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    <title>Transport Research International Documentation (TRID)</title>
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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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      <title>AZ TripUSA™</title>
      <link>https://trid.trb.org/View/2683248</link>
      <description><![CDATA[Northern Arizona has more than 6 million visitors per year. More than 2 million of these visitors will explore the World Wide Web to learn more about their destination prior to their trip. For this reason, the Federal Highway Administration, in partnership with the Arizona Department of Transportation (DOT), sponsored the development of the AZ TripUSA™ Rural Model Deployment Initiative (MDl)/Field Operation Test (FOT) in Northern Arizona along I-40. The result was TripUSA™ (developed by Castle Rock Consultants). This program is a public/private partnership created to improve traveler mobility, enhance economic development for the area, and enrich the overall experience of travelers. AZ TripUSA™ was successfully deployed during the first 6 months of 1998. TripUSA™ allows travelers who use the Internet to discover a wealth of information about their destination for use in trip and travel planning. Upon arriving at their destination, travelers can use interactive touch-screen kiosks to check road and weather conditions, find lodging and restaurants, and obtain directions to attractions.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:27:30 GMT</pubDate>
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      <title>Branson Travel and Recreational Information Program (TRIP)</title>
      <link>https://trid.trb.org/View/2683247</link>
      <description><![CDATA[The city of Branson, Missouri, has a permanent population of around 4,400 and measures approximately 7 mi (11.3 km) along its main thoroughfare, State Highway 76. Branson attracts more than 6 million visitors per year. The congestion on State Highway 76 is severe (Level of Service F) during peak and some off-peak periods. The lack of right-of-way makes it impossible for the city to "build" out of its congestion problems. With this in mind, the Branson Travel and Recreational Information Program (TRIP) project was created. The Branson TRIP project was one of five Advanced Rural Transportation System Operational Tests selected in August 1997 by the Federal Highway Administration for deployment. The Branson area provided an excellent opportunity to test Advanced Traveler Information System (ATIS) technologies in a rural tourist destination.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683247</guid>
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      <title>Points of Interest in Smart Cities and Visitor Behavior</title>
      <link>https://trid.trb.org/View/2579246</link>
      <description><![CDATA[Smart cities leverage technology and data to enhance the quality of urban life, including the management of points of interest (POIs) and visitor experiences. This paper explores the relationship between POIs and visitor behavior in smart cities, examining the impact of technology-driven solutions on understanding, analyzing, and optimizing visitor experiences. It highlights the importance of data-driven approaches in identifying and managing POIs, enhancing visitor satisfaction, and driving economic growth. The paper reviews existing literature, discusses key concepts, and presents case studies to illustrate the role of POIs in smart cities and their influence on visitor behavior. Our major contribution is a data driven approach to extract useful information from real data to municipality decisions and understand the problem. It concludes with recommendations for future research and practical implications for city planners, policymakers, and tourism authorities.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579246</guid>
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    <item>
      <title>Mining Tourists’ Movement Patterns in a City</title>
      <link>https://trid.trb.org/View/2579244</link>
      <description><![CDATA[Although tourists generate a large amount of data (known as “big data”) when they visit cities, little is known about their spatial behavior. One of the most significant issues that has recently gained attention is mobile phone usage and user behavior tracking. A spatial and temporal data visualization approach was established with the purpose of finding tourists’ footprints. This work provides a platform for combining multiple data sources into one and transforming information into knowledge. Using Python, we created a method to build visualization dashboards aiming to provide insights about tourists’ movements and concentrations in a city using information from mobile operators. This approach can be replicated to other smart cities with data available. Weather and major events, for instance, have an impact on the movements of tourists. The outputs from this work provide useful information for tourism professionals to understand tourists’ preferences and improve the visitors’ experience. Management authorities may also use these outputs to increase security based on tourists’ concentration and movements. A case study in Lisbon with 4 months data is presented, but the proposed approach can also be used in other cities based on data availability. Results from this case study demonstrate how tourists tend to gather around a set of parishes during a specific time of the day during the months under study, as well as how unusual circumstances, namely international events, impact their overall spatial behavior.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579244</guid>
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    <item>
      <title>AI-based Quality-of-Life-aware route planning: A Phuket case study</title>
      <link>https://trid.trb.org/View/2676207</link>
      <description><![CDATA[Assessing travel-related Quality of Life (QoL) is a significant challenge due to its reliance on subjective human perception, which is difficult to measure. Traditional studies are often constrained by the limited scope of costly, manual data collection. The significant contribution of this research is the introduction of a new framework that can analyze tourist perception on a large and systematic scale. The novelty of this work lies in the utilization of Google Street View (GSV) as a large-scale visual data source, coupled with the use of a sequence-based model to analyze travel routes, which allows for the simulation of the ever-changing travel experience better than the analysis of single, static images. This framework was applied to a dataset in Phuket, consisting of 456 driving routes, totaling 4,560 sequential images. Visual features were extracted from this image data using Object Detection and Semantic Segmentation techniques and then used to build prediction models with LSTM and KNeighborsRegressor for analysis in conjunction with QoL scores from questionnaires. The analysis revealed key insights into tourist perception patterns, identifying a significant divergence between the culturally homogeneous group of Thai tourists and the highly diverse group of foreign tourists. Furthermore, it was found that tangible quality of life dimensions (e.g., Material Well-being) was perceived more consistently than abstract dimensions (e.g., Emotional Well-being). In conclusion, this research shifts the paradigm of route analysis from focusing solely on efficiency to a human-centric process, providing a new tool for urban planners and stakeholders to understand and improve the quality of the journey in a more profound way.]]></description>
      <pubDate>Tue, 24 Mar 2026 16:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676207</guid>
    </item>
    <item>
      <title>Rethinking Europeanness through travelling imaginative geographies</title>
      <link>https://trid.trb.org/View/2643450</link>
      <description><![CDATA[Tourism mobilities play a significant role in how European identity and the European space are imagined, encountered, and negotiated, but this role has rarely been interrogated. To address this gap, the present article discusses the nexus between tourism and Europeanness, drawing from the concept of ‘travelling imaginative geographies of Europe’. This concept was developed from literature that identifies Europeanness as everyday practice, geographical imaginations, tourism, and mobilities. To test the concept, a limited number of tourist pictures that were shared in a pilot study conducted with 24 ‘students-cum-tourists’ – international Master’s-level students who had gone on tourist trips across Europe – are presented. The pilot study results revealed that travelling imaginative geographies reproduces and challenges consolidated narratives on Europeanness, including the role of cultural heritage and tourism mobilities, urban sociability and public space, embodied semiotics, and perceived boundaries. Considering the multiple crises presently ongoing in Europe, this article proposes that further research on travelling imaginative geographies may support their contribution to the evolution of Europeanness and, specifically, the question of Otherness as it pertains to the European space.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643450</guid>
    </item>
    <item>
      <title>Behavioral Intentions Related to Mode Switching to Electric Motorcycles in Bali</title>
      <link>https://trid.trb.org/View/2646018</link>
      <description><![CDATA[This study aims to investigate the behavioral intention to switch to electric motorcycles (e-MCs) in Bali. Data from 473 local respondents in Bali were used for the analysis. Structural Equation Modeling was used to build a conceptual model of switching behavioral intention to e-MCs in Bali. The study-appropriate variables were based on research gaps identified by a comprehensive analysis of existing literature. As a result, this study proposes nine hypotheses that were analyzed based on the collected data. The analysis showed that only four of the nine hypotheses can be accepted with statistical significance. The study found that the intention to switch to e-MC is directly influenced by risk perception, effort expectation, environmental concern, social influence, and hedonic motivation. Further research should give attention to domestic and international tourists visiting Bali, as many tourists ride motorcycles during their stay in Bali.]]></description>
      <pubDate>Thu, 12 Mar 2026 16:30:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646018</guid>
    </item>
    <item>
      <title>Tourist Safety on the Polish Baltic Coast: Analysis of Risks and Preventive Strategies</title>
      <link>https://trid.trb.org/View/2624157</link>
      <description><![CDATA[The Polish Baltic Sea coast is a popular destination for tourists seeking picturesque landscapes, sandy beaches, and a rich cultural heritage. Each year, millions of visitors flock to the region to enjoy its natural beauty and recreational opportunities. However, the increasing number of tourists also brings unique challenges related to safety and risk management. Ensuring the safety of visitors is a multifaceted task that requires collaboration between local authorities, businesses, and emergency services. The paper explores the primary risks faced by tourists on the Polish Baltic coast, including natural hazards, infrastructure challenges, and human-related factors such as crime or accidents. Furthermore, it examines the strategies and measures implemented to mitigate these risks, highlighting the importance of education, preparedness, and effective communication. By analyzing both threats and preventive actions, this study aims to provide valuable insights for policymakers, local stakeholders, and tourists themselves to enhance safety and promote a secure environment for all.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624157</guid>
    </item>
    <item>
      <title>Love at first sight? Segmenting tourists’ attitudes toward autonomous boats in Kaohsiung’s Love River</title>
      <link>https://trid.trb.org/View/2632166</link>
      <description><![CDATA[Integrating autonomous technology for waterborne vessels is gaining momentum with several services launched in recent years. However, compared to other autonomous public transportation modes, passenger intentions, key influencing factors, and variations across different user segments remain underexplored. Our work addresses the research gap by focusing specifically on tourists, contrasting with the prevailing literature emphasis on economic aspects of cargo vessels. This study investigates intentions to adopt autonomous boats at Kaohsiung’s Love River in Taiwan through a pre-trial on-site survey of 491 domestic visitors. The findings reveal that perceptions of performance effectiveness, ease of use, and enjoyment significantly drive adoption intentions, while concerns related to unfamiliarity and anxiety about new technologies inhibit acceptance. Utilizing an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model, structural equation modeling, and response-based segmentation (REBUS), three distinct user segments were identified: Pleasure Seekers, Cautious Enthusiasts, and Skeptics, each characterized by varying levels of enthusiasm and apprehension. This research contributes to transport and tourism literature by providing deeper insights into tourists’ acceptance of autonomous boats, emphasizing the roles of performance expectancy, hedonic motivation, and psychological barriers. Conceptually, we extend the UTAUT2 model with Technology Interest, Anxiety, and Trust. We apply REBUS-PLS to analyze heterogeneity, providing new methodological directions. The practical implications offer valuable insights for policymakers and operators to develop targeted marketing strategies and policies, foster user acceptance, enhance safety and emotional assurance, and effectively promote the distinctive experiences provided by autonomous maritime tourism.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632166</guid>
    </item>
    <item>
      <title>Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia</title>
      <link>https://trid.trb.org/View/2590557</link>
      <description><![CDATA[The demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590557</guid>
    </item>
    <item>
      <title>Development of a multimodal route choice model for visitors</title>
      <link>https://trid.trb.org/View/2613535</link>
      <description><![CDATA[Revitalizing a city center requires not only changes in land use patterns, but also improvements in the level of transportation services. This study aims to develop a multimodal route choice model for visitors in the central area of Kanazawa City, using a prism-constrained recursive logit model. The proposed model integrates both mode choice (walking, car, and bus) and route choice behavior. The parameters were successfully estimated for the study area with a high degree of accuracy. The results demonstrate the model’s capability to evaluate the impact of land use and transportation policies, such as reallocating parking spaces, enhancing bus services, and increasing sidewalk widths, on visitors’ mode and route choice behavior.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:17:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613535</guid>
    </item>
    <item>
      <title>Uncovering urban tourist mobility patterns based on large-scale mobile phone data</title>
      <link>https://trid.trb.org/View/2633632</link>
      <description><![CDATA[Amid the rapid global recovery of tourism and the increasing popularity of urban travel, cities are experiencing mounting spatial and functional pressures driven by tourists' unique mobility patterns. Although urban managers and researchers have focused on tourists' impact on urban space and transportation, systematic empirical studies at the individual level remain scarce, particularly in the detailed characterization of spatiotemporal behavior patterns. This study, based on high-frequency mobile phone data, develops an integrated analytical framework that combines tourist identification, activity inference, and multidimensional mobility indicators to systematically uncover the behavioral patterns and underlying mechanisms of tourist mobility in urban space. The results show that tourists, compared to residents, exhibit stronger spatial exploration tendencies and greater behavioral diversity, reflecting distinct non-routine mobility characteristics. Tourist activities are organized around accommodation locations, forming anchor-based activity chains. Tourist visitation is highly concentrated in certain attractions, resulting in spatially uneven “tourism corridors”. In addition, the integration of POI features and machine learning methods enables the inference of tourist activities, further enhancing the understanding of individual-level behavioral heterogeneity. This study not only extends urban mobility theories to tourism contexts but also provides data-driven insights for spatial governance, transportation planning, and service optimization in tourism-oriented cities.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633632</guid>
    </item>
    <item>
      <title>Digital Trojan horse – urban tools in a non-urban environment. How to inform tourist travel decisions by means of a multi-service mobile app</title>
      <link>https://trid.trb.org/View/2604787</link>
      <description><![CDATA[This study examines the impact of a multi-service mobile app on tourist decision-making across three key stages: destination selection, long-distance travel, and local mobility at the destination. Utilizing discrete choice experiments (DCEs) with 266 survey participants, the study evaluates the effects of an app that integrates information and booking interface for transportation, attractions, and local facilities. The results highlight that while the impact of the app is generally low, its added value becomes more substantial in complex and time-sensitive stages of travel, such as on-site mobility. The app’s availability increases the market share of destinations by up to 4.5 percentage points and increases the attractiveness of rail travel by 27 % under specific scenarios. The willingness to pay for the app is in the range of 7-12 € per night for destination choice, 10–20 € for mode choice for long-distance travel to destination, and 1-1.28 € per trip for local trips at the destination, indicating that it is commercially viable to develop and maintain. Despite these benefits, the app's effectiveness is contingent on user awareness and integration with high-quality local transport services. The study introduces the app as a "digital Trojan horse," leveraging its functionalities to unobtrusively promote sustainable travel options. This research underscores the need for collaborative app development across destinations and suggests further investigation into large-scale deployment and heterogeneity in user preferences.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604787</guid>
    </item>
    <item>
      <title>Where They're Coming From and How They're Getting Here: A Mobility Assessment on Little Tokyo’s Nonresident Population</title>
      <link>https://trid.trb.org/View/2613660</link>
      <description><![CDATA[Los Angeles Metro's Regional Connector is slated to open in 2020 with one of three new stations located near the heart of Little Tokyo at 1st Street and Central Avenue in Los Angeles. Coupled with the growth of Little Tokyo as a commercial destination, these transit investments have raised concerns that the changing landscape will jeopardize the preservation of culture within the historic Japanese-American neighborhood. To better understand the potential effect of these new transportation investments, this study examines the travel trends and behaviors of Little Tokyo's nonresident population. Using two public data sources and data collected through two travel surveys (an employee electronic travel survey and a visitor intercept travel survey), the research analyzes commute mode choice and distance, travel time, home destination, trip purpose, and demographic characteristics.]]></description>
      <pubDate>Mon, 15 Dec 2025 15:41:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613660</guid>
    </item>
    <item>
      <title>Research on Holiday Passenger Flow Prediction for Urban Rail Transit in Tourist Cities under Limited Sample Conditions: A Case Study of China</title>
      <link>https://trid.trb.org/View/2613599</link>
      <description><![CDATA[Holiday passenger flow in urban rail transit systems is characterized by considerable fluctuations and unpredictability, posing significant challenges for operational management. In tourist cities, these challenges are compounded by an influx of visitors during holidays, leading to pronounced passenger flow peaks and making accurate prediction especially difficult. Traditional forecasting methods often fail to effectively capture the spatiotemporal features of holiday passenger flow, particularly when only limited holiday data samples are available, resulting in reduced prediction accuracy. To tackle these issues, this paper proposes an XGBoost-based transfer learning model for time-series prediction (XGB-TL-TSP), which utilizes regular Saturday data to train for holiday scenarios in China. Experiment results show that the XGB-TL-TSP model notably improves holiday passenger flow prediction accuracy while demonstrating robust performance and strong generalization in noisy data environments and across diverse holiday contexts. The proposed model offers effective technical support for holiday passenger flow prediction in tourist cities.]]></description>
      <pubDate>Mon, 27 Oct 2025 11:30:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613599</guid>
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