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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Rethinking cost–benefit analysis for transformative cycling policies: Integrating behavioral change and the logsum method</title>
      <link>https://trid.trb.org/View/2681487</link>
      <description><![CDATA[Transforming urban transport systems toward sustainability requires a critical re-evaluation of the appraisal methods used in policy analysis. While cycling infrastructure offers clear environmental, health, and livability benefits, its economic evaluation through cost–benefit analysis (CBA) remains underdeveloped. This paper presents a CBA of the “E-Bike City” concept in Zurich, which involves a radical reallocation of road space from cars to bicycles and e-bikes, implemented within a large-scale MATSim agent-based transport model. We address methodological challenges in applying CBA to cycling projects, specifically demand forecasting, valuing consumer surpluses, and treating subjective safety. Consumer surpluses are calculated using both the conventional value of travel time savings (VTTS) rule-of-half approach and the logsum method derived from discrete choice models. We compare results with and without assuming behavioral change in response to the new transport supply. Our findings demonstrate that the transport demand model as well as the consumer surplus methodology significantly affect appraisal outcomes. Without accounting for preference change, both methods yield negative net present values (NPVs). In contrast, when behavioral adaptation is included, the logsum method produces strongly positive NPVs. The analysis also reveals substantial reductions in external costs, crashes, and greenhouse gas emissions. However, long-term decarbonization goals remain out of reach without further systemic changes, given projected population growth. We conclude that CBAs focusing on transformative, sustainable mobility policies require methodologies that reflect long-term behavioral adaptation and utility beyond travel time savings, making the logsum method a more suitable tool for sustainable transport appraisal.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:56:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681487</guid>
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    <item>
      <title>Evaluating the distributional impact of urban rail improvements: Logsum accessibility measures incorporating income class and household type</title>
      <link>https://trid.trb.org/View/2652394</link>
      <description><![CDATA[Recent discussions on transportation project evaluation have increasingly emphasized the importance of incorporating equity considerations. This study developed a travel behavior model that incorporates individual attributes and empirically assessed the distributional impacts of urban rail improvement projects in the Tokyo metropolitan area. Travelers were classified in this area into 24 attributes based on four income classes and six household types, and we estimated mode choice models for home-to-work and home-to-private trips. These models were then used to calculate the logsum accessibility measures, to predict user benefits from urban rail projects completed in 2019 and 2023. Distributional analyses revealed that for home-to-work trips, the median user benefit increased with income, but the interquartile ranges remained similar across most income groups above two million JPY/year. For home-to-private trips, benefits are higher for households with only one or two workers or for higher-income groups, reflecting greater variation in travel behavior and the value of travel time.]]></description>
      <pubDate>Thu, 26 Mar 2026 16:59:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652394</guid>
    </item>
    <item>
      <title>Exploring effects of expressway accident on drivers’ adaptive behavior in different scenarios: A comparative evaluation of machine learning techniques and discrete choice models</title>
      <link>https://trid.trb.org/View/2618109</link>
      <description><![CDATA[The congestion and time loss caused by expressway accidents are significant, and existing studies have not fully explored the differences and mechanisms of drivers' adaptive behaviors before, and during expressway driving. Based on 30,000 decision choices from 2500 drivers in western Japan, this study uses latent class analysis (LCA), XGBoost (eXtreme Gradient Boosting), and discrete choice model (DCM) to identify key influencing factors, compare model performance, and make policy recommendations. The LCA divided drivers into three categories; Results of XGBoost shows that there are significant differences in the importance of factors such as age, trip purpose, and accident clearance time among different scenarios and driver groups, and the model outperformed DCMs in the individual cluster analysis, with cluster 3 achieving a prediction accuracy of 61.1 % (area under the curve value); and SHAP (SHapley Additive Explanation) analysis indicates that information accuracy, alternative expressways and queue dynamics are dominative factors affecting Cluster 1, 2 and 3 respectively by using important features, and young drivers are more sensitive to real-time information than middle-aged and older drivers, and commuters are significantly more inclined to change routes than leisure travelers (P < 0.01). It is recommended to strengthen visual early warning for low-sensitivity groups, provide accident details for safety-priority groups, optimize the layout of expressway entrances and exits, and implement hierarchical information release based on age and travel purpose. This study provides an empirical basis for the design and policy formulation of dynamic transportation systems across scenarios and clusters.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:53:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618109</guid>
    </item>
    <item>
      <title>Assessing on-road cyclist behavior: A pilot evaluation of the cyclist behavior questionnaire for U.S. riders</title>
      <link>https://trid.trb.org/View/2612120</link>
      <description><![CDATA[Cycling is an affordable and environmentally sustainable mode of transportation that promotes physical activity. However, the rising cyclist-motor vehicle crashes in the U.S. highlights the need to understand behavioral factors associated with cyclist crashes. This study aimed to develop and evaluate a Cyclist Behavior Questionnaire (CBQ-US) for the U.S. riders. This study employed a mixed-method validation by combining a self-reported survey (N = 224) with a scenario-based bike simulator study. The psychometric structure was examined using Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA). Predictive validity was tested in a bike simulator using ordinal logistic regression, and ANOVA was used to reveal demographic differences. PCA identified a four-factor model (violation, aggressive violation, positive behavior, and distraction and forgetfulness), that explained 66.9 % of the variance. CFA confirmed the structure with adequate model fit. Simulator-based scenarios significantly predicted the CBQ-US subscales. Demographic analyses demonstrated that male cyclists exhibited higher rates of aggressive violations. Middle-aged cyclists and those with a history of crashes with non-motor vehicles reported more distraction and forgetfulness. Notable differences were also observed across states, indicating the influence of inconsistent infrastructure and traffic laws. The CBQ-US demonstrated strong psychometric properties, and the findings support the need for targeted interventions, such as restricting phone use, promoting bone-conductive headphones, infrastructure improvements and educational campaigns, to reduce crash risks. Despite having some limitations, the CBQ-US can be used as a useful tool in behavioral research and policy developments to promote cyclist safety and encourage active travel.]]></description>
      <pubDate>Tue, 18 Nov 2025 09:30:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612120</guid>
    </item>
    <item>
      <title>Modeling the Brand Choice Behavior of Shared Micro-Mobility Users: A Case of Electric Scooter Sharing</title>
      <link>https://trid.trb.org/View/2549290</link>
      <description><![CDATA[The growing number of shared micro-mobility service providers coexisting in the market has diversified the market composition, leading to challenges in expanding their own-brand market share. Therefore, developing effective strategies for competition and marketing requires a deeper understanding of users' choice behavior among heterogeneous service providers. However, most previous studies often overlook the heterogeneity among service providers, resulting in limitations in precisely explaining users' choice behavior. To address this research gap, a hybrid choice modeling approach is employed to explore the brand choice behavior within the same transport service. The proposed brand choice model integrates various latent variables, such as brand attitude and shared micro-mobility usage characteristics, to capture the key factors influencing users' service provider choices. Using electric scooter sharing (ESS) as a case study, stated preference data were collected to analyze the choice behavior of ESS users. The results show that travel attributes, latent variables, and socioeconomic characteristics have significant direct effects on choice probability, whereas brand attitude has substantial mediation effects, revealing the importance of brand evaluation on users' choice behavior. The managerial insights derived will enhance the competitive and marketing strategies of ESS service providers, while the policy implications will provide direction for government planning.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:24:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549290</guid>
    </item>
    <item>
      <title>What drives people to choose robo-taxis when scenario-specific factors change? An experimental investigation in Chinese cities</title>
      <link>https://trid.trb.org/View/2583140</link>
      <description><![CDATA[This study investigates the cognitive and contextual determinants of robo-taxi adoption in Chinese cities through a factorial vignette-based experiment. Integrating the Theory of Planned Behavior with Prospect Theory, the research examines how individual-level beliefs and scenario-specific factors shape adoption intentions among both users and non-users in cities where robo-taxis operate. The design manipulates perceived economic benefits and driving task aversion (DTA) across realistic mobility scenarios to test for asymmetric behavioral responses.Findings reveal that high economic benefits and high DTA significantly increase adoption attitudes and intentions, while low levels of these factors do not produce symmetrical negative effects, consistent with loss aversion principles. Attitude formation differs by experience: users are most influenced by economic benefits, while non-users respond more strongly to perceived system intelligence. Urban Mobility Ecosystem Integration (UMEI) emerged as a driver of favorable attitudes, particularly when platform compatibility and payment integration were emphasized. The results challenge linear adoption models by demonstrating reference-dependent, nonlinear patterns of evaluation, especially for economic and driving-related attributes.This study contributes to adoption research by combining behavioral theories in a scenario-based design and highlights the need for experience-sensitive models in emerging mobility systems. Policy implications include the importance of privacy assurance, user segmentation, and localized integration strategies for maximizing uptake. Findings support the refinement of adoption models to better reflect asymmetric effects and boundedly rational user behavior in complex urban ecosystems.]]></description>
      <pubDate>Tue, 09 Sep 2025 08:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2583140</guid>
    </item>
    <item>
      <title>Impact analysis of positive utility and built environment on car use for non-work trips: an ensemble approach</title>
      <link>https://trid.trb.org/View/2576450</link>
      <description><![CDATA[Although the Positive Utility of Travel (PUT) has been well discussed in the relevant literature, there remain research gaps in practice, particularly regarding how this utility can be effectively incorporated into conventional transportation models. Additionally, there is a need for further exploration of how the built environment influences car use for non-work trips with different levels of intrinsic utility. In this regard, distinguishing trips by the context of destination and/or purpose (as an approach in previous studies) is not a perfect method, as the intrinsic utility of trips may be different for each trip regardless of these contexts. To address this gap, the authors modified the conventional travel survey and collected the required data in fall 2022 and spring 2023 from 1170 respondents residing in Shiraz, Iran. Based on a series of questions about each reported trip, two types of travel were recognized by fuzzy C-means clustering: Low PUT and High PUT. Interestingly, about 56% of non-work trips were undertaken both to reach a destination and to travel itself. Furthermore, using a novel ensemble analysis approach, the authors found that five variables, including population density, block density, outdoor space proximity, positive attitude toward transit and family size, significantly affect car use for both types of travel. However, the importance of these variables differs for different trip types. According to the Multiple Criteria Decision Making (MCDM) results, motorcycle ownership and outdoor space proximity are the most important variables for predicting car use for trips with low PUT, while family size and population density are the most influential variables for explaining car use for trips with high PUT.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:54:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2576450</guid>
    </item>
    <item>
      <title>Integrating user values assessment with the augmented RFM model to enhance bus transit systems’ market segmentation</title>
      <link>https://trid.trb.org/View/2562531</link>
      <description><![CDATA[Understanding “who deserves retention” is pivotal to save the recent bus ridership decline. Despite efforts in segmenting bus riders into various behavior statuses, which riders who need, and even deserve, intervention policies to retain are largely unknown, making it impossible to allocate limited retention resources in a precise manner. To address this critical issue, the authors augment traditional RFM models with retention policy-sensitivity and transit mode-preference dimensions to capture bus riders' user value index by employing the multi-category factor analysis to evaluate riders’ behavioral loyalty, intentional loyalty, consumption potential, and recent activity level with the Analytic Hierarchy Process and expert interviews. Based on the four evaluation metrics, they cluster the riders into five segments: High-potential, Possible churn, Regular, Bus-dependent, and Loyal and recommend targeted retention policies to suit the characteristics of these five segments. The proposed transit rider segmentation framework is supposed to provide references for transit operators to find the potential churners and those who need to be retained among the identified potential churners.]]></description>
      <pubDate>Thu, 26 Jun 2025 16:12:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562531</guid>
    </item>
    <item>
      <title>Improving Mobility Options through Transit Signal Priority (TSP)</title>
      <link>https://trid.trb.org/View/2553163</link>
      <description><![CDATA[Transit Signal Priority (TSP) seeks to optimize the interaction between busses and the infrastructure, creating a minimum resistance path for transit buses through signalized intersections. TSP may improve travel time reliability (TTR), schedule adherence, and ultimately the quality of service and ridership for transit systems. Bhat and Sardesai explicitly includes of TTR in mode choice, demonstrating the significance of this measure, with higher sensitivity seen for those with less flexible work schedules. The importance of TTR in mode choice is further shown through survey results by Li et al., as well as in the consideration of individual’s public transport route selection by Swierstra et al. Thus, improving transit service quality has high potential benefits, with transit disproportionately serving essential workers and traditionally underserved communities. Additionally, improved quality of service may encourage traveler mobility choice behavior to switch from personal vehicles to transit, with associated benefits of reduced congestion, vehicle miles traveled, and emissions. An efficiently designed TSP system can lead to a more sustainable and equitable transportation system.

Past TSP research has mainly focused on developing optimization strategies to improve bus performance, typically while limiting impacts to the general traffic. Although, the acceptable level of impact to general traffic (typically single occupancy vehicles) is a policy decision that should be explicitly considered by agencies, while recognizing the potential constraints such policies place on transit performance. Adaptive TSP with online optimization has been studied and are increasingly being piloted. Existing adaptive TSP algorithms are primarily analytical models and mathematical programming, integrating real-time data for traffic state definition and actuation triggering. Most of the proposed strategies need further evaluation and testing to achieve field ready status. Additionally, for widespread adoption, more efficient real-time optimization algorithms need to be developed.

Focusing on real-time operational control, this project will develop and test novel artificial intelligence/machine learning (AI/ML) based TSP actuation and optimization algorithms in a simulated environment. The algorithms shall seek to integrate automatic passenger counting (APC), automatic vehicle location (AVL), connected vehicle (CV) data, and real time signal phasing and timing (SPaT) data. The project builds on a recently completed Georgia Department of Transportation (GDOT) funded study that explored TSP fundamental principles in a simulated environment and laid the groundwork for more advanced TSP algorithms.]]></description>
      <pubDate>Thu, 15 May 2025 14:46:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553163</guid>
    </item>
    <item>
      <title>COVID-era built environment and travel: Insights from location-based services data</title>
      <link>https://trid.trb.org/View/2534603</link>
      <description><![CDATA[This study revisits the linkage between land use interventions and travel behavior in the COVID era using increasingly available cell phone-based individual mobility data. Reducing the carbon emissions associated with personal vehicle travel is crucial to achieving climate targets; policies such as California's Senate Bill 375 require that land use planning achieve climate targets at the regional level. The implementation relies heavily on local placemaking efforts such as higher density infill development and walkable streets which have long been considered potential reducers of automobile travel. However, the rise of telework, decline of transit, and increase in pedestrian deaths following the pandemic have cast doubts on the efficacy and cost-effectiveness of strategies seeking to foster low-Vehicle Miles Traveled (VMT) location patterns. This study uses StreetLight Insight data on vehicle trip origination at the census tract-level before and after the emergence of COVID-19 (2019 and 2021) to assess the contribution of several built environment measures to VMT and to the share of short trips in the 6-county Southern California region. Despite concerns over COVID-induced changes, the authors find that several built environment measures remain solidly associated with travel efficiency in multivariate models investigating VMT levels, VMT rebound, and the share of trips that are shorter than two miles. While the prevalence of neighborhood-scale destinations is an activity generator, it also fosters shorter trips, and a region-level measure of job accessibility provides some evidence that more populated areas nearer the region's core did indeed struggle to return to pre-COVID activity levels. After the first year of the pandemic, VMT rebound was most pronounced in tracts with a high share of residents under the age of 18, suggesting that while many adults did not return to prior activity patters (e.g. due to telecommuting), children mostly did. Findings suggest that local policies and placemaking efforts, including 15-min communities, may still be promising trip reducers, while near real-time data provides a mechanism for far faster performance evaluation.]]></description>
      <pubDate>Tue, 13 May 2025 09:54:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534603</guid>
    </item>
    <item>
      <title>Generation of Mobility Patterns for Private Vehicles using Multi-headed Sequence Generative Adversarial Networks</title>
      <link>https://trid.trb.org/View/2493117</link>
      <description><![CDATA[Effective transport planning strategies seek to improve the urban transportation systems by addressing traffic congestion problems, identifying emission hotspots, incentivizing public transit to discourage private vehicle usage and adoption of electric vehicles. Strategy planners and policy makers are increasingly relying on new emerging methods such as full population scaled agent-based macroscopic transport simulation models to study the interaction between mobility patterns of the commuters and their impact on the transportation systems. This is because the behavioural changes in daily travelling patterns of commuters play a major role in predicting the future travel demand for the development of a robust and reliable urban transportation system. Traditionally, household travel surveys have been conducted periodically to understand the current travel behaviour and plan for the future development. However, it is impossible for traditional surveys to sample a large majority of the daily commuter trips, particularly for private vehicles and therefore in its raw form, it cannot be used in a full sample agent-based simulation. In this paper, the authors use the household travel survey data from Singapore to generate the synthetic mobility patterns for the complete population of private vehicle users that is capable of preserving the data privacy whilst retaining the statistical features of the original data. This is accomplished by employing a multi-headed gated recurrent unit (GRU) based generative model that enables the generation of synthetic mobility patterns that provide a suitable representation of the real mobility patterns of the larger set of commuters. The authors define and utilize two accuracy evaluation metrics that quantify the quality of the generated synthetic mobility trips. The authors' experimental results have shown that the authors are able to capture 90% of the correctness of the original mobility dataset of private vehicle users.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493117</guid>
    </item>
    <item>
      <title>Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto</title>
      <link>https://trid.trb.org/View/2496634</link>
      <description><![CDATA[As global efforts to combat climate change intensify, transitioning to sustainable transportation is crucial. This study investigates decarbonization strategies for urban traffic in downtown Toronto through microsimulation, focusing on eco-routing and eco-driving strategies, as well as the adoption of different fuel technologies: Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and conventional vehicles. A total of 140 scenarios are analyzed, incorporating varying levels of Connected and Automated Vehicle (CAV) penetration, anticipatory routing strategies, and driving behavior. Using transformer-based prediction models, the study evaluates Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions, average speed, and travel time. The findings demonstrate that 100% BEV adoption can reduce GHG emissions by 75%, but infrastructure and cost challenges persist. HEVs achieve moderate GHG reductions of 35%–40%, while e-fuels offer limited reductions of 5%. The study also highlights the role of eco-routing and eco-driving strategies in reducing emissions and improving travel time. However, it acknowledges potential unintended consequences, including modal shifts from active and public transportation to EVs, which could increase Vehicle Kilometers Traveled (VKT) and congestion, potentially offsetting some benefits of vehicle electrification. Integrating CAVs with anticipatory routing shows additional gains in reducing emissions and optimizing traffic flows. By providing a comprehensive evaluation of fuel technologies, traffic management strategies, and driving behaviors, this study offers actionable insights for policymakers to balance the benefits of electrification with its broader transportation impacts, supporting the development of sustainable urban mobility systems.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2496634</guid>
    </item>
    <item>
      <title>Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior</title>
      <link>https://trid.trb.org/View/2442240</link>
      <description><![CDATA[The COVID-19 pandemic emerged as a very influential occurrence with a profound impact on a global scale. The onset of the pandemic abruptly disrupted the regular course of everyday activities, primarily impacting urban regions. Hence, it is imperative to understand the effects of the COVID-19 pandemic on modern urban areas. This study seeks to analyze the effect of the pandemic on travel behavior by utilizing GPS data obtained from taxis, with a specific focus on spatial socioeconomic features. The M2 metro line in Istanbul has been selected for evaluation. In this analysis, four distinct periods are considered: total, off-peak, morning, and evening peaks. The stations are categorized using K-means clustering. The estimation models are constructed using ordinary least squares (OLS), spatial autoregression (SAR), and geographically weighted regression (GWR) techniques, which are applied to the variation in daily average cab trips and the characteristics of stations. The GWR models provide superior performance in comparison to the other two models, with notable distinctions observed in peak times, particularly morning peak when compared to total and off-peak counts. The findings indicate that factors such as population, population density, socioeconomic status, and the quantity of shopping malls are influential variables in elucidating and forecasting the fluctuations in taxi trip counts.]]></description>
      <pubDate>Tue, 12 Nov 2024 09:13:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2442240</guid>
    </item>
    <item>
      <title>Multiple shared mobility services under competition: Empirical evidence for public acceptance and policy insights to sustainable transport</title>
      <link>https://trid.trb.org/View/2417210</link>
      <description><![CDATA[Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers’ decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies.]]></description>
      <pubDate>Mon, 16 Sep 2024 09:00:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417210</guid>
    </item>
    <item>
      <title>Modeling urban resident travel satisfaction during the morning and the evening peak hours: A case study in Beijing</title>
      <link>https://trid.trb.org/View/2393208</link>
      <description><![CDATA[Understanding how various factors influence travel satisfaction can assist in traffic policy-making. In the study, the authors aim to develop innovative urban resident travel satisfaction evaluation models by building a comprehensive travel satisfaction evaluation index system and considering the asymmetric traffic flow and difference in travel time urgency during the morning and evening peak hours. The authors consider both the internal factors reflecting resident-related characteristics including socio-economic attributes and travel characteristics, and the external factors reflecting road-related characteristics including traffic facilities, road traffic conditions, traffic environments, and service levels. Then, for the morning and the evening peak hours, the authors respectively build a structural equation model (SEM) to capture the intrinsic interactions between latent factors, and an ordered logit model to describe the direct influencing factors of travel satisfaction considering its ordered nature. Finally, the authors examine their proposed models with the travel survey data collected in the Yizhuang district of Beijing, China. The numerical results show that both the internal and the external factors have significant impacts on travel satisfaction. The SEM models capture the interactions between the latent variables such as the positive relationship between traffic facilities and traffic environments. The ordered logit modeling results show that most external factors except the satisfaction of the road obstacles have positive influences on travel satisfaction. The authors’ research findings provide a better understanding of the intrinsic interactions between latent variables and direct influencing factors of travel satisfaction and offer the government valuable guidance on how to improve travel satisfaction.]]></description>
      <pubDate>Thu, 11 Jul 2024 13:53:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2393208</guid>
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