Data Collection Mechanisms

Quasi-Sparsity in Transportation Origin-Destination Demand

Quasi-sparsity (QS) indicates that for a large-scale transportation network, most origin-destination (OD) demands are concentrated on a small fraction of the OD pairs, while majority of the OD pairs exhibit small (maybe non-zero) travel demands. One example is the King County network (the area that includes the City of Seattle in the State of Washington):

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Michigan Mobility Metrics (M3): An Outcome-Focused, Multi-Year Survey Deployment and Data Collection Effort

Transportation modes, technologies, and the broader context within which people travel have evolved rapidly over the last decade. Examples of such changes include the introduction of: new/emerging modes like ridesharing and micromobility, electric and automated vehicle technologies, information and communication devices; and the increase in remote and hybrid work due to COVID-19. It is critical

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Investigation of Emerging Sensing and AI/ML Technologies to Enhance the Safety of Vulnerable Roadway Users at Signalized Intersection

Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network

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Blockchain Application on Smart Transportation Systems

Blockchain technology, predominantly utilized within cryptocurrency, is being increasingly adapted across diverse sectors, and transportation systems is not an exception. Despite presenting several challenges, blockchain technology also offers various advantages. Understanding Blockchain’s potential applications and benefits in addressing future urban challenges is an emerging field of research which has not been fully investigated. In fact,

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From Cross-Sectional to Longitudinal: The Impact of Sampling Strategies on Measuring Mobility Choices

The transportation profession has long relied on surveys as a main source of data. These surveys are used across a broad range of applications, including but not limited to, travel demand forecasting, travel behavior analysis, policy evaluation, environmental impact assessment, equity analysis, and economic evaluation. Despite the increasing accessibility of passive data collection methods, such

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Exploring the Changing Dynamics of Household Vehicle Ownership and Use in the U.S.

This project is driven by a pressing need to understand the rapidly evolving landscape of household vehicle dynamics amidst technological advancements and significant societal changes. It focuses on the growing urgency of climate change mitigation and adaptation, a push for equitable mobility for all, and the transition towards vehicle electrification. Aiming to fill the knowledge

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A Pilot Study to Integrate Mobility Data Collection APPs with Personalized Recommendation Systems

Recent years have witnessed many efforts to use smartphones to collect travel data. Typical examples include the automatic collection of sensor data such as location, accelerometer, or microphone readings, and personalized recommendation/behavior modification by gamifying travel and providing incentives for particular mode choices or building route choice models for active transportation modes such as bicycling.

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Measuring the Last-Mile: A Comprehensive Evaluation of Synthesis Approaches to Address Data Gaps for Local Freight Decision-Making (Phase 1)

Currently, few municipal or regional authorities have access to the disaggregate freight activity data needed for planning, operational decision-making, freight externality evaluation (e.g. air pollution, collision risk), or equity analysis. Due to stakeholder privacy concerns, freight data are often aggregated by geography and/or commodity, limiting direct applicability of published data for local analysis. As a

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The Transportation Heartbeat of America Survey

TBD will deploy a comprehensive longitudinal travel behavior and demand survey across the nation for six years to obtain a statistically representative depiction of attitudes, values, choices, socio-economics, demographics, well-being, and mobility and accessibility. The unique aspect of this endeavor is that it will also include a survey of businesses and employers. Through the incorporation

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