Project Description
Mapping the Past, Forecasting the Future
TransitPulse Explorer
Problem Statement:
As outer suburbs in Victoria continue to experience rapid population and housing growth, one of the most pressing challenges facing by the Victorian Government is ensuring equitable access to public transportation services, including buses and trains. The Victorian Government’s Growing Suburbs Fund (GSF) is a targeted initiative aimed at addressing the high-priority local infrastructure needs of rapidly expanding outer suburbs. This fund seeks to increase social and economic participation, meet health and wellbeing needs, and enhance local economic activity through strategic investments in infrastructure.
Public transportation, including both train and bus services, is a critical component of the infrastructure, as it plays a crucial role in supporting commuting, connectivity, and overall community engagement. While the GSF aims to address a range of infrastructure needs, the issue of inadequate public transportation services remains a significant concern that cannot be overlooked. Former Public Transport Users Association president Daniel Bowen has pointed out that many rapidly growing suburbs, including Wyndham Vale, Tarneit, Cranbourne, and Clyde, face challenges with insufficient local bus routes. Similar issues are evident with train services in densely populated suburbs, such as Tarneit and Criaigeburn, which have seen substantial influxes of new migrants. This indicates a gap that needs to be addressed even as broader infrastructure improvements are underway.
In response to this challenge, it is essential to first understand the current state of public transportation by conducting a thorough historical analysis and developing forecasts for the future. This approach requires data-driven insights to identify key trends and pinpoint where gaps in bus and train services are most pronounced. Only with a clear, evidence-based understanding of how transportation services have evolved and where demand is headed can we ensure that future infrastructure investments are strategically targeted.
This problem touches on several critical areas:
-- Distribution of Bus and Train Services: Ensuring that public transportation is equitably distributed across all outer suburbs is important. Rapidly growing areas with limited services need to be prioritized, and the availability of transport should be aligned with both current and projected population and housing growth.
-- Impact on Life Satisfaction: Access to reliable public transportation has a direct effect on people's overall life satisfaction. Commute times, convenience, and access to jobs or services influence well-being and social participation. Effective transportation planning can improve quality of life, particularly in suburbs where long or inefficient commutes are common.
-- Social and Economic Participation: Public transportation plays a critical role in connecting people to economic opportunities, education, healthcare, and social networks. A lack of adequate services can lead to isolation and reduced economic participation, further exacerbating inequality in rapidly growing suburbs.
Certainly, additional factors such as connectivity, timeliness, and other critical areas of public infrastructure need to be considered for effective community planning. These will be addressed in future plans as we work with the team and the government to enhance our initiative.
Project Description:
TransitPulse Explorer is an innovative application designed to help city planners, policymakers, and the broader community understand potential future scenarios of public transport provisions through the analysis of historical and current planning data. It serves as a comprehensive decision-support tool that not only helps policymakers formulate sustainable urban development strategies but also provides valuable insights for the general public to make informed decisions about their future movements.
By integrating past trends with future forecasts, TransitPulse Explorer helps to identify emerging needs, optimize transportation planning, and enhance community engagement, ultimately supporting more effective and responsive urban development.
On this application, stakeholders can visualize the population, household, and dwelling growth patterns between 2021 and 2026 to gain insights into the evolving situation in Victoria, Australia.
We also construct a Transit Equity Index for each Local Government Area (LGA). This index measures the number of bus and train trips relative to the population, households, or dwellings within each LGA. It is calculated as follows:
Transit Equity Index = Number of Bus and Train Trips / Population or Household or Dwelling
This index reflects the relative accessibility and adequacy of public transportation services within each LGA. A higher index value indicates a greater availability of public transport relative to the number of people, households, or dwellings, suggesting better access to transit services. Conversely, a lower index value points to a potential shortfall in public transportation coverage, highlighting areas where additional services may be needed.
By evaluating the Transit Equity Index, stakeholders can identify disparities in transit accessibility, prioritize areas for infrastructure investment, and develop strategies to ensure equitable public transportation services across all regions, particularly in rapidly growing suburbs.
Goals:
Provide advanced visualization and forecasting tools to help city planners, policymakers, and the public make informed decisions about future transportation infrastructure.
Align public transportation planning with the Victorian Government’s 20-minute neighbourhood principles to ensure that residents have access to essential services within a 20-minute commute.
Use the Transit Equity Index to identify accessibility disparities and guide targeted infrastructure investments, while providing data-driven insights to support sustainable urban development and effective use of the Growing Suburbs Fund.
Future Plans:
Building on our initial analysis and forecasting of bus and train services, our future efforts will expand to include a broader range of transportation and public infrastructure metrics. We will enhance our approach by incorporating:
Connectivity: Evaluating how well different transportation modes (buses and trains) are integrated to provide seamless and efficient travel experiences.
Timeliness: Assessing the punctuality and reliability of public transport services to ensure they meet commuter needs effectively.
In addition, we plan to upgrade TransitPulse Explorer to CivicPulse Explorer, expanding the analysis to include other critical areas of public infrastructure, such as:
Healthcare Facilities: Forecasting the accessibility and distribution of hospitals, clinics, and emergency services to ensure they can accommodate increasing healthcare needs.
Educational Institutions: Evaluating the proximity and accessibility of schools and universities to support the growing educational demands of expanding communities.
Recreational and Green Spaces: Analyzing the quantity, quality, and accessibility of sports facilities, community centers, parks and natural areas to cater to the increasing demand for recreational and social activities.
Utility Services: Evaluating the provision of essential utilities like water, electricity, and waste management to ensure they can support the growing population and maintain equitable access.
As we advance, we aspire to conduct these analyses and forecasts at a more granular grid-cell level, where data allows.
Sources:
https://www.suburbandevelopment.vic.gov.au/grants/growing-suburbs-fund
https://www.planning.vic.gov.au/guides-and-resources/strategies-and-initiatives/20-minute-neighbourhoods
https://www.abc.net.au/news/2024-08-13/public-transport-in-outer-melbourne-outpaced-by-population-boom/104152220
https://www.theguardian.com/australia-news/2023/oct/22/how-public-transport-deserts-entrench-inequality-melbourne-suburbs
Tools:
Logo - Microsoft Designer
Picture (in video) - Canvas
Data Story
To address the problem and calculate the Transit Equity Index, we use two key datasets and adopt several methodologies:
VIF2023 LGA Population, Household, and Dwelling Projections to 2036: This dataset provides both historical data from 2021 and future projections for 2026, 2031, and 2036. These projections are crucial for understanding the demographic context and calculating the Transit Equity Index.
Timetable and Geographic Information - GTFS: We use this dataset to extract detailed information about bus and train services. This dataset, with its latest update on 13/06/2024, includes raw files that define the schedule and location of public transportation services.
We download and process the GTFS files to obtain the number of bus and train trips at each stop (bus stop or station) along with their coordinates. We then intersect the trip number dataset with location information and the LGA shapefile. The purpose is to aggregate the data to the LGA level as the demographic dataset is at LGA level.
We merge the aggregated transportation data with the LGA projection dataset to compute the Transit Equity Index. This index is calculated by dividing the number of bus and train trips by the population, households, or dwellings in each LGA, providing a measure of public transportation accessibility.
With the computed index and other available data, we create visualizations to illustrate patterns and disparities in transit equity across different LGAs in Victoria. These visualizations help in identifying areas with inadequate public transportation services and support strategic planning for future infrastructure investments.
Scenario Analysis: We conduct scenario analyses assuming different growth rates (1%, 5%, 10%, and 15%) for population, households, and dwellings up to 2051, with the current model assuming uniform growth rates. More sophisticated and comprehensive options are under development.
Conceptual Framework for AI and machine learning: We provide a conceptual framework for integrating AI and machine learning in forecasting future dynamics among these variables, with options for including additional factors. We will use Autoregressive Integrated Moving Average (ARIMA) for time series forecasting. Other machine learning techniques, such as random forest and graph neural networks can help us improve the accuracy of forecasting. Given the complexity and regulatory considerations of AI in public sector projects, we aim to work closely with government departments to ensure our AI models adhere to Australian regulations and effectively support sustainable urban planning.
