Research sponsored by the Federal Highway Administration (FHWA) to explore transportation cost savings opportunities led to the creation of an elasticity-based spreadsheet model in Excel to project how "repricing" transportation might affect behavior, household costs, and external impacts in 50 states.

Transportation repricing transforms fixed costs of owning a car to incremental costs of using a car, giving drivers more control over how much they spend on transportation, while lowering barriers to vehicular mobility. This research covers conversion of the following costs: 1) vehicle sales taxes, 2) vehicle registration fees, 3) vehicle property taxes, 4) vehicle insurance, 5) at-work parking, and 6) leased-vehicle depreciation fees. Translating lump-sum to usage-based pricing encourages drivers to optimize their vehicular travel, yielding benefits in personal and societal transportation costs, congestion levels, and safety outcomes. The analysis shows how revenue policies can be designed and modeled to be revenue neutral and how much average household travel costs could be reduced by repricing.

This model taps state and national driving cost and other data and policy-specific data such as newer versus older vehicle fuel efficiency and annual mileage and market-specific parking prices. The model includes adjustments to capture rebound effects (increased auto ownership and fewer uninsured motorists) and separates out which drivers are affected by policies in each state.

This presentation describes an analytical framework developed and implemented to evaluate and rank capacity expansion projects for inclusion in the IDOT Data-Driven Decisions tool.

The ILSTDM is a trip based travel demand model which includes a MOVES based emission post processor. Using the emission post processor, emission impacts were measured for all Illinois counties for base 2045 conditions. The project build conditions were compared to the base condition to estimate the emission impacts for each proposed project for emissions of CO, NOX, VOC, PM10, PM2.5 and CO2E. Emission difference results were reported for the county(s) that the project was located and ranked.

To develop an overall project rank, the individual rankings are combined. Since CO2E comprises all greenhouse gas emissions, which are of primary concern in the emissions metric, CO2E is  weighted as 50% of the weighted ranking score. The other five pollutants are given a 10% weight in the overall ranking. The weighted rank is accompanied by a criteria score, which simply transforms the weighted rank into a number ranging between 0 and 1. The highest ranked project has a criteria score of 1, and the lowest ranked project has a score of 0.

A summary of the overall emission rankings listed each of the projects weighted rank and criteria scores. Each project was listed on an individual sheet that contained the overall project emission ranking, the daily change in emissions per day per mile of the project by each pollutant, and the ranking of each individual pollutant. Vehicle miles traveled data is displayed in a project map showing the change in vehicle miles traveled compared the base conditions in addition to a table that lists the autos, single-unit trucks, and multi-unit trucks VMT differences.

Mobile phone and shared mobility data are transforming how researchers and planners understand urban movement. These fine-grain data sources move beyond simple proximity measures to show how people actually navigate routes, respond to barriers, and use public spaces.

This presentation examines Loring Park in Minneapolis during 2024 as a case study. Using data from a mobile location provider, we track monthly visitation patterns, highlighting clear peaks during major events, and delineate a “pocket” of access defined by the Mississippi River and interstate highways. We then compare visitor trade areas and distance bands to neighborhood population characteristics, contrasting casual and frequent users. Publicly available micromobility trip data are used to illustrate how these barriers shape trip origins and destinations to and from the park.

By linking mobility data with the spatial realities of barriers and networks, this session demonstrates how fine-scale analysis can clarify who is and who is not accessing key urban destinations, and how these insights can inform planning, land use, and policy decisions

The TREDLite platform is a cloud-based scenario planning tool designed for public agencies to efficiently assess the impact of land use decisions on transportation demand and Vehicle Miles Traveled (VMT). 

TREDLite is used to streamline the project review process for public agencies, facilitating more effective collaboration between project applicants, agency staff, and consultants. TREDLite is also used to better understand the environmental considerations of zoning changes and land use planning by agencies when updating housing elements or making other planning decisions related to land use and transportation planning.

This presentation and subsequent break-out session will cover the development of TREDLite, including the integration and customization of the underlying data, the connection between the platform and public agency planning, additional use cases for the platform, case study examples, and an overview of future enhancements planned for the platform.

Understanding the complex interplay between the built environment, demographics, and travel behavior is crucial for promoting sustainable transportation. Neighborhood typologies, which categorize areas based on shared characteristics, offer a holistic approach to analyzing these relationships, potentially informing more effective land use and transportation policies. However, existing research using neighborhood typologies to explain travel outcomes shows inconsistent results. This inconsistency may stem from variations in methodological approaches, including the geographic scale of analysis (national vs. state) and the inclusion of socio-economic factors alongside built environment variables.

This research systematically investigates the impact of different methodological strategies on the development of neighborhood typologies and their subsequent relationship with travel outcomes. We address three key questions: (1) How do neighborhood typologies differ when classified at the national versus state level? (2) How does incorporating socio-economic factors alongside built environment attributes influence the resulting typologies? (3) How do these different classification strategies affect the observed relationship between neighborhood types and travel behavior?

In addition to the common factor analysis and cluster analysis methods, we include the decision tree method that balances prediction accuracy and interpretability to classify census block groups into neighborhood types, using data from multiple sources. Built environment variables (density, diversity, design, destination accessibility, and transit) are drawn from the EPA Smart Location Database 2.0. Traveler socio-economic variables and travel outcome data (trip frequency, length, mode share) are derived from the 2017 National Household Travel Survey (NHTS). We construct neighborhood typologies representing data from different geographic scales (national, state) and variable inclusion (built environment only, built environment + socio-economics). We then statistically analyze the relationship between each typology and the NHTS travel outcomes.

This research directly aligns with the MOMO 2025 "Connect" theme by connecting methodological choices in research to their practical implications for transportation planning. By systematically comparing different approaches to neighborhood typology development, this study provides actionable guidance for researchers and planners. Our findings demonstrate that the relationship between neighborhood types and travel outcomes is sensitive to different classification strategies. The results will inform:

• More effective land use and transportation policies: by identifying neighborhood characteristics that are most strongly associated with desired travel outcomes.

• Improved travel demand models: by incorporating more representative neighborhood typologies.

• Improved understanding of the relationship between land use and transportation: instead of reducing the relationship to a simple elastic measure, neighborhood typologies make it possible to consider nonlinear and synergistic relationships.

This research bridges the gap between academic methodology and practical planning application, ultimately contributing to more effective and sustainable transportation systems. It empowers planners to make data-driven decisions based on a better understanding of the complex relationship between place and travel behavior.

Widespread adoption of smartphones has revolutionized household travel diary survey data collection by integrating GPS, allowing trip-making to be detected in real time and prompting users to provide responses as soon as their travel is completed, rather than requiring participants to recall their previous day's travel.  Smartphones have also facilitated multi-day collection and enable path information to be collected.  The GPS path data has been underutilized relative to the core household, person, and trip information that household travel surveys have traditionally provided.  The Metropolitan Transportation Commission and San Francisco County Transportation Authority have recently begun to leverage GPS path data collected with the 2023 Bay Area Travel Study (BATS) to develop user profiles of managed lanes, toll bridges, and other freeway facilities.  We will present the methods for developing user profiles of roadway facilities, including routing GPS path data onto an Open Street Map network using open source tools and using weighted survey records to develop volume estimates and margins of error.  We will then present user profiles for facilities of interest, including breakdowns by household income, race and ethnicity, home county, mode, and trip purpose.