Being close to public transit does not necessarily mean being well connected.

A resident may live a short distance from a bus route, but that does not tell planners whether they can actually use that service to reach employment, education, healthcare, shopping or other everyday destinations — or whether they can make the return journey at the time they need to.

This was the challenge facing the Ithaca-Tompkins County Transportation Council (ITCTC) in New York.

With housing pressures increasing and new development needing to be considered across a diverse mix of urban and rural communities, ITCTC wanted a more meaningful way of understanding the relationship between housing, transportation and access to opportunity.

Working with Basemap and using TRACC, the team developed two new measures: the Transit Housing Accessibility Index (THAI) and the Cycle Housing Accessibility Index (CHAI). Rather than simply measuring proximity to transportation, the indices measure what residents can realistically reach, at the times they need to travel.

As Thomas Knipe, Director of ITCTC, explains:

About Ithaca-Tompkins County

Ithaca-Tompkins County is home to around 100,000 residents and combines the urban centre of Ithaca with smaller towns, villages and extensive rural communities.

Cornell University and Ithaca College are major generators of employment, education and travel demand, while residents across the wider county need to access jobs, schools, healthcare, retail and other services concentrated in and around Ithaca.

That creates a complex transportation picture.

A location that appears geographically close to Ithaca may not necessarily have good access by public transit. Conversely, some communities farther away can have relatively strong accessibility because of the frequency, timing and direction of the services available.

ITCTC already had strong local knowledge about where connectivity worked well and where gaps existed. The challenge was converting that knowledge into a consistent, measurable and repeatable framework that could be applied across the entire county.

The problem: moving beyond “is there a bus nearby?”

Traditional approaches to Transit-Oriented Development often begin with proximity: how close is a development to a transit stop or corridor?

That is useful, but it only tells part of the story.

Two homes might both sit 400 metres from a bus stop, for example, while offering completely different levels of accessibility. One may have frequent services providing direct connections to major employment areas throughout the day. The other may have only a small number of departures, poor connections or no practical return journey.

ITCTC therefore wanted to answer a much more useful question:

What opportunities can someone actually reach from this location using the transportation network?

The project also presented some particularly local challenges. Parts of the county's public transit network operate using hail-and-ride arrangements, meaning the network data needed to reflect how services operate in reality rather than simply accepting the published network at face value.

TRACC allowed the transportation network to be modelled and adjusted before undertaking the accessibility analysis.

Developing the Transit Housing Accessibility Index

Rather than applying a generic accessibility score, ITCTC and Basemap developed the THAI methodology together.

ITCTC identified the destinations that were important to residents, when people were likely to travel to them, the maximum journey times that could reasonably be expected, and the relative importance of each type of opportunity.

TRACC was then used to calculate travel times across the network and combine millions of individual journeys into a single connectivity score from 0 to 100.

The destinations included:

• Cornell University and other colleges
• employment locations, including non-standard employment
• elementary, middle and high schools
• food stores
• hospitals, urgent care facilities and doctors
• parks
• commercial centres
• Collegetown and Downtown Ithaca
• Dollar General stores and other locally important services.

This meant THAI was not based on an abstract definition of accessibility. It reflected how people living in Tompkins County actually travel and the opportunities that matter to them.

Different journeys require different assumptions

One of the important parts of the methodology was recognising that people do not make every journey at the same time.

Someone travelling to work or university is likely to travel during the morning peak and return later in the day. A trip to a doctor, grocery store or commercial centre is more likely to take place during the middle of the day. Leisure journeys may take place at weekends, while employees working non-standard hours may need transportation very early in the morning or late at night.

The THAI analysis therefore used different travel periods for different journey purposes.

Education, employment and university journeys were assessed using Tuesday morning travel between 7am and 9am, with the corresponding return journey assessed between 5pm and 7pm.

Shopping, healthcare and other daytime journeys were modelled between 11am and 1pm, with return journeys between 1pm and 3pm.

Parks and leisure access were assessed on a Saturday, while non-standard employment was separately analysed for early-morning and late-evening travel.

Maximum acceptable journey times were also varied according to the destination. Reaching an elementary school, for example, was given a shorter acceptable travel time than travelling to a major employment centre or university.

The result was a model which reflected journey purpose, time of day and the realities of the local transit network, rather than relying on a single catchment around a stop.

A journey has to work both ways

Another important difference was the decision to analyse both directions of every journey.

Accessibility analysis can sometimes focus on whether someone can reach a destination. But being able to reach a job, school or healthcare appointment is only useful if there is also a practical way to get home.

ITCTC therefore produced three measures:

Inbound | Outbound | Worst Case

The Worst Case score takes account of the weaker direction of the journey, exposing places where a service may appear accessible in one direction but fails to provide a practical round trip.

The analysis highlighted this particularly clearly around communities including Newfield and Freeville, where the availability or timing of return journeys reduced the effective accessibility available to residents.

This provides a much more realistic picture of whether a location is genuinely connected to opportunity.

What did the THAI analysis show?

The analysis confirmed Ithaca itself as the strongest connectivity hub in the county, reflecting the concentration of transit services, employment, education and other destinations around the city.

Strong connectivity was also visible along several of the major transportation corridors, including routes towards Trumansburg and Dryden.

However, the analysis also revealed significant differences elsewhere.

Areas west of Ithaca generally had poorer accessibility, with some communities particularly affected by the ability to make the return journey from major destinations. Newfield was one example where the two-directional analysis highlighted a weakness that would be much harder to identify from simply looking at proximity to transit.

Freeville also showed areas of lower transit connectivity despite its relative proximity to stronger-performing corridors.

This is where THAI becomes particularly valuable for planning: it does not simply confirm where transit exists. It exposes where the network successfully connects people to opportunities and where it does not.

From a map to understanding how many people are affected

A connectivity map is useful, but ITCTC wanted to take the analysis further.

The THAI results were therefore combined with Census population data, allowing the team to move from asking “where is accessibility good?” to “how many people experience good accessibility?”

The preliminary Worst Case results showed:

• 40% of residents received a zero score
• 41% achieved a connectivity score of 60 or above
• 22% — around 23,440 people — achieved a score of 90 or above.

This adds an important additional dimension to the analysis.

A poorly connected area containing very few residents presents a different policy challenge from one containing a significant population. Equally, proposed improvements can be evaluated not simply according to how much geographic area they affect, but according to how many residents benefit from the change.

Thomas Knipe said:

Bringing the trail network into the picture

Public transit was only one part of the project.

Tompkins County has also undertaken significant work to develop and improve its trail and cycling network, creating an extensive network approaching 1,000 miles when considered alongside the wider cycling network.

ITCTC wanted to understand what this meant for access to opportunity.

Using the same principles developed for THAI, the model was adapted to create the Cycle Housing Accessibility Index (CHAI).

Journey times and assumptions were adjusted for cycling, but the underlying principle remained the same: rather than simply asking whether cycling infrastructure exists nearby, the analysis measures what destinations residents can actually reach using it.

The CHAI results show Ithaca as a strong cycling accessibility hub, but importantly they also show accessibility extending well beyond the city.

Areas around Freeville, Dryden and Trumansburg perform strongly, while improved connections north from Lansing and east into more rural parts of the county demonstrate the role that the wider trail network can play in opening up access.

The analysis therefore provides evidence of how cycling can complement public transit rather than treating the two modes independently.

Testing future transportation investment

Because the methodology is network-based, it can also be used to test proposed improvements before they are built.

ITCTC supplied five proposed additions to the trail network, which were added to the model and the CHAI analysis rerun.

This allowed the county to compare the existing and proposed networks directly.

Under the existing network, 57% of residents — approximately 60,350 people — scored above 50.

With the proposed network in place, this increased to 57.2%, or around 60,500 residents.

The headline percentage change appears relatively small, but looking only at that measure would hide the wider effect. Across the county, 10.8% of residents experienced an improvement in their individual connectivity score under the proposed network.

That illustrates another benefit of accessibility modelling: an intervention does not need to move someone across an arbitrary score threshold to provide a meaningful improvement.

Supporting better housing and transportation decisions

THAI and CHAI now provide ITCTC with a consistent framework that can be used for much more than producing accessibility maps.

The methodology can support the comparison of potential housing sites, identify gaps in existing transit provision, examine the relationship between transportation and development and test the impact of future network improvements.

It also allows ITCTC to bring demographic information into the analysis.

Because TRACC outputs can be incorporated into the Council's existing GIS workflows, the accessibility results can be combined with other local datasets for further analysis.

As Thomas Knipe explains:

This turns accessibility from an isolated transportation measure into a dataset that can contribute to wider housing, land-use and policy decisions.

From proximity to opportunity

The central lesson from the project is simple:

Being near transit is not the same as being connected by transit.

Instead of asking:

“Is this development close to transit?”

THAI allows planners to ask:

“Can someone living here reach the opportunities they need — and can they get home again?”

By bringing together local knowledge, public transit schedules, cycling networks, journey purpose, time of travel, destination importance and Census population data, Ithaca-Tompkins County has created a much richer picture of accessibility across the county.

And because the methodology is repeatable, it can evolve alongside the transportation network — allowing ITCTC to measure existing conditions, test proposed interventions and understand who stands to benefit.

For housing and transportation planning, that moves the discussion beyond proximity to transportation and towards something much more meaningful:-

Access to Opportunity

Turn accessibility evidence into better decisions

The approach developed with Ithaca-Tompkins County shows how TRACC can help planning and transportation teams move beyond simple measures of proximity and understand what people can actually reach. Whether you are assessing new housing locations, reviewing the effectiveness of an existing transit network, testing proposed service changes or measuring the impact of active travel investment, TRACC provides the detailed, repeatable evidence needed to support better decisions. With the flexibility to incorporate local datasets, edit networks and analyse different modes, times and journey purposes, the same approach can be adapted to the priorities of any city, county, MPO or transit agency.

If you would like to explore what a THAI/CHAI-style accessibility assessment could look like for your area, get in touch with Basemap.