STUDI: A model to simulate the impacts of new metro lines on urban development in London

Transcription

1 STUDI: A model to simulate the impacts of new metro lines on urban development in London by Aris Christodoulou A thesis submitted to the University College London for the degree of Doctor of Philosophy Centre for Transport Studies January 21 University College London

2 Declaration I, Aris Christodoulou, confirm that the work presented in this dissertation is my own. Where information has been derived from other sources, I confirm that this has been indicated in the dissertation. Aris Christodoulou 2

3 Acknowledgements The following are acknowledged for their assistance during the conduction of this thesis: Roger Mackett for his supervision, guidance, support and encouragement during the past three years, the time he spent is appreciated greatly; Transport for London for its financial support and for providing data; more specifically, Shashi Verma for the interesting discussions and comments, Lauren Sager Weinstein for organising the provision of resources, and Mike Collop, Simon Cooper and Clifford Chow for providing data; Peter Jones for the discussions in the early stages of the research; Kamal Achuthan for his help with ArcGIS and Evgenios Xanthis for his advice on various IT issues. Finally, all members of the Centre for Transport Studies for providing an excellent research environment. 3

4 Abstract Urban systems are complex and change as a result of the interactions between their main elements. In order to model urban systems effectively, the dynamics of the relationships between these elements need to be considered. This thesis investigates the interactions between transport and urban development, focusing on the impacts of new metro lines. A new model is developed for this purpose: the STUDI (Simulation of Transport and Urban Development Interactions) model. The main concept underlying the STUDI model is that the impacts of new transport infrastructure on urban development are reflected in the interactions between the main agents involved in the process, which are authorities, developers, businesses and population. The STUDI model contains three main interrelated sub-models: the development, the business and the population sub-models. The development sub-model is a regression model forecasting the number of new commercial and residential premises, and the business and population sub-models are microsimulation models. The business sub-model simulates business start-ups and closures and business location and relocation decisions. The population sub-model simulates in- and out-migration, demographic and employment change, and residential location decisions. The main results include changes in the spatial distributions of development, businesses and population over time under different transport supply scenarios. The STUDI model has been developed for London in order to test the wider impacts of new metro lines. First it has been applied to evaluate the impacts of the Jubilee Line Extension (JLE) and then it was used to forecast the impacts of a line to open in the future: the East London Line Extension. Both cases indicate the positive impact of new transport infrastructure on urban development. 4

5 Contents 1 Introduction Agents of urban development Authorities Developers Organisations - Businesses Population Residence Employment Summary of impacts of new transport investments on urban development Interactions between the agents of urban development Review of land use transport models Optimising models (mathematical programming models) Forecasting models Aggregate forecasting models Regression Models Spatial Interaction Models Spatial Input Output Models Activity Based Models Disaggregate forecasting models Discrete Choice Models (Random Utility Models) Random Bid Models Cellular Automaton Models Microsimulation Models Summary Data Travel time estimates CAPITAL Railplan Combination of CAPITAL and Railplan data Development data Business data Population data Total-sample population compatibility STUDI model Theoretical description Development sub-model Business sub-model Connection with development and population sub-models Forecast of business closures and start-ups Business closures Business start-ups Business relocation Selection of businesses to look for new location Location choices of businesses Formation of the new business database

6 5.2.7 Aggregate results Population sub-model Connection to development and business sub-models Migration Out-migration In-migration Identify households to migrate in London Job allocation Residence allocation Population demographics Ageing Deaths Births Household dissolution Household formation Employment Employment status and employment location change Employment location decisions Residential location Location choice by separated people who did not find a match Search for dwelling by newly formed couples Residential location decisions Estimation, Calibration and Validation of the STUDI model Calibration Development sub-model Commercial premises Residential premises Business sub-model Businesses-GDP relation Business relocation Population Migration Employment location Residential location Connection between sub -models Validation Development sub-model Residential development Commercial development Business sub-model Population sub-model Stochastic variation Commercial development Businesses Population Summary

7 7 The Jubilee Line Extension (JLE) application Comparative statistics of the different runs of STUDI Development sub-model Residential development Commercial development Business sub-model Population sub-model Demographics Residential location Employment Summary The East London Line Extension (ELLX) application Travel-time changes due to East London Line Extension Development sub-model Residential development Commercial development Business sub-model Population sub-model Summary Discussion Connection of the main sub-models Development sub-model Business sub-model Population sub-model Other issues Conclusions References Appendix

8 List of Figures Figure 2.1: Map of the JLE (taken from Transport Studies Group, 24) Figure 2.2: Procedures of urban development Figure 4.1: Boroughs of London Figure 4.2: Changes in travel times from Westminster due to JLE Figure 4.3: Changes in travel times from Lambeth due to JLE Figure 4.4: Changes in travel times from Southwark due to JLE Figure 4.5: Changes in travel times from Greenwich due to JLE Figure 4.6: Changes in travel times from Tower Hamlets due to JLE Figure 4.7: Changes in travel times from Newham due to JLE Figure 5.1: Agents of urban development Sub-models of the STUDI model Figure 5.2: The main sub-models of the STUDI model... 7 Figure 5.3: Adding new businesses using zone attractiveness Figure 5.4: Business closures and start-ups... 8 Figure 5.5: Selection of businesses considering relocation Figure 5.6: Modelling of business location decisions Figure 5.7: In-migration Figure 5.8: Births Figure 5.9: Household formation... 1 Figure 5.1: Employment status changes Figure 5.11: Employment location decisions Figure 5.12: Households considering relocation Figure 5.13: Residential location decisions Figure 6.1: Forecast - Actual proportional distributions of change of dwellings Figure 6.2: Forecast - Actual proportional distributions of change for commercial premises Figure 6.3: Forecast - Actual proportional distributions of change in the number of businesses Figure 6.4: Total number of commercial premises in each borough (forecast) Figure 6.5: Change in the number of commercial premises in each borough (forecast) 153 Figure 6.6: Total number of businesses in each borough (forecast) Figure 6.7: Changes in the number of businesses in each borough Figure 6.8: Total population in each borough (forecast) Figure 6.9: Population changes in each borough Figure 7.1: Boroughs of London Figure 7.2: Difference of number of new dwellings forecast with and without the JLE 168 Figure 7.3: Difference of the number of new commercial premises forecast with and without the JLE Figure 7.4: Annual change of the number of commercial premises in Tower Hamlets. 171 Figure 7.5: Annual variation of vacant and commercial premises in Lambeth Figure 7.6: Industrial sector distribution of businesses in London in years 1995 (actual) and 26 (forecast) Figure 7.7: Difference of the number of new businesses added from 1995 to 26 as forecast with and without the JLE Figure 7.8: Annual change of the number of businesses in Southwark

9 Figure 7.9: Annual change of the number of businesses in Tower Hamlets Figure 7.1: Annual change of accessible employees Figure 7.11: Annual change of attractiveness of Greenwich Figure 7.12: Annual change of number of businesses in Greenwich Figure 7.13: Age distribution of population in London in 1995 (LATS data) and 26 ( forecast) Figure 7.14: Distribution of population in London according to their household structure in 1995 and in Figure 7.15: Employment status distribution of London population in 1995 (LATS data) and 26 (forecast) Figure 7.16: Household income distribution of London population in 1995 (LATS data) and 26 (forecast) Figure 7.17: Difference of population changes from 1995 to 26, forecast with and without the JLE Figure 7.18: Annual change of accessible businesses Figure 7.19: Annual variation of attractiveness of Lambeth and Southwark Figure 7.2: Population in Tower Hamlets - time series Figure 7.21: Difference of employment changes from 1995 to 26 forecast with and without the JLE Figure 8.1: East London Line Extension (TfL 29) Figure 8.2: Boroughs of London and ELLX boroughs... 2 Figure 8.3: Changes in travel times from Hackney due to ELLX... 2 Figure 8.4: Changes in travel times from Tower Hamlets due to ELLX Figure 8.5: Changes in travel times from Southwark due to ELLX Figure 8.6: Changes in travel times from Lewisham due to ELLX Figure 8.7: Changes in travel times from Croydon due to ELLX Figure 8.8: Difference of number of new dwellings forecast with and without the ELLX Figure 8.9: Difference of the number of new commercial premises forecast with and without the ELLX Figure 8.1: Annual change of the number of commercial premises in Lewisham Figure 8.11: Annual change of the number of commercial premises in Hackney Figure 8.12: Difference of the number of new businesses added from 26 to 216 forecast with and without the ELLX Figure 8.13: Annual change of the number of businesses in Hackney Figure 8.14: Annual change of the number of businesses in Lewisham Figure 8.15: Annual change of accessible employees Figure 8.16: Annual change of attractiveness of Hackney Figure 8.17: Annual change of attractiveness of Lewisham Figure 8.18: Difference of population changes from 26 to 216, forecast with and without the ELLX Figure 8.19: Annual change of accessible businesses Figure 8.2: Annual change of population in Lewisham

10 List of Tables Table 2.1: Interactions between the agents of urban development Table 3.1: Land use - transport modelling methodologies Table 4.1: Data sources of the development sub-model Table 4.2: Industrial sectors... 6 Table 4.3: Size of businesses... 6 Table 4.4: Business Growth Table 4.5: Business database (individual business records) Table 4.6: Variables of the population database Table 4.7: Boroughs in LATS data Table 4.8: Population data Table 6.1: Explanatory regression analysis of the commercial development location process using travel time to Westminster Table 6.2: Explanatory regression analysis of the commercial development location process using travel time to London Bridge Table 6.3: Explanatory regression analysis of the residential development location process using travel time to Westminster Table 6.4: Explanatory regression analysis of the residential development location process using travel time to London Bridge Table 6.5: Forecast of number of businesses according to GDP Table 6.6: Explanatory regression analysis of the business location process using travel time to Westminster and accessible workforce Table 6.7: Explanatory regression analysis of the business location process using travel time to London Bridge Table 6.8: Explanatory regression analysis of the residential location process using travel time to Westminster and accessible workforce Table 6.9: Explanatory regression analysis of the residential location process using travel time to London Bridge Table 6.1: Validation of the simulation results for 21 against the actual data Table 6.11: Validation of the simulation results for 26 against the actual data Table 6.12: Changes over time in the number of residential premises Table 6.13: Validation of the simulation results for 21 against the actual data Table 6.14: Validation of the simulation results for 26 against the actual data Table 6.15: Changes over time in the number of commercial premises Table 6.16: Validation of the business simulation results for 21 against the actual data Table 6.17: Validation of the business simulation results for 26 against the actual data Table 6.18: Changes over time in the number of businesses Table 6.19: New businesses in London from 1995 to Table 6.2: Validation of the population simulation results for 21 against the actual data Table 6.21: Validation of the population simulation results for 26 against the ONS mid-year population estimates Table 6.22: Comparison of the ONS mid-year estimates and Census data for

11 Table 6.23: Reference codes of London Boroughs Table 7.1: Comparative statistics for commercial premises (no JLE) Table 7.2: Comparative statistics for businesses (no JLE) Table 7.3: Comparative statistics for population (no JLE) Table 7.4: Comparative statistics for commercial premises (JLE) Table 7.5: Comparative statistics for businesses (JLE) Table 7.6: Comparative statistics for population (JLE) Table 7.7: Residential development in 26 in aggregate areas (forecast) Table 7.8: Commercial development in 26 in aggregate areas (forecast) Table 7.9: Total number of businesses in aggregate areas (forecast) Table 7.1: Difference of the number of new businesses added from 1995 to 26 as forecast with and without the JLE Table 7.11: Annual ranking of London boroughs according to attractiveness as resulted from the simulation with the JLE Table 7.12: Annual ranking of London boroughs according to attractiveness as resulted from the simulation with the JLE Table 7.13: Total population in 26 in aggregate areas (forecast) Table 7.14: Difference of population changes from 1995 to 26, forecast with and without the JLE Table 7.15: Annual ranking of London boroughs according to attractiveness as resulted from the simulation with the JLE Table 7.16: Annual ranking of London boroughs according to attractiveness as resulted from the simulation without the JLE Table 7.17: Annual change of dwellings due to JLE Table 7.18: Annual change of population due to JLE Table 7.19: Total employment in 26 in aggregate areas (forecast) Table 8.1: Total number of residential premises in 216 in aggregate areas Table 8.2: Total number of commercial premises in aggregate areas in Table 8.3: Total number of businesses in aggregate areas (forecast) Table 8.4: Population in 216 in aggregate areas Table 8.5: Annual ranking of London boroughs according to attractiveness as resulted from the simulation without the ELLX Table 8.6: Annual ranking of London boroughs according to attractiveness as resulted from the simulation with the ELLX Table A Table A

12 1 Introduction This study explores the interactions between transport and urban development focusing on the dynamics of the impact of new metro lines. For this purpose a new model has been developed: the STUDI (Simulation of Transport and Urban Development Interactions) model deals with the interactions in question in the context of urban systems modelling. Urban systems are complex as are the interactions between transport and urban development and they involve dynamic relationships. New transport infrastructure can be the result of increasing demand for transit, but also the main policy-tool for the development of an area; it is expected to attract new development, and development of an area which can be boosted by relevant policies is expected to increase the demand for transport. Population is attracted by accessible housing and employment supply in one area increases as businesses move into that area or as accessibility to business areas increases. Businesses make location choices considering several factors, including accessibility to the workforce, other businesses and markets, and availability of premises. Developers, either by forecasting or by responding to the increased floorspace demand, make choices about new developments. According to this brief description of location-related procedures of urban development, the four key agents (actors) involved in these procedures can be identified: authorities (i.e. government, local authorities, policy makers, transport authorities etc.), developers (i.e. construction companies, real-estate agents), businesses and population. The main concept of the STUDI model is that the procedures of urban development and the impact of new transport infrastructure on urban development are reflected in the interactions between the main agents of urban development. These interactions occur over time and hence they should be modelled in a dynamic framework. Most models simulating the interrelation of transport and land-use focus on road networks, including public transport as a variable. Additionally, while travel demand models consider the impacts of land-use on transportation systems, most of them lack the ability to reflect the implications of transportation investments on the patterns of land 12

13 development, except in a limited way (Zhao and Chung, 23). In this study, the impacts of new public transportation on land use are modelled explicitly. The STUDI model has been designed to be comprehensive in order to represent all the main factors of urban development. It simulates the interactions between developers, businesses and population and models the impacts of various factors including transport on their location decisions. Authorities are not included because their decision-making processes are affected by unmeasurable and subjective factors; decisions made by authorities (i.e. policies) are imported exogenously in the STUDI model in order to be tested. Location decisions of developers refer to decisions about where to develop new residential or commercial premises. Business location decisions refer to choices about where to locate their establishment. Population location decisions include residential and employment location choices. Location decisions involve behavioural factors and to be modelled realistically such factors should be considered. A disaggregate model can address the behavioural characteristics of choices in more detail than an aggregate one. Also it can provide more flexibility and better monitoring of location changes, as the origin and destination of an agent who is relocating can be tracked, and it can represent the relationships between the agents of urban development in detail. The current form of the STUDI model is highly disaggregate at agent level, moderately disaggregate at sectoral level and aggregate at spatial level. Spatial aggregation at borough level was chosen for several reasons. One was that various datasets, specifically those on development were available only at borough level. Furthermore, the computational speed of the model was benefited by this decision as having fewer zones improves the running time of the model. In general, a model that runs relatively fast is needed, as speed is a key element of the operationality of the model. The core aim of the STUDI model is the modelling of urban systems in order to understand, simulate and forecast the wider impacts of new public transport infrastructure in large cities, i.e. the impacts on the agents of urban development, on their interactions and on their location choices. The STUDI model has been developed for London and the Jubilee Line Extension (JLE) on London Underground is used to validate it. It is also 13

14 applied to forecast the impacts of the opening of the first phase of the East London Line Extension (ELLX). The STUDI model was developed in order to be operational and these two applications are used to test its operational potentials. During its development, the potentials of more applications and wider use were also considered. In accordance with the conceptual approach described earlier, the STUDI model consists of three sub-models: the development, the business and the population sub-models. The STUDI model runs over time simulating development, business and population processes for a number of simulation periods. The three sub-models are interconnected and exchange information dynamically. The development sub-model uses regression analysis to estimate the number of new commercial and residential premises to be added in each zone in every simulation period. The business sub-model is a microsimulation model, which simulates the decisions of each business separately. It simulates business start-ups and closures and relocation of existing businesses. It is applied to the total business population (individual business records) of London, which has been synthesized by using the Annual Business Inquiry data. The population sub-model is also a microsimulation model, which operates either at individual or at household level. It simulates demographic changes, migration, and employment and residential location decisions. It is applied to the raw LATS (London Area Transport Survey) data of 21. According to the discussion so far, the main features that were considered during the development of the STUDI model were: Dynamic representation Comprehensiveness Choice modelling Disaggregation Operationality Transport representation 14

15 Modelling of large cities Transferability In Chapter 2, the four agents of urban development are presented and the impacts of new transport infrastructure on each agent are examined. Methodologies and results of several studies that investigate the impacts of major transport investments on urban development are reviewed and the influence of JLE on each agent is discussed. In Chapter 3, the most important models and methodologies on the interactions between transport and land use are reviewed in order to identify the ones that best comply with the key desired modelling elements. The preferred modelling methodology is chosen and justified. In Chapter 4, the data used in the STUDI model are described and procedures followed in order to transform the data in the desirable forms are discussed. In Chapter 5, a new model, the STUDI model, is presented. The various procedures followed in the development, business and population sub-models are described. This includes the commercial and residential development models, the modelling of business start-ups and closures, the simulation of business location and relocation decisions, the simulation of demographic changes, in- and out-migration, and the modelling of employment and residential location decisions. The links between the three sub-models representing the interactions between the agents of urban development over time are also discussed. In this context, reference to assumptions and areas that can be improved is also made. In Chapter 6, the estimation, calibration and validation procedures of the STUDI model are presented. At first the equations used in the STUDI model are estimated and key indicators are determined. Then the results of the STUDI model are validated and the impacts of stochastic variation are examined. The forecast development, business and population distributions are compared to the real ones for two years, one in the middle of the whole simulation period and one at the end. 15

16 In Chapter 7 the STUDI model is applied to estimate the impacts of the JLE on urban development. Results presented include development, business and population distributions with and without the JLE in order to capture the impact of the new line, and relevant differences as forecast by the STUDI model. Moreover, results related to the distributions of employment positions and employed population with and without the JLE are shown. In Chapter 8 the STUDI model is applied to estimate the impacts of the ELLX which is expected to open in the near future. Future business start-ups and closures and in- and out-migration are forecast based on economic growth. Results presented include distributions of commercial and residential development, businesses and population with and without the ELLX as forecast by the STUDI model. In Chapter 9, issues raised during the conduct of the study and others that need to be further researched in the future are discussed. In Chapter 1 a research summary in the form of conclusions is presented. 16

17 2 Agents of urban development In order to model the interactions of the main agents of urban development (i.e. authorities, developers, businesses and population), the relationships between them and the impacts of transport infrastructure on their location decisions need to be understood. In this chapter, the agents of urban development are analytically presented and their relationship with transport infrastructure is discussed. Methodologies and results from several studies, which investigate the impacts of major transport investments on urban development, are reviewed. In this context the case of the Jubilee Line Extension on London Underground is also examined. The JLE (Figure 2.1) opened in 1999 and starts from Westminster; towards the east it runs along the southern part of the Thames until Canada Water, it then crosses three times the river Thames before it turns to the North until Stratford. The JLE includes the following stations: Westminster, Waterloo, Southwark, London Bridge, Bermondsey, Canada Water, Canary Wharf, North Greenwich, Canning Town, West Ham and Stratford. It runs through an area which used to be among the most deprived in UK (mainly the eastern part of JLE), although neighbouring with highly developed areas of London. The JLE has contributed significantly to the improvement of the areas included in its corridor, especially the London Docklands area (i.e. the area around Canary Wharf station) where the most intense development occurred. The history of the development of the JLE is presented in Willis (1997). New transport infrastructure affects urban functions in various ways. Moon (199) argued that the primary positive impacts of rapid transit systems on economic development and land use are environmental and federal policies and regulations, demographic changes, changes in city functions and a rising demand for developable property. Two factors that continue influencing the relationship between transit systems, economic development and land use change are traffic congestion and the related commuting habits of individuals. He also argues that economic development is certainly 17

18 occurring around the stations. Such impacts will be described through the interactions of the main actors of urban development. Two general conclusions that came up from many studies on land use impacts of new urban rail transit investments are the following (Cervero and Landis, 1993): Urban transportation investments will generate significant land-use impacts only if a region s economy is growing and if there are complementary development programs in place, such as zoning policies, which support higher densities or more intense land uses. Figure 2.1: Map of the JLE (taken from Transport Studies Group, 24) 18

19 2.1 Authorities Authorities (e.g. municipalities, government etc.) are responsible for the legal environment in which population, developers and organisations function. They make development policies; for example they can make land available for development, they define land use, they can give incentives to attract development etc. Additionally, they make decisions about new transport infrastructure for various reasons including the attraction of development and the fulfilment of the needs of population. On the other hand, development of an area may have adverse impacts on incumbent residents; rapid development of an area can increase land and property values and as a result the incumbent population may be forced out of the area. For example, the development of Canary Wharf attracted thousands of new jobs, but it had little positive impact on the employment of local population, which suffered historically from high unemployment (Transport Studies Group, 24). Authorities interact with organisations. They try to attract big multinational companies in order to strengthen the national economy and the international position of the country and to reduce unemployment. The development of Canary Wharf attracted many multinational companies from the financial sector. Some of them might had gone to the City, but others might had chosen other cities within Europe to allocate their headquarters; this was a great benefit for London in both economic and employment terms. In most cases, authorities expect to have long-term monetary (e.g. from taxes or fare revenue) or political benefits from new transport investments. The decision procedure for a new project involves the interaction of various governmental sectors with local authorities and developers. Authorities can take measures in order to promote development or in support of a new project (e.g. to support a new transit line they can give transit oriented development incentives), which will affect developers decisions. Economic growth of an area may increase the demand for transportation systems and new transport infrastructure is expected to attract development, but not without support. In the 19