Integrated Land Use and Transportation: Network Dynamic - Term Paper Example

INTEGRATED LAND USE AND TRANSPORTATION (NETWORK DYNAMIC) Name Institution Course Date Integrated land use and transportation (Network dynamic) 1.0 Introduction There has been considerable remark on the wants, reward as well as opportunities that result from assimilating land use and transport planning. Decisions concerning transport investments, modes as well as locations have impacted hugely on the growth of Australia’s regions as well as metropolitans. However, in scores of cases land use planning along with decision making has happened with slight or no concern for consequential impacts on transportation along with movement, financial support or venture (Newman, 2010). 1.1 Statement of problem Even though laws have been constituted that explicitly require the coordination between land use plans and transportation plans, none of these laws specify the methods that should be used to achieve this integration. Generally, land use plans are developed by local government; regional land plans are rare, and only a few states have established state-wide frameworks for land use planning (Parsons, 1998). On the other hand, transportation plans are developed by “state Departments of Transportation (DOT), Metropolitan planning Organizations (MPOs)” as well as transit agencies with significant funding support and regulation from the federal government. Since different organizations make public decisions on transportation planning and land use planning, harmonization is complicated. Even in the same authority like city governments, transportation and land use are repeatedly handled by different departments, with engineers in charge for transportation decisions while planners are accountable for land use planning. Consequently, transportation engineers, land use planners, and decision makers may have varying or even conflicting goals and objectives (Parsons, 1998). 2.0 Literature review: 2.1 Importance of Integrated Land Use and Transportation Model So as to tackle both federal legislation and the ever-increasing concerns about transportation system crises, the integrated land use and transportation (urban) model can be perceived as a tool that strengthens the coordination between land use plans as well as transportation plans at different levels of governments. An integrated model may as well be used to explore the correlation linking land use with transport in a systematic way. Decision makers can thus be able to model results to determine the effect of land use progress on transportation structures, as well as the impact of transportation policies on land use development (Bertolini et al., 2005). Improving transportation makes the land to be more accessible, hence increasing the probability that “it will be developed or redeveloped. In response, providers of transportation are more and more being requested to evaluate the probable development impacts, and to initiate mitigation plans for unconstructive impacts. Transportation agencies are increasingly becoming acquainted with induced land development as an impact of transportation capacity projects. These impacts are being documented both during analysis conducted through the National Environmental policy Act (NEPA) as well as in system or other planning activities” (Ducruet, & Lugo, 2013, pp 347). Growth, particularly the one that is scattered, may lead to severe ecological troubles. Scattered growth, featured by lesser population densities, a small number of transport choices, as well as inflexible separation of habitations, occupations and superstores, have the likelihood of exacerbating water and air pollution, loss of habitat, in addition to declining the ecosystem functions. It may as well increase the demands on the transportation system in addition to reducing the “efficiency of the system, since the same number of people and same level of economic activity generates more and longer trips” (Broitman, & Koomen, 2014, pp.43). Control these predicaments could be difficult especially when transport structures and land use are planed independently, as it is the case in most localities (Broitman, & Koomen, 2014). 2.1 Connection between Transportation and Land Use “Land use refers to the manner in which land is used; the buildings on the land like stores, houses, offices, factories, etc. as well as the activities taking place buildings like shopping, working, education, etc. and other activities such as farming, grazing and site-seeing among others” (Miller, 2004, pp 87). Participating in out-of-house activities like shopping and working necessitates travelling on transportation networks. For instance, movement of people from home to pace of work and raw materials from source to factory for processing cannot be attained without the support of transportation systems. It has been recognized widely that there is a considerable interactive connection between land use and transportation. Transportation demand is brought about by land use development by offering ease of access (Miller et al, 1999). Accessibility is the nexus between land use and transportation systems. The relations between land use and transportation can be gauged by accessibility, which displays both pleasant appearance as well as easiness of reaching destination (Handy, 1996). The term accessibility refers to a function of land use development (urban activity) distribution and transportation system configuration. The pattern of land-use development has major effects on accessibility because it establishes the distribution of attractiveness in terms of urban activities. The configuration and capability of the transportation system have an effect on the accessibility as notably as land-use pattern since it determines the easiness of reaching urban activities. For instance, reducing the cost of transportation in terms of time or money between two destinations will result in increased interaction between them (Bramley et al., 2009). 2.3 Background The emergence of explicit transportation crises like congestion and pollution has led to the increased governmental and public concerns regarding urban development. These predicaments are mainly cause by both transportation system design and land use planning (urban sprawl). There has been increasing recognition that “the relationship between land use and transportation needs to be understood in a consistent and systematic way” (Miller, 2004, pp. 88). There have been connections linking land use with transportation structures during the course of urban development. For instance, a new urban road will promote the development of adjacent land. As the land is being developed, there will be increased demand for travel, which will therefore lead to congestion on this new road. As the traffic increases, the read will require to be improved or a new road will be built altogether. The new highway will further promote additional land development, and the cycle continues. In regard to environmental and economic constraints, it is unlikely to “build our way out of congestion” by continuing new highway construction. This cycle has led to reshaping policy for city planning (Downs, 1992). The interaction between transport and land use needs be understood well as we struggle to resolve urban problems and work toward sustainable development. Several statistical models and techniques have been developed and revealed significant predictors of integrated land use with transportation system. Measuring the incorporation can be of great help in understanding the connection between transport network and urban structure. The rationale of understanding incorporated land use with transportation is to enhance network accessibility as well as connectivity to urban structures, building forms, land use locations, in addition to development designs (Batty, 2012). There is a strong affiliation linking land use systems with transportation system. Any new development on land will increase pressure on an area’s existing transport network. Integrated land use and transport has been studied across several disciplines such as accessibility, centrality and connectivity with various methods applied. The effect of land use features can be recognized through 3D (Density, Diversity and Design). 2.3.1 Density The term ‘density’ can be used to refer to “the number of units – for instance, people, dwellings, buildings of different use- in a given area. On the other hand, density and mix refers to the amount of development on a given piece of land as well as the range of uses. Density influences the intensity of development, and in combination with the mix of uses, can affect a place’s viability and character. There are a number of ways through which density can be defined and measured. Some density data can be collected using Geographic Information Systems, while others are hard to get through this or any other method” (Barthélemy, 2011, pp 57). The higher the residential densities, the higher the need for transportation systems and hence the need to comprehend the interactions linking land use with transportation (Kirk et al., 2010). The “density of development is usually measured in terms of population density and to a lesser extent employment density. Much of the study concerning land use and travel patterns has paid more attention o the relationship between population density and travel patterns” ECOTEC (1993, p. 33). According to ECOTEC (1993, p. 33), there are mainly four explanations as to why populace density may be connected to travel patterns. The first reason is that higher inhabitants densities increases the choice of openings for the growth of local individual interactions as well as actions that can be sustained with no resort to motorised journey. The second reason is that a higher population density increases the variety of “services that can be supported in the local area, reducing the need to travel long distances. Thirdly, higher density patterns of developments tend to reduce average distances between homes, services, employment as well as other opportunities which reduce travel distances. Lastly, high densities could be more amenable to public transport operation and use and less amenable to car ownership along with use which have implications for modal choice” (Xie, & Levinson, 2007, pp. 340). 2.3.2 Diversity/ Land use mix Diversity of land use and its properties in the city provides a distinct role as an interaction and link between these uses and different sectors, and which achieves the mobility. Diversity “describes the heterogeneity of land uses in geographically defined areas. Measures of land use mix typically include residential, institutional, commercial, recreational, industrial as well as agricultural uses. The mixing of land uses has an effect on the physical separation of activities and is thus a determinant of travel demand” ECOTEC (1993, p. 33). A number of studies have suggested that the diversity of land uses is not as significant as density in impacting travel demand. However, the level of diversity could “contribute to travel demand especially through the decentralization of less specialized employment. The mixing of land uses is commonly measured using job ration, the ratio of jobs in the area to workers resident in that area” (Derrible, & Kennedy, 2009, pp.77). Ewing et al. (1996, pp. 98) have examined how different “land use mix characteristics affect the trip generation including the balance of homes and jobs”. Their study has shown that “there is no statistically considerable correlation between the balance of homes and jobs and journey frequency” (Ewing et al., 1996, pp. 98). 2.3.3 Design “Urban design is concerned with the layout as well as design and construction of buildings and the spaces between them” (Masucci, Stanilov, & Batty, 2013, pp. 125). The design and size of settlements have an influence on the variety of neighbourhood occupations along with services that may be sustained and affects the choice of community transportation services which can be made available. Therefore, minute settlements that are not capable of supporting a wider variety of services along with amenities may compel local inhabitants to go longer distances so as to reach the facilities and services they are missing in their locality. This may lead to congestion in the transport network available and hence trigger the demand for construction of more roads, thus impacting the environment. However, very huge and centralized residential areas might as well result to “longer travel distances as the distance between homes and the urban centre increases. Huge settlements with a very wide variety of jobs and services could as well attract people living far away to travel to them” (Masucci, Stanilov, & Batty, 2013, pp. 126). These factors may influence the travel patterns and trigger the need for constructing new transport networks and thus affecting the environment (Masucci, Stanilov, & Batty, 2013). 2.3.4 Destination accessibility The good accessibility to reach the destination of residents’ activities needs to be provided and it is very essential part of spatial planning as well as transport planning considering the operation of integrated transportation system on the area. It is the main factor –together with costs of travelling- that determines whether the inhabitants will use private car or public passenger transport. The impact of final transport coordination to the decisions of passengers has to be noted and considered while planning the integrated transport system (Parthasarathi, Hochmair, & Levinson, 2010). The function of integrated transport system is to offer the good accessibility which can be competitive with use of car. 2.4 Overview of previous research “Research projects have assessed several corresponding dimensions concerning firstly the connection between land use patterns in metropolitan areas at various spatial scales (neighbourhood, municipality, the whole metropolitan area), the transport infrastructures as well as the travel behaviour of residents; secondly the effects that transport (reciprocally land use) policies have on land used patterns (reciprocally travel behaviour), such as inhabitants location; and thirdly LUTI (Land Use Transport Interaction) has been a significant topic with the execution of a number of models (such as UrbanSim or MARS)” (Derrible, & Kennedy, 2009, pp.134). Various significant results have been shown. Global research has now evidently brought to light a number of the mutual connections linking land use, particularly metropolitan sprawl with polycentrism, as well as transport. However, land use patterns affect the requirement for transportation infrastructures as well as the mobility performance of the residents. For example, “the sprawl of metropolitan areas and the development of polycentric patterns have resulted to increased length of average trip distance” (Schwanen et al., 2004, pp. 590). “On the other hand, and reciprocally, transport infrastructures together with conditions (prices, accessibility) influence spatial patterns: for example, sprawl and the growth of employment sub-centres outside the central business district have been enabled by the acquirement of cars” (Schwanen et al., 2004, pp. 590). Several experimental studies have investigated the relationships between numerous scopes of land use, particularly density (Newmann and Kenworthy, 1989), diversity as well as design (Ewing and Cervero, 2010) in addition to the mobility activities of residents: standard length/ number/ length of every day trips, selection of modes, energy utilization as well as green house gas (GHG) emissions. Furthermore, the relationships between monocentrism, polycentrism as well as travel behaviour has been a significant research topic (Schwanen et al., 2004). While there is close relations linking transportation with land use, previous study in the field of LUTI has also shown that the links between transport and land use are hard to separate empirically for the reason that there is a massive amount of simultaneous changes of other factors, together with socioeconomic aspects (such as income) and attitudinal aspects (such as preferences for residential locations and travel behaviour). Research has as well stated that there is a need for “a better coordination between land use and transport policies to attain sustainable travel behaviour in metropolitan areas” (Cascetta, &Cantarella, 1993, pp. 77). Cervero and Kockelman (1997) suggested that the 3D of land use together with ease of access have considerable effects on the transportation use. Normally, the majority of study outcomes imply that use of community transportation increases with higher density, diversity, ease of access, along with more walking-supportive city plan. On the other hand, “both choice modelling and multivariate regression studies presume a homogenous connection linking demand and land use so the spatial reliance of the variables cannot be observed” (Cervero and Kockelman, 1997, pp. 89). Lastly, LUTI modelling has been a significant area of study both in Australia and internationally. The Oregon Department of Transportation’s “Transport and Land Use Model Integration Project” (TLUMIP, 1996) as well as the State of Utah’s “Quality Growth Enhancement Tools (QGET)” are some of the global hard work that paved the way for the progress of novel incorporated models that would assess the connections linking land use with transportation. UrbanSim was built up as an element of TLUMIP and comprised of a new city-scale land use model for incorporation with transport models. 2.4.1 Gravity-based integrated urban models Gravity-based integrated urban models start off from the Lowry model, which was developed for the city of Pittsburgh (Lowry, 1964). The most common and extensively used successor to Lowry’s model is integrated transportation-land use package (ITLUP). 2.4.1.1 Lowry Model The Lowry model approximates spatial distribution of household along with employment based on the theory of gravity. The original Newton’s law states that “any two bodies attract one another with a force that is proportional to the product of their masses and inversely proportional to the square of the distance between them”. By considering the theory of distance, the Lowry model presumes that the likelihood of making a trip is inversely proportional to the travel time (length of trip) between the starting point and ending point of the journey. It shows that the longer the time of travel between two places, the less likely an individual will make trip between them. In regard to this theory, “the probability for a worker to choose a residential location is inversely proportional to the travel time between the place of residence and the workstation”. 2.4.1.2 Integrated Transportation-Land Use package ITLUP is the mainly extensively used integrated urban model. It was designed “under contract with the U.S. Department of Transportation” (Putman, 1983). The model was developed to enhance long-range projecting results by ascertaining the connection linking transport planning with land use. ITLUP is comprised of two key model constituents: “a land use model and a transportation model. The land use model is composed of disaggregate residential allocation model (DRAM) and employment allocation model (EMPAL), which were developed by Putman and colleagues” (Putman, 1983, 1988, S.H. Associates, 2001). 2.4.1.2.1 The Land Use Model DRAM works on the logic that the likelihood for an employee to select an area as a residential area is proportional to the ease of access as well as prettiness of that area. The number of employees in area j who are willing to select a residential area i are determined by the ratio of prettiness and ease of access of area i to all other areas’ attractiveness and accessibility. EMPAL on the other hand works on the assumption that “the probability for a household to choose a zone in which to work is proportional to this zone’s attractiveness and accessibility”. 2.4.1.3 Input-Output-Based Model This model is based o the relationship among various economic sectors. The interaction or relationship linking these financial sectors is used to predict the distribution of urban activity, individual trips as well as flow of commodity. 2.4.1.3.1 Input-Output Framework This framework was introduced by Leontief (1967) and it formulates an urban system as a system of equation using various financial sectors. Household and employment are divided into various financial components based on industry and household income. 2.4.1.4 The MEPLAN Model The centre feature of this model is the relationship between diverse financial segments in a region. The model works on the assumption that the produce of each sector in a zone will be transported into all zones for consumption, thus generating financial interactions among various economic sectors in zones. 2.4.1.5 Discrete Response Simulation Model 2.4.1.5.1 Discrete Choice Model The model is used to assess the choice between mutually exclusive alternatives on the basis of attributes of these alternatives. It is used to predict which region among all regions in a city will be selected by a family or employer to live in. UrbanSim is the most popular urban model that is founded on discrete choice theory and was formulated under “the program of travel model improvement program (TMIP)” (Waddle, 2002). In UrbanSim, a city is subdivided into several grid cells in order for the families and employers to select their residents among these cells. 2.4.1.5.2 Bid-Rent Model This model is based on the concept that customers (families as well as developers) choose their residential area based on the lowest price, and land owners want to sell their land for maximum profit. 2.4.2 Spatial Analysis of Public Transport Accessibility (SNAPTA) SNAPTA was planned to assess the spatial ease of access as well as the communal impartiality of a city community transportation system. Good accessibility is viewed as a driver to financial development as well as competitiveness through “providing access to markets and enhancing the attractiveness of cities as focal business locations and tourism” (Scottish Executive, 2004, pp. 18). “SNAPTA is GIS based accessibility instrument which defines accessibility as whether or not people can get to services and activities at a reasonable cost, in reasonable time and with reasonable ease” (CEC, 2007, pp. 82). There are three indicators or measures of accessibility: Time access to city centre by public transport­: “from each area during the actual morning peak hour travel to the Central Business District (CBD)”. A contour measure: this indicator calculates the overall number of financial activities or destinations within highest voyage time by public transport for various trip reasons. A potential accessibility measure: “A gravity-based measure utilizing the morning peak hour travel time between data zones, weighted by the quantity of activity opportunities per zone”. Thus, the instrument “focuses on the land use and transport element of urban interactions as well as the availability of opportunities during the morning peak hour which can be accessed by public transport” (Gutiérrez et al., 1996, pp. 45). The three pointers have been utilized extensively and they depend on various methods to assess ease of access. The main distinction between them is that “the time access to city centre and contour indicators focus on the separation between location while the potential indicator focuses on the interaction between locations” (Gutiérrez et al., 1996, pp. 45). The device focuses on collections of persons, and presumes that these people have a set of communal as well as financial activity wants to be fulfilled at various “destinations, and that travel demand will be determined by the attractiveness of these locations along with the quality of the transport infrastructure linking these places” (Gutiérrez et al., 1996, pp. 45). The main disadvantage of SNAPTA is that zonal centroids are utilized and thus it presupposes that all people are assembled at the centroid and benefit from the equal level of accessibility. Another disadvantage is that the openings that are situated immediately adjacent to the modelled region even by only some seconds are ignored. However, the main benefit of SNAPTA is the capability to employ a set of accessibility pointers by means of tiny physical partitions, and with various choices of land-use as well as socio-demographic data. Moreover the limitation of one indicator can be easily dealt with by using another indicator in the package. 2.5 Limitations of existing models Integrated urban models have in the recent past been initiated and put into practice in a number of metropolitan areas as well as regions for diverse application purposes. Since these models target big urban regions, they lack the flexibility to be functional in small urban areas. The majority of integrated urban models employ the conventional four-step travel demand model as the transportation model. There are very few efforts that have been made to integrate the joint journey distribution-assignment transportation model and the land use model. Lastly, the current urban models demand a huge budget along with professional personnel to conduct the land use model. Small urban and regional areas do not have the ability to afford the budget as well as the professional crew required to develop these land use models. 2.6 Need for new research in the field of LUTI Until now, LUTI connections have not yet been understood well. Furthermore, essential society challenges such as climatic change, rising energy prices, as well as the need to minimize GHG emissions, environmental demands and societal disparities, along with “the development of new transport systems (cleaner and more integrated systems) and information and communication systems, demand for investigation into novel topics in the field of the multifaceted relationships between land use, transportation, energy, greenhouse gasses and more generally sustainable growth” (Batty, 2013, pp. 57). Moreover, the financial crisis necessitates the need to re-assess a number of the connections linking land use with transportation. For instance, the low earnings of workforce could have some bearing on the selection of habitation and hence the travel alternatives as well as patterns, whereas the cutback of community financial support or private venture for huge transportation road and rail networks possibly will affect the locality of residency as well as travelling. Thus there is need for research “to provide elements of decision for policy makers at the various spatial scales in the fields of land use and transport planning” (Sarkar, 2013, pp. 215). Currently, Australia and the world at large has witnessed rise of energy costs as well as the increase of ecological distresses, which may result to a alteration in location policies of both families along with financial performance, as well as resulting in modification of travel behaviour. Thus, this has the ability to alter the land use transportation relations and particularly the connections between density, design, diversity as well as transportation in addition to the connection linking monocentrism, polycentrism and transport. Therefore, there are several questions concerning the strategies (especially incorporated strategy packages) that planners need to build up in terms of spatial planning and in particular the regional or urban structure design (Chen, Claramunt, & Ray, 2014). There are also significant questions about “the strategies that policy makers need to develop in term of spatial planning and particularly regional or urban structure design according to the spatial scale: a neighbourhood (using this scale density, compacity seem significant) or the entire metropolitan area: for example, must polycentrism be promoted, and what type of polycentrism?” (Zhong, et al., 2014, pp. 2190). Finally, there is need for research in the area of LUTI modelling. The major concerns that relate to this topic incorporate the planning of models with: “satisfactory spatial resolution, modern activity-based modelling techniques, ability to link to advanced environmental sub-models, dynamic and more disaggregate, high degree of transparency, sufficient behavioural and empirical validity, easiness of use, high computational performance, flexibility to adapt to different user’s needs as well as sufficient data quality” (Waddell, 2011, pp. 220). References: Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1), 1-101. http://dx.doi.org/ 10.1016/j.physrep.2010.11.002 Batty, M. (2012). Building a science of cities. Cities, 29, Supplement 1, S9-S16. http://dx.doi.org/http://dx.doi.org/10.1016/j.cities.2011.11.008 Batty, M. (2013). The New Science of Cities. Cambridge, Massachusetts: The MIT Press. Bertolini, L., le Clercq, F., &Kapoen, L. (2005). Sustainable accessibility: a conceptual framework to integrate transport and land use plan-making. Two test-applications in the Netherlands and a reflection on the way forward. Transport Policy, 12(3), 207-220. http://dx.doi.org/http://dx.doi.org/10.1016/j.tranpol.2005.01.006 Boyce, D. E. and F. Southworth (1979), “Quasi-Dynamic Urban-Location Models with Endogenously Determined Travel Costs”. Environment and Planning A, Vol.11, p.575-584. Bramley. G., Dempsey, N., Power, S., Brown, C. and Watkins, D. (2009). ‘Social sustainability and urban form: evidence from five British cities’. Environment and Planning A 41 pp.2125-2142. Broitman, D., &Koomen, E. (2014). Regional diversity in residential development: a decade of urban and peri-urban housing dynamics in The Netherlands. Letters in Spatial and Resource Sciences, 1-17. http://dx.doi.org/10.1007/s12076-014-0134-y Cascetta, E., &Cantarella, G. E. (1993). Modelling dynamics in transportation networks: state of the art and future developments. Simulation practice and theory, 1(2), 65-91. Cervero, R., and Kockelman K. (1997), “Travel Demand and the 3ds: Density, Diversity, and Design”. Transportation Research Part D, Vol. 2, No. 3, 1997, pp. 199-219. Chen, S., Claramunt, C., & Ray, C. (2014). A spatio-temporal modelling approach for the study of the connectivity and accessibility of the Guangzhou metropolitan network. Journal of Transport Geography, 36, 12-23. City of Edinburgh Council (2007), ‘Local Transport Strategy 2007-2012’, the City of Edinburgh Council, Edinburgh. Derrible, S., & Kennedy, C. (2009). Network Analysis of World Subway Systems Using Updated Graph Theory. Transportation Research Record: Journal of the Transportation Research Board, 2112, 17-25. http://dx.doi.org/doi:10.3141/2112-03 Downs, A. (1992). “Stuck in Traffic: Coping with Peak-Hour Traffic Congestion”. The Brookings Institution and Cambridge, Washington, D. C. Ducruet, C., & Lugo, I. (2013). Structure and Dynamics of Transportation Networks: Models. The SAGE handbook of transport studies, 347. ECOTEC (1993), “Reducing transport emissions through land use planning”. HMSO, London. Ewing R., Pendall R., and Chen D. (2002), “Measuring sprawl and its impact, volume 1, technical report”, Smart Growth America, Washington, DC. Ewing, R. (1996), “Pedestrian- and Transit-Friendly Design”. Report prepared for the PublicTransit Office, Florida Department of Transportation. Ewing, R., & Cervero, R. (2010), “Travel and the built environment”. Journal of the American Planning Association, 76(3), 265-294. Ewing, R.; DeAnna, M. and Li, S-C. (1996), “Land use impacts on trip generation rates”. Transportation Research Record, Vol. 1518, pp. 1-6. Gutiérrez, J., González, R. and Gómez, G. (1996), ‘The European high-speed train network: predicted effects on accessibility patterns’, Journal of Transport Geography, vol.4, no.4, pp. 227-238. Handy, S. (1996), “Urban form and pedestrian choices: study of Austin neighbourhoods”. Transportation Research Record, Vol. 1552, pp. 135-144. Kirk, S. F. L., Penney, T. L., & McHugh, T. L. F. (2010), “Characterizing the obesogenic environment: the state of the evidence with directions for future research”. Obesity Reviews, 11(2), 109-117. Leontief, W. (1967), ‘Input-Output Economics’. Oxford University Press, New York. Lowry, I. S. (1964). ‘A Model of Metropolis, RM-4035, Rand Corp.’, Santa Monica, CA Masucci, A. P., Stanilov, K., & Batty, M. (2013). Limited urban growth: London’s street network dynamics since the 18th century. PLoS One, 8(8), e69469. Miller, E. J. (2004), ‘Land Use-Transportation Modeling (Draft, Chapter 5). Transportation Engineering Handbook: Planning Methods and Applications’. CRC Press LLC. FL. Miller, E. J., D. S. Kriger, and J. D. Hunt (1999), “Integrated Urban Models for Simulation of Transit and Land Use Policies: Guidelines for Implementation and Use”. TCRP report 48. TRB, National Research Council, Washington, D.C. Newman P. W. G. and Kenworthy J. R. (1989), “Cities and Automobile Dependence”. Gower Technical, Aldershot, Eng. Newman, M. (2010). Networks: an introduction: Oxford University Press. Parsons Brinckerhoff Quade & Douglas, Inc. (1998), “Land Use Impact of Transportation: A Guidebook”. NCHRP report 423A. TRB, National Research Council, Washington, D.C. Parthasarathi, P., Hochmair, H., & Levinson, D. M. (2010). Network structure and activity spaces. Available at SSRN 1736218. Putman, S. H. (1983), “Integrated Urban Models: Policy Analysis of Transportation and Land Use”. Pion, London. Britain. Putman, S. H. (1988), ‘Results from Implementation of Integrated Transportation and Land Use Models in Metropolitan Regions’. Network Infrastructure and the Urban Environment, Springer, New York. Putman, S. H. Associates, Inc. (2001), ‘Integrated Transportation and Land Use Forecasting: Sensitivity Tests of Alternative Model systems Configuration’. TMIP. Publication FHWA-EP-01-025. U.S. Department of Transportation. Sarkar, S. (2013). Street network analysis for understanding typology in cities: case study on Sydney CBD and suburbs. Paper presented at the State of Australian Cities (SOAC) Conference. Sydney, Australia Schwanen, T., Dijst, M., & Dieleman, F. M. (2004), “Policies for urban form and their impact on travel: the Netherlands experience”. Urban studies, 41(3), 579-603. Scottish Executive (2006), ‘Scotland’s Transport Strategy, Scottish Executive’, Edinburgh. Waddle, P. (2002). UrbanSim: Modeling Urban Development for Land Use. Journal of the American Planning Association, Vol68, p.297-314. Waddell P. (2011) “Integrated Land Use and Transportation Planning and Modelling: Addressing Challenges in Research and Practice”, Transport Reviews, 31: 2, pp. 209-229. Xie, F., & Levinson, D. (2007). Measuring the Structure of Road Networks. Geographical Analysis, 39(3), 336-356. http://dx.doi.org/10.1111/j.1538-4632.2007.00707.x Zhong, C., Arisona, S. M., Huang, X., Batty, M., & Schmitt, G. (2014). Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science, 28(11), 2178-2199. http://dx.doi.org/10.1080/13658816.2014.914521 Read More