2 May 2023

WHERE TO FROM HERE?Andrea Colaiacomo

A MODELLING METHODOLOGY FOR MEASURING LAND-USE AND PUBLIC TRANSPORT ACCESSIBILITY

Accessibility indices – How many?

Accessibility indices – LUPTAI

It is then possible to compare accessibility to different type of attractions:• Schools• Hospitals• Shopping centres• Amenities• Etc.

And assess the accessibility looking also at socio-economic variables:• Accessibility to schools from zones with high density of young families• Accessibility to health centres from zones with high density of elderly people

LUPTAI (Land Use and Public Transport Accessibility Index) links active and public transport availability and key destinations.

nowherenowhere200 trains per hour

schoolhospital2 trains per hour

Accessibility indices – LUPTAILUPTAI is calculated starting from destinations tracing backwards to the origins within given thresholds.

Destination

Stop within 400m (walk egress)

Public transport journey

Stops within public transport travel time threshold

350m from the stop plus 50m buffer (total 400m from the stop)

Source: Land Use & Public Transport Accessibility Index (LUPTAI) Tool - The development and pilot application of LUPTAI for the Gold Coast

Accessibility indices – LUPTAIA series of indices (1 to 5) are then assigned to each geographical unit based on the combinations of:• PT frequency and walking distance• PT travel time (by mode) and walking distance

Source: Land Use & Public Transport Accessibility Index (LUPTAI) Tool - The development and pilot application of LUPTAI for the Gold Coast

Accessibility indices – LUPTAI

Walk accessibility

PT accessibility

Combined accessibility Source: Land Use & Public Transport Accessibility Index (LUPTAI) Tool - The development and pilot application of LUPTAI for the Gold Coast

LUPTAI and the modelOBJECTIVE:Calculate LUPTAI using model outputs. This allows us to take in account the impact of changes in traffic flow conditions.

FORESEEN CHALLENGES:Model outputs are aggregated by travel zone pairs, while accessibility is typically calculated at each individual element of the geographical grid.

Outputs used in order to calculate LUPTAI consistently in the presented methodology are:• Walk travel time• PT travel time• Walk access and egress distances

LUPTAI and the modelDestinations - Schools

There are many schools in the MSD.

Schools are evenly distributed across the MSD.

No particular accessibility issues are anticipated.

LUPTAI and the modelDestinations – Shopping centres

Fewer in number if compared with schools.

Still pretty evenly distributed across the MSD.

Some interesting patterns in accessibility are anticipated.

LUPTAI and the modelDestinations – Hospitals

Hospitals are even more sparse than shopping centres.

More hospitals are located in the eastern suburbs than to the west.

LUPTAI and the modelDestinations – Universities

Universities are concentrated in the CBD, with a few exceptions.

Some extreme patterns in accessibility are anticipated.

LUPTAI and the modelBase ScenarioBase scenario – Road network

LUPTAI and the modelBase ScenarioBase scenario – Public transport network

LUPTAI and the modelAccessibility to schools

Accessibility to schools is good every where.

LUPTAI and the modelAccessibility to shopping centres

Corridors with better accessibility start being visible around rail lines.

LUPTAI and the modelAccessibility to hospitals

Corridors are better defined and it is pretty clear that western suburbs have lower access to hospitals.

LUPTAI and the modelAccessibility to universities

Corridors are well defined around rail lines. Western suburbs and areas around the end of rail line show the lowest accessibility indices.

LUPTAI and the model

The unexpected!

LUPTAI and the modelUNFORESEEN CHALLENGESLUPTAI only includes walking distance and travel time, but it’s mostly affected by the walking distance. This may end up in misrepresenting a change in the user behaviour.

For example in the base scenario a user may go from A to B with:• 600m access and egress distance• 30 minutes PT travel time• (and 3 interchanges)

While in the project scenario a user may go from A to B with:• 900m access and egress distance• 30 minutes PT travel time• (and 1 interchange)

In this case LUPTAI would show a worse accessibility index. Is this actually the case?

LUPTAI and the modelThe way to go: use the generalised cost.

The generalised cost includes all the parameters used by the model to replicate user behaviour:• In-vehicle travel time• Access / egress time• Waiting time• Fare factor• Access / egress penalties• Transfer penalties• Stop choice and route choice parameters

An index based on the generalised cost will then be consistent with what has been assumed to influence the user decisions.

LUPTAI and the model - ScenariosTest scenario 1 – Increased frequency along Frankston line

LUPTAI and the model - ScenariosTest scenario 2 – New university in Footscray

LUPTAI takes into account the destinations available. This means that even without changing the PT network, accessibility can be improved by providing services in the right location.

LUPTAI and the model - ScenariosTest scenario 1 – Change in accessibility

Even doubling the frequency along the Frankston line, accessibility doesn’t change significantly with the exception of few zones around the rail corridor.

LUPTAI and the model - ScenariosTest scenario 2 – Added an additional university

While adding a new university in Footscray would affect a wider area by improving the accessibility from the western suburbs (including Melton and Sunbury)

LUPTAI and the modelThe strength of the accessibility index is that it includes the interaction between the PT system and the available destinations.

The index could be further improved by including:• population densities, • crowding, • number of accessible destinations, • availability of additional PT modes and/or stations