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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Evaluating Crowd Flow Forecasting Algorithms for Indoor Pedestrian Spaces: A Benchmark Using a Synthetic Dataset</title>
      <link>https://trid.trb.org/View/2561854</link>
      <description><![CDATA[Crowd management plays a vital role in urban planning and emergency response. Accurate crowd prediction is important for venue operators to respond effectively to adverse crowd dynamics during large gatherings. Although many studies have tried to predict crowd densities or movement dynamics with data-driven predictive models, their validation is often limited to data within the same scenario. As a result, the predictability of the data-driven model in unseen scenarios, such as evacuation scenarios, remains unknown due to the challenges of collecting out-of-distribution data regarding emergency conditions. To address this problem, we present an evaluation pipeline to evaluate different kinds of data-driven models. A method is proposed to generate realistic scenarios by simulation and collect synthetic data from these scenarios to acquire a comprehensive dataset. With these synthetic data, we evaluated different predictive models, from traditional machine learning methods to deep learning time-series prediction models, to explore their generalizability. Furthermore, we propose a weighted average metric, which is better suited to determine the performance of forecasting algorithms under adverse conditions. Through extensive experimentation, we showcase the heterogeneity and diversity of the simulation dataset. The evaluation results also revealed that all the data-driven models performed poorly in unseen scenarios, highlighting the urgent need to develop a robust and generalizable model for predicting crowd flow in indoor spaces.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561854</guid>
    </item>
    <item>
      <title>Hybrid machine learning and physics-based modeling of pedestrian pushing behaviors</title>
      <link>https://trid.trb.org/View/2614756</link>
      <description><![CDATA[In high-density crowds, close proximity between pedestrians makes the steady state highly vulnerable to disruption by pushing behaviors, potentially leading to serious accidents. However, the scarcity of experimental data on pushing behaviors has hindered systematic investigations into the underlying mechanisms and the development of accurate models. Using behavioral data from bottleneck experiments, we analyze the heterogeneity of pedestrians’ internal pushing tendencies, revealing that pedestrians tend to push under high-motivation conditions and in wider corridors. In addition, we introduce a spatial discretization method to encode the state of pedestrian neighbors into feature vectors, serving together with pedestrian internal pushing tendency as the input of random forest classifiers to predict whether a pedestrian would engage in pushing behaviors. By analyzing speed-headway relationships, we reveal that pushing behaviors correspond to an aggressive space-utilization movement strategy. Consequently, we propose a hybrid machine learning and physics-based model integrating the heterogeneity of internal pushing tendencies, the random forest-based prediction of pushing behaviors, and multiple movement strategies associated with pushing and non-pushing behaviors. The proposed model is calibrated using experimental data, and parameter sensitivity analysis is conducted. Validation results demonstrate that the hybrid model effectively reproduces experimental crowd dynamics, particularly in high-motivation scenarios. Moreover, the hybrid structure of the proposed model is suitable for incorporating additional behaviors, providing a solid foundation for advancing the understanding and simulation of complex pedestrian dynamics.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614756</guid>
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    <item>
      <title>Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space</title>
      <link>https://trid.trb.org/View/2591775</link>
      <description><![CDATA[Safe, socially compliant, and efficient navigation of low-speed autonomous vehicles (AVs) in pedestrian-rich environments necessitates considering pedestrians' future positions and interactions with the vehicle and others. Despite the inevitable uncertainties associated with pedestrians' predicted trajectories due to their unobserved states (e.g., intent), existing deep reinforcement learning (DRL) algorithms for crowd navigation often neglect these uncertainties when using predicted trajectories to guide policy learning. This omission limits the usability of predictions when diverging from ground truth. This work introduces an integrated prediction and planning approach that incorporates the uncertainties of predicted pedestrian states in the training of a model-free DRL algorithm. A novel reward function encourages the AV to respect pedestrians' personal space, decrease speed during close approaches, and minimize the collision probability with their predicted paths. Unlike previous DRL methods, our model, designed for AV operation in crowded spaces, is trained in a novel simulation environment that reflects realistic pedestrian behaviour in a shared space with vehicles. Results show a 40% decrease in collision rate and a 15% increase in minimum distance to pedestrians compared to the state of the art model that does not account for prediction uncertainty. Additionally, the approach outperforms model predictive control methods that incorporate the same prediction uncertainties in terms of both performance and computational time, while producing trajectories closer to human drivers in similar scenarios.]]></description>
      <pubDate>Thu, 30 Oct 2025 16:48:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591775</guid>
    </item>
    <item>
      <title>Experimental study on pedestrian movement on elevated platforms</title>
      <link>https://trid.trb.org/View/2518206</link>
      <description><![CDATA[Comprehending pedestrian movement behavior on platforms is crucial for enhancing the safety of railway tunnels, particularly during emergencies. This study performed a series of pedestrian walking and running experiments on a 0.6 m high platform. By analyzing the movement capability and spatial distribution characteristics, the potential influences and differences of crowd movement under unprotected and restricted boundaries were explored. The results reveal that both the motion modes and boundary types influence the behavior mechanism of pedestrians. Compared to the restricted boundary condition, it is found that the impact of unprotected boundary on pedestrian movement is primarily reflected in increased speed for walking (7 % faster) and increase of headway distance (about 25 %) for running. Pedestrians moving on the unprotected side exhibit smaller boundary distances than those on the restricted side but show greater heterogeneity in their distribution. The nearest neighbor distribution indicates that a higher acceptance of front neighbors among pedestrians near boundaries. Those running near the unprotected side need a larger movement comfort radius to accommodate their motion. This study provides empirical data to address the gap in the field, which can serve as a basis for modeling and enable the accurate simulation of pedestrian flows on platforms, thereby facilitating the development of more reasonable crowd guidance measures and ultimately enhancing tunnel evacuation safety.]]></description>
      <pubDate>Mon, 07 Apr 2025 17:01:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2518206</guid>
    </item>
    <item>
      <title>Study of Factors Affecting Pedestrian Movement in Mass Leisure Gatherings</title>
      <link>https://trid.trb.org/View/2407341</link>
      <description><![CDATA[Mass leisure gatherings have a huge chance of creating a disaster due to a lack of proper infrastructural support, proper exit points, sufficient width for walking, proper knowledge of the overall crowded place and poor crowd management systems. Therefore, for a proper crowd management system, a detailed analysis of different crowd profiles, pedestrians’ behaviors, obstacles hindering the path of pedestrians, effects of the opposing pedestrian interaction, the delay time, and change of speed due to several hindrances are needed to culminate. This paper aims at identifying factors and their effects on the change in the speed of pedestrians during important religious gatherings in India. From the study, it is seen that density, flow in opposite direction, and the number of people stopping on the road causes a decrease in the speed of pedestrians. The results from this study can be used for better representation of pedestrians in microscopic simulation models in different scenarios which can be used for planning purposes of pedestrian prioritized streets.]]></description>
      <pubDate>Tue, 17 Dec 2024 17:09:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407341</guid>
    </item>
    <item>
      <title>Modified social force model considering emotional contagion for crowd evacuation simulation</title>
      <link>https://trid.trb.org/View/2251024</link>
      <description><![CDATA[Pedestrian behavior in emergency situations is an important part of emergency management, and crowd evacuation simulation provides a low-cost and low-risk method for investigating pedestrian evacuation dynamics by describing pedestrian evacuation behavior and predicting evacuation outcomes under hypothetical situations. This study proposed a modified social force model that combines dynamic hazard and emotional contagion frameworks to investigate the effect of panic on pedestrian evacuation dynamics under heterogeneous personality trait distributions. First, the emotional contagion framework was improved, and an updated emotional model was developed considering personality characteristics. Second, the individual self-driving force in the social force model was transformed from a physical drive mode to a psychological drive mode by introducing panic factors. Lastly, new rules for the spread of panic within small groups were formulated based on the important influence of small group behavior on evacuation. The simulation results confirmed the feasibility of analyzing crowd evacuation from the perspective of social psychology, and revealed that the spread of panic was closely related to the distribution of personality characteristics, individual walking speed, exit usage, evacuation efficiency, and small groups. In addition, the model could reproduce self-organizing behavior in crowd evacuation, such as fast is slow and herd behavior. Further, the results revealed that moderate panic can help improve evacuation efficiency, whereas excessive panic can result in uneven utilization of exits. In addition, the presence of small groups in panic crowds increased the crowd evacuation time.]]></description>
      <pubDate>Wed, 15 Nov 2023 09:19:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2251024</guid>
    </item>
    <item>
      <title>Pedestrian small group behaviour and evacuation dynamics on metro station platform</title>
      <link>https://trid.trb.org/View/2160814</link>
      <description><![CDATA[Crowd at metro stations is usually a mixture of individuals and small groups of families or friends. However, limited research has focused on small group behaviours for metro safe evacuation evaluation and planning. In this study, a field observation at metro stations and a questionnaire survey were conducted to reveal the small group behaviour characteristics with different decision patterns and compactness. A cellular automaton (CA) based simulation model was proposed to reproduce small group behaviours of independent or joint decision pattern, with loose or close contact, reflecting the real-time trade-off between individual efficiency and group coherence. Impacts of small group behaviours on crowd dynamics were investigated by simulation experiments under diverse scenarios. Simulation experiments revealed that joint decision pattern and close contact of small groups were more likely to lead to longer evacuation time, lower average speed and stronger interference on the individuals. Deviations of estimated evacuation time due to small group behaviours were investigated and found to be common and widespread with different group decision pattern and compactness, congestion levels, proportions of groups in the crowd and exit layouts.]]></description>
      <pubDate>Mon, 17 Jul 2023 09:13:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2160814</guid>
    </item>
    <item>
      <title>An investigation of how context affects the response of pedestrians to the movement of others</title>
      <link>https://trid.trb.org/View/2026494</link>
      <description><![CDATA[Identifying how pedestrians respond to the movement of others in emergencies is an essential topic that is directly relevant to building evacuation and safety management. Here, the authors hypothesise that pedestrian following behaviour depends on the context. They identify three essential contextual factors: spatial information, the size of crowds and the distribution of individuals across exits. They conduct a virtual experiment with over 500 participants who have to decide whether to follow a crowd in scenarios capturing these different contextual factors. Their findings suggest pedestrians have an innate preference to avoid the exit chosen by a majority of people but also that they prefer exits that are associated with shorter escape routes, even if these exits are used by more people. However, if one exit is not used at all, these preferences are altered and pedestrians prefer following others regardless of exit properties. In contrast to the relative usage of exits, the overall size of the crowd does not affect pedestrian exit choice in their experiment except for the case when all pedestrians choose the same exit. They call the change in exit choice behaviour depending on how pedestrians are distributed across exits ”split effect”. Simulation results show how the split effect can lead to unbalanced route usage and reduce the efficiency of pedestrian flow in certain circumstances, such as when the arrival rate of pedestrians is low. The authors' work adds to a growing body on pedestrian exit choice and highlights the importance of precise control of contextual factors in research.]]></description>
      <pubDate>Thu, 27 Oct 2022 13:46:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2026494</guid>
    </item>
    <item>
      <title>A Modified Social Force Model (SFM) for Pedestrian Behavior in the Presence of Autonomous Vehicles (AVs)</title>
      <link>https://trid.trb.org/View/2015420</link>
      <description><![CDATA[The social force model (SFM) is a widely used method to predict pedestrian behavior. This paper develops a modified social force model to understand pedestrians’ behavior at a signalized crosswalk with only autonomous vehicles (AVs) on the road. Previous research indicated that pedestrians are likely to feel less safe around driverless “ghost vehicles” like AVs, and a repulsive behavior of the pedestrians is observed toward AVs. Hence, a new repulsive force is incorporated into the traditional SFM to account for this phenomenon, and the modified SFM’s principle used to simulate pedestrian behavior. Simulation of pedestrian behavior is performed using the open-source crowd simulation software, VADERE. Simulation results show that pedestrians’ walking behavior becomes chaotic indicating that pedestrians may require more time to cross the road in this scenario. The paper discusses the results of these simulation experiments and their implications on transportation engineering.]]></description>
      <pubDate>Wed, 05 Oct 2022 16:54:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2015420</guid>
    </item>
    <item>
      <title>Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends</title>
      <link>https://trid.trb.org/View/1983066</link>
      <description><![CDATA[Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: “Pedestrian crash frequency models”, “Pedestrian injury severity crash models”, “Traffic engineering measures in pedestrians’ safety”, “Global reports around pedestrian accident epidemiology”, “Effect of age and gender on pedestrians’ behavior”, “Distraction of pedestrians”, and “Pedestrian crowd dynamics and evacuation”. Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely “Collision avoidance and intelligent transportation systems (ITS)”, “Epidemiological studies of pedestrian injury and prevention”, “Pedestrian road crossing and behavioral factors”, “Pedestrian flow simulation”, and “Walkable environment and pedestrian safety”. Finally, “autonomous vehicle”, “pedestrian detection”, and “collision avoidance” themes were identified as having the greatest centrality and development degrees in recent years.]]></description>
      <pubDate>Mon, 18 Jul 2022 09:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1983066</guid>
    </item>
    <item>
      <title>Follower-leader concept in microscopic analysis of pedestrian movement in a crowd</title>
      <link>https://trid.trb.org/View/1977437</link>
      <description><![CDATA[This paper presents a microscopic analysis of factors influencing pedestrian movement and interactions with their surroundings for two considered modes: independent movement influenced only by the surrounding conditions and synchronized movement based on following another pedestrian. This study analyses which of these effects prevail in different phases of the movement. The results show that the significant value of correlation between pedestrian velocity and corresponding individual density is observed mainly during approaching the crowd. Contrarily, in the segment of pedestrian trajectory which corresponds to movement inside the crowd, correlation between the velocity of a follower and a leader is more important. This confirms that the pedestrian behaviour in a crowd is a complex field.]]></description>
      <pubDate>Wed, 08 Jun 2022 13:28:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977437</guid>
    </item>
    <item>
      <title>Investigating pedestrians’ obstacle avoidance behaviour</title>
      <link>https://trid.trb.org/View/1977418</link>
      <description><![CDATA[Modelling and simulating pedestrian motions are standard ways to investigate crowd dynamics aimed to enhance pedestrians’ safety. Movement of people is affected by interactions with one another and with the physical environment that it may be a worthy line of research. This paper studies the impact of speed on how pedestrians respond to the obstacles (i.e. Obstacles avoidance behaviour). A field experiment was performed in which a group of people were instructed to perform some obstacles avoidance tasks at two levels of normal and high speeds. Trajectories of the participants are extracted from the video recordings for the subsequent intentions:(i) to seek out the impact of total speed, x and yaxis (ii) to observe the impact of the speed on the movement direction, x-axis, (iii) to find out the impact of speed on the lateral direction, y-axis. The results of the experiments could be used to enhance the current pedestrian simulation models.]]></description>
      <pubDate>Wed, 08 Jun 2022 13:27:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977418</guid>
    </item>
    <item>
      <title>Parameter calibration in crowd simulation models using approximate Bayesian computation</title>
      <link>https://trid.trb.org/View/1977410</link>
      <description><![CDATA[Simulation models for pedestrian crowds are a ubiquitous tool in research and industry. It is crucial that the parameters of these models are calibrated carefully and ultimately it will be of interest to compare competing models to decide which model is best suited for a particular purpose. In this contribution, I demonstrate how Approximate Bayesian Computation (ABC), which is already a popular tool in other areas of science, can be used for model fitting and model selection in a pedestrian dynamics context. I fit two different models for pedestrian dynamics to data on a crowd passing in one direction through a bottleneck. One model describes movement in continuous-space, the other model is a cellular automaton and thus describes movement in discrete-space. In addition, I compare models to data using two metrics. The first is based on egress times and the second on the velocity of pedestrians in front of the bottleneck. My results show that while model fitting is successful, a substantial degree of uncertainty about the value of some model parameters remains after model fitting. Importantly, the choice of metric in model fitting can influence parameter estimates. Model selection is inconclusive for the egress time metric but supports the continuous-space model for the velocity-based metric. These findings show that ABC is a flexible approach and highlights the difficulties associated with model fitting and model selection for pedestrian dynamics. ABC requires many simulation runs and choosing appropriate metrics for comparing data to simulations requires careful attention. Despite this, I suggest ABC is a promising tool, because it is versatile and easily implemented for the growing number of openly available crowd simulators and data sets.]]></description>
      <pubDate>Wed, 08 Jun 2022 13:27:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977410</guid>
    </item>
    <item>
      <title>An artificial neural network framework for pedestrian walking behavior modeling and simulation</title>
      <link>https://trid.trb.org/View/1977404</link>
      <description><![CDATA[Movement behavior models of pedestrian agents form the basis of computational crowd simulations. In contemporary research, a large number of models exist. However, there is still no walking behavior model that can address the various influence factors of movement behavior holistically. Thus, we endorse the use of artificial neural networks to develop walking behavior models because machine learning methods can integrate behavioral factors efficiently, automatically, and data-driven. In this paper, we support this approach by providing a framework that describes how to include artificial neural networks into a pedestrian research context. The framework comprises 5 phases: data, replay, training, simulation, and validation. Furthermore, we describe and discuss a prototype of the framework.]]></description>
      <pubDate>Wed, 08 Jun 2022 13:27:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977404</guid>
    </item>
    <item>
      <title>Experimental study on the influence of background music on pedestrian movement in high densities</title>
      <link>https://trid.trb.org/View/1977384</link>
      <description><![CDATA[It is interesting to investigate the effect of background music on pedestrian movement. This paper investigates the properties of crowd motion with external rhythms. With rhythm, pedestrians stop more frequently than without any rhythm. The stopping also increases with the increment of the tempo. Velocity and flow with rhythms are lower than that without any rhythm at high densities due to the more frequent stopping. Stepping behavior analysis shows that the step frequency with rhythms is smaller than that without any rhythm, especially at high densities. Dynamic coordinated behavior is weakened by music, which also affectsthe stepping behavior. Our study will be helpful for understanding the effect of background music on pedestrian movement.]]></description>
      <pubDate>Wed, 08 Jun 2022 13:27:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977384</guid>
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