ARTICLE IN PRESS Transport Policy 15 (2008) 372–378
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AMELIA: A tool to make transport policies more socially inclusive Roger L. Mackett , Kamalasudhan Achuthan, Helena Titheridge Centre for Transport Studies, University College London, Gower Street, London WC1E 6BT, United Kingdom
a r t i c l e in f o
a b s t r a c t
Available online 30 January 2009
Transport policy should take into account the needs of those who are socially excluded. To facilitate this, a software tool, AMELIA, is being developed. After a description of AMELIA and how it is used, the paper continues with a discussion about the analysis of the increase in the number of elderly people who can reach the centre of St Albans in Hertfordshire in Great Britain as the result of the implementation of four policy actions, including the cost implications. The paper is concluded with discussion about the potential role of AMELIA as a tool for policy analysis. & 2008 Elsevier Ltd. All rights reserved.
Keywords: Transport policy Social exclusion Social inclusion Accessibility
1. Introduction Social inclusion is an area of growing concern. There is a wide range of characteristics that are associated with being socially excluded, for example, having a disability, being elderly, having a low income or being a single parent (Mackett et al., 2004). Usually those who are socially excluded have two or more of these characteristics, for example unemployed teenagers and low-income people living in rural areas. Social inclusion involves many issues that have nothing to do with transport, including politics, poverty and the nature of society. However, better transport can help to overcome many problems associated with social exclusion by enabling people to reach opportunities that can help them earn money, improve their health and enjoy a rich social life, all of which can help make people to feel more included. Hence, it is increasingly being recognised that transport policy should take into account explicitly the needs of those who are socially excluded. However, there is currently no comprehensive way to ensure that transport policies do take social inclusion into account. This issue is being addressed in a research project being carried out in the Centre for Transport Studies at University College London as part of the work programme of the Accessibility and User Needs in Transport in a Sustainable Urban Environment (AUNT-SUE) consortium (see http://www.aunt-sue.info/). In this part of the programme, a software tool, A Methodology for Enhancing Life by Increasing Accessibility (AMELIA) is being developed to test the extent to which transport policies can increase social inclusion (Mackett, et al., 2007, 2008a, b). AMELIA is a user-friendly, policy-based interface to a Geographical Information System (GIS). Following a scoping study, funding for the development of AMELIA began in
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May 2004 and continues until April 2010. This includes a phase involving consultation about AMELIA with people who are socially excluded. In the next section of this paper, the role of transport in addressing social inclusion is considered. Then AMELIA is described, followed by discussion of the study area being used to test the software, and a demonstration of the application of the software.
2. Transport and social inclusion Even within the transport sphere there are many aspects of accessibility that are very difficult to address using transport models, for example, information provision. This means that the types of barrier to access that can be investigated with AMELIA are physical ones, and so the types of social exclusion that can be addressed are ones related to physical access, such as being in a wheelchair, or related to travel cost and time, such as reaching employment by public transport. Since about 14% of adults in Britain have at least one disability (Martin et al., 1988) improving accessibility for them will improve life for a significant number of people. These are important issues, and can be examined using a GIS approach, as shown, for example in research into wheelchair access using GIS (Matthews et al., 2003; Beale et al., 2006). In England, local authorities outside London with transport planning responsibilities are required by the Department for Transport (DfT) to produce a Local Transport Plan (LTP) which is part of a bidding process to obtain funding from central government for expenditure on local transport policies and projects. Local authorities are required to ensure that their LTP shows clear connections between targets for local transport, and targets for social inclusion (and economic growth and housing) (Department for Transport, 2004). This is the rationale behind the development of AMELIA, since it will enable planners to test explicitly that their policies do increase social inclusion
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(within the limitations discussed above). There are other tools available to assist transport planners in this area (Mackett and Titheridge, 2004), in particular, Accession (2006), which is software commissioned by the DfT to assist in the process by showing changes in accessibility by public transport brought about by changes in the transport system. AMELIA goes further than this by considering all modes, explicitly linking policies with the changes in accessibility and incorporating benchmarks, so that planners can explore policy options to test how many more people reach the benchmarks with policy interventions. In England, the construction of new infrastructure and changes to the street layout must now comply with the Disability Discrimination Act 1995, which gives people with disabilities a right of access to goods, facilities, services and premises. In order to help in this process DfT has issued Inclusive Mobility Guidelines (Department for Transport, 2005). These provide guidance on the design of a variety of aspects of the urban environment, including, footways, pedestrian areas, bus stops, access to and within transport-related buildings, signage and lighting. This list indicates the range of detail to which infrastructure must be designed to comply. However, the basis for some of the advice is not clear. There is some guidance in the Inclusive Mobility Guidelines on the physical parameters of people with different mobility characteristics (Department for Transport, 2005). The source of these figures is not stated explicitly, with the text saying, for example, ‘‘Walking distances were researched in some detail in the late 1980s and, based on the findings from these studies, the following are recommended’’, referring to a table which shows the maximum distances that people with various degrees of disability can walk. These are understood to have been based upon work carried out at the University of Leeds (May, 2008) and presented in a report by the Institution of Highways and Transportation (1991). It is stated in the Inclusive Mobility Guidelines that seats should be provided at intervals of no more 50 m in commonly used pedestrian areas, but no evidence is offered to support this assertion. However, a figure of 100 m is recommended elsewhere (Department for Transport, 2007; ECMT, 2006). Other factors also have different values: Monheim and Frankenreiter (2000) say that the minimum absolute width of footways varies from 1.4 m in France to 2.0 m in Norway and Switzerland, with a normal value ranging from 1.5 to 3.0 m in Finland to 2.0 to 4.0 m in Norway (no figures were given for Great Britain). There are also some anomalies. For example, very detailed specification is given for the dimensions of toilet facilities within transport buildings such as stations, but there is no guidance on the number of public conveniences that are required on the street. For many people, the lack of suitable facilities is a very real barrier to making journeys on foot. This reflects the difficulty of determining appropriate design parameters. This issue is addressed in AMELIA by providing the relevant information in the form of guidance and letting the planner using the tool use his or her judgement. Anomalies such as this, plus the fact that older areas do not comply with the guidelines, mean that even with comprehensive inclusion policies there may be barriers to access for members of groups who wish to reach the facilities in urban areas. Implementation of policy actions to overcome the barriers would cost money. Whilst new facilities are often required by law to be accessible to various sections of the community, such as those in wheelchairs, much of the work in this area involves increasing the accessibility of existing infrastructure. This costs money. Whilst such schemes are rarely subject to conventional economic appraisal, there is a need to ensure that public funding is being spent as effectively as possible, and to promote schemes that give the greatest benefits. Currently there is no systematic way of doing this. Developing a methodology to help address these issues is part of the research described in this paper.
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3. AMELIA The purpose of AMELIA is to present the user with a set of possible policy actions relevant to the policy objective being considered, and then to quantify and map the effects of these policy actions to help the user to assess which is the most effective. The policy objective is normally defined in terms of increasing accessibility for members of a particular group to a set of opportunities, such as shops or medical facilities. Sometimes a mode of travel such as walking is specified. Alternatively, the policy objective might be formulated in terms of overcoming a barrier to movement. AMELIA requires data on the population in the group being considered (the elderly, those in wheelchairs and so on), the nature of the facilities that they wish to reach (shops, jobs, health facilities and so on) and how they can travel there. AMELIA can then be used to see how many more of this group can reach the opportunities as a result of the policy actions. In order to assess whether a policy action is effective, it is necessary to use benchmarks representing a ‘reasonable’ level of access (Mackett, 2006; Titheridge and Solomon, 2007). AMELIA is used to see how many members of the group meet the benchmark with and without the intervention represented by the policy action. To date, most of the analysis carried out with AMELIA has been at the microscale, examining barriers to walking (Mackett et al., 2008a, b). The key elements of AMELIA are shown in Fig. 1. Fig. 2 shows how AMELIA is used. Having set the general policy objective of increasing accessibility, it can be focussed on particular groups in society or modes of travel by selecting the relevant characteristics. These will be used by AMELIA to identify some suitable policy actions. Some of these can take different values, such as the angle on dropped kerbs, so suitable values need to be selected. Guidance is provided on this, drawing on various sources such as the Inclusive Mobility Guidelines (Department for Transport, 2005). Cost data are also provided for some policy actions, since this may influence the scale of implementation. The data for testing the policy action then have to be set up by making appropriate changes to the GIS representing the study area. Advice is provided on how to do this through a ‘help’ system. A suitable benchmark can be selected, on the basis of judgement about a ‘reasonable’ distance or level of expenditure of time or money. AMELIA is then run and the results examined, possibly in the light of the cost of implementing the policy action. AMELIA can be run again using different values for the policy action or another policy action. The user can repeat this process until satisfied that a policy action has been identified which is effective in meeting the accessibility needs of the group being considered. AMELIA has an information system built into it and that identifies suitable policy actions that can be implemented to help achieve the chosen objective. When a policy action is tested with
Policy objective
Benchmarks
Policy actions to achieve the objective
Analysis of the impact of the actions
Data on the population in a socially-excluded group
Changes in the number of socially excluded people meeting the benchmarks
Data on the local area (transport networks, opportunities, etc)
Fig. 1. The components of AMELIA.
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Set the policy objective Identify the relevant characteristics
AMELIA information system
Select a policy action Guidance on values Set the values for the policy action
Costs
Set up the data for testing the policy action Run AMELIA
Set the benchmark
Examine the results
Fig. 2. The procedure for using AMELIA.
AMELIA, the key output is the increase (or, possibly, decrease) in the number of people in a particular group who can reach the opportunities being considered as a result of the implementation of the policy action. Typically, about four policy actions are identified to achieve the policy objective. They can all be tried by the user by modifying the GIS. AMELIA will identify different numbers of people who can reach the opportunity as a result of the policy action. In fact, each policy action will have probably a number of ways that it can be implemented. For example, one way of increasing accessibility for people in wheelchairs is to introduce dropped kerbs (curb ramps) at road crossings. In an area with a large number of crossing points without dropped kerbs, there will be a very large number of combinations of places where they could be built. Each combination will help different numbers of people. The places most likely to be useful are on routes that link locations which large numbers of people travel between. It is important that such routes are comprehensively fitted with dropped kerbs, with no breaks in the route, since one missing dropped kerb is sufficient to pose a major barrier. It is of little comfort for a wheelchair user to know that almost all the road crossings have dropped kerbs on his or her route if there is one point that cannot be managed. Engineers wishing to install dropped kerbs will not be able to install all the facilities at the same time, and they may well be subject to budget constraints.
4. The study area The design of AMELIA requires an area to be defined for testing the tool and local authority involvement in the design process. The county of Hertfordshire, which is the area immediately north of London, has been chosen for this purpose. It was chosen mainly because Hertfordshire County Council is a non-academic partner in the AUNT-SUE work programme, is able to supply data for the project, and is involved in the testing and validation of AMELIA. A database is being set up for Hertfordshire. Macro-level data based upon the local authority’s information systems and other sources such as the 2001 Census of Population are being assembled for the whole county. Micro-level data based upon street audits, including details such as steps, slopes, access to individual buildings and obstructions on the pavement are being incorporated into the database. These more detailed data are only for the city of St Albans since it is not feasible to collect such data for the whole of Hertfordshire (Mackett et al., 2008b).
St Albans (population 129,005 in the 2001 Census) was chosen because it is a compact city within Hertfordshire which offers a variety of facilities, including retail, medical, entertainment and administrative, which need to be accessed by various members of society, and includes some interesting barriers to movement because of its historic origins as the Roman city of Verulamium. As part of the model development process, detailed data for St Albans were collected on the street by the authors. Data were collected on the following: buildings, characteristics of the footway, road crossings, bus stops, car parking and features. While it would be possible for others who wished to use AMELIA to collect such data for other areas, it would be labour intensive and expensive. It is possible to obtain much of the data from various sources in Great Britain. For example, data on destinations and buildings can be obtained from the Ordnance Survey MasterMap Address Layer which holds precise location data on all commercial and residential buildings classified by their usage and also includes some nonaddress points (Ordnance Survey, 2008b). The commercial vendor Experian (2008) provides data on commercial and retail locations through their Shop Point data for high streets in urban areas at a fine level of detail and at postcode level for other areas. These are available in digital format for purchase and integration into the GIS databases. Data on footways and pavements are maintained by the Highways Division of most UK County Councils as part of their asset management system. These can be imported and used with GIS. While data on the location of bus stops were obtained from Hertfordshire County Council, bus stop location data can be obtained from the National Public Transport Data Repository (NPTDR) maintained by the organisation Thales (2008). The data being used for St Albans has been entered into a GIS database using digital data from the Ordnance Survey Land-Line Plus data as the base. The building polygons were extracted from it and populated with the data collected in the field as attributes. The buildings were further grouped into different category levels based on the Ordnance Survey Points of Interest (POI) classification scheme (Ordnance Survey, 2008a). The location data for car parking and features were mapped as point features and linked with their attributes. Using the footways and crossing data collected, a detailed pedestrian network layer of the link-node structure was created by manually digitizing the pavements and crossings using the Land-line data as a backdrop. Once digitized, the network data were subject to further editing to include nodes at all decision points such as crossings and intersections. The links representing footways and crossings were used to store the respective attribute information collected, which could be modelled for network analysis purposes as the cost of traversing a particular link or as a barrier. Data have been extracted from the Census of Population 2001 for Census Output Areas in St Albans to facilitate accessibility analysis of specific groups of people.
5. Implementing the policy tests In order to demonstrate how AMELIA is used to identify ways of increasing accessibility, the impact of four policy actions in the city centre of St Albans is described in this paper:
putting in dropped kerbs at existing road crossings; putting in new road crossings every 100 m; widening the pavement to allow wheelchairs to progress; putting in benches so that people can rest every 100 m.
Fig. 3 shows the centre of St Albans with the existing benches, footways and crossings. The policy actions are considered in terms of how many more people are able to reach the centre of the city, represented by the Old Town Hall which is adjacent to the main
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shops and the street market. Fig. 4 shows the location of the new crossings and widened pavements. The locations of the new benches are not shown because it was not necessary to locate them explicitly in order to implement this policy test. Approximate costs have been introduced for each of these policy actions: £25,000 for a new road crossing based on figures from the London Borough of Camden (2005) Spending Plan, £2000 for new or replacement dropped kerbs (i.e. £1000 for each side of the road), £65 per square metre for new pavement, both based on figures obtained directly from the London Borough of Camden, and £300 for a new bench based on figures from Spon’s Price Book (Davis Langdon, 2008). The population being considered is the 19,231 residents of St Albans who are aged 65 years or over living in private residences. The elderly population is heterogeneous, some able to walk easily, others having difficulties. The shortage of road crossing points and benches to rest on are barriers to those who cannot walk very far.
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The lack of dropped kerbs at existing crossings is a barrier to those who cannot walk up a step or in a wheelchair. Some people may have both difficulties. Detailed data on the capabilities of the elderly population of St Albans are not available, so estimates have been made by applying rates based on data from the Survey of Disabled Adults in Private Households in Great Britain which were used to produce the report Disability in Great Britain (Martin et al., 1988). Table 1 shows the numbers of people aged 65 or over living in St Albans assumed to be incapable of walking up a step or capable of walking various distances by applying these rates. In the analysis it was assumed that those who could not walk at all would use wheelchairs and that the rest could walk the distances shown in Table 1. In order to be inclusive the most pessimistic assumptions were made: for example, where the Survey of Disabled Adults showed people could walk between 46 and 183 m it was assumed that 46 m was the maximum distance they could walk (using the mean or median point would not have been
Fig. 3. The existing layout of benches, crossings and footways in the centre of St. Albans.
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Fig. 4. The proposed layout of the new crossings, existing crossings with new dropped kerbs and the widened pavements.
Table 1 The number of residents of St Albans aged 65 or over with various walking capabilities. Group
Cannot walk up one step
Can walk up one step
Total
Cannot walk at all or can walk less than 46 m Can walk 46 m but not 183 m Can walk 183 m but not 402 m Can walk 402 m Total
2500 77 77 0 2654
0 1077 692 14,808 16,577
2500 1154 769 14,808 19,231
Source: Based on rates calculated from The Survey of Disabled Adults in Private Households in Great Britain (Office of Population Censuses and Surveys, 1989). Note: The Survey of Disabled Adults used measurements in yards which have been converted to metres in the above table: 46 m ¼ 50 yards, 183 m ¼ 200 yards and 402 m ¼ 440 yards.
possible for the large numbers who could walk 402 m or further without making an arbitrary assumption about how far they could walk).
It was necessary to estimate how these people would have been likely to travel to the city centre. It was assumed that all those who, according to the Census of Population in 2001, lived in
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Census Output Areas within 800 m of the centre of St Albans (taken to be the Old Town Hall) would have walked there (or travelled there by wheelchair). The rest were assumed to travel by bus or car. They were split between the two modes based on the relative usage of the two modes by people aged 65+ according to the Great Britain National Travel Survey (Department of the Environment, Transport and the Regions, 2003a, b). Those assumed to be coming by bus were allocated to the most appropriate bus stop within 400 m of the city centre according to where they live. Those assumed to be coming by car were allocated to car parks within 400 m of the city centre in proportion to the size of the car park. The distance from the arrival point in the city centre to the Old Town Hall was used as the benchmark in this example.
6. Results of the policy tests Table 2 shows the numbers that were estimated to use each mode to access the city centre and the total numbers able to walk or use a wheelchair to reach the Old Town Hall from their arrival point (the bus stop, the car park or home in the case of those not using buses or cars). It can be seen that 71% of those arriving in the City Centre were calculated to be able to walk (or use a wheelchair) to reach the Old Town Hall from their arrival point. This meant that 5557 people could not. Bus users had the highest proportion able to reach the Old Town hall because the bus stops tend to be nearer the final destination than the car parks or the homes of those living within 800 m and walking or using a wheelchair all the way. Table 3 shows how many more of the 19,231 people aged 65 or over that AMELIA estimated would be able to reach the Old Town Hall as a result of the policy actions. It can be seen that providing more road crossing points would make no difference. This is because there are already so many crossing points provided (233) that an extra 11 would not reduce the distance that any of the
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people arriving in the city centre would have to walk (or use a wheelchair) to make it possible for any more to reach the Old Town Hall. Providing wider pavements would enable 13 people, all car users, to reach the Old Town Hall. These would all be people who use wheelchairs to travel from the car park to the final destination. Narrow pavements are assumed not to be a barrier to walkers. Installing dropped kerbs at existing road crossings would enable 24 more people, also all car users, to reach the Old Town Hall. The fourth policy action, providing benches every 100 m, would have the greatest impact, enabling 524 more people to reach the Old Town Hall, spread across the three access modes. This represents an increase from 71% to 74% of the elderly population of St Albans. Whilst this may be regarded as valuable in social terms, AMELIA facilitates consideration of the cost implications. Table 4 shows the total cost of implementing each of the policy actions. The cheapest is the installation of dropped kerbs at existing crossings at £46,000 while the most expensive is providing road crossings every 100 m at £275,000. However, as shown above, these policy actions benefited different numbers of people. Here there are large differences with the provision of benches every 100 m being the most cost effective because it would cost £180 per beneficiary, while providing dropped kerbs at existing crossing would cost £1916 per head. The third most costeffective policy action would have been providing pavements, which costs much more at £18,447 per head, partly because of the relatively small number of beneficiaries. Because providing more road crossing points would not increase access to the Old Town Hall for anyone, the costs per head are, in effect, infinite. It is recognised that many assumptions have been made in carrying out this analysis, for example about the capabilities of the elderly population of St Albans and the impact of the policy actions on them. Also, the cost figures are indicative, and the total costs would be different if different layouts for the new infrastructure had been selected. In practice, a planner using AMELIA would test various layouts and assumptions about the implementation of the policy test, the costs and the possible
Table 2 Numbers of people arriving in the city centre and at the Old Town Hall.
Total numbers using each mode to arrive in the city centre Total numbers able to reach the Old Town Hall % able to reach the Old Town Hall
Walk or wheelchair all the way
Bus and either walk or wheelchair
Car and either walk or wheelchair
Total
485 161 33
2793 2151 77
15,953 10,838 70
19,231 13,674 71
Table 3 Increases in the numbers who can reach the Old Town Hall as a result of the policy actions. Policy action
Walk or wheelchair all the way
Bus and either walk or wheelchair
Car and either walk or wheelchair
Total
Providing Providing Providing Providing
0 0 0 7
0 0 0 56
24 0 13 461
24 0 13 524
dropped kerbs at existing crossings crossings every 100 m wider pavements benches every 100 m
Table 4 Costs of implementing the policy actions. Policy action
Unit cost
Number of units installed
Total cost
Cost/head
Providing Providing Providing Providing
£2000 each £25,000 each £65 per m2 £300 each
23 11 3689 m2 314
£46,000 £275,000 £239,805 £94,200
£1916 – £18,447 £180
dropped kerbs at existing crossings crossing every 100 m wider pavements benches every 100 m
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responses of the population being studied. Notwithstanding these issues, the example above shows that it is possible for AMELIA to provide advice for planners to use in their decisions about the best strategy to improve accessibility for socially excluded people. 7. Conclusions This paper has discussed the development of the software tool AMELIA which is designed to show the impacts of transport policy on social inclusion. The effects of some policy tests to improve accessibility for elderly people using the city of St Albans in Hertfordshire as an example have been illustrated. The analysis suggested that providing benches would provide the most costeffective increase in accessibility. It is recognised that many assumptions have been made in doing this, but, AMELIA does offer a systematic approach to the issue of increasing accessibility. It does not take decisions. Rather, it helps planners and others to explore policy options and see the likely impacts. With costs included in AMELIA, planners can compare the impacts of different policy actions and decide which is the most cost effective. This is a major improvement on the conventional use of GIS by a planner. AMELIA can be used in the public consultation process, allowing the public to see the cost-effectiveness of policy actions suggested by both the planners and themselves. In fact, the next major phase of the development of AMELIA is to take it to meetings with groups of people who are socially excluded to see if the assumptions embedded in it reflect their views and behaviour. It will be used to let them see if they can devise ways to improve their lives by increasing accessibility to the opportunities that they value but have difficulty accessing. Whilst it is quite clear that AMELIA can only address a small part of the social exclusion problem, it does offer a systematic approach to the very important issue of increasing accessibility to opportunities that many people in society lack. There is much more work to be done, but as shown in this paper, the potential for the use of AMELIA is huge. Acknowledgements This paper has been written as part of a project entitled ‘Accessibility and User Needs in Transport’ which is being funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant GR/S90867/01 as part of its Sustainable Urban Environments Programme. The co-operation of the Environment Department of Hertfordshire County Council is greatly appreciated. The use of data on disability from the Social Surveys Division, OPCS, which was supplied by the UK Data Archive, is acknowledged. OPCS and the UK Data Archive bear no responsibility for the further analysis of this data or its interpretation contained within this article. References Accession, 2006. Helping make places more accessible. Available from /http:// www.accessiongis.com/S.
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