Can a polycentric structure affect travel behaviour? A comparison of Melbourne, Australia and Riyadh, Saudi Arabia

In cities of Australia and New Zealand, residential dispersion has occurred but this has not led to a similar form of employment dispersion. This is partly due to the development of white-collar office jobs, as seen in the Melbourne CBD, where between 1996 and 2006 100,000 new white-collar jobs were created (Mees et al. [10]).

The overcentralisation of office employment in Australian CBD areas has been linked to significant levels of long travel distance and commuting time, traffic congestion, poor balance between employment–housing structures, significant levels of subsidy for public transport and excessive costs for office leases. Urban traffic congestion has grown significantly, and is recognised as a key policy area and optimal growth and decentralisation policies have been proposed as a response to these issues [2].

Review of research into employment decentralisation policies and effect on transport has been developed [2]. Transport outcomes are different in different cities, with decentralisation to rail-based localities in Japan and Singapore being extremely successful in decreasing traffic congestion and increasing the performance of transit systems. Additionally, decentralisation in London over the 1960s–1970s has also decreased traveller flows into the CBD.

In New South Wales (NSW), the government has promoted a secondary centre in Parramatta. Parramatta started with 10,000 jobs in 1971 and reached 40,000 jobs by 2005, and Parramatta contributed to reduction in travel time. In a recent master plan, the NSW government (City of Cities) proposed to strengthen two new regional subcentres which are Liverpool and Penrith [2].

However, in San Francisco, employment decentralisation has had little impact on reduction of mean distances or travel times for commuters, with a lack of development of self-containment. Within Stockholm, the development of urban fringe residential areas for workers has led to increased mean commuting distances, and ultimately led to higher staff turnover [2].

While there appears to be positive outcomes to decentralisation, there have been negative outcomes as well. Little is known about how long it takes workers and households to transition to new spatial arrangements after a workplace relocation, say, by moving house, changing to a job located elsewhere or changing schools for children. There is a risk that decentralisation can lead to higher proportion of suburb-to-suburb commutes which may entail increases in car use as more workers travel farther [9].

The polycentric urban structure policy of Melbourne and Riyadh need to be better understood based on the specific context of these two cities. Most of the studies conducted for Melbourne and Riyadh have been qualitative rather than quantitative studies. In Melbourne, the Department of Planning and Community Development [11] mentioned that shifting from one main centre to more alternative centres has a similar CBD area function, which will reduce congestion and allow residents to spend less time travelling to and from work. Meanwhile, in Riyadh, the Arriyadh Development Authority (ADA) [8] mentioned that ‘sub-centres will create the opportunity for a balance between work and residence in the new growth sectors of city suburbs. The sub-centres will have significant effects to the pattern of the daily trips between the suburbs and the city centre by creating new workplaces. This also will reduce the total of number daily trip, because may residents will be living and working in the same section of the city and therefore the movement will be directed away from the city centre area, instead to the current pattern of going to the city centre. There has been no clear view about how this shift’s dynamic process works.

This study aims to contribute to the better understanding of the impact of polycentric structure. A model analysis was conducted to quantify the impact of the shift from monocentric structure to polycentric structure in Melbourne and Riyadh. The analysis focusses on the impact of variants in activity and residential redistribution as shown in Fig. 1.

One of the major tasks in this study was to collect data used to develop a land use/transport model. Data collection was divided into two groups: land use and demographic information, and transport data. In Riyadh, the demographic and population data were collected by the Municipality of Riyadh (MOR) in 2008; however, the land use data were conducted by the ADA in 2002.

In Melbourne, the demographic and population data were collected by the Department of Transport of Victoria in 2008; however, the land use data were collected by the Local Municipalities Councils (2009).

The explanatory variables applied to the model are based on data availability. The variables were formed in relation to the socioeconomic characteristics of households, demographic data, land use and urban form. The data for Riyadh were sourced from the 2008 survey dataset, created by the MOR; and for Melbourne the data were sourced from the Department of Transport (DOT), Melbourne, Victoria. The data were within the city’s Traffic Analysis Zones (TAZs), with Riyadh featuring 2,166 TAZs and Melbourne having 2,253 TAZs.

Riyadh’s trip data were obtained from the 2008 MOR report, while for Melbourne they were sourced from the 2008 DOT data. The data focused on trip purposes, including home-based work (HBW); home-based education including primary, secondary and high school; home-based recreation; home-based shopping; home-based other; and non-home based.

The origin and destination (OD) trip data for Riyadh were also sourced from MOR (2008) and for Melbourne they were similarly sourced from DOT (2008). Trip data are available for the AM peak period (2 h) in Riyadh, this period was measured across 2 h in Melbourne, and the OD trip matrix was organised by car, public transport and walking modes. Trip distance is calculated as the shortest distance between two centroid points which were sourced by MOR and DOT in 2008 in Riyadh and Melbourne, respectively.

At present, Riyadh has no tram and train service but it has been designed to be used in the upcoming years. Public transport data have been collected from MOR (2008). Also, the data of walk trip distance were extracted from Koushki’s study (1989) and used to predict the walk trip mode share in Riyadh.

Socioeconomic data for Melbourne were applied at the TAZ level and used as an independent variable of the models in this study, and the socioeconomic variables included the number of employed residents, the number of households, age group (0–17, 18–64, and 65+), the number of jobs and the number of students.

Finally, land use data were also applied as an independent variable, with independent variables organised into two categories. Firstly, socioeconomic variables and land use variables were broken into building gross floor area (GFA), and secondly the number of buildings and activities in each zone was measured. The GFA for Riyadh was sourced from the ADA (2002), and for Melbourne it was sourced from the Local Municipalities Councils (2009). The number of buildings and activities in each zone measured the amount of retail, shopping areas and universities, with the last measure providing strong analysis for trip attraction in Riyadh. Land use data for Melbourne were not readily available for this study and were not used.

This section describes the land use model and transport model used in the analysis.

Table 1 shows the comparative analysis of trip distance between scenarios by HBW and NHBW purposes in both Melbourne and Riyadh.

This section will display the trip time based on the different scenarios in both Riyadh and Melbourne for HBW and NHBW trip purpose.