Why should we use the best available evidence to drive Public Transport Network design?

HomeNewsBlogWhy should we use the best available evidence to drive Public Transport Network design?

This is the blog piece I have been threatening to write for quite a while. It rained last week, so I wrote it.

When you think about it, Canberra has incredibly good public transport assets: a modern well-maintained fleet of buses and light rail vehicles, a (generally) customer focussed work force, and a low-cost fare regime. Those factors would ordinarily have people flocking to public transport in times of increased household costs, increasing congestion and constrained parking arrangements. But they aren’t! Why is this so?

My take on it is that the leadership of Transport Canberra is unable to see the “wood for the trees” and been unable to optimise utilisation of its resources to match the needs of the community. My primary evidence for this statement can be seen in the frequent comments made by people to the PTCBR Facebook presence, at the PTCBR Public Meetings and regularly seen in local media, such as the Canberra Times’ letters page or radio talkback. Of course, as a public transport user I experience many of these disincentives myself.     

As far as I can tell, the current network has been designed primarily based on the data coming from the MyWay ticketing system, with some findings from the Australian Population Census (once every 5 years, and only captures data on journeys to/from work) and the infrequent survey of ACT and Queanbeyan residents travel patterns. The other remaining factor is influence exerted by individuals and groups.

So, while it is important that the MyWay fare box data be collected to record the revenue collected from passengers, it seems unreliable for designing public transport networks for several reasons:

  1. Fare evasion: Fare box data typically relies on the assumption that all passengers pay their fares, which is not always the case.
  2. Fare box data may also not capture passenger travel patterns, such as transfers between different modes of transport or trips that do not involve fare payment.
  3. Fare box data will not account for external factors such as changes in population, land use, economic conditions, or travel behaviour. These factors do not provide a complete picture of potential passenger demand for designing public transport networks.

In summary, while fare box data can provide some insights into passenger demand and revenue generation in public transport systems, it will be unreliable for designing public transport networks. Additional data sources and methodologies are needed to ensure a more comprehensive and accurate understanding of passenger demand for effective public transport network planning.

An alternative transport planning possibility

Spatial data analytics, which involves the analysis and interpretation of geographic data, can provide significant advantages when used to design public transport networks. Here are some key advantages:

  1. Optimal route planning: Spatial data analytics can help identify the optimal routes for public transport networks by analysing various factors such as population density, travel patterns, existing transportation infrastructure, and geographic features like roads, landmarks, and topography. This can result in more efficient and effective public transport routes that minimize travel time, reduce congestion, and maximize accessibility to key destinations.
  2. Demand forecasting: Spatial data analytics can help predict and forecast demand for public transport services by analysing data on population density, demographic profiles, travel patterns, and other relevant factors. This can enable public transport planners to better understand the demand for different routes and adjust the network accordingly, resulting in more responsive and demand-driven public transport services.
  3. Cost-effective resource allocation: Spatial data analytics can help optimize resource allocation for public transport networks by analysing data on travel patterns, population density, and other factors. This can help determine the optimal locations for stops, transfer points and hubs, as well as the appropriate frequency and capacity of services. This can result in more cost-effective utilisation of resources, especially buses, and reduce operational costs while maintaining service quality.
  4. Accessibility and equity considerations: Spatial data analytics can help address accessibility and equity considerations in public transport network design by analysing data on social, economic, and demographic factors. This can help identify areas with limited access to public transport and ensure that the network is designed to serve all segments of the population, including underserved or marginalised communities. This can contribute to a more equitable and inclusive public transport system.
  5. Environmental sustainability: Spatial data analytics can support the design of environmentally sustainable public transport networks by analysing data on emissions, energy consumption, and other environmental factors. This can help identify opportunities for optimizing routes, reducing fuel consumption, and minimising the environmental impact of public transport services, contributing to more sustainable and environmentally-friendly transportation options.
  6. Data-driven decision making: Spatial data analytics enables data-driven decision making in the design of public transport networks, allowing for evidence-based planning and design. By analysing relevant data, public transport planners can make informed decisions on route design, stop locations, service frequency, and resource allocation, leading to more effective and efficient public transport systems.

Potential data sources

There are sources of geospatial data on human movement available now that can provide valuable insights. Some of the best sources of geospatial data on human movement include:

  1. Mobile phone data: Mobile phones generate vast amounts of location-based data, including GPS coordinates, cell tower locations, and Wi-Fi access points. This data can be used to track human movement patterns, such as commuting routes, travel behaviour, and activity hotspots. Companies that collect and aggregate mobile phone data, such as telecommunications providers, often offer data products for research and analysis purposes.
  2. Global Positioning System (GPS) data: GPS data is collected by satellites and can provide precise location information on human movement. GPS data is commonly used in transportation analysis, outdoor recreation, and sports tracking. GPS data can be obtained from various sources, including GPS-enabled devices, wearable fitness trackers, and GPS loggers.
  3. Social media data: Social media platforms, such as Twitter, Instagram, and Facebook, often contain location information in posts and check-ins. This data can be used to infer human movement patterns, such as travel destinations, points of interest, and social activity hotspots. Social media data can be obtained through APIs (Application Programming Interfaces) provided by the respective platforms or through data scraping techniques.
  4. Satellite imagery: High-resolution satellite imagery can provide visual information on human movement, such as vehicle movement on roads, pedestrian activity in urban areas, and changes in land use. Satellite imagery can be obtained from commercial satellite providers, such as Maxar, Airbus, and Planet, or from free sources, such as Google Earth and NASA Earthdata.
  5. Sensor networks: Sensor networks, such as weather stations, air quality sensors, and smart city sensors, can provide geospatial data on human movement and environmental conditions. This data can be used to study patterns of human movement in relation to environmental factors, such as weather, air quality, and noise levels. Sensor data can be obtained from public agencies, research institutions, or private sensor network providers.
  6. Data mining and machine learning: Advanced data mining and machine learning techniques can be applied to data to identify travel patterns. For example, clustering algorithms can group mobile phone users based on their travel behaviour, while predictive models can forecast travel patterns based on historical data.

The data and skills may already be available

Anecdotally, it has been suggested that a lot of these resources already exist within the ACT Government, not much has been applied to the operations of the transport network. For example, we are told that we have a network of bluetooth sensors around the network, used for managing traffic lights. Presumably, this would also suggest people experienced with such technology are already in the Government’s employment.

While it is important to note that accessing and using geospatial data on human movement can raise privacy and ethical implications, these concerns can be mitigated by strong rules on depersonalisation and following applicable laws and regulations.

To put some context into this, other parts of the world use geospatial data to assist there planning. Here is an article about what is happening in Copenhagen. And here is a piece about what can be done.

In summary, I am discussing an option for improving the targeting of a $200 million plus per annum tax-payer investment that currently seems to rely on a “rear vision mirror view” to plan ahead. What do you think?