Introduction Physical activity is a critical factor in the prevention of chronic disease and in achieving and maintaining good health. However, little is understood about the link between the built environment and physical activity levels in regional areas. Objectives We linked geospatially-assessed walkability with population health surveillance data gathered from urban and regional locations across Tasmania, Australia, to assess if walkability was associated with physical activity. Methods We used linear regression analysis to determine associations between physical activity duration and the walkability index level generated by geospatial assessments. Results We demonstrated a clear association between higher walkability and greater physical activity duration, which was more pronounced in regional areas when compared to urban areas. Conclusions Geospatially-assessed walkability may influence physical activity levels in regional areas. Implications for Public Health Designing the built environment to increase walkability may be a simple but effective strategy for enabling more physical activity, especially in regional settings.

This paper presents an activity-based transport demand modelling framework for regional contexts, where inter-regional commuting is important and behavioural data are often sparse. Using Greater Bendigo, a regional city in Australia, as a case study, we develop daily activity chains and subsequent activity-based demand for use in agent-based simulation models. The methodology integrates statewide travel flows with detailed local modelling through demographic synthesis, activity-chain generation, spatial location assignment, and mode-choice modelling. The key contribution is a multi-scale location assignment framework that prioritises the mandatory activities, work and education, before allocating non-mandatory activities. Work locations are assigned to reproduce observed inter-regional commuting flows and distance distributions derived from Journey to Work (JTW) data, while education locations are allocated subject to facility-level capacity constraints. Secondary activity locations are assigned using a two-sided gravity-based approach conditioned on preceding and subsequent activity anchors, land-use-based destination attractiveness, and empirically observed mode-specific distance distributions. Trips entering and leaving the local study region are explicitly incorporated to maintain internal behavioural consistency. The activity-based demand is assessed against observed population totals, activity timing, trip-length distributions, commuting patterns, and regional mode shares. The comparison results show close agreement with key calibration benchmarks and plausible agreement with complementary survey-based checks. The generated demand includes about 13% cross-boundary work trips, underscoring the importance of explicitly accounting for external travel in regional demand models. Overall, the proposed methodology provides a scalable and transferable approach for generating activity-based demand for regional cities while preserving cross-boundary travel consistency.

Addressing priorities such as health, liveability, and climate resilience, many global governments are exploring xminute city and x-minute neighbourhood policies. Addressing a lack of operational models for x-minute cities, we developed a model to assess accessibility and utilisation implications of their implementation, testing the model by applying it to the low-density city of Melbourne, Australia, where 20-minute neighbourhood (20MN) policy focuses on walkable access to daily destinations within 10 minutes (20 minute round trip), and applying the model to people living within 10-minute walking distance 'catchments' of mixed-use activity centres. We curated a list of 14 destination types (such as supermarkets and primary schools), and developed a method to model notional placement of additional destinations to place at least 80 % of people in each catchment within a 10-minute walk of each destination type. We extended accessibility considerations to cycling, based on a 10-minute oneway ride. Our results show improvements in accessibility across the city as a whole, particularly in inner urban areas; but with significant improvements in outer urban areas, characterised by low housing density, when cycling is promoted. Our utilisation analysis demonstrates feasibility challenges when implementing x-minute city policies in less densely populated locations. Our conclusions underscore the importance of supporting xminute city policies with safe cycling infrastructure and careful urban densification strategies. Our findings are relevant to many cities seeking to implement x-minute city or neighbourhood policies worldwide, especially cities facing challenges of low density.

Agent- and activity-based transport models when combined link transport network flows to the individuals generating them, enabling exploration of the interplay between travel needs and transport network capacity.This study expanded a recently developed multi-modal agent- and activity-based transport model of Greater Melbourne, Australia by incorporating travel mode choices of demographic cohorts, a more accurate representation of public transport trips, and walking or driving to train stations. Separate mode choice models were developed for five unique cohorts of travellers. The model was calibrated and validated for each cohort using Victorian Integrated Survey of Travel and Activity data. The model simulated a Baseline scenario of existing transport infrastructure and an alternative scenario that includes Stage 1 of the Suburban Rail Loop, or SRL East, a new rail network traversing under-served middle suburbs. Results show SRL East will increase walking trips and average daily walking distances, and decrease car trips in areas surrounding SRL East train stations. The average walking distance between SRL areas increased between 4.9% and 11.1% for the respective cohorts, with the largest increase for those aged between 20 and 24.Results suggest SRL East will change people’s travel behaviour and influence the uptake of active transport, with the cohort results useful for age and gender-based interventions. The research is significant for showing SRL East’s impacts on active transport up-take and for influencing city-wide transport and urban planning. Future research modelling local and city-wide impacts of all stages of the SRL are warranted.

With urban densification and the proliferation of high-rise structures, residents' apartment views are getting obstructed from surrounding nature, especially greenery. Existing approaches often rely on simplified proxies or aggregated building- or floor-level metrics which does not capture individual-level variation. Most of them use coarse spatial data or subjective self-reports, lacking the granularity and precision to quantify the greenery visible to each resident from their own living space. This study introduces a conceptual and methodological framework for objectively modelling green views at the individual apartment level. Our Apartment Greenery View Measure was developed and assessed by (1) geocoding individual observer positions at window-level within apartment buildings, (2) implementing GIS-based three-dimensional viewshed analysis using high-resolution environmental datasets to objectively quantify views, and (3) examining agreement between modelled views and 445 residents' self-reported perceptions using the green-to-grey ratio. The method was applied to 30 apartment buildings across Melbourne, Australia. Findings reveal variability in green view exposure by building height, floor-level, and apartment orientation. A moderate correlation (r = 0.556, ICC = 0.521) shows the agreement between objective and perceived view measures, with 39.1% participants overestimating and 60.9% underestimating their views. This underscores the need for objective, standardised measures that move beyond perception alone. The workflow supports aggregation at multiple spatial scales, from individual units to floors and buildings, providing a flexible framework for assessing visual green equity citywide. This provides a scalable, low-cost tool for planners, designers, and health researchers seeking to integrate visual greenery into urban housing, policy, and equity-focused interventions.

In this paper, we present an activity-based model for the Greater Melbourne area, using a combination of hierarchical clustering, probabilistic, and gravity-based approaches. The model outlines steps for generating a synthetic population-a list of agents with their demographic attributes-and for assigning activity patterns, schedules, as well as activity locations and modes of travel for each trip. In our model, individuals are assigned activity chains based on the probabilities of their respective demographic clusters, as informed by observed data. Tours and trips then emanate from these assigned activities. This is innovative compared to the common practice of creating trips or tours first and attaching activities thereafter. Furthermore, when selecting activity locations, our model incorporates both the distance-decay of trip lengths and the activity-based attraction of destination sites. This results in areas with higher attractiveness for various activities showing a greater likelihood of being selected. Additionally, when assigning the location for the next activity, we take into account the number of activities an agent has remaining to ensure they do not opt for a location that would be impractical for a return trip home. Our methodology is open and replicable, requiring only publicly available data and is designed to produce outcomes compatible with commonly used agent-based modeling software such as MATSim. Each sub-model is calibrated to match observed data in terms of activity types, start and end times, and durations.

Riding a bicycle, particularly for transport purposes, offers substantial environmental and health benefits. Suitable bicycle infrastructure is crucial for promoting bicycle use, but remains scarce and fragmented in Australia, particularly in regional cities. In this context, understanding cyclists' route choices is a key input in informed infrastructure planning to increase cycling mode share. This study uses data from a map-based public participatory route survey to spatially capture bicycle route choice in the regional city of Greater Bendigo, Australia. The model incorporates attributes such as level of traffic stress (LTS), slope, tree canopy cover, intersection density, and route directness, along with an adjustment to account for overlap among alternative routes. The results show that cyclists strongly prefer routes with the lowest traffic stress, lower gradients, and greater network connectivity, while avoiding circuitous paths. Cyclists are also more likely to choose routes that are distinct and share fewer common segments with alternative routes. Segmentation by gender and age reveals notable behavioural differences: female cyclists are considerably more sensitive to traffic stress and slope, prioritising safety and comfort, whereas male cyclists exhibit greater tolerance to stress and prioritise route efficiency. Middle-aged cyclists (40-60 years) exhibit the strongest aversion to stressful routes, while younger cyclists (18-40 years) demonstrate greater flexibility in route choice. These findings highlight the importance of age and gender considerations in bicycle infrastructure planning as well as prioritising low-stress, well-connected, and direct cycling corridors. The findings also highlight the value of map-based public participatory surveys as a cost-effective means of collecting route data in regional settings with small populations.

In car-dominated cities like Melbourne, Australia, limited data on cyclists' travel patterns and socio-demographic differences complicate understanding of the effectiveness of infrastructure investment interventions aimed at promoting cycling. Recent advancements in city-scale transport modelling enable virtual testing of such interventions. However, the application of agent- and activity-based models for large-scale cycling simulations has been constrained by data and complexity. In this study, we developed a city-scale agent-based simulation model for Greater Melbourne to evaluate changes in travel mode share from cycling infrastructure modifications. We clustered bicycle riders into five demographic groups: Maverick Males, Motivated Adults, Conscientious Commuters, Young Sprinters, and Relaxed Cruisers, estimating mode choice parameters for each group. Using aggregated smartphone application data, we developed a cycling trip routing methodology to incorporate road infrastructure impacts. Results indicated that travel time significantly influences mode choice across all clusters. Cycling infrastructure was crucial for four clusters, and travel cost influenced four clusters. The calibrated model assessed the potential impact of fully implementing Greater Melbourne's strategic cycling corridors, a network of key cycling routes. Simulations suggested an initial 30% increase in cycling use, raising the mode share to approximately 2.6%, indicating a modest overall impact. Further analysis showed that even with full implementation, on average about half of the lengths of the routed bikeable trips would still occur on roads without any cycling infrastructure. This underscores the need to improve infrastructure on both major corridors and minor roads, and to complement these improvements with behavioural interventions.

Activity-based models for simulating transport systems have become prominent, particularly in metropolitan areas. However, adapting these models for regional cities presents unique challenges, including limited data availability and the need to account for a large number of trips originating outside the study region. This research introduces a comprehensive workflow for developing an activity-based demand generation model for Greater Bendigo, a large regional city in Victoria, Australia. The model uses a synthetic population table representing all individuals residing in the State of Victoria (rows) and demographically representative of census data (columns) as its input. These individuals were categorised into three groups: Greater Bendigo residents with local trips, residents commuting to outside regions, and non-residents working in Greater Bendigo. The first step involved clustering individuals from the input synthetic population based on their main activities to identify distinct cohorts. Subsequently, activity distribution tables were computed for each cohort using the Victorian Integrated Survey of Travel and Activity (VISTA) data.Distinct daily activity chains were generated for each cohort, producing 24-hour itineraries accurate to 30-min time bins with VISTA activity types, start time, and durations. A combination of location-allocation and gravity-based models were employed for assigning activity locations, accurately representing their spatial distribution. In this step, the realistic location of a person’s main activity was assigned first, followed by the selection of secondary activities’ locations to create a sensible return loop back home. Approximate locations were used for trip origins or destinations outside Greater Bendigo.The model workflow can be used to generate demand for other regional cities with significant inflow and outflow of daily travellers. The generated travel demand, together with a suitable transport network, can be used in agent-based traffic models to examine transport interventions in regional areas, providing a valuable tool for regional transport planning.

Existing liveability indices are limited as they cannot be used to accurately monitor changes in liveability of a place over time. This study introduces a new method, the Adjusted Mazziotta-Pareto Index (AMPI) approach, to calculate the liveability index and accurately measure its changes. This liveability index was calculated for 403 suburbs in Melbourne, Australia, in 2016 and 2021. It consisted of walkability components, social infrastructure, regular public transport, public open space, local employment opportunities, and housing affordability as the constituent indicators. By employing the AMPI approach, these indicators were normalised into a time-independent scale and aggregated by incorporating a penalty for any imbalances among them. This approach provided a balanced assessment of liveability, which is comparable across multiple time points. Our analysis of within-suburb changes in the liveability index found that, while liveability in most suburbs remained stable over a five-year period, some outer suburbs in Melbourne exhibited considerable changes, primarily driven by changes in access to regular public transport and daily living destinations. Understanding changes in liveability has the potential to advance both research and policy practice on improving liveability. Future research could apply the AMPI-based liveability index to investigate to what extent neighbourhood liveability changes over time can influence residents' health and wellbeing trajectories. Practitioners and policy makers can make evidence-informed decisions in identifying areas where investments are needed to improve liveability.

Accessibility models explore how land use and transport systems interact to facilitate access to activities and daily needs. Existing applications generally model accessibility based on distance or travel time. For pedestrians and cyclists, the street-level environment (e.g., green visibility, streetside amenities, dedicated infrastructure) significantly influences people's willingness and ability to travel. Incorporating these features into accessibility models can help them to be more representative of active travellers' experienced environment.This study presents a methodology for incorporating the street-level environment into active mode accessibility. First, micro-scale built environment data from multiple sources are harmonised into a high-resolution digital representation of the land use and transport system. Second, a compute-optimised framework is developed for modelling accessibility at the micro-scale (i.e., each dwelling separately) incorporating the street-level environment. The methods build upon the open geodatabase OpenStreetMap and open-source MATSim project, facilitating expandability and transferability to other contexts. We apply this methodology to develop policy-relevant accessibility indicators for Greater Manchester.In the results, we observe that the street-level environment can cause accessibility indicators to vary at the micro-scale, especially in less connected neighbourhoods where the choice of routes is limited. We also observed that for cyclists, the accessibility advantage over walking reduces substantially when traffic stress is considered. Our findings support further adoption of micro-scale built environment data and high-resolution analysis methods for active travel accessibility modelling in research and practice.

Cycling for transport is a sustainable alternative to using motorised vehicles for daily trips and is a key form of micromobility. Travel time is a critical factor influencing cycling route choice behaviour and uptake. Thus, it is important to understand the factors affecting cycling travel time and speed and their impact on cycling behaviour. In this study, an agent-based transport simulation model with heterogeneous cycling speeds was developed and used for Melbourne to study the impact of a hypothetical traffic signal optimisation intervention along six key cycling corridors. Linear regression and random forest models were used to identify factors affecting cycling speed, which informed the parameters of the agent-based model. Simulation outputs showed, on average, an increase of 4.1% in the number of cyclists on the corridors, as existing cyclists chose to use these corridors, and an average reduction in cyclists’ moving travel time of 6.2% for those using the intervention corridors (excluding time spent waiting at traffic signals). The findings provide insights into the effects of road attributes on cycling speed and behaviour, as well as the effectiveness of interventions aimed at reducing cycling delays. These insights are valuable for developing solutions to optimise urban infrastructure for micromobility, enhancing the efficiency and appeal of cycling as a viable transport option.

As cities continue to densify and high-rise developments become more prevalent, residents' views of their surroundings are often obstructed, limiting their visual exposure to natural features such as greenery. Given the increasing amount of time individuals spend indoors, assessing their green views from buildings has become essential, as greenery contributes to livability and well-being. This study develops an observer-centric measure to quantify individual green views from apartment buildings using a spatially explicit framework. The proposed method leverages GIS-based 3D techniques and spatial analysis to capture and quantify green views. Additionally, grey views (built-up environments) and water views (surface water) were quantified to account for the mix of natural and urban features within an observer's views. The framework was tested on sample buildings in Melbourne, Australia, using high-resolution environmental datasets and 3D building models. The results demonstrate significant variations in observers' green views based on building type, floor level, and surrounding urban form. The approach provides valuable insights for urban planners, architects, and policymakers, enabling data-driven strategies to optimize greenery access, enhance urban design, and promote healthier, more sustainable cities. Additionally, it supports health-related policies by facilitating the assessment of green exposure's impact on wellbeing, informing interventions to improve mental and physical health in urban environments.

In this paper, we present an algorithm for creating a synthetic population for the Greater Melbourne area using a combination of machine learning, probabilistic, and gravity-based approaches. We combine these techniques in a hybrid model with three primary innovations: 1. when assigning activity patterns, we generate individual activity chains for every agent, tailored to their cohort; 2. when selecting destinations, we aim to strike a balance between the distance-decay of trip lengths and the activity-based attraction of destination locations; and 3. we take into account the number of trips remaining for an agent so as to ensure they do not select a destination that would be unreasonable to return home from. Our method is completely open and replicable, requiring only publicly available data to generate a synthetic population of agents compatible with commonly used agent-based modeling software such as MATSim. The synthetic population was found to be accurate in terms of distance distribution, mode choice, and destination choice for a variety of population sizes.

Urbanisation is occurring globally and rapidly with potential to compromise the development of sustainable, liveable and healthy cities. Urban observatories have also existed for many years addressing a range of relevant urban issues. These observatories provide a unique method to translate research into practice, support evidence-informed policy and planning, target actions of the sustainable development goals, address spatially based health inequities and improve the liveability of cities. This paper provides an analysis of the Australian Urban Observatory, a digital liveability planning platform using urban analytics to observe and enhance understanding of liveability inequities in Australian cities that is linked to policy and planning. The analysis aims to share learnings about development of the Australian Urban Observatory, including the conceptual framework of liveability, planning tools, and the resulting impact in policy and planning applications. This is the first urban observatory in Australia that will continue to expand and develop over time, supporting urban governance, democratic process and creating real world policy impact through partnership between academia, government, industry and the community.

Introduction: Being physically active has multiple health benefits and contributes to the reduction of co-morbidities and mortality from chronic diseases. Active transport (walking and cycling) contributes to population health by enabling physical activity. We developed a simulation model to measure health impacts of transport scenarios for Melbourne, Australia. Our aim was to demonstrate active transport health impacts and support the materialization of policies for healthy cities and people. The model measures health impacts of increased physical activity from replacing short car trips for any purpose or for commuting under 5 km by walking and cycling. Methods: We developed a micro-simulation model of physical activity and disease risk in combination with the well-established proportional multi-state life table model. We quantified life course health including health adjusted-life years, life years, new cases of diseases prevented, and deaths prevented for 14 chronic diseases associated with physical inactivity for the adult population of people from Melbourne, Australia in 2017. Results: Over the life course of the Melbourne adult population of 3.6 million people in 2017, gains in health-adjusted life years ranged from 5,100 (95% Uncertainty Interval (UI) 3,700 to 6,500) for the scenario replacing commute trips by car under 1 km with walking up to 738,800 (95% UI 546,000 to 935,000) when replacing car trips under 2 km with walking and between 2 km and 5 km with cycling. We also estimated benefits in terms of reductions of new cases of diseases and deaths prevented, with the greatest gains for ischemic heart disease, stroke, Alzheimer's and other dementias and type 2 diabetes. Conclusions: We found that shifting car travel to active modes would accrue important health benefits for the 2017 Melbourne population. Our results support policies and strategies for sustainable transport planning to contribute to reduce the burden from chronic diseases and environmental impact of car-oriented cities.

This paper describes the design, development, and testing of a general-purpose scientific-workflows tool for spatial analytics. Spatial analytics processes are frequently complex, both conceptually and computationally. Adaptation, documention, and reproduction of bespoke spatial analytics procedures represents a growing challenge today, particularly in this era of big spatial data. Scientific workflow systems hold the promise of increased openness and transparency with improved automation of spatial analytics processes. In this work, we built and implemented a KNIME spatial analytics (“K-span”) software tool, an extension to the general-purpose open-source KNIME scientific workflow platform. The tool augments KNIME with new spatial analytics nodes by linking to and integrating a range of existing open-source spatial software and libraries. The implementation of the K-span system is demonstrated and evaluated with a case study associated with the original process of construction of the Australian national DEM (Digital Elevation Model) in the Greater Brisbane area of Queensland, Australia by Geoscience Australia (GA). The outcomes of translating example spatial analytics process into a an open, transparent, documented, automated, and reproducible scientific workflow highlights the benefits of using our system and our general approach. These benefits may help in increasing users’ assurance and confidence in spatial data products and in understanding of the provenance of foundational spatial data sets across diverse uses and user groups.

Urban liveability is a global priority for creating healthy, sustainable cities. Measurement of policy-relevant spatial indicators of the built and natural environment supports city planning at all levels of government. Analysis of their spatial distribution within cities, and impacts on individuals and communities, is crucial to ensure planning decisions are effective and equitable. This paper outlines challenges and lessons from a 5-year collaborative research program, scaling up a software workflow for calculating a composite indicator of urban liveability for residential address points across Melbourne, to Australia's 21 largest cities, and further extension to 25 global cities in diverse contexts.