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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Complementary or Competing? Studying the Relationship between E-Scooter Sharing and Bikesharing in Austin, Texas</title>
      <link>https://trid.trb.org/View/2694349</link>
      <description><![CDATA[The correlation between e-scooter sharing (ESS) and docked bikesharing (DBS) remains ambiguous. This study compared usage patterns between the two modes in Austin, Texas, aiming to unveil their evolving relationship. To account for nonlinear effects, generalized additive mixed models were employed. The findings indicate that both ESS and DBS programs achieved success in densely populated urban areas, areas with younger and higher-income populations, as well as on university campuses. However, a one-sided competitive relationship emerged, with ESS surpassing DBS. For policy implications, local governments should reassess and harmonize their policies to determine the cost-effectiveness of preserving a declining DBS program.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694349</guid>
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    <item>
      <title>Integrating Social and Economic Sustainability in Urban Air Mobility Network Planning</title>
      <link>https://trid.trb.org/View/2689381</link>
      <description><![CDATA[Urban air mobility (UAM) is an innovative concept with the potential to create numerous economic opportunities. However, the integration of UAM into existing urban environments presents significant challenges. Factors such as aircraft noise, community acceptance, and economic feasibility are primary concerns for investors, governments, and users. Recognizing the critical role of network and infrastructure design in a sustainable UAM system, this work focuses on enhancing UAM’s social and economic sustainability from a network planning perspective. Specifically, the authors plan the configurations of vertiports and flight corridors in a city by considering several key socioeconomic and environmental factors. The proposed methodology consists of social and economic factors identification, geospatial data integration, vertiport location selection, and flight corridor optimization. The methodology is applied in a detailed case study of the Austin, Texas, metropolitan area, in which the authors present and discuss a series of sustainable UAM network planning results across various cost-benefit scenarios.]]></description>
      <pubDate>Wed, 15 Jul 2026 09:23:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689381</guid>
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    <item>
      <title>Impacts of mobility and social networks on social activity-travel participation using location-based social network data</title>
      <link>https://trid.trb.org/View/2658022</link>
      <description><![CDATA[This paper investigates the relationship between social networks and the activities they generate, by exploring inter-social-activity durations as a proposed measure of social activity participation frequency. To model the proposed measure, data were collected and processed from a publicly-available dataset sourced from the location-based social networking service Gowalla. The data include information from 3065 Texas Gowalla users, regarding social activity-travel behavior, and performance of modularity- and surprise-based community detection. To account for the longitudinal nature of the data, and for possible spatial instability of the model parameters across two major Texas cities, a grouped-random-parameters hazard-based duration modeling approach with heterogeneity in means is employed, and separate models are estimated for Austin and Dallas users. The results suggest that social activity participation frequency is affected by individual mobility, and by a number of social network effects, such as ego social network size, social group variety, and local closeness centrality. The findings call for a thorough investigation of the transportation system and social network interrelationships.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658022</guid>
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    <item>
      <title>Factors influencing service times for urban deliveries in the United States</title>
      <link>https://trid.trb.org/View/2685547</link>
      <description><![CDATA[Efficient urban logistics systems are crucial for ensuring the timely and reliable delivery of goods, especially with the growth of e-commerce and the increasing frequency of household deliveries. This research investigates the determinants of delivery service time—defined as the time required to deliver the package once the delivery person has arrived at the package’s delivery location—across five U.S. metropolitan statistical areas (MSAs): Austin, Boston, Chicago, Los Angeles, and Seattle. It applies parametric duration models and finds that the log-logistic distribution best fits the data across all MSAs, identifying key spatial, socio-economic, temporal, and package-related factors that influence delivery service time. In addition, the Interaction Index is used to identify economic poles in each MSA, enabling a more nuanced understanding of urban spatial structure and its impact on last-mile performance. Results show that proximity to these poles has MSA-specific effects; shorter service times are associated with larger household sizes and lower incomes, while longer times are linked to higher population density, larger packages, higher incomes, and deliveries made during peak hours. Based on these insights, the research provides actionable recommendations for policymakers and practitioners, such as applying freight-efficient land use planning principles, investing in infrastructure improvements that directly support last-mile logistics, and implementing off-hour delivery programs to reduce delays during peak periods. Future research should build on this work by validating socio-economic drivers and exploring additional determinants of delivery service time.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685547</guid>
    </item>
    <item>
      <title>Comparing delay-, distance-, and cordon-based congestion pricing strategies via large-scale simulation</title>
      <link>https://trid.trb.org/View/2685542</link>
      <description><![CDATA[This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685542</guid>
    </item>
    <item>
      <title>Exploring the relationship between daytime and nighttime mobility and park visitation: A case study of Austin, TX</title>
      <link>https://trid.trb.org/View/2680665</link>
      <description><![CDATA[Urban green space disparities persist amid rapid urbanization, widening the supply-demand gap between parks and developed area. Population density is a critical determinant in estimating park visitors, defining suitable park locations, and allocating facilities for park accessibility. Conventionally, population density data were used as a foundational basis for urban green space planning decisions, often derived from sources like the US Census Bureau, primarily reflecting “nighttime residential” distribution. However, this approach fails to capture the dynamic urban life where daily routines and mobility significantly shape park usage. This study bridges this gap by exploring the relationship between daytime and nighttime mobility patterns and their influence on park visitations across diverse park types during weekdays, using Austin, TX as study area. Methodologically, we employ a fixed effects regression analysis integrating longitudinal data from SafeGraph for park visitation and LandScan USA for daytime-to-nighttime population density ratios, within 1 km buffers around each park. Control variables encompass socio-economic factors at the block group scale, park attributes, and weather conditions. Findings suggest that neighborhood and pocket parks demonstrate positive associations with daytime population density, while district and metropolitan parks exhibit stronger ties with nighttime population density. Further, median age, unemployment rate, and higher education attainment exhibit positive correlations with park visitation, especially during daytime. Park amenities, especially playgrounds and water features, significantly contribute to increased visitation across all park types. The findings offer valuable guidance for policymakers and urban planners, informing the reimagining of park distribution strategies, optimizing facilities, and fostering inclusive park spaces accessibility.]]></description>
      <pubDate>Thu, 25 Jun 2026 09:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680665</guid>
    </item>
    <item>
      <title>A Dynamic Analysis of the Built Environment-Travel Behavior Relationship Using Three Activity-Travel Surveys in the Austin, Texas Region</title>
      <link>https://trid.trb.org/View/2712626</link>
      <description><![CDATA[This study investigates the dynamic effects of the built environment on travel in Austin, Texas, over a 20-year period. Using three waves of household travel surveys from 1997, 2006, and 2017, the research employs a repeated cross-sectional approach to address the limitations of traditional longitudinal and cross-sectional studies, and to more accurately estimate effect sizes. Methodologically, we introduce a novel integration of machine learning and inferential modeling to uncover non-linear relationships and threshold effects of the built environment characteristics on travel. Using Gradient Boosted Decision Trees (GBDT) and Partial Dependence Plots (PDPs), we first identify optimal threshold points in the relationships, which are then incorporated into piecewise multilevel models. Findings from the study reveal that the built environment serves as a sustainable tool for managing travel in the long term, contributing 50% or more to the total feature importance in predicting individual travel—surpassing the combined effects of personal and household characteristics. Improved transit accessibility, enhanced local and regional accessibility, higher population and employment densities, and greater diversity are all associated with significant reductions in travel— particularly within their identified thresholds—though the magnitude of their influence varies across time periods and shows diminishing marginal returns. These findings highlight the potential of smart growth policies—such as expanding transit accessibility, promoting high-density and mixed-use development, and discouraging single-use development and peripheral sprawl—as effective strategies to reduce car dependency and manage travel demand. Moreover, the study demonstrates that the proposed integrated approach can effectively capture complex non-linear effects while enhancing flexibility and interpretability, reducing researcher bias, and enabling statistical inference—ultimately providing more robust and policy-relevant insights.]]></description>
      <pubDate>Mon, 15 Jun 2026 08:40:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712626</guid>
    </item>
    <item>
      <title>Smart charging of fleet and personal electric vehicles through joint vehicle-to-grid optimization</title>
      <link>https://trid.trb.org/View/2702201</link>
      <description><![CDATA[As electric vehicle (EV) adoption accelerates, vehicle-to-grid (V2G) strategies offer advantages over unmanaged charging (V0G) by enhancing grid stability, reducing fleet operation costs, and supporting integration of variable generation resources. This research develops a day-ahead optimization framework linked with agent-based simulations to evaluate coordinated V2G participation by fleet and personal EVs under 5 energy-pricing settings in Austin, Texas. Three scenarios (V0G, fleet-only V2G, and joint-V2G) are examined, considering real-time price and grid profiles, health-damage costs, and operational constraints for both fleet and personal EVs. Results show how V2G scenarios shift fleet EV charging to mid-day while enabling strategic battery-discharge during evening peaks, mitigating grid stress and lowering EV energy costs. V2G delivers close to 80% energy-cost savings for a 2000-EV fleet in Austin on grid-stressed days, with 55% lower charging pollutant outputs. Joint-V2G amplifies system-level benefits by complementing fleet discharge, but smart-charging equipment costs can offset those benefits.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702201</guid>
    </item>
    <item>
      <title>Interactions between Climate Policy and Technology-Influenced Travel Behavior: Mitigating Induced Demand from Cooperative Adaptive Cruise Control</title>
      <link>https://trid.trb.org/View/2701222</link>
      <description><![CDATA[Advances in vehicle and computing technologies have influenced the development of automated vehicle systems, and vehicles that do not require human intervention are already deployed on roadway networks. While these advances are proving to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional impacts on mobility, land use, energy consumption, and emissions. This study demonstrates a multimodel approach to analyze the effects of vehicle automation and the potential of deep decarbonization policies to mitigate associated increases in energy use and emissions, over a period from 2020 to 2040 in Austin, Texas. We use the global change analysis model with state-level resolution (GCAM-USA) to simulate the evolution of the US energy system under reference case and deep decarbonization scenarios. Fleet characterization and fuel prices projected by GCAM-USA are passed to the SMART Mobility modeling workflow. This large-scale simulation framework combines the POLARIS activity-based travel-demand model and mesoscopic traffic simulator, the Autonomie vehicle energy consumption model, and the UrbanSim land-use simulator, to jointly explore the mobility and energy use outcomes of the Level 4 automation with cooperative adaptive cruise control (L4-CACC) and a set of decarbonization policy responses. Results suggest that the introduction of L4-CACC vehicles could increase fuel consumption when no decarbonization policies are implemented, raising 2040 vehicle miles traveled (VMT) by approximately 9% and fuel use by about 13% relative to a no-automation reference case. Deep decarbonization policies, including energy pricing and vehicle electrification incentives, offset part of these increases by reducing fuel consumption by 22% relative to the automated case and 27% relative to the reference case, while also reducing overall well-to-wheel greenhouse gas emissions by shifting travel toward more efficient vehicle technologies. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation produced by the expected higher VMT. Finally, our analysis indicates the relevance of introducing land-use processes such as household and workplace choices in vehicle automation studies, because of the influence on the VMT that these decisions have in the long term.]]></description>
      <pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701222</guid>
    </item>
    <item>
      <title>Impacts of spatial resolution on agent-based transportation simulations with shared autonomous vehicles</title>
      <link>https://trid.trb.org/View/2655630</link>
      <description><![CDATA[Agent-based transportation models have been used to simulate shared autonomous vehicle (SAV) fleet operations, enabling a growing understanding of SAVs' operations, impacts, and opportunities. This paper investigates the issue of spatial resolution, since most studies have been conducted on coarsened networks, with many missing links and with aggregated addresses for trip origins and destinations. This work presents simulation results for dynamic traffic assignment with SAV fleet operations in Austin, Texas, comparing outcomes across two networks and two sets of addresses for trip ends in the region's six counties. The comparison involves the Capital Area Metropolitan Planning Organization's (CAMPO's) planning network with addresses highly aggregated (census block centroids supplemented with business establishment information), versus OpenStreetMap's (OSM's) real network with actual addresses sourced from OpenAddresses. CAMPO's network contains 40.6 % of the OSM lane-miles, while the aggregated address points are highly concentrated in the urban core and represent 23 actual addresses on average. Agent-based simulation results using the POLARIS model suggest that omitting a large share of collector and residential links significantly affects network flows, increasing VMT and VHT along non-expressway arterials by 18.9 % and 10.4 %, respectively, for the case of Austin. By contrast, address aggregation (at least at the level implemented in this study) has little impact on traffic. SAVs benefit from increased network connectivity and alternative routes in the complete network to reduce passenger pickup distances and ridepooling detours, lowering VMT by 10 % per SAV—nearly five times the reduction seen in network-wide VMT—and empty VMT (%eVMT) by 2.5 to 3.5 percentage points.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655630</guid>
    </item>
    <item>
      <title>Disparities in pedestrian crossing and driver yielding behaviors: Evidence from a large-scale observational study at urban intersections</title>
      <link>https://trid.trb.org/View/2673269</link>
      <description><![CDATA[Pedestrian safety remains a critical challenge in urban environments, marked by rising fatalities and persistent disparities across sociodemographic groups. Uncovering the drivers of these disparities requires a deeper understanding of both pedestrian and driver behaviors. This study examines how individual attributes, social context, and time-of-day/weather conditions shape pedestrian crossing and driver yielding decisions. The authors analyzed over 1,000 hours of video footage from two intersections in Austin, Texas, documenting over 20,995 pedestrian crossings and 3,124 pedestrian-vehicle interactions. Manual annotation of this footage enabled the estimation of two binary logit models: one predicting non-compliant pedestrian crossings (NCPC) and the other predicting driver unyielding (DUY) (that is, driver failure to yield to pedestrians). The results indicate that male pedestrians, Black pedestrians, those displaying visible signs of housing insecurity (VHI), and individuals crossing solo are significantly more likely to cross non‑compliantly and to encounter lower driver‑yielding rates. Runners also exhibit higher NCPC rates than walkers, with peak non‑compliance occurring during late night and dawn periods. On the driver side, pedestrian NCPC behavior is the strongest predictor of failure to yield. DUY behavior is also more likely during morning periods and among drivers of personal (non-commercial) vehicles, and when the pedestrian in question is older, Black or Brown, and exhibits VHI. These findings highlight the importance of addressing social and behavioral factors in pedestrian safety interventions. By revealing how marginalization and context interact to shape risk, this research contributes to the transportation equity literature and supports interventions that go beyond infrastructure, such as education campaigns, bias reduction, and community-led safety initiatives.]]></description>
      <pubDate>Wed, 18 Mar 2026 08:59:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673269</guid>
    </item>
    <item>
      <title>Advanced Transportation Optimization and Modeling (ATOM)</title>
      <link>https://trid.trb.org/View/2676009</link>
      <description><![CDATA[The U.S. transportation system is experiencing increasing complexity driven by evolving infrastructure, land-use patterns, travel demand, demographic shifts, and rapid advances in vehicle and mobility technologies. Emerging behaviors such as telecommuting, ridesharing, and micromobility, along with changing attitudes toward public transit and vehicle ownership, are reshaping how people and goods move across regions. To ensure that transportation investments remain efficient, resilient, and cost-effective, transportation agencies require advanced, data-driven tools to anticipate and evaluate the system-level impacts of these changes.  

This project develops an advanced transportation modeling and optimization pipeline in Austin, Texas, to evaluate the impacts of alternative strategies and technologies through scenario-based analysis. The system will be built around the Behavior, Energy, Autonomy, and Mobility (BEAM) model. BEAM is an open-source, agent-based regional transportation model that enables realistic simulation of travel behavior, mode choice, fuel consumption, and system performance, and associated community-level impacts under different “what-if” scenarios.  

By leveraging BEAM’s scalable, modular architecture, the project will address key limitations of conventional four-step and activity-based transportation models, providing a robust framework for testing strategies such as emerging technologies, infrastructure enhancements, and new mobility services before deployment. The pipeline will be developed and extended to assess additional impacts (via coupling to additional models) and therefore to serve as a decision-support tool for engineers, planners, and service providers, allowing them to evaluate performance outcomes and trade-offs across multiple metrics relevant to both economic productivity and community outcomes. Model calibration and validation of the Austin BEAM Core pipelines will utilize highly resolved local datasets on traffic flows, speeds, and network performance. These data will enable precise representation of real-world operating conditions in the Austin region and ensure the model’s reliability for planning and investment analysis.  

Scenario development will be coordinated with implementation partners regional stakeholders identified through a stakeholder mapping exercise. These scenarios will reflect practical policy and technology options under active consideration in Texas, ensuring alignment with state and regional priorities. The resulting pipeline will be structured for extensibility, allowing future integration with additional datasets and modeling components for use in other applications. Project outcomes will be shared broadly through technical reports, workshops, and data portals to facilitate adoption by other agencies, research institutions, and industry partners.  

Ultimately, this project supports goals of enhancing efficiency, safety, and reliability, while strengthening economic competitiveness and enabling informed, data-driven investment decisions. By combining open-source modeling innovation with public–private collaboration, the project will provide a replicable framework for modern, performance-based transportation system management.  

Moreover, the pipeline embraces and deploys advanced and transformative research: using an open-source, agent-based framework (BEAM) exceeds conventional planning methods. The stakeholder-co-development model (with public and industry partners) ensures that this research is not only theoretically innovative but also rooted in real-world deployment potential. This initiative empowers decision-makers to implement policies that enhance safety, the economy, and with various co-benefits to communities.  ]]></description>
      <pubDate>Tue, 03 Mar 2026 16:42:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676009</guid>
    </item>
    <item>
      <title>Autonomous Vehicle Traffic Delay Incident and Rapid Response</title>
      <link>https://trid.trb.org/View/2562044</link>
      <description><![CDATA[The paper focuses on a case study of a Cruise autonomous vehicle (AV) involved in a significant traffic delay incident in Austin, TX, in June 2023. The pre-programmed travel routes of the AVs avoided the major arterial streets and may not be the most direct or shortest paths. The incident event procedure and the interpretation of the remote assistance provided by the AV fleet operations control center (OCC) to the two Cruise vehicles are thoroughly discussed. The incident was caused by a group of illegally roadside-parked cars on a narrow street, and the difficulty of the Remote Assistance role is evaluated. This case study and the analysis illustrate that the extenuating circumstances of the incident and the difficulty of navigating around the blockage in the AV’s travel path provided a good and reasonable resolution of the incident, representing what is now being called “Remote Assistance” in recent SAE-recommended practice standards.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562044</guid>
    </item>
    <item>
      <title>Transportation and Mental Health in Central Texas Using 211 Call Center Data – An Exploratory Analysis</title>
      <link>https://trid.trb.org/View/2652177</link>
      <description><![CDATA[Mental health is an important part of an individual’s well-being and has been included as a key topic by the U.S. Centers for Disease Control and Prevention. Lack of access to affordable and efficient transportation can isolate individuals, limiting their ability to maintain employment, attend healthcare appointments, or engage in social and recreational activities—all of which are vital for mental well-being.  Long commutes, traffic congestion, and unreliable transit can contribute to chronic stress, anxiety, and fatigue, especially in urban environments. Active transportation options like walking and cycling not only reduce stress but also promote physical activity, which can reduce symptoms of depression.  
The research project aims to understand the multifaceted relationships between transportation and mental health by conducting a literature review using Latent Dirichlet Allocation (LDA) in topic modeling to identify prevailing themes and research trends in transportation and mental health. Also, through collaboration with United Way for Greater Austin, this project will incorporate insights from 211 Call Center staff and volunteers to better understand transportation-related mental health concerns, from a frontline service perspective. The project will then analyze 211 Call Center data provided by the United Way for Greater Austin. This analysis will explore spatial and temporal variations in mental health-related issues and examine how transportation correlates with mental health concerns. Caller comments, when available, will complement the quantitative data by providing personal context and deepening the understanding of lived experiences. Ultimately, the findings will inform policy recommendations aimed at addressing transportation barriers as a means to improve mental health outcomes in communities.    
]]></description>
      <pubDate>Tue, 13 Jan 2026 15:19:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652177</guid>
    </item>
    <item>
      <title>Efficacy or Equity? A Public Transit Operations Method for Balancing Costs and Local Need: A COVID-19 Case Study in Austin, TX</title>
      <link>https://trid.trb.org/View/2624176</link>
      <description><![CDATA[This study explores the operational dynamics of public transit during the early stages of the COVID-19 pandemic, focusing on the Capital Metro Transit Authority in Austin, TX. The pandemic induced a dual challenge of declining ridership and the urgent need to serve transit-dependent populations, particularly essential workers who are predominantly from lower income and minority backgrounds. Using the analytical hierarchy process (AHP), this research develops a method for transit authorities to balance demand, supply, and equity in real-time transit operations. Daily operational metrics and demographic data spanning from January 2019 to May 2022 within a 0.25-mi radius from transit stops were utilized in the analysis. The study revealed that traditional metrics often overlook the intricate needs of transit-dependent populations. Specifically, the AHP model indicated that certain routes, which had been canceled, should instead have continued at a reduced rate because of their high equitable need. Particularly affected by these operational changes were foreigners and individuals residing more than 5?mi from the central business district, who suffered disproportionately from the lack of adequate transit services. By pinpointing these routes, the model can ensure that critical transit services align with the needs of the most dependent community members. This strategic approach supports essential mobility and access to resources, promoting urban transit equity.]]></description>
      <pubDate>Fri, 14 Nov 2025 08:46:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624176</guid>
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