and promotion sought (see ITDP,

2009), together with the removal of barriers to connecting people with places. This is

supported by a number of research studies undertaken, which are discussed in section

3, providing evidence that the question of accessibility is of world wide importance.

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2 CURRENT UK GUIDANCE AND SOFTWARE

This section sets out the existing guidance in the UK, which was created to enable

Local Authorities to develop their own accessibility action plans as part of the Local

Transport Plan. This section of the paper is included as an example of how

Accessibility Planning can be incorporated in planning policy and action plans.

This section also gives information on the software available in the UK (Accession)

which has been used and adapted in the research, discussed later in this paper. This

includes the issues faced from the outset of the research, and which through finding

the solutions unlocked the capability of applying Accession to the North American

example.

Current guidance

The current UK guidance is enshrined within the Local Transport Plan which has the

overall aim of ‘making sure that everyone can get to work, schools, healthcare, food

shops and other key services’ (DfT AP Guidance, 2005). For this research, guidance

is only sought from the perspective of completing area wide accessibility audits (GIS

assessments and demographic profiling) but the full guidance gives information on

forming partnerships and completing local accessibility studies.

The guidance used is based on the core accessibility indicators (found in section 4 of

the DfT technical guidance paper) and is listed in figure 2 below.

Figure 2: Core Indicators

Core accessibility indicators are to be calculated for each LTP area using multiple

threshold based accessibility measures. The use of thresholds may make these

indicators more suitable for monitoring changes than for planning interventions.

In addition, threshold based measures which consider the nearest opportunity do

not fully reflect the degree of choice available to an individual. As a result,

continuous measures will also be calculated for the same indicator set. The core

indicators to be used are:

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% of a) pupils of compulsory school age ; b) pupils of compulsory school age in

receipt of free school meals within 15 and 30 minutes of a primary school and 20

and 40 minutes of a secondary school by public transport;

• % of 16-19 year olds within 30 and 60 minutes of a further education

establishment by public transport;

• % of a) people of working age (16-74); b) people in receipt of Jobseekers'

Allowance within 20 and 40 minutes of work by public transport;

• % of a) households b) households without access to a car within 30 and 60

minutes of a hospital by public transport;

• % of a) households b) households without access to a car within 15 and 30

minutes of a GP by public transport; and;

• % of a) households; b) households without access to a car within 15 and 30

minutes of a major centre by public transport.

Accession software

In conjunction with the guidance given by the Department for Transport in 2004,

software was developed to assist in calculating the indicators in figure 2, this being

Accession software.

Accession is described by its developers as the ‘first software package to fully address

all aspects of travel time and cost mapping using digital road networks and public

transport timetable data” (Citilabs, 2009). Accession was developed by MVA

consultants in preparation on LTP2 completion (developed according to UK

Department for Transport’s specifications) and is now owned and distributed by

Citilabs.

Adapting UK guidance to this research

When taking this study forward from the preparation work it was found that the data

available (in the Twin City region) was not as detailed as first thought (in that data

such as GP surgeries was not available and, the census outputs did not include

elements such as car ownership); as such the research concentrates on the application

of gravity based assessments (using Hansen, 1959, opportunity based calculations).

By using the Hansen type measure it will be possible to test for equity across the

study areas, enabling comparison. This type of study compares to Halcrow studies in

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Worcester and work done by El-Genidy and Levinson, 2006, and follows a

methodology suggested by the Department for Transport (technical appendix 6

within ‘Guidance on Accessibility Planning’, 2006).

For future studies however, it is envisaged that the above criteria could be applied (or

a variation of) if the study was working with partners who could supply the data.

Adapting UK Software to this research

Issue 1: Accession software is provided by Citilabs with one coordinate system, this

being UK national grid; a coordinate system being the ability to map spatial objects

within it.

Therefore so that it is possible to use Accession software, additional software (GIS)

has been used to transfigure any data (bus stop locations, road networks for example)

based on a USA projection to that of the National Grid4.

Issue 2: Accession has a host of import facilities for Public Transport data (NapTAN,

Atco-CIF formats for example) but these are to UK data standard settings. To carry

out an Accession run for a NOAM city the Accession database was ‘broken’ into and

manipulated in MS Access, then re-imported back to Accession. The alternative, and

long winded, option was to code in all public transport services and timetables

manually, which would be time and, subsequently, cost consuming.

Issue 3: The outputs will need to be re-transformed to USA projection so that

demographic analysis can be undertaken and outputs produced.

It has been possible to solve all the above issues including being able to import the

public transport data into Accession without a full recode

4 As the research is regional/local in nature, using the UK national grid should not be

a problem as a projection for a NOAM City (issues for larger studies could be

impacted by the curvature of the earth for example).

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The next section examines existing research in both North America and Europe on

Accessibility Planning, and examines the lessons learned from the work completed

already.

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3 ACADEMIC RESEARCH

North America

An academic search of accessibility projects over the last ten years, set in North

America, shows a number of projects mirroring projects undertaken in the UK; in

terms of being able to access employment, schools and leisure facilities by modes of

transport and in most of these studies there was a deep emphasis on the need of equity

of service/facility distribution.

Where the research has examined public transport accessibility it has been based on

transport assessment zones (TAZs), which are outputs from transport planning

models. For other modes of accessibility (car and walk) the research studies have been

based on C++ programming or other GIS software (ArcView).

Research by Badoe and Miller, 2000, give the overarching global accessibility issue

faced that:

“over the last two decades there has been increased concern in metropolitan

regions with the decline in air quality, increased congestion in both urban

and suburban areas, and negative impacts to the natural environment

resulting from last development patterns overwhelmingly favourable to the

automobile”.

Handy, 2002, states that “the concept of accessibility has been coin in the

transportation planning field for almost 40 years. Improving accessibility is a common

element in the goals section in almost all transportation plans in the US”.

Of the research conducted for this study, the closest match of a UK style accessibility

audit was undertaken by El-Genidy and Levinson (2006) which looked at access to

employment and how opportunity changes over time (1990 to 2000) in the

Minneapolis City area (incorporating the Twin City region). The study uses common

accessibility indicators such as gravity-based measures, as developed by Hansen,

1959, together with cumulative measures.

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Sanchez, 1999, also discusses the role of public transport accessibility to combat the

issue of employment in the cities of Portland, OR, and Atlanta, GA. Sanchez argues

that little research has been carried out on public transport access and instead focus

only on the automobile and people with jobs and as such ignore those who are

unemployed and by default have no car.

Research by Talen, 2001, discusses the link between school location, access and

opportunity, and the importance of school quality with that of social, political and

economic life. The author discusses the massive geographical changes in US schools

over the last several decades in terms of locations and that the geographical

implications of consolidation have been ignored. The author adds (pg 465) that in

“terms of literature on access, scant research is devoted to school locations” and that

the study of school accessibility is important for three reasons (at least):

• the basic question of fairness;

• social equity; and;

• student performance.

Nicholls, 2001, discusses the ever increasing use of GIS of government agencies to

enhance the planning and management of facilities; and in particularly for access to

leisure supports the use of GIS to provide leisure service agencies with opportunities

to enhance the planning and management of facilities. However the author highlights

that little research of spatial nature of access and equity has been undertaken with GIS

and proposes a method to improve simple methods of using a geometric perspectives

(straight-line distances).

European research

The research exercise has also shown a number of recent pieces of research

undertaken in Europe; with Vandenbulckle, Steenberghen and Thomas, 2008, discuss

the use of accessibility as a tool of land use and transport planning in Belgium; Lopez,

Sanchez and Vicente, 2006, using GIS based accessibility indicators in Madrid; and

Vega and Reynolds-Feighan, 2009, undertaking an accessibility based project in

Dublin.

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Halden (2005) states that in Europe accessibility is measured at one of three main

geographical levels:

• Local accessibility to facilities in the neighbourhood

• Regional accessibility often for cities and their hinterlands

• Interregional accessibility to measure connectedness of a region or country.

Following this introduction, the paper will draw upon literature in the United

Kingdom (existing guidance); together with information on the software used, and in

other parts of the world supporting this paper (sections 2 and 3). The methodology

(section 4) will then build on discussion in section 1 to develop further the techniques

used to produce the results (section 5). The paper will then conclude (section 6) with

the results of the research and finally give a number of recommendations to take this

work forward (section 7).

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4 METHODOLOGY

The following section sets out the methodology employed to calculate the network

and local accessibility levels, including the assumptions made and parameters used in

Accession. It is not intended within this paper to describe in detail the work

completed (as it involves complex GIS and data analysis procedures), however an

overview is given for understanding.

Network Accessibility

There are two forms of network accessibility results, the traditional in the form of

‘threshold measures’ where an origin should be within X amount of minutes to be

classed as accessible (i.e. within 30 minutes of a Hospital). The second form being a

‘weighted continuous measures’ (also known as a Hansen measure after Hansen,

1959) in which accessibility is considered to many destinations (combining the results

to form one score of accessibility, termed opportunity) using a distance decay

function. Additional information is found in deterrence function section below. For

this research the latter of the methods have been adopted and guidance sought from

Department of Transport Accessibility Guidance website.

Accession Inputs

Accession works by importing data on origins, destinations and public transport data

(this also includes a road network, but for this research this was not used. These are

discussed next:

Origins

Due to the large study areas being considered, in both cases a 250 metre grid

was drawn around each study area and imported to Accession.

Destinations

The list of destinations included in this study is:

• Education;

• Employment;

• Hospitals;

• Supermarkets/shopping

centres; and;

• Towns and Cities.

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For education this includes all schools in the local area in both regions (in

Greater Manchester, excluding Primary schools).

For employment, this was based on number of daytime jobs within Greater

Manchester Census Super Output Areas (SOAs), of which there are

approximately 2,568; in the Twin City region the number of jobs in Census

TAZ areas, of which there are 1,201.

Public Transport Network

Data was downloaded from the National Public Transport Data Repository

(www.NPTDR.org.uk with permission from GMPTE) and from the Metro GIS

data repository (Twin City region data) in April 2009.

The data for Greater Manchester was directly imported into Accession through

the CIF importer. Data for Twin City region was manipulated to ensure it was

in Accession ‘friendly format’, and imported into Access (which provides the

database background to Accession).

Calculations

Network accessibility was then calculated to each destination set based on a

number of travel time bands, for example access to employment was set for a

weekday time period and travelling between 7:30am and 9am.

Deterrence Function

To form a score of accessibility, considering access time to a number of destinations,

a distance decay function was applied. An extract taken from DfT Accessibility

Guidance (Technical Appendix 6 – Information on deterrence parameters) states that:

“within the continuous measures proposed for accessibility

planning, the deterrent effect of travel time is modelled by means

of a negative exponential function of the form exp (-?t) which is

hypothesised to describe the relationship between travel duration

and the likelihood of travel”.

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The purpose of applying the deterrence parameter, which differs by destination type,

is to imply that people would travel further for one destination type over another

either though a greater pull (i.e. employment) or through lack of supply (i.e.

hospitals). The lower the deterrence parameter the greater the opportunity over time –

this is demonstrated in figure 3, which shows how opportunity changes over time by

level of exponential factor (orange line = 0.036, blue = 0.042, red = 0.061 and green =

0.085). Note figure 3 shows the opportunity to a single destination (where travel time

= 0, opportunity =1).

Figure 3: Deterrence Value and the distance decay function

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 40 45 50 55 60

Travel Time

Op

po

rtu

nit

y

Local Accessibility

As noted in the introduction, the research also assesses the ‘local accessibility’ of a

bus network, calculated by looking at the percentage of population within desired

distances of the bus network (stop points) based on the criteria given:

“250 metres of a half hourly or better service or 400 metres of a 15

minutes or better service in the weekday daytime;

Or

250 metres of an hourly or better service and 400 metres of a half

hourly or better service at all other time (weekday evening and

Sundays)”

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This is defined as criteria ‘C’ (with the individual criteria, 250metres and 400 metres,

being A and B respectively) which has been developed as a standard measure of

accessibility by Greater Manchester Passenger Transport Executive (GMPTE).

Criteria ‘C’ calculates the percentage of population that live within an acceptable

distance from a frequent route/stop. Note distance increases with frequency as this

assumes a bus stop with more frequent services is more attractive and therefore a bus

user would walk further to access it..

The findings of the local accessibility analysis will show for the current bus network

the total number of households that are within the above criteria.

Population data (applied to Network and Local results)

Note all the population analysis within this research is based on a methodology where

the population data (from Census sources) is disaggregated from closed regions (i.e.

Census Output Areas in the UK and TAZ zones in USA) to an origin grid. To reflect

accurate population the grid is weighted by location (if within residential area and

close to road network) and as such is considered, in the absence of detailed data to be

robust enough for this research.

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5 RESULTS

This section of the paper compares the results for each region and compares both

regions in comparative terms.

Network Accessibility

In the following section, the results of the research are presented using a number of

maps and tables as examples.

Notes for the presented maps: the accessibility results (opportunities) are presented as

a series of indexes to show where areas of high and low opportunity reside. They have

been indexed as the range of destinations are not consistent within the two areas, thus

actual levels would be misleading. The maps range from areas of high opportunity

(green) to areas of low opportunity (blue); note they are presented as a continuous

space and therefore do not reflect where populations reside.

Notes for the tables: the accessibility results (opportunities) are presented also as a

series of indexes and relate to the maps presented, in addition subsets of population

have been created and analysed. These subsets are populations considered most in

danger of being socially excluded5 (shown in figure 4) or those who live within the

region core6.

Note not all maps and tables are presented in this paper, but are available from the

author of this paper.

5 Twin City regions this is populations within low income areas, for this Greater Manchester within the

socially/economically deprived areas

6 Greater Manchester, within the M60; for Twin City region, within the Interstate Highway that forms the

ring road around Minneapolis and Saint Paul

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Figure 4: Areas of deprivation (Greater Manchester) and low income (Twin City

region)

Findings – Network Accessibility

In tables 1 and 2, the distribution of access to employment is shown for Greater

Manchester and Twin City Region respectively, for all population groups, populations

in socially/economically deprived areas and the inner core.

Cells (in % diff to all columns) that are coloured green show for the sub population

categories where there is significant positive (actual) difference in that category to the

total population, orange cells being the reverse (significant negative change). Cells (in

% cum. Diff to all columns) that are coloured dark blue show, cumulatively, where

the distribution is significantly higher (>25%) and light blue less, but still high and

positive change (>5%).

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Ultimately tables 1 and 2 tell the same story, in that show in population within

targeted groups have higher employment opportunities (through bus travel).

Table 1: Indexed Employment Opportunity: Greater Manchester

Greater Manchester

Access to Employment

All 10% deprivation Central core Index

% Cum.

% %

Cum.

%

%

diff

to all

%

Cum.

diff to

all

% Cum.

%

% diff

to all

%

Cum.

diff to

all

<0.1 (poor) 3.9% 100.0% 0.3% 100.0% -3.7% 0.0% 0.0% 100.0% -3.9% 0.0%

0.1<0.2 20.3% 96.1% 11.6% 99.7% -8.7% 3.7% 0.0% 100.0% -20.3% 3.9%

0.2<0.3 24.3% 75.8% 20.7% 88.1% -3.6% 12.4% 0.8% 100.0% -23.5% 24.2%

0.3<0.4 18.8% 51.5% 17.5% 67.4% -1.3% 16.0% 8.4% 99.2% -10.5% 47.7%

0.4<0.5 14.5% 32.6% 17.1% 49.9% 2.6% 17.3% 28.7% 90.8% 14.1% 58.2%

0.5<0.6 10.5% 18.1% 14.3% 32.8% 3.8% 14.7% 35.0% 62.2% 24.5% 44.1%

0.6<0.7 5.4% 7.6% 12.9% 18.4% 7.5% 10.8% 19.1% 27.2% 13.7% 19.6%

0.7<0.8 1.7% 2.2% 5.0% 5.6% 3.3% 3.3% 6.1% 8.1% 4.4% 5.9%

0.8<0.9 0.4% 0.6% 0.6% 0.6% 0.1% 0.0% 1.5% 2.0% 1.1% 1.5%

0.9<1

(good) 0.1% 0.1% 0.0% 0.0% -0.1% -0.1% 0.5% 0.5% 0.4% 0.4%

Total 100.0% - 100.0% - - - 100.0% - - -

Table 2: Indexed Employment Opportunity: Twin City Region

Twin City Region

Access to Employment

All Low Income central core Index

% Cum.

% %

Cum.

%

% diff

to all

%

Cum.

diff to

all

% Cum.

%

% diff

to all

%

Cum.

diff to

all

<0.1 (poor) 18.5% 100.0% 1.7% 100.0% -16.8% 0.0% 4.2% 100.0% -14.3% 0.0%

0.1<0.2 18.2% 81.5% 2.9% 98.3% -15.4% 16.8% 8.7% 95.8% -9.6% 14.3%

0.2<0.3 18.0% 63.3% 14.1% 95.4% -3.9% 32.1% 18.7% 87.2% 0.7% 23.9%

0.3<0.4 14.2% 45.3% 19.3% 81.3% 5.1% 36.0% 19.6% 68.5% 5.4% 23.1%

0.4<0.5 15.1% 31.1% 21.4% 62.0% 6.3% 30.9% 23.1% 48.8% 8.0% 17.7%

0.5<0.6 8.5% 16.0% 17.9% 40.6% 9.4% 24.6% 13.7% 25.7% 5.2% 9.7%

0.6<0.7 5.2% 7.5% 15.0% 22.7% 9.8% 15.2% 8.3% 12.0% 3.2% 4.5%

0.7<0.8 1.8% 2.3% 6.4% 7.7% 4.6% 5.4% 2.9% 3.7% 1.1% 1.4%

0.8<0.9 0.3% 0.5% 0.8% 1.2% 0.5% 0.8% 0.5% 0.7% 0.2% 0.3%

0.9<1

(good) 0.1% 0.1% 0.5% 0.5% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1%

Total 100.0% - 100.0% - - - 100.0% - - -

So we know that the bus network serves key areas of the population, but how do the

networks in Greater Manchester and the Twin City region compare? Table 3 shows

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the comparison between tables 1 and 2 (actual and cumulative). Cells that coloured

green show where there is a significant positive difference when comparing Greater

Manchester to the Twin City region, i.e. the percentages are higher in Greater

Manchester than the Twin City region. The reverse relationship is shown by blue

coloured cells.

The relationship is quite clear in that green cells show more prominently higher in the

categories (especially for the central core), however interestingly when comparing the

cumulative values for the 10% deprived / low income group, a higher amount of the

Twin City group features higher in the opportunity values showing the bus network in

the Twin City region offers greater opportunity to employment to this group, than the

comparable level (index) in Greater Manchester.

Table 3: Comparing Twin City to Greater Manchester

Figures 5 and 6 show geographically how the opportunity indexes range across the

two study areas, both showing the dominant ‘hearts’ of employment opportunity in

the regions centres (City core).

Comparing Twin City, USA to

Greater Manchester, UK

Access to Employment (Actual) Index

ALL

10%

deprived

or low

income

Central

Core

0.1 0.0% 0.0% 0.0%

0.2 14.5% 1.4% 4.2%

0.3 12.5% -7.3% 12.8%

0.4 6.1% -13.9% 30.7%

0.5 1.5% -12.1% 42.0%

0.6 2.1% -7.8% 36.4%

0.7 0.1% -4.2% 15.2%

0.8 0.0% -2.1% 4.4%

0.9 0.1% -0.7% 1.3%

1 (good) 0.0% -0.5% 0.3%

Comparing Twin City, USA to

Greater Manchester, UK

Access to Employment (Actual) Index

ALL

10%

deprive

d or low

income

Central Core

0.1 -14.5% -1.4% -4.2%

0.2 2.1% 8.7% -8.6%

0.3 6.3% 6.6% -17.9%

0.4 4.6% -1.8% -11.2%

0.5 -0.6% -4.3% 5.6%

0.6 2.0% -3.6% 21.2%

0.7 0.2% -2.1% 10.8%

0.8 -0.1% -1.4% 3.1%

0.9 0.1% -0.2% 1.0%

1 (good) 0.0% -0.5% 0.3%

23

Figure 5: Indexed Opportunity to Employment: Greater Manchester

Figure 6: Indexed Opportunity to Employment: Twin City Region

24

Like for like analysis was also completed for opportunities to:

• Education;

• Food (for the UK this was access to Supermarkets, in the USA access to

shopping centres as a proxy);

• Towns and Cities (both as proxy’s to key services) in the weekday time

periods of morning peak, daytime and evening; and;

• Hospitals in the weekday time periods of daytime and evening, and also

Sunday daytime.

As noted previously the results showed a similar tendency to that of employment,

with Greater Manchester results showing greater distributions in the higher

opportunity indexes; and in both regions the targeted population groups show positive

correlations of higher levels of opportunity to that of the general population.

However, the analysis of the networks has shown that where destinations are more

spread across the regions (unlike employment where the largest destinations are in the

central core), for example access to ‘Towns and Cities’, indexed opportunity in

Greater Manchester shows much better access than in Twin City Region. The cause of

this is the much better service provision to the central (City) areas of Twin City,

compared to other parts of the region (this will be shown later in the local

accessibility analysis). Thus in the central areas opportunity is high, but outside,

access is very low, hence more blue areas on the map (see figures 7 and 8 for

comparison).

In Greater Manchester the picture is very different, in that outside the central core,

opportunity is still relative high and comparable. This suggests that in Greater

Manchester the bus network is designed more, than in the Twin City region, to serve

areas other than the key centre; a reflection perhaps of overall planning policies

within the two regions/countries.

25

Figure 7: Indexed Opportunity to Cities and Towns: Greater Manchester

Figure 8: Indexed Opportunity to Cities and Towns: Twin City region

26

Overall the research shows, at the very highest level that the bus network in Greater

Manchester provides a more consistent level of opportunity/accessibility to that seen

for the Twin City region; and this is seen across all destination types and time bands.

Analysis of targeted populations who live in areas of low income (USA)/deprivation

(UK) shows that in the Twin City region the bus network offers higher access when

compared to the general population. In the UK, there is a similar but more modest

improvement when compared to the total population.

Therefore, at a very simplistic level, it could be argued that the bus network in the

Twin City area, in particular, provides access to key services to the most needy of it

populous, i.e. those with low income and those within the core area.

Local Accessibility

The following section discusses, for both study areas, the amount of population that

live within x metres from a bus stop with a desired frequency, over time.

A series of maps and tables are presented: the maps (figures 9 and 10) are examples

given for the weekday PM peak, and show the catchment areas for Greater

Manchester and the Twin City region, respectively, for the criteria set (criteria C). The

‘red’ areas show the area captured by criteria C of the local accessibility, while the

‘yellow’ shaded area shows the catchment of all bus services (800m from a bus stop).

Using demographic analysis these areas can then be used to calculate how many

people reside within them. Equivalent maps and subsequent population analysis has

been completed for each time band with the results found in the tables presented.

The figures show clearly that in Greater Manchester the applied criteria is true up to

the region boundary, but in the Twin City region the same is not true, with sparse red

colouration around the perimeter.

Tables (4 to 6, respectively) show (i) the percentage of population (including subset

population groups) that fall into the criteria set (criteria C) for both regions; (ii) the

comparison of general population to the sub group categories (low income/areas of

27

deepest deprivation and central core); and (iii) how the population catchments in

Greater Manchester compare to that in the Twin City region.

The key finding from this section of research is that (in tables 4 to 6), when looking at

the general population, 80 to 90% of the population are covered by the criteria

assessment in Greater Manchester whereas in the Twin City Region 27 to 44%, which

is a notable standout difference. A simple assessment of bus stop catchments, i.e. the

percentage of population within 800metres of any bus stop, the results show, in both

regions, more than 90% of the population is captured (99.7% in Greater Manchester

and 90.7% in the Twin City region).

This draws two potential conclusions, both of which enables greater understanding of

the network results; one being in the outer areas of the Twin City region bus services

are typically infrequent (or at least not better than 1 per hour in the weekday and less

so in the evenings and Sundays); two that the population in Greater Manchester live

closer to the bus network than in the Twin City region. The second of these

conclusions is drawn from comparing the percentage captured within 400 metres

(criteria C) and the percentage captured within 800 metres of a bus stop generally.

Tables 4 and, in particular, 5 also show, when comparing the general population to the

subsets selected, that both networks provide better accessibility to those who may

have greatest social need (i.e. low income/living in most deprived areas). In the Twin

City region the difference is most noticeable with 65 to 82% of the population in low

income areas are captured within the criteria set, a significant difference to the noted

general population (99.3% are within 800 metres of a bus stop).

Population groups within the inner core also show higher levels of catchment,

although not as significantly different for low income groups in the Twin City

Region); with approximately 20% more captured within the criteria when compared

to the general population (46 to 65% compared to the 27% to 44% of the general

population).

28

From this research it is clear that this measure of ‘local’ accessibility is set to show in

Greater Manchester (and would probably so do in other UK metropolitan areas) very

high percentage catchments, and therefore a measure of good accessibility.

Figure 9: Local accessibility catchment: Greater Manchester

Figure 10: Local accessibility catchment: the Twin City region

29

Table 4: Local Accessibility: Percentage of the population by criteria

Time Period Criteria GM Total GM Total (10%

deprived)

GM Total (Central Core)

TC Total TC Total (Low

Income)

TC Total (Central Core)

A 72.5% 82.9% 78.1% 36.8% 69.8% 54.2%

B 79.4% 89.6% 88.4% 29.0% 63.9% 47.5% Weekday AM Peak

C 89.5% 96.5% 94.3% 43.7% 81.7% 65.2%

A 76.7% 87.3% 81.0% 28.3% 62.2% 45.8% B 81.9% 91.6% 90.3% 19.4% 51.0% 32.7%

Weekday Daytime

C 89.1% 96.4% 94.2% 32.4% 71.1% 52.5%

A 75.3% 86.0% 78.9% 36.8% 69.8% 54.2% B 81.0% 90.5% 89.3% 30.9% 64.0% 48.3%

Weekday PM Peak

C 88.2% 95.8% 93.1% 43.9% 80.8% 64.7%

A 70.3% 81.7% 74.6% 32.2% 66.0% 48.5% B 76.1% 86.2% 86.7% 29.5% 65.4% 48.8%

Weekday Evening

C 84.8% 93.8% 90.9% 39.5% 79.5% 60.3%

A 66.6% 78.7% 77.5% 28.3% 61.8% 44.2% B 68.6% 81.2% 85.5% 26.2% 61.4% 43.9%

Sunday Morning

C 79.9% 89.8% 91.8% 34.7% 74.8% 54.8%

A 72.3% 84.5% 79.7% 29.0% 62.4% 44.8% B 76.4% 87.7% 88.7% 27.3% 62.5% 45.4%

Sunday Afternoon

C 85.7% 95.2% 93.4% 35.7% 75.7% 55.9%

A 68.7% 80.5% 74.9% 22.5% 54.5% 37.8% B 69.2% 81.6% 83.1% 19.4% 51.2% 33.3%

Sunday Evening

C 82.1% 92.3% 90.3% 27.3% 65.3% 46.0%

Access to bus stop 99.7% 99.9% 100.0% 90.7% 99.3% 97.5%

Table 5: Local Accessibility: Percentage change across population groups

Time Period Criteria

GM (ALL compared to

10% deprived)

GM (ALL compared to

Central Core)

TC (ALL compared to

Low Income)

TC (ALL compared to

Central Core)

A 10.3% 5.6% 33.1% 17.5% B 10.2% 9.0% 34.9% 18.5%

Weekday AM Peak

C 7.0% 4.8% 38.0% 21.6%

A 10.7% 4.3% 33.8% 17.4% B 9.7% 8.4% 31.7% 13.4% Weekday Daytime C 7.3% 5.1% 38.7% 20.1%

A 10.8% 3.6% 33.1% 17.5% B 9.5% 8.3% 33.1% 17.4%

Weekday PM Peak

C 7.6% 4.9% 36.9% 20.8%

A 11.4% 4.3% 33.8% 16.3% B 10.1% 10.7% 35.8% 19.2% Weekday Evening C 8.9% 6.0% 39.9% 20.8%

A 12.1% 10.9% 33.5% 15.9% B 12.6% 17.0% 35.3% 17.7% Sunday Morning C 9.8% 11.9% 40.1% 20.1%

A 12.2% 7.4% 33.4% 15.9% B 11.3% 12.3% 35.1% 18.0% Sunday Afternoon C 9.4% 7.6% 40.0% 20.2%

A 11.8% 6.2% 32.0% 15.3% B 12.5% 13.9% 31.8% 13.9% Sunday Evening C 10.2% 8.2% 38.0% 18.7%

Access to bus stop 0.3% 0.3% 8.6% 6.8%

30

Applying the measure across to the Twin City Region, and probably to other major

Cities in the United States, the results show much less catchment (less than 50% of

the general population), and therefore would not be a meaningful measure (as a means

of providing information to planners to provide improvements).

Table 6 shows the comparison between the results for Greater Manchester and the

Twin City Region., and shows the disparity clearly between the two study areas.

However, drilling down the population to those living in areas of relative low income

and the measure, as it currently stands, becomes much viable; with potential use by

bus service planners to improve access to those falling outside the catchment.

Table 6: Local Accessibility: Percentage change comparing Greater Manchester to

the Twin City region: by day of week and study area

Time Period Criteria TC compared to GM (ALL)

TC compared to GM (low income/10% deprived)

TC compared to GM (Central

Core)

A -35.8% -13.0% -23.9% B -50.5% -25.7% -40.9%

Weekday AM Peak

C -45.8% -14.8% -29.0%

A -48.3% -25.2% -35.2% B -62.5% -40.6% -57.6% Weekday Daytime

C -56.7% -25.3% -41.7%

A -38.5% -16.2% -24.7% B -50.0% -26.5% -40.9%

Weekday PM Peak

C -44.3% -15.0% -28.5%

A -38.1% -15.7% -26.2% B -46.5% -20.8% -38.0% Weekday Evening

C -45.3% -14.3% -30.6%

A -38.3% -16.9% -33.3% B -42.4% -19.7% -41.7% Sunday Morning C -45.3% -15.0% -37.0%

A -43.3% -22.2% -34.9% B -49.1% -25.3% -43.4% Sunday Afternoon C -50.0% -19.4% -37.5%

A -46.2% -26.0% -37.1% B -49.8% -30.4% -49.8% Sunday Evening C -54.8% -27.0% -44.3%

Access to bus stop -9.0% -0.6% -2.5%

31

6 CONCLUSIONS

At a macro scale, from the evidence presented in this paper, and the techniques

employed to do so, it can be concluded that (i) it is possible to conduct Accessibility

Planning techniques and apply it to a City/region outside of the United Kingdom; and

subsequently (ii) indicators of (UK standard) accessibility can be successfully applied

to a North American region.

The research review, looking at evidence in North America in particular, shows that

there is a desire to conduct accessibility planning; however nothing as sophisticated or

detailed as Accession software has been used, suggesting there is a market

(potentially world wide) for its use and application.

On a more focused level, fundamentally the research has shown that the Greater

Manchester region provides a higher / more consistent level of accessibility (network

and local) than that seen in the Twin City region.

The results suggest that network planning in the Twin City region is primarily set to

provide access to the core centre, while in Greater Manchester networks are more

consistent to serve the entire county area. In particular buses in Greater Manchester

would appear to serve urban areas within more penetration, i.e. going into housing

estates, while in the Twin City region the network (particularly in the outer areas)

designed along side major roads.

Crucially, for both regions, the bus networks have been proved to serve ‘best’ sections

of society who could be argued to be more reliant on a bus service to reach key

destinations, for example those who live in the most socially/economically deprived

areas (in Greater Manchester) and those on low income (in Twin City region). This is

especially the case in the Twin City region where accessibility levels to low income

groups is significantly better than that to the general population. A similar set of

results, although less so in magnitude, was seen for accessibility within the central

core of the two regions.

It could be fairly argued that comparing the two regions like-for-like is ambiguous,

with different historical drivers of policy affecting how both have developed as urban

32

areas and how bus services are provided by the operators and local government

agencies. These factors, amongst others, however lie outside of the remit for this

research paper, but there could be an opportunity to develop further.

The final, but most important, point is that this research has proved that Accession

and accessibility planning in general can be applied to Cities / regions outside of the

United Kingdom. This point is taken forward in the final section of this paper, looking

at recommendations to take this research forward.

33

7 RECOMMENDATIONS

Overall the paper has achieved the objectives set in that Accession has been

successfully translated to an area outside of the United Kingdom, and indicators of

accessibility have been successfully applied to the Twin City region.

The recommendation is that this approach could be applied to enable planners in the

United States, and in other areas, to provide information on public transport reviews

and the locations of new developments are located in places that ensure they are in

sustainable locations (i.e. close to the public transport network).

The ‘local’ accessibility indicators used are UK based and probably should be refined

to take into account the different urban conditions (if taken forward). In this research

the ‘local’ accessibility indicator was measured, over the total population, to cover

less than 50% of the population; thus the results had no meaningful purpose (you

cannot take promote policies of social inclusion if less than half the population is

included).

However if the indictor was refined, for example, increasing the population catchment

area (from 400m to 800m) and halving the required frequency (minimum number of

buses in defined period) much larger ‘captured’ populations would have been

calculated, leaving smaller pockets of areas that then could be targeted; therefore the

refinement could lead to policies that promote improvements to accessibility.

Alternatively, the indicators could be applied to areas where social exclusion is more

likely to occur (low income areas) where access to the public transport system is more

important. The research showed that in these areas accessibility was higher than the

general population, however work could be done to highlight in where in these areas

access is poorest, hence leading to policies of promotion of bus services / facilities

and removal of transport barriers.

The final recommendation is that a partner organisation (Transit authority for

example) is found and a full accessibility audit undertaken for the area the authority

represents.

34

8 REFERENCES