Developed with support from the Illinois Department of Transportation, the Sustainable Neighborhoods Toolkit brings together data and analysis from across the CUUATS modeling suite to analyze neighborhood-level mobility, accessibility, and health in Champaign County. The results from the toolkit are published in the Access Score web application:
Transportation planning operates at a regional scale, but mobility, accessibility, and health are influenced by neighborhood-level factors and decisions. This makes it difficult to observe differences in infrastructure and behavior across the region.
The Sustainable Neighborhoods Toolkit integrates data from the models and tools in the CUUATS modeling suite to evaluate neighborhood-level mobility, accessibility, and health as experienced by members of the community. At the heart of the modeling suite are the four-step travel demand model (TDM) and land use model. These models forecast how population, employment, and travel behavior will change in Champaign County over time. They provide the big-picture trends that undergird the other models and analyses.
Using outputs from the TDM and land use model, the Social Cost of Alternative Land Development Scenarios (SCALDS) and Motor Vehicle Emissions Simulator (MOVES) models predict the social and environmental impacts of future development and transportation projects. They forecast future energy and infrastructure needs, as well as greenhouse gas emissions, factors that influence the sustainability of future scenarios.
Developed using the Sustainable Neighborhoods Toolkit, Access Score is the latest addition to the modeling suite. An open source web application, it takes the regional data and analysis from the other models and brings it down to the neighborhood scale, where members of the community make their travel choices. Using Access Score, community members can select from four modes of transportation and ten destination types. An interactive map updates in real time to display a customized block-level accessibility analysis, showing how easy or difficult it is to reach the selected destination types using the selected modes of transportation from anywhere in Champaign County. Users can also select a future scenario from Long Range Transportation Plan 2045 to see how proposed transportation projects, as well as population and employment changes, will impact accessibility in the future.
The analysis displayed in Access Score was performed using the three primary tools in the Sustainable Neighborhoods Toolkit:
- Level of Traffic Stress Assessment
- Accessibility Assessment
- Health Impact Assessment
The sections that follow describe each of these tools in detail.
Level of Traffic Stress Assessment
Level of Traffic Stress (LTS) is a rating given to a road segment or crossing indicating the traffic stress it imposes on the user. LTS levels range from one to four. An LTS of one represents conditions that are comfortable for users of all ages and abilities, while an LTS of four represents conditions that are comfortable only for experienced and confident users of that mode. In the LTS methodology, each segment or crossing is scored based on a series of criteria. The worse score received is then assigned as the overall score.
Since the four LTS categories are designed to correspond to different types of transportation users (e.g., Geller’s four types of cyclists), they indicate the degree to which the transportation network serves everyone in the community. By contrast, previous assessment methods, such as Level of Service (LOS), reflect the performance of the transportation system, but not necessarily its impact on particular types of users.
LTS Scoring Logic
Because LTS measures follow a “weakest link” logic, the stress level is assigned based on the lowest-performing attribute. For example, BLTS considers bike lanes with adjacent parking lanes. Assuming posted speed of 30 MPH and annual average daily traffic (AADT) between 1,000 and 3,000, the street cross section pictured below would be assigned an LTS of 3. The bike lane on the right plus its adjacent parking lane are 150 inches wide, while the left is 162 inches wide. Based on the bike lane on the right, the segment would be assigned an LTS of 3, but based on the left it would receive an LTS of 2. Using the “weakest link” method, the final score is the worst score received based on all the criteria assessed.
Modal LTS Assessments
- Bicycle Level of Traffic Stress: BLTS is the most established of the LTS assessments and is based on methodology developed at the Mineta Transportation Institute. It evaluates street segments, crossings and intersections, and turning lanes. Features like buffered bicycle lanes and low traffic counts decrease BLTS, while higher posted speed limits and more vehicle travel lanes increase it.
- Pedestrian Level of Traffic Stress: PLTS is based on methodology developed by the Oregon Department of Transportation. Like BLTS, it provides a series of segment and crossing criteria, including sidewalk condition, buffer, and land use criteria. Factors like low speed limits and physical buffers decrease PLTS, while missing sidewalks or those in poor condition increase it.
- Automobile Levels of Traffic Stress: ALTS was developed by CUUATS staff using methods that mirror those in BLTS and PLTS. It focuses on criteria that capture interactions and potential conflict points with other modes. Factors like good roadway condition and high posted speed limits decrease ALTS, while on-street bicycle facilities and heavy vehicle traffic increase it.
Transit is not included in the LTS analysis. Since the experience of riding transit does not change substantially from one bus route to another, the primary criteria that that influences its use is travel time, including wait time, ride time, transfer time, and walk time. These factors, rather than LTS, are used to evaluate transit accessibility in the Accessibility Analysis. CUUATS staff explored the possibility of adding credits or penalties to account for the presence or absence of transit facilities, such as shelters and benches. However, further research is needed to determine how much weight these factors have in influencing transit users’ travel choices.
Technical Documentation and Code
Technical documentation and code related to LTS are available in the CUUATS Level of Traffic Stress Assessment repository on GitLab. The Python code in this repository takes a transportation network GIS layer using a standard data schema and assigns LTS scores based on a set of criteria for each mode. The data schema and the criteria are described in detail in the technical documentation.
Accessibility reflects how easy or difficult it is to reach a destination from a starting point, or origin. It is based primarily on two factors: the quality (LTS) of the transportation infrastructure, and the distance to the destination. The combination of these factors can be used to calculate a “cost” for each link in the transportation network. The purpose of the Accessibility Assessment is to find the path with the lowest cost from each intersection in Champaign County to the nearest destination or destinations.
Calculating Trip Cost
The example below shows the trip cost calculation for a bicycle trip to work consisting of two street segments. The first segment has a bicycle level of traffic stress (BLTS) of 1 and a length of 1 mile. The segment has a BLTS of 3, representing more challenging riding conditions, and a length of 0.5 miles. As a result of its low LTS score, the cost of biking the second segment is actually higher than the cost for the first segment, event though the first segment is twice as long. The total cost of the trip is the sum of the two segment costs.
Calculating Accessibility Scores
CUUATS staff identified ten common types of destinations that represent locations members of the community visit on a regular basis. A database of locations matching these destination types was compiled using business data from Emsi, as well as other local data sources. For some destination types, such as schools and public facilities, the destinations are further classified into subtypes. This process reflects the reality that most households, for example, need access to both a library and a post office.
The Accessibility Assessment uses a Python network analysis library called Pandana to calculate the least cost, or most easily accessible, path from each intersection to a destination or destinations of each type or subtype. In most cases, the single most easily accessible destination is used. For example, the most easily accessible park determines the accessibility for the park destination type. For retail stores, however, households need access to a variety of different stores. Therefore, the fifth most easily accessible store is used to calculate the accessibility for the retail stores destination type.
The output from Pandana is a series of costs for each mode-destination combination at each intersection. To generate an access score, the costs are scaled according to a set of thresholds for each mode, where any trip with a cost at or above the thresholds receives a score of zero, and a trip with a cost of zero receives the maximum score of 100. The thresholds represent the most difficult trip a typical user of that mode would be willing to make:
- Pedestrian: 1.5 miles / 30 minutes at LTS 2
- Bicycle: 4 miles / 30 minutes at LTS 1
- Bus: 60 minutes (no LTS)
- Vehicle: 10 miles at LTS 2
Technical Documentation and Code
Technical documentation and code related to accessibility are available in the CUUATS Accessibility Assessment repository on GitLab. The Python code in this repository takes LTS scores generated by the Level of Traffic Stress Assessment, transit data from a GTFS feed, and destination locations, and calculates the access scores using Pandana. It produces output in the format expected by the Access Score web application. The technical documentation describes the input and output data formats and scoring procedures.
Health Impact Assessment
CUUATS aims to increase understanding about the different ways the transportation system is connected to population health. One of the most widely documented impacts of transportation infrastructure on health outcomes is manifested through individual travel mode choices as they are related to physical activity. People who routinely walk and bike are more likely to get the recommended levels of daily physical activity for good health, which can lower the risk of certain health conditions such as obesity, hypertension, and type II diabetes.
In 2014, CUUATS staff published a Health Impact Assessment that established a relationship between transportation and built environment characteristics and obesity in the community. During 2019 and 2020, the Health Impact Assessment was integrated into the Sustainable Neighborhoods Toolkit. The assessment was updated to draw on score data from Access Score instead of drawing directly on characteristics of the transportation system. The access scores represent how easy or difficult it is to access ten common types of destinations using four modes of transportation. The Health Impact Assessment uses the bicycle and pedestrian access scores, since these modes of transportation are associated with increased physical activity.
CUUATS obtained aggregated data from a major health provider about the number of patients treated for obesity, hypertension, and type II diabetes between 2013 and 2017 in the Champaign Urbana Urbanized Area. Using these data, CUUATS staff created a regression model that associates the number of patients with the average bicycle and pedestrian access score in each traffic analysis zone for the 2015 Baseline scenario created as part of the Champaign-Urbana Long Range Transportation Plan (LRTP) 2045. Controlling for race and age, the model showed a significant inverse relationship between pedestrian and bicycle accessibility and the number of patients treated for these diseases.
Evaluating Future Scenarios
While the relationship between bicycle and pedestrian accessibility and health does not establish causation, scientific evidence suggests that even small increases in active transportation could lead to significant decreases in chronic disease. As such, it is appropriate to explore the possible impact of future transportation projects on population health.
CUUATS staff applied the model from the Health Impact Assessment to access scores from the Champaign-Urbana LRTP 2045 Preferred Scenario. This scenario reflects ambitious implementation of bike and pedestrian recommendations in current plans, projected transit system changes, future environmental considerations and actions, and an emphasis on infill (over peripheral or sprawl) development.
Preliminary modeling suggests that bicycle and pedestrian accessibility improvements in the 2045 Preferred scenario could lead to 292 fewer patients (a 1.7% reduction) requiring treatment for obesity, hypertension, and type II diabetes per year compared to the 2015 Baseline scenario. These predicted reductions do not take into consideration population growth.
Technical Documentation and Code
Technical documentation and code related to health are available in the CUUATS Health Impact Assessment repository on GitLab. The R code in this repository takes accessibility data from the Accessibility Assessment and a traffic analysis zone data. It integrates these data with American Community Survey demographics automatically retrieved from the Census API to fit a regression model. The model is then applied to future scenario accessibility scores to predict health outcomes. The technical documentation describes the input data format and configuration options.
Neighborhood Assessments for Better Planning
Through a suite of three integrated technical tools and one web application, the Sustainable Neighborhoods Toolkit offers planners a way to assess mobility, accessibility, and health at the neighborhood level. The data and analysis generated using the toolkit are already being used by CUUATS for planning applications, including the LRTP 2045 performance measures and the Title VI Report. In addition, the Access Score web application provides the public with a way to explore accessibility at the neighborhood level. Work is already underway to create a next-generation tool that will leverage Access Score to engage public meeting participants around questions of accessibility, further strengthening the planning process.