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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Do light-based nudges influence pedestrian exit choice? A case study in a university building</title>
      <link>https://trid.trb.org/View/2706580</link>
      <description><![CDATA[Lighting is an integral element of every pedestrian environment, making it a promising tool for crowd management. However, limited knowledge exists on how different lighting conditions shape pedestrian choice behavior. This study systematically examines how both light intensity and light color influence pedestrian exit choice using data from a large field experiment in which varying light settings were applied to two building exits. Two multinomial logit (MNL) models, a light-intensity model and a light-color model, were estimated to quantify these effects. Findings indicate that only a limited subset of light-intensity and light-color conditions meaningfully influence pedestrian exit choice, with Off-Neutral, Bright-Neutral, White-Green, and Red-Green showing moderate, time-dependent effects. At the same time, contextual factors such as origin, local density, and time of day remain far stronger predictors of behavior. Moreover, learning effects emerge selectively and often counterintuitively, with pedestrians increasingly favoring the darker or red-lit exits in conditions where opposite directional responses are expected. The MNL models suggest that lighting can modestly influence pedestrian routing, provided it is applied with careful attention to contextual conditions and time of day.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706580</guid>
    </item>
    <item>
      <title>Simulation-to-artificial intelligence framework for real-time evacuation decision support in emergency response</title>
      <link>https://trid.trb.org/View/2731096</link>
      <description><![CDATA[Accurate and rapid prediction of evacuation time is critical for improving emergency response in densely populated academic buildings. While traditional simulation tools provide detailed egress analysis, their high computational cost limits real-time application during emergencies. Herein, the authors propose a simulation-to- artificial intelligence framework that integrates crowd simulation, machine learning, and a large language model for real-time evacuation time prediction. The authors developed a comprehensive simulation model of an academic building using Pathfinder software, generating a robust dataset through 4900 distinct simulations. Machine learning models were used for predicting evacuation times, with the random Forest algorithm exhibiting the highest accuracy of 85.7%. To validate the approach, the authors conducted a large-scale evacuation drill involving 2329 participants, which confirmed the predictive efficacy of the models. Notably, the application of machine learning reduced prediction times from 45.300 s to 0.003 s for complex scenarios, significantly enhancing computational efficiency. Furthermore, Shapley additive explanation analysis was used for improving the transparency and trustworthiness of the model, identifying average evacuee speed and total number of occupants as the most critical factors influencing evacuation time. This study provides a scalable, efficient, and interpretable decision-support tool for building evacuation management and real-time emergency response.]]></description>
      <pubDate>Thu, 13 Aug 2026 10:29:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731096</guid>
    </item>
    <item>
      <title>Modeling the impact of clothing and shoe type on pedestrian behaviour in Abuja and Kano, Nigeria</title>
      <link>https://trid.trb.org/View/2698992</link>
      <description><![CDATA[The rise of overcrowded pedestrian walkways, sidewalks, and bridges due to a growing population, poor design considerations, or design replication has called for an apt investigation and regional standards for facility designs. This study investigated the flow and speed of pedestrians in facilities across 5 locations in Abuja and Kano, Nigeria, to identify the impact of clothing type on pedestrian behaviour. The outcomes suggest that Nigerian pedestrians walk slower (47.43 m/min or 0.79 m/s) than those in the US, Australia, and New Zealand (0.8 m/s to 1.44 m/s). Ease of movement was linked to the 10 clothing and shoe types worn in Nigeria (English wear, Short Africa wear, Kaftan, Jallabiya, Hijab, Wrapper, Abaya, Cover, Canvas, sandals, Slipper sandals, and Slippers). At the macroscale, results also suggested that walkway pedestrians (W002) had the highest speed distributions for males with lighter and freer shoes (SH002) and clothing (CL001), while bridges (B001 and B002) were consistently the lowest. A nonlinear relationship was found between pedestrian flow and density, and flow decreased with increasing density. Males and Females also exhibited a decrease in speed with increasing density, and lighter footwear returned higher speeds. This study proposed that pedestrian-designed standards should be region-specific, as various levels of service (LOS) in infrastructural planning may need to consider cultural and environmental factors affecting movement.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698992</guid>
    </item>
    <item>
      <title>Dynamics of bidirectional pedestrian flow on a ramp at different movement urgency levels</title>
      <link>https://trid.trb.org/View/2726720</link>
      <description><![CDATA[Understanding dynamics of pedestrian flow on ramps is crucial for crowd management in public places in daily life as well as in an emergency. However, research about pedestrian flow on ramps is limited, especially for bidirectional flow. In this study, an experiment of bidirectional flow at different urgency levels (normal walking and fast walking) are conducted on a ramp. The lane formation, crossing behavior, nearest neighbor, fundamental diagrams, congestion level and crowd danger are analyzed. It is shown that more lanes form in balanced flow compared to that in unbalanced flow. In unbalanced flow, pedestrians in minor flow need more time to cross the corridor compared to that in major flow, especially under fast walking conditions. In bidirectional flow, most of the nearest neighbors will stay at the two sides of the ascending and descending pedestrians, but the nearest neighbors of pedestrians in minor flow walk in front and behind more frequently under both normal and fast walking conditions. According to density-speed and density-specific flow relations, the speed and specific flow under fast walking conditions are both higher at the same density than those under normal walking conditions. The flow ratio will also influence the density-speed relation. The crowd risk of pedestrian flow on the ramp is the highest under fast bidirectional flow conditions. The authors hope the study is helpful for the safety management of bidirectional flow on the ramp.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:49:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726720</guid>
    </item>
    <item>
      <title>Development of Methods for High-Density Crowd Measurement and Tracking in Railway Station Concourses</title>
      <link>https://trid.trb.org/View/2688836</link>
      <description><![CDATA[Understanding the characteristics of pedestrian flows in large-scale railway stations is demanding. Not only for designing new stations or renovating existing ones, but also for applying new technologies such as autonomous robots for goods transportation, it is essential to conduct appropriate evaluations based on a fine measurement of pedestrian flow. However, there are only a limited number of cases of large-scale measurements in public spaces, and quantitative verification of tracking accuracy targeting high-density crowds has not been conducted. Furthermore, there are no publicly available 3D point cloud datasets for station environments. In this research, we developed a wide-area, high-density pedestrian flow measurement and tracking system using twenty 3D-LiDAR sensors in a real-world environment to accurately capture and quantify pedestrian flow in crowded large-scale station concourse. By employing an offline person detection model using deep learning and tracking compensation processing using both past and future point cloud data, we confirmed that high-accuracy person detection and tracking with HOTA exceeding 90% is possible in an area of approximately 900 m². Using the developed system, we successfully measured walking trajectory data with high accuracy and visualized flow trends using basic indicators and heatmaps derived from the measurement data, thereby evaluating the pedestrian flow characteristics in the station concourse environment.]]></description>
      <pubDate>Wed, 15 Jul 2026 09:23:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688836</guid>
    </item>
    <item>
      <title>Understanding walking route choice preferences and pedestrian network flows: From individual trajectories to city-scale patterns using empirical data from Sydney</title>
      <link>https://trid.trb.org/View/2685544</link>
      <description><![CDATA[This study investigates the influence of built environment factors on pedestrian route choices in an urban context using passively collected mobile phone trajectory data from Sydney, Australia. We estimate and compare multiple discrete choice models including C-Logit, Path Size Logit (PSL), and Error Component (EC) models to quantify associations between pedestrian route choices and route characteristics such as distance, slope, turns, crossings, amenities, and greenery. The models are applied to a high-resolution sidewalk network to simulate pedestrian flows across the city. Our findings are broadly consistent with existing literature, highlighting the importance of route simplicity, directness, and terrain in walking behavior. A key contribution of this study is the integration of passively collected GPS trajectories with route choice modeling and network-level flow assignment, demonstrating a scalable framework for understanding and forecasting pedestrian behavior. The approach enables city-scale assessments of pedestrian infrastructure and offers valuable insights for data-driven planning of walkable urban environments.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685544</guid>
    </item>
    <item>
      <title>Passenger-Luggage Combined Motion Model and Abnormal State Recognition in Public Transportation Hubs</title>
      <link>https://trid.trb.org/View/2617827</link>
      <description><![CDATA[In public transportation hubs, the combined motion (CM) between a passenger and his luggage is a primary movement form. The state of CM always influences the stability of the movement of passenger crowds. While there is still a lack of systematic studies on the passenger-luggage combined motion, even most state-of-the-art literatures focus only on the impact of luggage on the overall efficiency of crowd evacuation. To fill this academic lag, this study investigated the motion relationship between passengers and luggage and then proposed a passenger-luggage combined motion model (PLCMM), which helps determine the relevance between passengers and luggage. Furthermore, a novel energy-based criterion for the stability of PLCMM and an abnormal state recognition model were established. Finally, the proposed model and criteria were validated in the waiting hall of Shanghai Hongqiao High-speed Railway Station, as a typical large public transportation scenario. The luggage abnormality recognition model can identify three abnormal states, i.e., luggage falling off, tail dumping, and sudden acceleration. A performance comparison between the proposed PLCMM and five other representative models was conducted from the aspects of scenarios, algorithm complexity, and research focus. The detection accuracy of the PLCCM was 96.154% and the algorithm complexity was considerably lower than the complexity exhibited by the purely computer vision-based approaches. The results demonstrated that PLCMM aligned closely with the ground truth, and outperformed other state-of-the-art models. The stability criterion can be employed to assess the impact of lateral perturbation forces on the stability of the passenger-luggage combined motion, to support the daily passenger flow control and management in more public traffic areas.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617827</guid>
    </item>
    <item>
      <title>Mean field dynamics for pedestrian interaction in quantized observation-based 3D route choice modeling</title>
      <link>https://trid.trb.org/View/2679106</link>
      <description><![CDATA[This paper proposes a novel approach to estimate a network-based 3D pedestrian route choice model from uncertain observations, while modeling interaction effects between pedestrians via mean field dynamics inspired by Mean Field Game (MFG) theory. We assume sensor-based observations using such as Bluetooth Low Energy (BLE) to capture pedestrian behavior in indoor or 3D spaces, which is difficult to measure with conventional GPS-based methods. To address the high-level uncertainty, we introduced a probabilistic framework consisting of quantization and observation manifold, allowing us to robustly estimate models even with noisy data. The validity of the proposed framework is examined through numerical experiments, showing that the model accuracy is improved compared to conventional methods as the observation uncertainty increases. Here we set the baseline as the deterministic approach that sequentially identifies a single link at each time step, while the proposed quantized approach allows multiple possible state transitions at each timestep. We also conducted a field survey using BLE beacons to capture large-scale pedestrian trajectories inside JR Shibuya station in Tokyo, Japan. The estimation results using observed data demonstrate that our approach with mean field dynamics outperforms conventional methods lacking such interactions. Furthermore, the model provides interpretable insights into pedestrian flow dynamics in the station: specifically, it reveals that interaction effects differ between boarding and alighting passengers and vary depending on the time and day.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679106</guid>
    </item>
    <item>
      <title>Establishing Operating Characteristics for Non-Motorized Road Users</title>
      <link>https://trid.trb.org/View/2712196</link>
      <description><![CDATA[State departments of transportation (DOT) have qualitative design guidance available to them for walkable and bikeable transportation system improvements. However, the specific design of non-motorized transportation facilities is often selected based on the amount of space available, rather than the physical and operational characteristics of their users and equipment.

Bicycle-related research into operational characteristics is limited, and more information about bicycles and their riders is needed. Recently completed research has improved our understanding of bicyclist acceleration and speed on conventional bicycles, but more information is needed related to reaction time, deceleration, braking, lean angle, coefficients of friction, and lateral shy distance. Further, the research does not capture the full range of users, such as those using e-bikes and other micromobility devices.

Pedestrian traits such as walking speed and space requirements have been well-studied, but only in certain contexts. Pedestrian walking speed influences traffic signal timings, and walking speed information has been collected through a variety of methods. Sophisticated modeling of pedestrian flow is available to apply toward the design of infrastructure such as transit stations. However, available guidance does not fully capture how pedestrians, including those using mobility devices, operate in a typical transportation context.

 The objective of this research is to collect information about the basic operating characteristics of a wide range of pedestrians, bicyclists, and other micromobility users to better understand their spatial requirements along sidewalks, bikeways, and roadways. This research will be useful to transportation planners and designers seeking to develop safe and effective infrastructure for non-motorized users.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:35:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712196</guid>
    </item>
    <item>
      <title>Roles of physical collision forces and walking characteristics on pedestrian traffic flow status</title>
      <link>https://trid.trb.org/View/2663012</link>
      <description><![CDATA[The physical collision force between pedestrians affects the walking states of pedestrians. However, the relationship between physical collision force and pedestrian walking states remains unclear. To bridge this gap, a series of pedestrian-controlled experiments were designed. During the experiments, the physical collision forces were obtained using pressure sensors worn by the pedestrian. Walking characteristics, including speed and density, were obtained through video processing technologies from aerial videos taken by drones. Furthermore, the relationships between the physical collision forces and the walking characteristics were analyzed. The results show that, when the collision forces between pedestrians exceed 715 N, it will lead to the instability in the pedestrian traffic flow; when the collision forces are less than 400 N, the pedestrian walking state will remain stable; and when the collision forces are between 400 and 715 N, the pedestrian walking status may be described as in either an unstable or stable state.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663012</guid>
    </item>
    <item>
      <title>The Effects of Street Repurposing on Pedestrian, Vehicle and Visitor Patterns</title>
      <link>https://trid.trb.org/View/2702858</link>
      <description><![CDATA[COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of infection. In particular, many cities changed transportation infrastructure, including permitting sidewalk cafes that accommodated outdoor dining, reallocating street space from travel or parking to outdoor dining, and redesigning streets to accommodate a wide variety of users etc. What are the effects of these urban infrastructure innovations? How well do they draw visitors and support businesses nearby? What are their effects on the region’s traffic patterns? Are there spillover effects spatially? As cities emerge from COVID and re-imagine the future of our urban cores, answers to these questions are critical. Though the existing literature has a wealth of knowledge on the built environment effect on travel behavior, they are nearly exclusively at much larger scale (e.g., census tracts) and static (comparing different behavioral patterns between places with different built environment characteristics. There is little to no insight on how block-level urban infrastructure innovations lead to changes in visit patterns as well as nearby businesses. And yet, changes at this scale (block-level) are where local policy changes take place. This proposal is to answer these questions.]]></description>
      <pubDate>Thu, 14 May 2026 15:19:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702858</guid>
    </item>
    <item>
      <title>A quantitative and direct evaluation method of walking environment using location-based big data</title>
      <link>https://trid.trb.org/View/2670009</link>
      <description><![CDATA[Traditional walkability metrics, such as the Highway Capacity Manual (HCM) or static GIS-based scores, typically evaluate physical capacity based on absolute density or static environmental factors. However, these methods fail to capture the dynamic temporal variations and the specific “conflict” (intersection) of pedestrian flows that affect the walking experience. This study proposes a novel, dynamic walkability evaluation method using location-based big data. We introduce a “Walkability Index” calculated from the product of normalized Z-scores for inflow and outflow vectors within a mesh. This mathematical design is intentionally structured to detect flow intersection—where multidirectional movements occur simultaneously—rather than simple volume. We applied this method to two distinct urban areas in Saitama, Japan: Omiya Station (a major transport hub) and Kawagoe City (a tourist site). The statistical analysis revealed that in the Omiya area, the third quartile (Q3) of Z-scores remained below 0.1 across all time periods. This indicates that “low walkability” is not a uniform condition but is highly localized in specific statistical outliers (hotspots) representing the top 25% of meshes. In contrast, Kawagoe exhibited linear distribution patterns along tourist routes. While full ground-truth validation remains a future task, this method offers a scalable, quantitative tool for city planners to pinpoint specific dynamic bottlenecks, thereby complementing traditional static evaluations for targeted interventions.]]></description>
      <pubDate>Tue, 12 May 2026 09:11:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670009</guid>
    </item>
    <item>
      <title>From sensor counts to OD flows: The N-Step model for urban pedestrian mobility</title>
      <link>https://trid.trb.org/View/2697066</link>
      <description><![CDATA[In this study, we introduce a novel model called the N-Step Flow Model, designed to synthetically estimate pedestrian flow between urban areas using data from sensor-based pedestrian counts. Inspired by the D8 flow algorithm commonly used in hydrology, the N-Step Flow Model distributes pedestrian flow across all neighboring tiles in a proportional manner, allowing a detailed analysis of collective urban mobility. The model was tested in Melbourne, Australia, where square and hexagonal tessellations were used to segment urban space. The synthetic flows generated were compared to those derived from the gravity model, showing broadly stable similarity patterns across representative weeks, with correspondence varying by step. The N-Step Flow Model provides an explicit stepwise redistribution view of spatial flow propagation, which may support exploratory planning analyses. The proposed approach offers a flexible method for analyzing pedestrian dynamics at a macro level, providing new aspects for developing urban planning strategies and supporting decision-making for infrastructure improvements.]]></description>
      <pubDate>Thu, 07 May 2026 11:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697066</guid>
    </item>
    <item>
      <title>Capturing stochastic variabilities in pedestrian flows: A dynamic continuum modeling approach</title>
      <link>https://trid.trb.org/View/2661784</link>
      <description><![CDATA[Stochastic phenomena are commonly observed in pedestrian flow. However, the existing models for pedestrian dynamics rely on averaged inputs and yield deterministic outputs only, and thereby fail to capture the stochastic variabilities inherent in pedestrian dynamics. This study builds upon Hughes’ dynamic continuum model to develop mathematical models for stochastic pedestrian dynamics that explicitly consider two types of stochastic characteristics: demand stochasticity and behavioral stochasticity. The proposed system, represented as a set of time-dependent stochastic partial differential equations, is solved using a combination of the Monte Carlo (MC) method or Quasi Monte Carlo (QMC) method and efficient numerical schemes, such as a fifth-order weighted essentially non-oscillatory finite difference scheme and the fast sweeping method. Benchmarking scenarios are designed and simulated, and the numerical results demonstrate the convergence and computational performance of the MC and QMC methods. The advantage of stochastic modeling is evident given the significant differences between the averaged stochastic outputs and deterministic outputs, attributable to the strength of stochasticity and extent of stochastic dimensions. Moreover, based on stochastic data inputs, the proposed stochastic models and numerical solutions can clarify the probabilistic distributions of key indicators, such as density, which are valuable for the design and improvement of pedestrian facilities.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661784</guid>
    </item>
    <item>
      <title>Modeling proactive avoidance behaviors in pedestrian flows considering congestion anticipation</title>
      <link>https://trid.trb.org/View/2659400</link>
      <description><![CDATA[This paper investigates proactive avoidance behaviors in pedestrian flows by means of real-world experiments and a potential field model. Typical movement patterns of pedestrians related to the proactive avoidance behaviors are provided. Based on the observed behaviors, a potential field model is proposed to combine tactical-level and operational-level modeling frameworks. This model allows pedestrians to select and move toward temporary destinations instead of moving directly to their final destinations. Pedestrian movements are guided by the potential values associated with different positions in the focused space. Three types of sub-potentials are involved to reflect the effects of route attributes, spatiotemporal congestion anticipation, and potential conflicts on pedestrian movements. Numerical experiments demonstrate that the proposed model can effectively reproduce pedestrians’ proactive avoidance behaviors. The model is validated by comparing the simulation results with existing experimental data and our own experimental data. This investigation provides an alternative explanation for pedestrian movement mechanisms.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659400</guid>
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