Full report 210909 high res
-
Upload
keithdrew76 -
Category
Career
-
view
153 -
download
0
description
Transcript of Full report 210909 high res
1
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
2
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
3
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.
4
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
5
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).
6
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
7
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.
8
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:
9
% 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
10
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).
11
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.
12
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.
13
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.
14
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).
15
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.
16
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”.
17
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)”
18
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.
19
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
20
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%).
21
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
22
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