1 TRAINS, TRAMS, AND TERRACES: POPULATION GROWTH AND NETWORK 2 EXPANSION IN SYDNEY: 1861-1931 3 4 5 6 Bahman Lahoorpoor 7 [email protected] 8 9 David M. Levinson, Professor of Transport 10 School of Civil Engineering, 11 The University of Sydney 12 [email protected] 13 14 15 Word Count: 4048 words + 4 table(s) × 250 = 5048 words 16 17 18 19 20 21 22 Submission Date: August 1, 2019 Lahoorpoor and Levinson 2

1 ABSTRACT 2 This paper examines the changes that occurred in the tram and train networks and density of popu- 3 lation in Sydney between the early 1860s and 1930s when both trains and trams were developing. A 4 set of statistical analysis has been conducted using panel data representing 593 districts of Greater 5 Sydney at suburb (neighborhood) level over each decade from 1861 to 1931. We find that trams 6 and population density are positively associated in a positive feedback process, tram deployment 7 leads population growth and population growth leads tram deployment, both satisfying a Granger 8 causality test. 9 10 Keywords: transport, land use, railways, co-development, network growth, panel data, cross- 11 section time series Lahoorpoor and Levinson 3

1 INTRODUCTION 2 Land use and transport have a long and complex history of mutual influence. The invention of 3 steam engine locomotives, lead to the opening of the first railway in 1825 (1). After the onset 4 of the Industrial Era, due to the location of job opportunities (mostly in factories) in urban areas 5 and transporting agricultural goods from rural regions to the urban core, rural populations began 6 to migrate to cities, causing large scale urbanization and urban expansion (2). As cities began to 7 grow and walking distances increased, a new mode of transit was required to move the labor force 8 across urban areas. 9 The first widespread urban public transport dates back in the early 19th century with the de- 10 ployment of the horse-drawn omnibus in many cities. With the deployment of in-street rails, these 11 vehicles were transformed into horse-drawn tramways (streetcars) soon after, and then steam trams 12 appeared in the 1880s. The first public passenger railway opened in 1825 connecting Stockton and 13 Darlington (England) and the world’s first inter-city railway services were opened in 1863 as the 14 "London Underground" (3). Since then, railway transport systems have taken many alternative 15 forms such as train, tram, metro, light rail, monorail, and high-speed rail systems. 16 With advances in infrastructure and transport technologies, commuters could now travel 17 further distances by public transit (and soon by private vehicles) and city centers could draw on 18 a larger labor pool (4). Therefore, with the emergence of the railways and public transport (the 19 period between 1860s-1930s), the population dispersed unevenly across suburban neighborhoods 20 (5, 6). (The term ‘suburb’ is used in the Australian sense of a subunit (neighborhood) in a larger 21 metropolitan area). 22 The railway enables mass movement, and as such, it enables higher densities for certain 23 activities in some places. By increasing the ability of firms (and jobs) to cluster in the urban 24 core, rail networks are simultaneously creating a push factor decreasing housing densities in those 25 places by making housing in the core more expensive and and a pull factor making housing outside 26 the core have greater accessibility. The theory of transport-land use interaction is that this joint 27 co-development process of infrastructure and land development location is a positive feedback 28 cycle: transport infrastructure produces accessibility that induces land development which induces 29 transport demand and increases accessibility increasing the production of transport networks (i.e. 30 inducing supply) and further intensifying land development. (7–12). 31 This paper aims to test that theory and disentangle the questions of induced development 32 and induced supply. It asks if the development of a suburb supports the construction of a new 33 railway station or does the railway network act as a centralized (or decentralized) force to the 34 population growth? It is widely believed that high population density is an important factor in the 35 success of rail systems (density represents potential ridership). However, just because rail depends 36 on high population density for success does not necessarily mean that either high density areas 37 generate rail investment or rail creates high-density areas around stations (7). 38 To answer this question, Greater Sydney is selected as a case study. The remainder of this 39 paper proceeds as follows. Section 3 provides a brief literature review on the co-deployment of 40 railway and the population density. In Section 4, the theory and hypotheses are discussed. In 41 Section 5, the case study and the associated data are explained. In Section 6, the results of the 42 statistical experiments are provided. Finally, the concluding remarks are presented in Section 7. Lahoorpoor and Levinson 4

1 LITERATURE REVIEW 2 Network characterization studies 3 The properties of transit networks have long been the subject of interest of researchers and plan- 4 ners. In the past few years, a large variety of metrics to quantify transit networks such as the 5 heterogeneity, connection patterns, coverage, clustering, accessibility, discontinuity, connectiv- 6 ity and interconnectivity have been used Xie and Levinson, Derrible and Kennedy, Ducruet and 7 Lugo, Tian et al. (13–16). These metrics are based on principles adopted from graph theory and 8 spatial analysis techniques with which networks are transformed into graphs composed of edges 9 (links) and vertices (nodes) Newman, Lin and Ban (17, 18). Properties of network structure show 10 the quality of a network design and transport planning Xie and Levinson (13). In general, transit 11 networks layouts, including almost all road and rail systems, can be classified into following basic 12 types: radial, diametrical, tangential, circumferential, trunk with branches, trunk with feeder, and 13 loops Teodorovic and Janic (19). The LRT and railways networks of Sydney are a good illustration 14 of combination of these kinds of layouts. 15 These efforts have been limited: first, many studies review idealised road networks evo- 16 lution based on simple demand-supply interaction with static land use; second, a single abstract 17 mode of transit systems with infinite link capacity is considered; third, little empirical evidence 18 has been provided for validating the existing models.

19 Empirical network-land use coevolution studies 20 A related set of empirical studies investigate the effect of transport networks on land development 21 and population changes over real cases. 22 For instance,(20) showed that annual population growth in US counties increased 0.4% in 23 the 1850s due to the presence of one or more railway lines in a county. Beeson et al. (21) studied the 24 same trend for Midwest counties in the US for 150 years (during 1840-1990) and found 0.12% as 25 the annual population growth. In a similar study, Schwartz et al. (22) applied a weighted regression 26 analysis to show a high rail density lessened population decline in rural areas during the 1870s. 27 Felis-Rota et al. (23) studied the evolution of railway network alongside population growth 28 for England and Wales, and Darroch (3) descriptively studied the interrelationship between the 29 railway and land use in the central zone of London using Underground records and specialist 30 knowledge. For the case of England and Wales, Alvarez et al. (5) investigate the evolution of 31 railway coverage and its impact on population growth. Results indicate that a parish with a low 32 level of railway coverage tended to lose population while those with good coverage tended to gain 33 population. 34 Koopmans et al. (24) found the same result for municipal population. They studied the 35 relation between the transport network and population growth for the case of the Netherlands and 36 investigated the effect of accessibility improvements on the urban population growth. A multi- 37 modal transport network between 1840 and 1930 was digitized, and the accessibility measured 38 by the travel time between all municipalities. They demonstrated that the municipal population 39 growth is positively influenced by improved accessibility due to railway connections. 40 A fraction of the literature used the Granger causality to discover the relation between land 41 use and transport development. Levinson (7) analyzed the co-development between population 42 density and railway density for 33 boroughs of London. Railways acted as a decentralizing force 43 to depopulate the core, while dispersing population to the peripheries. Streetcar (tram) technology 44 led the process of suburbanization in US cities (25, 26). King (9) used the Granger causality Lahoorpoor and Levinson 5

1 models upon parcel-level data to explore the co-development of the railway and residential and 2 commercial land uses. He tested two alternative hypotheses; whether New York subway (Metro) 3 stations were a leading indicator of residential and commercial development or subway station 4 expansion followed residential and commercial construction. Results indicate that the subway 5 developed in an orderly fashion and grew densest in areas where there was growth in commercial 6 development. No correlation was found between residential and network development in New 7 York City. 8 The other side of the transit-land use interaction is also studied in the literature. Jiang 9 and Levinson (27) explores the network investment of Beijing subway. The locational and person- 10 weighted accessibility of public transit is measured from 1999 to 2014 in Beijing. Results reinforce 11 evidence of a positive feedback loop showing that more population attracts more transport invest- 12 ment, which increases accessibility, and attracts more population. 13 Cats (28) conducts a longitudinal analysis of the topological evolution of a rail network by 14 reviewing the dynamics of the network topology for the case of Stockholm, Sweden. Based on 15 the review, three distinctive periods (contraction, stagnation, and growth) in the evolution of the 16 network are recognized. Also, the evolution and relationship between different network indicators 17 have been investigated. The analysis reveals that network evolution can be characterised by abrupt 18 or incremental changes by investment/disinvestment process. The results show that in a network, 19 the exploration phase will extend the network coverage and regional accessibility, while the densi- 20 fication phase increases network efficiency and contributes to network robustness (e.g. scale-free 21 network). 22 Other research investigate the interaction through economy. Barthélemy and Flammini 23 (29) investigates city formation and analyzes how road network evolves and interacts with the 24 evolution of population density by considering the rent price and transport costs. Their ‘out-of- 25 equilibrium’ simulation process enables the city to evolve in time to adapt to continuously changing 26 circumstances. The self-organized pattern of streets emerges as a consequence of the interplay of 27 the geometrical disorder and the local rules of optimally. Results indicate that when transport costs 28 are moderate, the density is almost uniform and the road network is a typical planar network. If 29 transport costs get higher, a densely populated area will be in the core around which the density 30 decays exponentially. 31 As it emerges from literature, there some historical research on the consequences of trans- 32 port improvements on the population density in an urban area. However, there is no generalizable 33 rule for the outcomes. The results differ case by case. Moreover, dealing with the spatial data, the 34 research remains at the aggregate level. The reason is in part due to ease of analysis and in part 35 due to the lack of available historical data at finer gradations of details. To bridge this knowledge 36 gap, the paper aims to reveal the relationship between the population and the railway network over 37 a new case study with a different context of infrastructure and development in the world.

38 THEORY AND HYPOTHESIS 39 The interaction between land use and transport network has been discussed in the previous sections. 40 The co-development (co-deployment to be precise) theory being tested in this study is whether land 41 use development leads to the future development of transit network or in converse, the expansion 42 of the transit network drives land use. When and how much the development of one leads to more 43 development of the other is still a question. 44 This has been brought to light in the literature. In the core of London, existing development Lahoorpoor and Levinson 6

1 lead to the improvement of railway system which in turn enhance the commercial development 2 which leads to more rail investment. However, in the periphery of London, the transit network 3 increase the population density which attracts more investments on the network and essentially 4 leads to more land development (7). 5 Other cases are also possible. One expects that rail promotes suburbanization, shifting the 6 population from the core of a city to the periphery where the price of land is significantly lower than 7 the core, since the value of land in the core appreciated for commercial activities benefitting from 8 agglomeration economies, while decreasing for residential activities as the periphery supplies more 9 land for development within an accessibility travel time threshold. This will drop the residential 10 density significantly for the core while increasing it in the periphery. 11 In order to investigate the effect of railway on land use and how land use changes a railway 12 network (both tram and trains) the following hypothesis are tested based on the available historical 13 data: 14 1. Population density is positively associated with the lagged increase in density of 15 (a) tram stops 16 (b) train stations 17 2. Tram stop density is positively associated with the lagged increase in density of 18 (a) population density 19 (b) train stations 20 3. Train station density is positively associated with the lagged increase in density of 21 (a) population density 22 (b) tram stops 23 Three models are defined to predict residential density, tram stops density, and train sta- 24 tions density, respectively. As the density changes slowly, a one period (10 years) lag structure 25 is considered to conduct the causality test. The result will be used to test the enumerated hy- 26 potheses of the hypothesized mutual relationship between railway and land use development. A 27 cross-sectional database has been generated at the suburbs geographical level to estimate the three 28 independent models. Equation 1 predicts the population density based on the lagged population 29 and changes in the network density. Equation 2 and 3 predict the density of tram stops and train 30 stations, respectively.

Pi,t = α0 + α1Pi,t−1 + α2∆Ti,t,t−1 + α3∆Ri,t,t−1 (1)

Ti,t = α0 + α1Ti,t−1 + α2∆Pi,t,t−1 + α3∆Ri,t,t−1 (2)

Ri,t = α0 + α1Ri,t−1 + α2∆Pi,t,t−1 + α3∆Ti,t,t−1 (3) 31 where: 32 Pi,t denotes the population density of district (i) at time t; 33 Pi,t−1 is the lagged population density in the previous time of district i; 34 ∆Pi,t,t−1 is the change in the density of population on region i for one lag period (between t and 35 t − 1); 36 Ri,t indicates the density of train stations in region (i) at time t; 37 Ri,t−1 is the lagged train density in the previous time of district i; 38 ∆Ri,t,t−1 is the change in the density of train stations on region i for one lag period (between t and 39 t − 1). Lahoorpoor and Levinson 7

1 Ti,t represents the density of tram stops in region (i) at time t; 2 Ti,t−1 is the lagged tram density in the previous time of district i; 3 ∆Ti,t,t−1 is the change in the density of tram stops on region i for one lag period (between t and 4 t − 1); 5 6 All three models are estimated using the ‘xtpcse’ procedure in StataSE 14.0, which cal- 7 culates panel-corrected standard error (PCSE) regression estimates for linear cross-sectional time- 8 series models.

9 DATA 10 Sydney, as a rapidly developing city, had public railway transport services beginning in the 1850s, 11 which facilitated and responded to the development of suburbs. The advent of first steam railways 12 occurred in 1855 which formed the basis of the New South Wales Government Railways. The first 13 line was opened for passenger and freight trains between Sydney and Granville (near Parramatta, 14 Sydney’s second historical core and the colony of New South Wales’s first seat of government), 15 which at the time was a center of agriculture. Railways were soon complemented by an exten- 16 sive tram system, but the transit system was disrupted in the twentieth century by the rise of the 17 automobile. 18 As the aim of this study, only the development stage of the railway network is considered. 19 To test the aforementioned hypotheses, the railway network including tram and trains, and the 20 historical population data are essential. The two following subsections describe the 7 decades 21 (between 1861 and 1931) data being used in this study.

22 Network data 23 The historical railway systems in Sydney included the train network and the tram network. Al- 24 though, the technology of both system had evolved over time, only the presence of the network 25 is considered in this study. Therefore, all the stations, stops, and track lines were digitized and 26 geo-coded in QGIS (an open-source GIS platform) with the opening and closure dates. Then, 27 this geographical information is summarized on the currently defined state suburbs of New South 28 Wales, Australia. Figure 4 demonstrates the evolution of Sydney railway network during the time. 29 The evolution of Sydney railway network is also tracked by some network measurements. 30 Both the tram and trains network were converted to a graph network containing nodes represent- 31 ing stops and links which are the rail segments between two successive stops. The number of 32 stops/stations, the number of links, and the total network length are illustrated in Figure 1 and 33 Figure 2 for tram and train network, respectively. 34 As shown in the figures, both networks evolved during the study period. However, the 35 speed of changes on the tram network was faster. The tram network experienced a long birth stage 36 (from 1861 to 1879) and grew rapidly until the 1930s with two short stagnation periods in 1898 37 and 1905. The network declined gradually from 1930s with a rapid closure in the last 5 years of 38 its life cycle. 39 Unlike the tram system, the train network followed a different pattern. The network de- 40 veloped over a longer time and experienced a relatively slow development. Train system starts off 41 with a rapid expansion in terms of network coverage (length), followed by construction of infill 42 stations for three decades between 1855 to 1885. Three major network expansions and two fairly 43 long stagnation stages are observable since 1885. It is worth noting that minor closures occurred Results

SydneyLahoorpoor Trams and Levinson Network Evolution 8

Network Length Links Nodes 300 1200

250 1000

200 800

150 600 Nodes/Links

100 400 Total NetworkTotal Length (Km)

50 200

0 0 1911 1871 1881 1921 1861 1891 1931 1951 1901 1916 1961 1941 1876 1926 1866 1886 1896 1936 1906 1956 1946 Year

FIGURE 1: Sydney tram network evolution

1 in the network life cycle. The network are still expanding as new Metro and light rail lines are now 2 under construction. 3 It is also worth noting that while trams were primarily serving passenger movements (with 4 limited freight traffic), the railways were much more balanced between passenger and freight ser- 5 vices. The railways also served both local and intercity travel, while trams were almost entirely 6 urban in orientation.

7 Population Data 8 Alongside the network data, the population data between 1851 and 1931 are required to estimate 9 the co-development model. The historical census data were available at large-scale boundaries 10 (usually at local government boundaries). Moreover, the jurisdiction boundaries were not con- 11 sistent during time and changed many times. In order to test the discussed hypotheses using the 12 proposed methods, boundaries must be rectified to be consistent across the study period. 13 To address this challenge, first, all the historical census data were digitized within the origi- 14 nal published boundaries for the census date. Second, to redistribute the population, the population 15 is uniformly re-distributed across a finer mesh of blocks over the state suburbs boundaries propor- 16 tional to the number of dwelling in each block for each suburb by 2016. 17 The reason is twofold. Firstly, the number of dwellings is more correlated to the population 18 distribution rather than the area of each boundary. Secondly, most of the dwellings remain in place 19 since they were built. Results

SydneyLahoorpoor Trains and LevinsonNetwork Evolution 9

Network Length Nodes Links 450.0 300

400.0 250 350.0

300.0 200

250.0 150 200.0 Nodes/Links

150.0 100 Total NetworkTotal Length (Km) 100.0 50 50.0

0.0 0 1915 1910 1875 1885 1925 2015 1855 1865 1870 1880 1895 1920 1935 1955 1970 1975 1985 2010 1860 1890 1900 1905 1930 1950 1960 1965 1980 1990 1995 1940 1945 2020 2000 2005 Year

FIGURE 2: Sydney train network evolution

1 After the population distribution, the population density of each suburb is organized as 2 panel data by census decade. It is worth mentioning that population is interpolated where necessary 3 to account for missing data. Figure 5 illustrates the population density of NSW state suburbs for 4 the period between 1861 and 1921. In this study, 593 suburbs which are within the Greater Sydney 5 boundary are considered. 6 As mentioned earlier, the census boundaries were not constant historically, and many re- 7 gions were undeveloped rural or bush areas prior to the dispersion of the population. There is 8 no specific data about when exactly a region (or suburb) got developed and effectively became 9 part of greater Sydney. However, the population for Sydney and surrounding local government 10 areas are recorded (31). Figure 3 displays the evolution of developed area and population growth 11 historically.

12 RESULTS 13 Results from the regression models (Equation 1 to Equation 3) are presented in this section. All 14 three models are summarized in the following tables: Table 1 shows the model prediction for 15 population density, Table 2 illustrates the results for tram stops density, and finally Table 3 presents 16 the model for train stations density at the suburb level of the Greater Sydney. The following 17 subsections are identified by the hypotheses these models test. Lahoorpoor and Levinson 10

4,000,000 14000 Sydney Population Sydney Area 3,500,000 12000

3,000,000 10000

2,500,000 8000 2,000,000

Population 6000

1,500,000 KilometersSquare

4000 1,000,000

500,000 2000

- 0 1881 1891 1901 1911 1921 1933 1947 1954 1966 1976 1986 1999 Year

FIGURE 3: Sydney land and population growth. Source: Emerson (30)

1 Predicting population density 2 The result for population density is shown in Table 1 for all 593 suburbs in Sydney. Not surpris- 3 ingly, overall population density of region i at time t is positively and significantly correlated with 4 the lagged (10 years) population density of that region. Also, the population density is positively 5 associated with the lagged increase in the tram stops density. This finding corroborates our first 6 expectation (H:1(a)) that a new tram stops drives an increase in the population density. In contrast, 7 the lagged change in the train station density is not a significant predictor of population density, 8 refuting the second hypothesis (H:1(b)).

TABLE 1: Predicting population density

Explanatory variables Coeff. Std. Err. P>|z|

Lagged population density (Pi,t−1)(L10) 1.00E+00 5.48E-02 0.000 Change in tram stops density (∆Ti,t,t−1) (L10) 66.69E+00 24.21E+00 0.006 Change in train stations density (∆Ri,t,t−1) (L10) 23.46E+00 119.74E+00 0.845 Constant 150.90E+00 49.17E+00 0.002 Number of observations: 4,774 ; Number of groups: 593 ; Observations per group: 8 ; R2: 0.874 ; Dependent variable: Pi,t Lahoorpoor and Levinson 11

1 Predicting tram stops density 2 Table 2 presents the result for tram stops density. As expected, the dependant variable is highly 3 correlated with the lagged tram stops density. Furthermore, both changes in lagged population 4 changes and lagged change in the train stations density are positive in predicting the tram stops 5 density, although the change in population density is less significant. This supports both hypotheses 6 (H:2(a) and H:2(b)) that the tram network density is positively associated with the lagged increase 7 in density of population and train network. TABLE 2: Predicting tram stops density

Explanatory variables Coeff. Std. Err. P>|z|

Lagged tram stops density (Ti,t−1)(L10) 1.02E+00 1.61E-1 0.000 Change in population density (∆Pi,t,t−1) (L10) 2.82E-04 1.06E-04 0.008 Change in train stations density (∆Ri,t,t−1) (L10) 8.07E-01 2.83E-01 0.004 Constant 2.32E-01 1.13E-01 0.039 Number of observations: 4,774 ; Number of groups: 593 ; Observations per group: 8 ; R2: 0.612 ; Dependent variable: Ti,t

8 Predicting train stations density 9 Table 3 shows the result for train stations density model. The train stations density is positively 10 correlated with the lagged change in tram network, corroborating H:3(b). However, the lagged 11 changes in the population density is not able to justify the changes in the train network signifi- 12 cantly (P=0.715), refuting H:3(a). This finding negates the hypothesis that train network density is 13 positively associated with changes in population density. TABLE 3: Predicting train stations density

Explanatory variables Coeff. Std. Err. P>|z|

Lagged train stations density (Ri,t−1)(L10) 9.79E-01 9.44E-02 0.000 Change in population density (∆Pi,t,t−1) (L10) 1.23E-06 3.37E-06 0.715 Change in tram stops density (∆Ti,t,t−1) (L10) 6.88E-03 2.37E-03 0.004 Constant 1.76E-02 5.29E-03 0.001 Number of observations: 4,774 ; Number of groups: 593 ; Observations per group: 8 ; R2: 0.743 ; Dependent variable: Ri,t

14 Elasticities 15 The result of regression models can be evaluated with the elasticity index. Elasticity of one variable 16 shows the magnitude of change in the dependent variable by changing one unit (usually percent) 17 of that variable. To put in another word, the elasticity is the proportional change ratio of the 18 dependent variable over the change ratio of the intended factor. Elasticities values of each variable 19 are calculated and compared with the previous results in the literature. 20 Change in tram density increases the population and train density (supported by the regres- 21 sion models). A one percent change in tram density is associated with a later increase in population Lahoorpoor and Levinson 12

1 and train density of 0.02%, and 0.03%, respectively. Moreover, change in population density has 2 a significantly higher effect on tram density rather than train stations, which is to say 1% increase 3 in population density leads to 0.05% growth in the tram. Also, the result shows low elasticity of 4 change in train density over the population and vice versa, which reinforce the result that train 5 network and land development are independent of each other.

TABLE 4: Comparing the elasticities of different studies

Elasticities Sydney London (7) New York (9) Change in tram density on population density 0.0261 - - Change in train density on population density 0.0005 0.0023 - Change in population density on tram density 0.0557 - - Change in train density on tram density 0.0172 - - Change in population density on train density 0.0030 0.0023 0.0004 Change in tram density on train density 0.0300 - -

6 CONCLUSION 7 This article examined the population growth and rail network expansion in Sydney between the 8 years 1861 to 1931. Historical census and the railway network data were generated in GIS formats 9 and linear cross-sectional time-series models were estimated at the suburb level. The evolution of 10 railway network and the development are studied. The land development and the railway networks 11 have co-developed over the long period. This article found that the tram network was a predecessor 12 to the population growth and that increase in the population density drove the tram network in turn. 13 The role of the train network was different since it was initially developed for the intercity travel 14 and freight movements, and its deployment appears largely independent of population change in 15 the study period. Today’s conditions are different, and clearly population in greater Sydney is far 16 more concentrated around train stations than elsewhere, and new growth is more likely to occur in 17 train station catchment areas (32). Future research should examine this question more recently. 18 Results from the three models suggest that the expansion of the tram network led residential 19 construction (increasing population density), and profoundly shaped the Sydney landscape. The 20 result also support the hypothesis that the tram network is expanded in response to the increased 21 demand and where the train network acted as a complimentary mode. This result satisfy the the 22 Granger causality analysis showing the causation effects between transport and land use. The 23 Granger causality analysis assists to understand some real causality and to analyze the significance 24 of causation effects in the context of statistical regression (25). 25 It is important to note that, unlike the initial expectations, changes in residential density 26 could not predict the train network. The reason might be threefold. First, the train network did 27 not provide frequent services, and consequently, the accessibility provided by trains was highly 28 intermittent and insufficient to drive suburbanization. Second, the design of the train network was 29 a top-down decision-making process, and other factors were involved at the design stage, in par- 30 ticular serving freight and inter-city passenger markets rather than commuters. Third, the Sydney 31 train network partially had a small role in public transport since most of Sydney’s population was 32 well served by trams. This trend changed by opening the Harbour Bridge (1932) and opening the 33 City Circle to train system. Lahoorpoor and Levinson 13

1 There are some research suggestions that need to be discussed. First, in this study, the 2 prediction models were developed with only two regressors due to the lack of data. Other ex- 3 planatory variable such as accessibility would increase the performing of the regression models. 4 Accessibility reveals the effect of tram and train services on the distribution of population, and 5 that may justify why the population density is not associated with the train network growth during 6 the period of analysis. Second, the prediction models were developed for all suburbs. The spatial 7 stratification of suburbs may improve the developed model. 8 Future research should consider other factors involved in the co-development of land use 9 and transit network. Considering other modes of transport such as bus services and private vehicles 10 would also be beneficial. Finally, in this study, the number of train stations and tram stops in a 11 suburb were considered as the tram and train network in that suburb. More sophisticated indices, 12 such as the number of stations/stops reachable in the 15-minute walk from the suburb’s centroid 13 (32) should increase the accuracy of the prediction. Lahoorpoor and Levinson 14

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FIGURE 4: Evolution of Sydney railway network: tram and train Lahoorpoor and Levinson 17

FIGURE 5: Evolution of population density in Sydney