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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Exploring the Influence Path of the Built Environment of Residence and Workplace on Commuting Carbon Emissions: A Case Study of a Mountainous Big City</title>
      <link>https://trid.trb.org/View/2613149</link>
      <description><![CDATA[The mechanism of the built environment in the mountainous cities on commuting carbon emissions is unclear. This study aims to explore the influence path of the built environment of residence and workplace on commuting carbon emissions in a big mountainous city in southwest China. A path analysis model of commuting carbon emissions was established based on the neighbourhood-scale built environment data of commuters in Kunming, China. The model estimated results showed that the distance from the residential location to the city center and the land use mix at the residential area had a positive direct effect on commuting carbon emissions, while the land use mix at the workplace had a negative direct effect on commuting carbon emissions; some other built environmental variables of residence and workplace (population density, road network density, and number of bus stops or subway stations) had indirect effects through the commuting behaviors (distance, mode, and car ownership). The study also found that there were differences in the influence pathways of the built environment of residence and workplace on commuting carbon emissions. When designing urban carbon reduction strategies, it is necessary to consider the direct and indirect effects of the built environment on commuting carbon emissions. In particular, attention should be paid to the difference in the influence path and effect of the residence and the workplace built environment on commuting carbon emissions.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613149</guid>
    </item>
    <item>
      <title>Subsidy policy design for high-speed rail express</title>
      <link>https://trid.trb.org/View/2599135</link>
      <description><![CDATA[The growing demand for high-value cargo transportation and the development of high-speed rail networks have made high speed rail express (HSRE) feasible. Although high-speed rail offers lower carbon emissions, its higher operational costs necessitate government subsidies to balance corporate profitability and social benefits. To address the policy design of subsidies for HSRE, the authors first employed the Hotelling model to characterize logistics enterprises’ mode choice behavior, thereby determining the demand for both high-speed rail and road. Subsequently, a social welfare maximization model was developed incorporating producer surplus, consumer surplus, government expenditure, and environmental impact, deriving optimal subsidy values under different pricing scenarios. Finally, a case validation analysis was conducted using the HSRE service between Kunming and Chengdu in China. The research reveals that the subsidy value is proportional to the price difference between the two transport modes under fixed-prices scenario—the greater the disparity, the higher the required subsidy. While under equilibrium-prices scenario, the optimal subsidy value depends not only on intermodal cost differentials and carbon pricing but also on cargo time value, transport efficiency, terminal handling capacity, and transfer costs. These findings provide new insights for HSRE subsidy policies design.]]></description>
      <pubDate>Tue, 07 Oct 2025 08:21:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2599135</guid>
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    <item>
      <title>Evaluation of Traffic Efficiency in Highway-Weaving Areas Considering Risky Driving Behaviors Based on Vehicle Trajectory Data</title>
      <link>https://trid.trb.org/View/2588947</link>
      <description><![CDATA[Highway-weaving areas face significant traffic efficiency challenges due to frequent merging and diverging movements. This study quantifies the precise thresholds at which risky driving behaviors impact traffic efficiency, using high-resolution drone data from two weaving sections in Kunming, China, collected during peak hours. The data cover more than 2 h of detailed vehicle motion, including speed, acceleration, and lane-change activities. Four risky driving behaviors—rapid acceleration, rapid deceleration, lane changes, and speeding—were analyzed to assess their impact on traffic flow. A traffic efficiency model was developed using traffic volume and the duration of risky behaviors within a 30-s window, considering both upstream and weaving area conditions. Rapid deceleration was most frequent near merging points, creating bottlenecks, while rapid acceleration and speeding were common after diverging points. High converging ratios were linked to longer travel times and reduced traffic efficiency. Random forest models achieved the highest predictive accuracy (R²=0.612–0.723) among the machine learning approaches tested. SHAP analysis identified specific critical thresholds (e.g., mainline traffic volume > 14.22 pcu, merging traffic > 8.12 pcu) beyond which traffic efficiency significantly deteriorates. Notably, a nonlinear relationship was discovered where moderate levels of rapid deceleration before the weaving area (below 17.74 s) actually improved traffic flow. The study provides actionable parameters for traffic management systems to implement targeted interventions such as extended merging lanes and adaptive speed controls, moving beyond descriptive observations to prescriptive guidance for improving traffic flow in weaving areas.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588947</guid>
    </item>
    <item>
      <title>Investigating the associations of commuting behavior, perception, satisfaction, and subjective well-being: An analytical framework from the perspective of commuters' preferences and tolerance toward commuting time</title>
      <link>https://trid.trb.org/View/2584194</link>
      <description><![CDATA[Although commuting behavior and subjective well-being (SWB) have been widely shown to be statistically correlated, the complex relationships among commuting time dissonance and commuting modes, experiences, and SWB remain empirically unclear. From the perspective of commuters' preferences and tolerance toward commuting time, this study aims to reveal the associations of commuting time dissonance, perceptions, satisfaction, and SWB across commuting modes. This study redefines the levels of commuting time dissonance based on the extent to which individuals' actual commuting time deviates from their ideal and tolerable reference points. An analytical framework linking modes→dissonance→perceptions→satisfaction to SWB was developed. The authors obtained a sample of 356 urban commuters from different industries in Kunming, China, using stratified sampling in 2023. Finally, a path analysis model was used to identify the pathways of association between these subjective and objective commute variables and SWB. First, commuting time dissonance was negatively related to multidimensional commute experiences. Actual commute time activated tolerance threshold (severe dissonance), increasing the likelihood of perceived commuting fatigue, stress, and pain while reducing commute satisfaction. Second, these negative experiences exerted significant adverse mediating roles between severe dissonance and SWB. Specifically, high levels of perceived commute pain and low levels of commute satisfaction further reduced SWB. Third, commuting time dissonance mediated the relationships between commuting modes and both perceptions and satisfaction. Specifically, active commuters exhibited significantly lower probabilities of severe commute time dissonance but higher likelihoods of commute time consonance (actual commute time alignment with ideal reference point). This dual advantage indirectly reduced negative commute perception and enhanced commute satisfaction. To achieve synergistic policy objectives in active travel promotion, commute experience optimization, and well-being enhancement, evidence-based planning of urban commuting zones should incorporate residents' ideal and tolerable commute time thresholds.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2584194</guid>
    </item>
    <item>
      <title>Max Pressure Signal Control Method for Collaborative Optimization of Motor and Nonmotor Vehicles</title>
      <link>https://trid.trb.org/View/2553860</link>
      <description><![CDATA[This study proposes a traffic signal control method based on the max pressure (MP) control algorithm to optimize the coordination of motor and nonmotor vehicles. The vehicle arrival rates at downstream intersections are determined using upstream arrival rates and Robertson’s platoon dispersion model. The total delays for both vehicle types under varying congestion levels are analyzed based on different arrival scenarios. The queue lengths at downstream intersections are predicted using Robertson’s model. By applying the MP control characteristics, the green time for each phase is optimized based on queue lengths to maximize the traffic travel service and prevent queue overflow. A simulation environment using SUMO was developed to test the model, with Beijing Road in Kunming as a case study. The results show significant reductions in queue lengths and intersection delays for both motor and nonmotor vehicles. The proposed method outperforms traditional controls, effectively managing oversaturation, preventing queue overflow, and increasing traffic throughput. This method is particularly effective in environments with high numbers of nonmotor vehicles.]]></description>
      <pubDate>Wed, 18 Jun 2025 14:41:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553860</guid>
    </item>
    <item>
      <title>Investigating user preferences for dockless bike- and electric bike-sharing through tracking usage patterns</title>
      <link>https://trid.trb.org/View/2544292</link>
      <description><![CDATA[The integration of electric bikes has significantly boosted the popularity of shared micro-mobility. To promote the coordinated development of dockless bike-sharing (DBS) and electric bike-sharing (EBS), it is crucial to analyze the mechanisms influencing user preferences. However, capturing accurate usage patterns of users remains a challenge, hindering the optimization of shared micro-mobility services. Using one month of shared cycling order data from Kunming in 2022, this study tracks user travel patterns and categorizes them into three types: DBS-dominant, balanced, and EBS-dominant. To investigate the underlying mechanisms influencing these preferences, the study initially applies HDBSCAN clustering to identify users' frequent travel locations. A weighted Gradient Boosting Decision Trees (GBDT) model is employed to reveal the nonlinear relationship between explanatory variables and user preference types. The analysis considers factors from the perspectives of travel characteristics, built environment, and shared infrastructure systems. Results indicate that travel characteristics and the built environment significantly affect users' travel preferences. DBS-dominant users prefer short-distance, high-frequency trips, particularly in the Central Business District (CBD) and areas with complex road conditions. In contrast, EBS-dominant users favor long-distance travel and prolonged use, particularly in areas farther from the CBD. Balanced users exhibit flexibility, switching between DBS and EBS based on specific needs and conditions to maximize convenience. Targeted policy measures have been designed for various user groups to improve travel services and support the integrated development of the DBS and EBS systems. This study not only provides scientific decision-making support for shared mobility services but also assists market operators in refining their offerings.]]></description>
      <pubDate>Wed, 28 May 2025 10:13:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2544292</guid>
    </item>
    <item>
      <title>Understanding urban spatial structure through the lens of multiple modal accessibility</title>
      <link>https://trid.trb.org/View/2543835</link>
      <description><![CDATA[In contrast to most previous studies that investigated urban spatial structures via the accessibility pattern by a single transport mode, this study proposes a research framework based on the concept of modal accessibility gap(MAG). This framework uses cumulative accessibility measurements, spatial clustering methods with spatial constraints, and online map tools. These methods were employed in the urban spatial structure identification of Kunming, a major city in Southwest China. Findings can be summarized as follows: First, there are obvious spatial disparities in accessibility between motorized and non-motorized transport modes, which emphasizes the necessity of understanding urban structures with multiple modal accessibility. Second, combining the accessibility of the four common modes (driving, public transport, walking, and cycling) with spatial constraints is beneficial for maintaining spatial continuity. In addition, some substructures can be better depicted compared to urban structures detected by a single transport mode. Thus, introducing non-motorized transport modes into the MAG analysis is helpful for the urban planning aimed at green mobility, healthy lifestyles, and human well-being.]]></description>
      <pubDate>Wed, 14 May 2025 13:09:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543835</guid>
    </item>
    <item>
      <title>Is human-like decision making explainable? Towards an explainable artificial intelligence for autonomous vehicles</title>
      <link>https://trid.trb.org/View/2488463</link>
      <description><![CDATA[To achieve trustworthy human-like decisions for autonomous vehicles (AVs), this paper proposes a new explainable framework for personalized human-like driving intention analysis. In the first stage, the authors adopt a spectral clustering method for driving style characterization, and introduce a misclassification cost matrix to describe different driving needs. Based on the parallelism in the complex neural network of human brain, the authors construct a Width Human-like neural network (WNN) model for personalized cognitive and human-like driving intention decision making. In the second stage, the authors draw inspiration from the field of brain-like trusted AI to construct a robust, in-depth, and unbiased evaluation and interpretability framework involving three dimensions: Permutation Importance (PI) analysis, Partial Dependence Plot (PDP) analysis, and model complexity analysis. An empirical investigation using real driving trajectory data from Kunming, China, confirms the ability of the approach to predict potential driving decisions with high accuracy while providing the rationale implicit AV decisions. These findings have the potential to inform ongoing research on brain-like neural learning and could function as a catalyst for developing swifter and more potent algorithmic solutions in the realm of intelligent transportation.]]></description>
      <pubDate>Mon, 10 Feb 2025 14:29:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2488463</guid>
    </item>
    <item>
      <title>Urban natural gas pipeline operational vulnerability under the influence of a social spatial distribution structure: A case study of the safety risk patterns in Kunming, China</title>
      <link>https://trid.trb.org/View/2447588</link>
      <description><![CDATA[Frequent urban natural gas pipeline accidents pose a serious threat to the safety of people and property in surrounding areas. However, current research on natural gas pipeline risks primarily focuses on evaluating the pipelines themselves, with no established method for assessing the impact of pipeline disasters on surrounding areas. This paper proposes an urban natural gas pipeline risk assessment method that integrates the physical attributes of the pipelines with an analysis of social vulnerability based on urban social spatial distribution. Using urban Point of Interest (POI) data, a social spatial distribution model for potential natural gas pipeline accidents is constructed. The risk of pipeline failure is assessed based on physical vulnerability, while the consequences of failure are evaluated through social vulnerability. This method combines the analysis of physical and social vulnerabilities to achieve a comprehensive urban natural gas pipeline risk assessment. The results identified 68 out of 6148 pipelines in the study area as "double high" pipelines, characterized by high physical vulnerability (relatively high risk pipelines) and high social vulnerability (involving level IV areas). The high risk communities identified in the study area are the Cuihu West Road Community and the Daguan Commercial City Community, highlighting the characteristics of risk distribution. The findings suggest that this study contributes to improving urban resilience to natural gas pipeline incidents, reducing potential economic losses and public impacts, and enhancing urban public safety. It also provides new insights into natural gas pipeline risk assessment and urban public safety research.]]></description>
      <pubDate>Thu, 14 Nov 2024 09:48:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447588</guid>
    </item>
    <item>
      <title>Two-stage multilateral trade-based prediction model for freight transport carbon emission of Belt and Road countries along Eurasian Landbridges</title>
      <link>https://trid.trb.org/View/2431765</link>
      <description><![CDATA[Global freight distribution patterns have been affected by trading policies and the pandemic outbreak. The Belt and Road Initiative, trade conflicts, and the COVID-19 pandemic have changed the global logistics flow, shifting cargos from maritime and air transport to railway transport along the countries in the Eurasian Landbridge. Though railway freight emits less carbon than road truck transportation, the increased use of railway freight brings in a higher volume of carbon emissions to cities located along the landbridges. Achieving net zero carbon emission is becoming more important, but there is a lack of literature in assessing the environmental impact of cross-border railway logistics transportation among Belt and Road countries. A novel two-stage multilateral trade-based prediction model is developed, integrating a modified gravity model and nonlinear autoregressive neural network for trade and emission forecasting. The model evaluates railway freight along the landbridge over ten years and forecasts the impact of carbon emissions from trading and logistics along the corridor in the subsequent five years. It further analyses the emissions impact of the proposed Third Eurasian Landbridge and the extended Second Eurasian Landbridge. The findings provide insights for the development of railway freight transport, considering trade and logistics flow, carbon emission mitigation strategies, and sustainability impact between China and other Belt and Road countries. While countries such as India and Kazakhstan were forecast to have significant amounts of carbon emissions in the projected period, the rapid growths in locations with smaller emission amounts such as Kunming and Georgia should draw attention and require continuous monitoring.]]></description>
      <pubDate>Mon, 30 Sep 2024 17:21:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431765</guid>
    </item>
    <item>
      <title>Performance evaluation of a short-distance transition subgrade with pile-plank structures in high-speed railway</title>
      <link>https://trid.trb.org/View/2404051</link>
      <description><![CDATA[The transition subgrade is widely applied between the general subgrade and rigid structures (such as abutments, culverts, and tunnels) to reduce the degradation rate of track structures in high-speed railways. To address the problem that the common-type transition subgrade cannot be applied in mountainous areas due to the short distance between the tunnel and the subgrade (or abutment), a new type of pile-plank structure (PPS) transition subgrade for short-distance transition was proposed, and the PPS subgrade was successfully applied to a bridge-tunnel transition zone in the Shanghai-Kunming high-speed railway, China. A series of field tests, including vibration acceleration and velocity tests, were carried out in the bridge-tunnel transition zone to evaluate the transition performance of the PPS subgrade using the effective value and power spectral density method. In addition, an FE model that considers the coupled vehicle-track-subgrade-foundation interaction was established to fully reveal the dynamic performance of the PPS subgrade by characterizing the change rate of rail deflection, the dynamic response distribution of the track structure along the transition zone, and the running quality of the train comfort. The results show that the proposed PPS transition subgrade has a good transition performance and meets the requirements for excellent train comfort.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:09:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2404051</guid>
    </item>
    <item>
      <title>Characteristics of Fundamental Data for Railway Maintenance and the Construction of Integrated Data Platform</title>
      <link>https://trid.trb.org/View/2203454</link>
      <description><![CDATA[This paper summarizes the background information for the establishment of the Integrated Data Platform for Railway Maintenance (IDPFRM). It analyzes the spatial characteristics, the subject characteristics and the time characteristics of Fundamental Data for Railway Maintenance (FDFRM) generally. Then it proposes the thoughts of integrating the FDFRM based on these characteristics. It proposes to construct the IDPFRM through regarding the spatial location as ligament, the single facility as the center, and the maintenance management flow as the master line. Finally, it gives an application example of the IDPFRM in Kunming Railway Bureau.]]></description>
      <pubDate>Sat, 15 Jun 2024 16:42:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203454</guid>
    </item>
    <item>
      <title>Investigating the association between perceived risk levels and commuting mode shifts after the lifting of the COVID-19 pandemic prevention and control policies</title>
      <link>https://trid.trb.org/View/2378156</link>
      <description><![CDATA[There is a research gap in understanding people's perceived risks and their commute mode shifts after the major shift in anti-pandemic policies. The study aims to reveal the relationship between commuters' perceived risks and their commuting mode transfers in the specific context of canceling anti-pandemic policies. The authors conducted an online sample survey of residents in 6 neighborhoods after one month the lifting of anti-pandemic policies in Kunming, China. Measured perceived risk data suggested that a perceived risk score of 23 ∼ 30 accounted for 62 % of the respondents, who were defined as the high-perceived risk group; while the perceived risk score of 14 ∼ 22 accounted for 36 % of the respondents, who were defined as the middle-perceived risk group; only 2 % of respondents with a perceived risk score of 6 ∼ 14. Commuting mode transfer statistics showed that 22.2 % of the respondents switched from other commuting modes to private cars, of which 56.1 % came from public transportation. Conversely, out of 81 car commuters, only 3 respondents moved to other commuting modes. The authors used nonparametric tests to find that there were group differences in commuting mode shifts. Specifically, the proportion of commuters with high-perceived risk levels shifted from other travel modes to private cars was 11% larger than that of commuters with middle-perceived risk levels. Public commuters were more likely to switch to car commuting than active commuters. The nonparametric test results also showed that single variables such as car ownership, commute distance, age, and marital status was significantly correlated with the distribution of the shifting in commuting mode. Furthermore, the authors employed a binary logistic regression model to reveal that commuters with higher perceived risk levels, longer commuting distances, or car ownership were more likely to switch from other travel modes to private cars than other commuters. The conclusion of this study is that the lifting of COVID-19 pandemic prevention and control policies increases the perceived risk level of commuters, which pushes them to switch to private car commuting. It is necessary to pre-estimating people's perceived risk level, and pre-judging changes in daily commute behaviors before deciding to cancel the anti-pandemic policies.]]></description>
      <pubDate>Mon, 20 May 2024 17:06:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2378156</guid>
    </item>
    <item>
      <title>Investigating socio-spatial differentiation for metro travelers using smart card data: Older people vs. others</title>
      <link>https://trid.trb.org/View/2354084</link>
      <description><![CDATA[As population aging has been an issue worldwide, the mobility of older people have attracted the attention of scholars from urban planning, transport geography, and social science. However, few have investigated socio-spatial differentiation among mobility groups, considering their daily needs and activity spaces. To fill this research gap, the authors conducted a comparative analysis of socio-spatial differentiation, based on individual activity spaces. They used smart card data from Kunming, China, to identify selected individuals’ residential locations and travel patterns, and evaluate their accessed activity space. The authors performed a disaggregated analysis of the individual activity space, and then aggregated the activity counts on each grid. This study found that the residential locations of older metro travelers are significantly different from those of other metro travelers. In addition, socio-spatial differentiation was found to exist due to different daily requirements. The results were confirmed in three LASSO models with built environment variables. These findings are useful in urban and transportation planning to improve elder-friendly services.]]></description>
      <pubDate>Thu, 25 Apr 2024 09:41:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2354084</guid>
    </item>
    <item>
      <title>The travel pattern difference in dockless micro-mobility: Shared e-bikes versus shared bikes</title>
      <link>https://trid.trb.org/View/2359515</link>
      <description><![CDATA[To facilitate the tailoring of dockless bike-sharing and electric bike (e-bike) sharing services and assist in formulating effective regulations, this study aims to unravel the spatio-temporal travel patterns specific to e-bike-sharing and bike-sharing systems, utilising interpretable machine learning methods and a large-scale trip-level dataset in Kunming, China. The results show that shared bikes and e-bikes exhibit overall similarities and subtle differences in many aspects, such as trip attributes and spatial distribution. Additionally, both shared bikes and shared e-bikes have three basic temporal patterns for commuting and recreational purposes. Regarding the differences, e-bike sharing networks are more dispersed and bigger, and bike sharing tends to form densely connected clusters of flow, exhibiting a local concentration of activity. Besides, the commuting activities within e-bike sharing systems exhibit two patterns: direct travel to the destination and integration with public transit. In contrast, shared bikes predominantly rely on public transit transfers for commuting purposes.]]></description>
      <pubDate>Wed, 10 Apr 2024 11:38:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2359515</guid>
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