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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <language>en-us</language>
    <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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>PGRoute: Practical and Privacy-Preserving Group Ride-Sharing Matching for Online Ride-Hailing Systems</title>
      <link>https://trid.trb.org/View/2658927</link>
      <description><![CDATA[Privacy-preserving online ride-hailing (ORH) services can offer riders and drivers a more enhanced travel experience without disclosing their location privacy. Group ride-sharing is specifically designed for riders undertaking long-distance trips, allowing a group of riders with similar travel plans to share a single taxi. However, the lack of integrated route planning in existing privacy-preserving schemes prevents them from effectively matching riders with the most optimal taxi. In this paper, we propose a privacy-preserving group ride-sharing matching scheme, PGRoute, based on leveled fully homomorphic encryption (LFHE). In PGRoute, we propose a privacy-preserving path planning method and design a fast ciphertext-based distance matrix computation protocol for the key time-consuming modules in it to effectively improve the efficiency. PGRoute can plan routes for groups of riders, determine the optimal boarding order, and match the most suitable taxi while protecting the location privacy of both riders and taxi drivers. Theoretical analysis and experimental results demonstrate that PGRoute is secure and efficient within ORH systems. Compared to previous works, PGRoute reduces the pickup time for a group of riders by a factor of 2.9- $7.5\times $ , and achieves 2.7- $6.7\times $  higher computational efficiency.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658927</guid>
    </item>
    <item>
      <title>Heterogeneity in Women’s Nighttime Ride-Hailing Intention: Evidence from an LC-ICLV Model Analysis</title>
      <link>https://trid.trb.org/View/2669574</link>
      <description><![CDATA[While ride-hailing services offer increased travel flexibility and convenience, persistent nighttime safety concerns significantly reduce women’s intention to use them. Existing research often treats women as a homogeneous group, neglecting the heterogeneity in their decision-making processes. To address this gap, this study develops the Latent Class Integrated Choice and Latent Variable (LC-ICLV) model with a mixed Logit kernel, combined with an ordered Probit model for attitudinal indicators, to capture unobserved heterogeneity in women’s nighttime ride-hailing decisions. Based on panel data from 543 respondents across 29 provinces in China, the analysis identifies two distinct female subgroups. The first, labeled the “Attribute-Sensitive Group,” consists mainly of young women and students from first- and second-tier cities. Their choices are primarily influenced by observable service attributes such as price and waiting time, but they exhibit reduced usage intention when matched with female drivers, possibly reflecting deeper safety heuristics. The second, the “Perception-Sensitive Group,” includes older working women and residents of less urbanized areas. Their decisions are shaped by perceived risk and safety concerns; notably, high-frequency use or essential nighttime commuting needs may reinforce rather than alleviate avoidance behaviors. The findings underscore the need for differentiated strategies: platforms should tailor safety features and user interfaces by subgroup, policymakers must develop targeted interventions, and female users can benefit from more personalized risk mitigation strategies. This study offers empirical evidence to advance gender-responsive mobility policy and improve the inclusivity of ride-hailing services in urban nighttime contexts.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669574</guid>
    </item>
    <item>
      <title>Mining Hidden Ridesharing Patterns: A Data-Driven Gap Analysis of Chicago TNC Trips</title>
      <link>https://trid.trb.org/View/2673005</link>
      <description><![CDATA[This study paved the way for developing digital twins of smart and emerging urban mobility systems, using shared mobility services such as ridesharing as a key case study. As cities contend with challenges, such as traffic congestion, environmental sustainability, and transportation equity, shared mobility platforms (e.g., UberPOOL and Lyft Shared) have emerged as promising solutions. Leveraging Chicago’s Transportation Network Companies (TNCs) shared mobility data set, this research uncovers latent patterns in user behavior and trip-sharing dynamics through data mining and exploratory analysis. It distinguishes between trips, where users authorized ride-sharing and those that were actually pooled, revealing key spatial, temporal and behavioral difference. Economic factors also played an important role. For instance, the hourly gap between authorized and successfully pooled trips was narrower on weekends, suggesting more stable matching opportunities, while users who authorized but were not pooled tended to pay less per mile than the general trip population. Building on these insights, this study integrates both supervised and unsupervised machine learning methods to enhance the understanding of ridesharing dynamics. Density-based spatial clustering of applications with noise (DBSCAN) was employed to uncover latent trip groupings, which served as the foundation for developing predictive models that estimate the likelihood of successful ride matches. Multiple classifiers, including Logistic Regression, Random Forest, and XGBoost, were implemented and rigorously evaluated to identify the most effective predictive model. This integrated approach not only provides a comprehensive perspective on ridesharing behavior and trip shareability within current mobility platform, but also builds the foundation for early-stage digital twins that can simulate, optimize, and inform decision-making in future smart mobility systems, including autonomous vehicle fleet operations.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673005</guid>
    </item>
    <item>
      <title>Evaluating Behavioral Responses to Mobility Credits and Ridehailing Integration in a Digital Mobility System</title>
      <link>https://trid.trb.org/View/2702725</link>
      <description><![CDATA[Digital mobility platforms are increasingly adopted by public agencies to coordinate multimodal travel, streamline fare payment, and improve efficiency. However, there is limited empirical evidence on how users respond to platform-based incentives and integrated services in real-world settings, as most studies rely on stated preference data or simulations. This project analyzes user behavior on Vamos-EZHub, a public digital mobility platform that integrates trip planning, fare payment, and access to services including local transit and ridehailing. It evaluates behavioral responses to two sequential interventions on Vamos-EZHub: (1) the introduction of prepaid mobility credits and (2) the integration of a transit-triggered ridehailing credit. 

Using longitudinal platform telemetry, ridehailing trip records, transit fare activation data, and General Transit Feed Specification (GTFS) data, the project examines how mobility and ridehailing credits affect platform engagement, transit and ridehailing use, first/last-mile connectivity, and spatial and temporal patterns of linked travel. Two-way fixed effects and event-study models are used to identify behavioral changes associated with each intervention. A geospatial-temporal algorithm classifies ridehailing trips connecting to transit, and stop- level regression models identify transit service and network characteristics associated with demand for linked trips. 

Expected outcomes include quantitative estimates of the influence of mobility credits and ridehailing integration on multimodal coordination, identification of service characteristics associated with higher demand for linked trips, and a reproducible analytical framework. The results will inform data-driven platform design, operational planning, and integration strategies for public agencies managing digital mobility platforms, while providing evidence to guide coordination with private ridehailing partners to improve system efficiency and reliability.]]></description>
      <pubDate>Thu, 14 May 2026 16:36:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702725</guid>
    </item>
    <item>
      <title>To be integrated or not? Understanding continuance behavioral intention towards integrated ride-hailing services: Empirical evidence from Nanjing, China</title>
      <link>https://trid.trb.org/View/2656207</link>
      <description><![CDATA[Despite explosive growth of integrated ride-hailing services (IRHS), the impact on long-term behavioral pattern has been little examined. This study intends to investigate travelers’ continuance behavioral intention towards IRHS, using a theoretical framework based on Expectation Confirmation Model (ECM). Moreover, four IRHS-specific feature variables are included, such as compatibility, hassle cost, convenience, and security. Further, this study introduces habit as moderating variable. Moreover, socio-demographic factors are considered as control variables, including gender, age, income, and educational level. With data collected from Nanjing, China, an empirical analysis is conducted using hybrid approach of Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The findings indicate that perceived usefulness, satisfaction, and expectation confirmation are key determinants. Noteworthily, perceived usefulness exhibits as more important than expectation confirmation. Further, it shows that all IRHS-specific features play crucial roles. Specifically, compatibility and hassle cost show stronger influence on expectation confirmation, while convenience and security affect more on perceived usefulness. Habit acts as a moderator within relationships between expectation confirmation and satisfaction, and satisfaction and continuance behavioral intention. Additionally, travelers’ continuance intention is negatively related to age and education level. These findings shed valuable insights for understanding the general pattern of travelers’ behavior, and add practical value for platforms and policymakers.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:08:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656207</guid>
    </item>
    <item>
      <title>Security-Enhanced Spatiotemporal Ride-Hailing Demand Prediction—Part I: Horizontal Federated Learning</title>
      <link>https://trid.trb.org/View/2610657</link>
      <description><![CDATA[The burgeoning demand for ride-hailing services, coupled with the complexity of traffic patterns, underscores the need for advanced demand prediction models. Traditional approaches often suffer from data silos, which prevent the effective utilization of big data separately owned by multiple ride-hailing platforms. In this study, we propose TSAGE, a novel spatiotemporal deep learning model that combines GraphSAGE for spatial feature extraction and Gated Recurrent Units (GRU) for capturing temporal dependencies. To enable privacy-preserving collaborative learning, horizontal federated learning (HFL) is integrated into TSAGE, so that multiple ride-hailing platforms are allowed to jointly train models without sharing raw data. Moreover, we implement HFL using differential privacy technique to protect data privacy by adding noise to the locally trained model parameters before sharing them with the central server. Through a detailed case study in New York City for Uber and Lyft, we found that prediction accuracy for each ride-hailing platform correlates positively with the volume of ride-hailing demand and negatively with the volatility in demand. Consequently, areas with high demand demonstrate superior performance, and larger platforms with less demand volatility also exhibit better performance. Moreover, compared to scenarios where each platform individually predicts ride-hailing demand, HFL showed significant performance enhancements. Additionally, compared with other HFL methods, the HFL applied with TSAGE can effectively address the challenges posed by the non-independent and identically distributed data across different platforms.]]></description>
      <pubDate>Thu, 26 Mar 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610657</guid>
    </item>
    <item>
      <title>Motivations and Barriers to Electrifying Ridehailing Services: Evidence from California TNC Drivers</title>
      <link>https://trid.trb.org/View/2681316</link>
      <description><![CDATA[Battery electric vehicle (BEV) adoption among ridehailing drivers remains underexplored. This study examines factors impacting vehicle fuel type choices among California ridehailing drivers, with particular attention to whether drivers registered a pre-existing household vehicle or obtained one with the intention to use it for ridehailing work (ridehailing intentions). An integrated choice and latent variable model reveals that older drivers, renters in multi-family housing, and those who rely on ridehailing as their primary income source are more likely to obtain vehicles with ridehailing intentions. BEV adoption is positively associated with favorable attitudes and supportive social norms toward EVs, but negatively associated with perceived barriers. BEVs are more common among vehicles rented through ridehailing platforms. Home charging access matters more for drivers without ridehailing intentions, while public charging access has a greater impact for those with ridehailing intentions. Familiarity with federal BEV incentives significantly increases the likelihood of adoption.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681316</guid>
    </item>
    <item>
      <title>Mobility Shifts: Factors Influencing Ride-Hailing Adoption and Their Role in Travel Behavior in Kathmandu Valley, Nepal</title>
      <link>https://trid.trb.org/View/2674209</link>
      <description><![CDATA[Ride-hailing services (RHS) are rapidly expanding in cities with weak public transport, such as Kathmandu Valley, Nepal. Despite growing use of platforms like Pathao, In-Drive and Tootle, limited research has examined behavioral factors influencing RHS adoption and its impact on travel behavior. This study addresses that gap by identifying key determinants of RHS use and mobility shifts. Based on 548 valid questionnaire responses from urban youth (ages 16–45), combined with spatial mapping using GIS, Exploratory Factor Analysis revealed three main drivers: service quality, technology affinity, and cost sensitivity. RHS adoption significantly changed travel behavior; 66.61% reduced public transport use, 69.71% reported shorter travel times, and 17.52% made discretionary trips they otherwise wouldn't have. Adoption was highest in dense urban core areas. Findings highlight RHS's role in reshaping urban mobility and underscore the need for integrated transport planning, improved digital access, and stronger safety regulation.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674209</guid>
    </item>
    <item>
      <title>How can metro-integrated multimodal travel substitute for ride-hailing trips in the era of mobility as a service?</title>
      <link>https://trid.trb.org/View/2647732</link>
      <description><![CDATA[The rapid growth of ride-hailing services has reshaped urban mobility but also intensified congestion, emissions, and car dependency, raising concerns about sustainability. The metro-integrated multimodal travel (MIMT), enabled by Mobility as a Service (MaaS) platforms, offers promising low-carbon alternatives by integrating metro systems with convenient access and egress modes such as ridesplitting, buses, cycling, and walking. This study aims to develop a pratical analytical framework to assess the substitution potential of MIMT for ride-hailing trips in the era of MaaS. First, a trip reconstruction method is proposed to generate ten types of MIMT alternatives under identical origin–destination (OD) conditions. Second, a multidimensional evaluation model is established to quantify substitution benefits by jointly considering cost savings, carbon emission reductions, and time delays. Third, an interpretable machine learning approach (CatBoost integrated with SHAP and PDP) is applied to identify the travel and built environment factors influencing substitution potential. A case study based on 837,503 ride-hailing trips in Shanghai indicates that 56.46 % of trips could feasibly be replaced by MIMT alternatives. On average, each substituted trip yields a comprehensive benefits of 13.83 CNY, comprising cost savings of 24.03 CNY and carbon emission reductions of 1.29 kg, at the cost of an average travel time increase of 17.51 min. The results further reveal that substitution potential is primarily driven by route nonlinearity, trip distance, and metro accessibility. Travel-related variables account for 61.11 % of explanatory power, while built environment features contribute the remaining 38.89 %. Sensitivity analyses demonstrate that travelers' transition from ride-hailing to MIMT is predominantly influenced by the value of time, with current carbon pricing exerting only a marginal effect. These findings highlight the role of MaaS in promoting multimodal integration and provide actionable insights for policymakers and platform operators to reduce ride-hailing dependency and advance low-carbon urban mobility.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647732</guid>
    </item>
    <item>
      <title>How virtual experience reshapes commuters’ MaaS subscription and mode choice: Insights from an economic experiment</title>
      <link>https://trid.trb.org/View/2636354</link>
      <description><![CDATA[As a noteworthy example of subscription-based service in transportation, Mobility-as-a-Service (MaaS) provides seamless and integrated multimodal travel solutions through bundles, encouraging travelers to transition from private modes to sustainable travel options. While previous studies have primarily focused on the impact of MaaS bundles on mode preferences, the complicated and extensive MaaS-induced behavioral changes and their evolving impact have been overlooked. This study addresses this gap by investigating changes in users’ subscriptions and travel choices with accumulated virtual experience of MaaS bundle usage. Combining stated choice experiments and experimental economics, we conduct a four-part multimodal travel experiment targeting commuters, offering an engaging environment where participants make sequential decisions comprising MaaS bundle subscriptions and travel mode choices. Dynamic discrete choice models are formulated to calibrate participants’ dynamic decision-making processes under MaaS bundle subscriptions and behavioral changes over multiple virtual periods. The results indicate that the virtual experience of subscribing to a particular bundle would motivate them to subscribe to the same bundle again in subsequent periods. When MaaS subscribers make mode choices, their behavior is not simply making trade-offs between travel time and cost. Rather, they tend to consider the future use of their bundles fully, and they are more inclined to make travel decisions based on available bundle discounts. The impact of subscriptions is most pronounced in promoting ride-sourcing trips, followed by multimodal and single-mode public transportation options. These findings offer initial insights into the impact of MaaS subscriptions in reshaping traveler’ subscription and travel choices over a relatively longer period.]]></description>
      <pubDate>Thu, 26 Feb 2026 16:18:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636354</guid>
    </item>
    <item>
      <title>Integrative System Design and Implementation for Enhancing Public Transportation and Urban Mobility: A Case Study in Xi’an</title>
      <link>https://trid.trb.org/View/2613212</link>
      <description><![CDATA[The rapid advancement of technology and the urgent need for sustainable urban growth have driven the evolution of public transportation as a key component of smart city development. This paper explores an integrated city-level public transportation system designed for Xi’an, focusing on the seamless incorporation of advanced digital solutions to enhance urban mobility. The system comprises four core components: a mobile application, shuttle bus operations, an urban online bus-hailing service, and a 43-in. LCD electronic stop signs. These components are interconnected through a cloud-based data infrastructure that supports real-time data collection, analysis, and communication. By leveraging big data, IoT, and AI, the system facilitates adaptive service delivery, predictive route adjustments, and enhanced user experiences. The mobile application provides real-time navigation and trip planning, while the shuttle bus system incorporates GPS tracking, diagnostics, and passenger monitoring for safety and efficiency. The bus-hailing service uses data-driven algorithms to dynamically adjust routes based on passenger demand, improving coverage and reducing wait times. Additionally, the electronic stop signs ensure passengers receive timely updates at transit points. Despite the advancements showcased in this paper, challenges remain in scaling such comprehensive systems and integrating them within existing urban infrastructure. This study presents a scalable, adaptable model that demonstrates how intelligent, data-driven public transportation can be implemented to meet modern urban mobility needs, positioning Xi’an as a case study for other global cities.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613212</guid>
    </item>
    <item>
      <title>The influence of app function evolution on transport SuperApp use behaviour over time</title>
      <link>https://trid.trb.org/View/2590555</link>
      <description><![CDATA[In the past few decades, there has been a significant increase in smartphone apps that are designed to help users optimise their daily activities. As a result, there has been a noticeable impact on travel demand. Some of these apps have evolved with the incorporation of additional functions in a gradual transformation into multi-function apps or SuperApps, thereby providing users with more integrated and personalised services for a wider range of activities. Focusing on Transport SuperApps (TSA) in Indonesia, this study aims to investigate how app usage behaviour interacts with the evolving functions of these apps over time. The study further examines the influence of personality traits, socio-demographic factors, and residential location on app usage patterns. In this study, longitudinal data on TSA usage from 2015–2022 was collected from users in four Indonesian cities. The Latent Markov (LMM) and Negative Binomial (NBM) Models were used to analyse the transition of behaviours, app types, and the number of apps used. The findings reveal that transport and shopping services are the most popular and consistently utilised services by users. The results suggest that the introduction of new services has a positive impact on the number of TSA services used. However, some services were found to be used only temporarily, primarily serving as alternatives to support users’ daily needs and desires. Initial higher service usage was observed among educated users with sociable and disorganised personalities, while discontinuation of usage is associated with older users and affluent households. Higher transition and continuation to use more services are also observed in larger cities like Jakarta compared to smaller cities like Cianjur.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590555</guid>
    </item>
    <item>
      <title>Greening livestock supply chains: a conceptual framework from an empirical study in Vietnam</title>
      <link>https://trid.trb.org/View/2611257</link>
      <description><![CDATA[This paper aims to examine and conceptualise the greening practices of livestock supply chains. Based on a qualitative approach over a purposive sample in Vietnam, we explore the current green supply chain practices in the livestock industry and provide an understanding of the challenges companies face. Subsequently, data is analysed using qualitative content analysis before an integrated framework is proposed to conceptualise and provide guidelines for companies in the livestock industry when considering adopting green practices. The framework is developed from both supply chain and organisational approaches. The former includes all stages in the livestock supply chains from sourcing to consumption, while the latter highlights three critical prerequisites for successful implementation: resource recovery, efficiency improvement, and environmental handling. Implications for managers and policymakers are presented accordingly to provide suggestions on the focused area for improvements.]]></description>
      <pubDate>Wed, 14 Jan 2026 17:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611257</guid>
    </item>
    <item>
      <title>Impacts of vehicle restrictions on urban traffic speeds and transit ridership: an empirical analysis using high-frequency data</title>
      <link>https://trid.trb.org/View/2612505</link>
      <description><![CDATA[The impacts of vehicle restrictions in Santiago, Chile, on the average speeds of vehicle traffic and public buses as well as on transit card validations are quantified using novel high-frequency data, coming from millions of records from a ride-hailing service and the city’s public transport system. The restrictions were in force on weekdays during May through August between 7:30 am and 9 pm in urban districts, and applied on a given day to 20 % of the stock of vehicles registered before September 2011. The quality, frequency and spatial coverage of the data we use allow us to estimate not only classic methods like before-and-after and differences-in-differences, but also triple differences, which allows for a higher number of control variables. All these methods arrived at the conclusion that the restrictions produce small increases in speed vehicle traffic (between 3.3 % and 4.2 %) and bus speeds (between 1.8 % and 2.3 %); no increases in the use of public transport were detected. Three likely reasons for the size of the effects are the low percentage of vehicles subject to the restrictions on any given day (approximately 6.9 %), the tendency for frequent drivers to be from higher-income groups and thus to own newer vehicles, and widespread violation of the restrictions due to weak enforcement with low fines.]]></description>
      <pubDate>Wed, 14 Jan 2026 17:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612505</guid>
    </item>
    <item>
      <title>Integrated operation of ride-hailing and shared micromobility services in multimodal transportation networks with public transit: The unintended consequences of regulations</title>
      <link>https://trid.trb.org/View/2599158</link>
      <description><![CDATA[This paper investigates the integrated operation of ride-hailing and shared micromobility services provided by transportation network companies (TNCs) such as Uber, Lyft, and Didi, and examines the policy implications of prevalent TNC regulations under complex multimodal interactions. We consider a TNC platform that simultaneously coordinates a fleet of for-hire vehicles and deploys micromobility infrastructures to offer both ride-hailing and shared micromobility services, in conjunction with a public transit agency operating in the same transportation network. To capture the key elements of such a multimodal system, we develop a market equilibrium model that incorporates ride-hailing waiting times, micromobility access and egress times, the spatial distribution of micromobility infrastructure, passenger demand, platform pricing and fleet sizing, vehicle repositioning, and traffic congestion. The platform’s decision-making problem is formulated as a non-convex program, and a tailored solution method is proposed to efficiently compute the solution through problem reformulation and dimensionality reduction. Using the developed model, we analyze the implications of two prevalent TNC regulations: (a) a congestion charge on ride-hailing services aimed at mitigating traffic congestion; and (b) a vehicle density floor for shared micromobility services to promote spatial equity. Our results reveal several unintended consequences of these regulations due to the interplay between ride-hailing, shared micromobility, and public transit in a multimodal transportation network. Interestingly, we find that how the congestion charge on ride-hailing trips influences public transit ridership crucially depends on micromobility’s role as a feeder mode: when micromobility serves as a significant transit feeder, the congestion charge increases transit ridership; otherwise, the congestion charge on ride-hailing services could inadvertently reduce transit ridership. Furthermore, we find that imposing a vehicle density floor for micromobility services, while improving the spatial equity of micromobility, may inadvertently reduce the equity of ride-hailing services, ultimately widening the overall equity gap across the multimodal transportation network. These unintended consequences are observed only when all three modes-ride-hailing, shared micromobility, and public transit-are jointly modeled, underscoring the critical importance of accounting for multimodal interactions in the design and evaluation of TNC regulations.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2599158</guid>
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