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Live transport performance dashboards – a light example

Sydney bus performance dashboard

This shows an up-to-date overview of the current performance of the bus network in Sydney, queried every 15 minutes from the Transport for NSW Vehicles API. .

The infrastructure behind this is quite simple and powerful, and you can learn 90% of it through this tutorial.

Possible extensions
> Integrate real vs scheduled time
> Historic performance / animations throughout the day

Try:
> See the buses in the most congested traffic routes
> Query individual bus routes
> Analyse the occupancy of the bus network
> Scroll into individual areas to see changes in average km/h speed
> Summarise one of the above variables for the area within field of view
> Screenshot / compare different times of day
> Select one variable (such as ‘Standing Only’) and see the routes ranked by their count of this variable

Bus performance (last 15 minutes)

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Finding the centre point of Sydney

During this year’s GovHack our team was interested in visualising open economic data of Sydney. There were several outputs of the project ; and this was the least sophisticated but ended up the most interesting.

If you were to try and pick the centre of Sydney, where would you say it was? Probably first instinct is just to go where it says ‘Sydney’ on Google Maps, right?

Well we found it. This is the centre of Sydney! Recognise it?

2017-09-21 11_59_49-Durham St - Google Maps

No? Ok. What about this?

2017-09-21 11_58_38-PhotoMaps by nearmap

Still no? Well it is important as this is the forecast central location of Sydney’s projected population in 2040.

We calculated the average location of population and employment in Sydney from 2011 – 2040. This was based on the location of people and jobs in the Bureau of Transport Statistics’ forecasts, using a weighted average location based on the centre of travel zones and population/employment within them. This is what we ended up with (an interactive version below). The blue houses represent population, and the green buildings represent employment. The size of the icon denotes year, from 2011 to 2040.

2017-09-21 11_05_33-Economic Centres Map _ CARTO

As you can see, the forecasts show the centre of each moving west at about 50 metres each year, and it is definitely not very close to the ‘City of Sydney’ LGA as an outsider may expect.

In 2040, sprawling Sydney’s population centre will have moved from Halvorsen Park to Rose Hill, and the employment centre from Rhodes to Wentworth Point.

Some interesting further analysis would be in applying the same method to different cities. While it is a simple calculation it is something worth thinking about – particularly when trying to provide equal transport and social services or all.

Interactive map below:

Datasets: Bureau of Transport Statistics, Transport for NSW – Population and Employment Projection

Here’s an example of a different method applied to London:

London’s Real Centre Point


PopWeighted1

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Measuring accessibility – on the 30 minute city

30min2

One of the recent projects I’ve been involved in at Arup has been developing spatial, analytical tools to understand transport accessibility. In particular, this is to do with destination-based accessibility – so rather than assessing how well-performing a city is delivering transport at particular points (which could go anywhere), we looked at how this performs delivering to all other places in the city. In particular we were looking at places that are important to creating liveable environments – such as to education, parks, healthcare and our jobs.

For me, this topic was building well on research I had done in 2015 (See ‘Where to From Here? A Modelling Methodology for Measuring Land-Use and Public Transport Accessibility in Melbourne), which assessed destination-based accessibility within transport modelling software, restricted to travel zones. This time there were some major improvements to the method ; mostly from removing from a software shell to raw code, and much more disaggregate units of analysis.

We assessed Greater Sydney Sydney at a 300m x 300m grid level, producing over a million travel time isochrones for driving (including traffic), public transport and walking to assign accessibility values to liveability variables in approximately 120,000 small cells in the city. In a nutshell, our toolkit involved a bit of OpenTripPlanner, Python, Amazon Web Server and FME – all using Open Data sources. This means means the method is highly reproducible for both other cities, and applicable to the same city with a different network (which, could be used to evaluate transport network changes, or alternate land use scenarios). A web map has been produced to showcase some of the work done in this space is so far , exploring what the ’30 minute city’ means for Sydney:

30minutecity.arup.digital

30min

It is certainly exciting to see the potential of this thinking and method being applied to both Sydney and other cities. Accessibility and the impact on individual opportunities is often overlooked and undervalued in many forms of transport analyses. With the increasing richness of the data that is becoming available from the Government and other forms of Open Data; combined with open analytical and visual methods like these it is encouraging and clear that these analyses can potentially produce insight towards tackling some of our growing issues in Australian cities, such housing affordability, transport disadvantage, sustainability.

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