Search

• A pedestrian-vehicle coupling model with high simulation accuracy is proposed. • Six phase regions were acquired from the heterogeneous traffic phase diagram. • Obtained the difference between the fundamental diagram of heterogeneous and homogeneous traffic. • Tighter speed limits and wider sidewalks can reduce traffic conflicts. In urban streets, the intrusion of pedestrians into the motorway will pose a considerable challenge. However, modelling mixed pedestrian-vehicle traffic scenes is difficult. Pedestrians and vehicles have different motion characteristics and spatial dimensions, so it is hard to carry out unified modelling, and there are few related studies. This paper combines the multi-grid cellular automata model to explore these scenes to connect the vehicle and pedestrian models. An Improved Kerner-Klenov-Wolf (IKKW) model and a pedestrian motion model considering Time-To-Collision (TTC) are proposed, in which the spatial motion of a vehicle and a pedestrian is uniformly updated horizontally and longitudinally. The preliminary analysis of the model shows that it has high simulation accuracy. Applying the model to the simulation of real-life situations, research results reveal the impact of pedestrian intrusion behavior on traffic and the changes in vehicles' speed and flow rate caused by pedestrian intrusion behavior. The fundamental diagram of pedestrian-vehicle heterogeneous traffic shows significant differences, reflecting the impact of pedestrian intrusion on the state of traffic flow, and exposes six phase regions of pedestrian-vehicle mixed traffic flow. Moreover, this paper analyzes the conflicts between pedestrians and vehicles under different speed limits and sidewalk widths, showing that lower speeds and wider sidewalks can effectively reduce the frequency of pedestrian-vehicle conflicts. The peak conflicts' frequency when the vehicles' speed limit is 60.48 km/h exceeds three times that when the speed limit is 30.24 km/h. The cellular automata model proposed in this paper provides a good direction and idea for studying mixed traffic flow and has high scalability. [ABSTRACT FROM AUTHOR]

• A probability distribution model of platoon size of connected vehicles is proposed. • A fundamental diagram model of the mixed traffic flow is developed. • An analytical model of the capacity of mixed traffic flow is proposed. • A numerical experiment is designed to discuss the impact of penetration rate and maximum platoon size on road capacity. This paper proposes a capacity analysis model for a mixed traffic flow environment that considers the heterogeneity and maximum platoon size of connected vehicles. Firstly, we explore how the organization mode of platoon affects car-following characteristics in mixed traffic flow with connected and automated vehicles, connected human-driven vehicles, automated vehicles, and human-driven vehicles. Different car-following models are used to describe the car-following characteristics of different types of vehicles. Secondly, the probability distribution model is developed for the size of platoons considering the penetration rate and the maximum platoon size of connected and automated vehicles, connected human-driven vehicles. Thirdly, the fundamental diagram of mixed traffic flow is further derived based on the probability distribution of platoon size and car-following models. Then, the capacity model of mixed traffic flow is proposed. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates and maximum platoon sizes of connected and automated vehicles, connected human-driven vehicles on the capacity of the mixed traffic flow. The results show that (1) with a low penetration rate of connected and automated vehicles, connected human-driven vehicles, the change in the maximum platoon size has no noticeable effect on the distribution of platoon size of connected and automated vehicles, connected human-driven vehicles. (2) Road capacity increases with the maximum platoon size and the penetration rate of connected vehicles. (3) Compared with connected human-driven vehicles, the penetration rate of connected and automated vehicles has a more significant impact on road capacity. [ABSTRACT FROM AUTHOR]

• Propose a methodology that combines both empirical experiments and theoretical models to construct the fundamental diagram for automated vehicle traffic. • It is found that the traditional triangular fundamental diagram structure remains applicable to automated vehicle traffic. • The shortest automated vehicle headway setting can significantly improve the road capacity, while the other headway settings may decrease the road capacity. • Propose a parsimonious formula to estimate the fundamental diagram for mixed traffic with measures from pure human-driven traffic and pure automated vehicle traffic. Increasingly, commercial vehicles are equipped with automated vehicle (AV) features such as adaptive cruise control systems. The AV feature can automatically control the headway between the current vehicle and the preceding vehicle in an adaptive manner. The automatic control may lead to significantly different car- following motions compared with those of human-driven vehicles, which challenges the applicability of classic traffic flow theory to emerging road traffic with AVs. To investigate the impacts of commercial AVs on traffic flow, this paper proposes a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram (FD), i.e., the foundation for traffic flow theory for AV traffic. To demonstrate the empirical experiment settings, we collected high-resolution trajectory data with multiple commercial AVs following one another in a platoon with different headway settings. The field experiment results revealed that the traditional triangular FD structure remains applicable to describe the traffic flow characteristics of AV traffic. Further, by comparing the FDs between AVs and human-driven vehicles, it was found that although the shortest AV headway setting can significantly improve road capacity, other headway settings may decrease road capacity compared with existing human-driven-vehicle traffic. It was also found that headway settings may affect the stability of traffic flow, which has been revealed by theoretical studies but was first verified by empirical AV data. With these findings, mixed traffic flow FDs were derived by incorporating different headway settings and AV penetration rates. The method proposed in this paper, including experiment designs, data collection approaches, traffic flow characteristics analyses, and mixed traffic flow FD construction approaches, can serve as a methodological foundation for studying future mixed traffic flow features with uncertain and evolving AV technologies. [ABSTRACT FROM AUTHOR]

• This work shows that the fundamental relationship can rapidly change shape. • The rapid change is a systematic response to conditions in adjacent lanes. • Drivers adopt larger spacing when faced with high relative speed between lanes. • This adjacent lane dependency is absent from most micro and macro models. • The new found dynamics provide mechanisms to sustain synchronized flow. Much of traffic flow theory and highway design is based upon the fundamental relationship, FR, between speed, flow, and density. Traditionally, the FR is assumed to be a static curve over time and space. This paper presents new empirical findings that show the FR in one lane of a freeway can actually change shape in a rapid and systematic response to conditions in the adjacent lanes. This paper demonstrates the dependencies for a general purpose freeway lane that is operating in the congested regime. The drivers become more conservative in response to slower speeds in either adjacent lane, conversely, they become less conservative in response to higher speeds in either adjacent lane. It is also shown that as drivers become more conservative the signals and waves move upstream slower. So this inter-lane dependency has a direct impact on how fast signals propagate through the traffic stream. For the study site the empirical relationship between the wave speed and relative speed is close to linear. This interdependence between lanes is an important factor that it is absent from most traffic flow and car following models. Given these findings the discussion explores hypothetical examples showing how the inter-lane dependencies can give rise to mechanisms for waves in adjacent lanes to synchronize, as well as a mechanism for a queue spreading from one lane to another without any lane change maneuvers. Finally, this work closes with an exploration of the possible physiological mechanisms that might be occurring in the drivers. [ABSTRACT FROM AUTHOR]

• This work finds LWR and shockwave analysis can underestimate signal velocity. • The errors are due to unaccounted for nonstationary traffic conditions. • Traffic dynamics differ when the state includes free flow and queued vehicles. • Long headway vehicles violate LWR and result in faster signals than predicted. • Adjacent lanes can induce signals propagating upstream in free flow conditions. This paper undertakes a detailed empirical study of traffic dynamics on a freeway. The results show the traffic dynamics that systematically determine the shape of the fundamental diagram, FD, can also violate the stationarity assumptions of both shockwave analysis and Lighthill, Whitham and Richard's models, thereby inhibiting the applicability of these classical macroscopic traffic flow theories. The outcome is challenging because there is no way to identify the problem using only the macroscopic detector data. The research examines conditions local to vehicle detector stations to establish the FD while the single vehicle passage method is used to analyze the composition of vehicles underlying the aggregate samples. Then, traffic states are correlated between successive stations to measure the actual signal velocities and show they are inconsistent with the classical theories. This analysis also revealed that conditions in one lane can induce signals in another lane. Rather than exhibiting a single signal passing a given point in time and space, the induced and intrinsic signals are superimposed on one another in the given lane. We suspect the subtle dynamics revealed in this research have gone unnoticed because they are far below the resolution of conventional traffic monitoring. The findings could have implications to other traffic flow models that rely on the FD, so care should be taken to assess if a given model is potentially sensitive to the non-stationary dynamics presented herein. The results have a direct impact on practice. Traffic flow theory is a critical input to many aspects of surface transportation, e.g., traffic management, traffic control, network design, vehicle routing, traveler information, and transportation planning all depend on models or simulation software that are based upon traffic flow theory. If the underlying traffic flow theory is flawed it puts the higher level applications at risk. So, the findings in this paper should lead to caution in accepting the predictions from traffic flow models and simulation software when the traffic exhibits a concave FD. [ABSTRACT FROM AUTHOR]

This paper proposes the fundamental diagram model for a mixed traffic flow environment that considers different car-following modes under various levels of automation and connectivity. First, the SAE classification of vehicle automation levels, from L 0 to L 5 , has been further divided into connectivity and automation levels. Second, the car-following modes that affect car-following characteristics are analyzed in mixed traffic flow with different levels of connectivity and automation. Different car-following models are used to describe the car-following characteristics of different types of modes. Third, the fundamental diagram model of mixed traffic flow is further derived based on the different levels of automation and connectivity. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates (PRs) of the car-following modes on the traffic capacity of the mixed traffic flow. The results show that (1) with a higher PR of the automation level of the vehicle, the greater the traffic capacity. At the same time, as the automation levels increase, the change in the penetration rate of high automation levels has a more and more significant impact on traffic capacity. (2) Traffic capacity increases with increased PR of higher automation levels, representing an increase of approximately 11.1 % (3) Compared with connectivity, the PR of automation levels significantly impacts traffic capacity, which is approximately 52.6 %. [ABSTRACT FROM AUTHOR]

Abstract Single-file traffic is a simple and fundamental phenomenon that can be observed in vehicle transportation, pedestrian flow, animal movement and many others. In this paper, the fundamental diagram of single-file movement is studied by ant experiments and modeling. For ant, single-file traffic is a very important activity. From single-file movement experiments, ant follows the one in front can be observed. It is also observed that the critical density, i.e. the density corresponding to the maximum flow, is larger than 0.5. The flow–density curve present as an asymmetrical arch, which is different to those of traffic flow and pedestrian flow. In order to get insight of the phenomenon, we introduce a multi-grid NaSch (MG-NS) model, based on multi-grid cellular automata model and traditional NaSch (NS) model, to understand the single-file ant trails. The multi-grid method is convenient to refine the time and space, therefore one agent (ant) can occupy multiple grid sites and its movement can be calculated in detail. Based on the model, we get the theoretical relationship between flow and density with varying delay time t j. It is different from that of traditional NS model and agrees well with the simulation results. In addition, MG-NS model can match the result of ants experiment well with t j = 0. 25 (n = 4). The smaller the delay time t j (larger n) is, the larger the flow, speed and the critical density are. Through stability analysis, the smaller delay time t j means agents react faster and can keep the flow in steady state easier. It is also indicated that in the traditional NS model, because the size of grid equals to agent size, the delay time is always equal to or greater than one time step. But for the agent with sensitive response, the traditional NS model will be no more applicable. The MG-NS model proposed overcomes the disadvantages and thus is an improvement to the traditional one, thus can be used to reveal detailed mechanisms of traffic. Highlights • In this paper, we investigate the fundamental diagram of single-file movement by conducting ant experiments and multi-grid NaSch (MG-NS) model. • In ant experiment, it is also observed that the critical density is larger than 0.5. • MG-NS model is agreed with the result of ants experiment well with t j = 0. 25 (n = 4). • The smaller the delay time t j (larger n) is, the larger the flow, speed and the critical density are, and the smaller delay time t j means agents react faster. [ABSTRACT FROM AUTHOR]

This paper proposes a fundamental diagram of the mixed traffic flow for multi-lane road segments with connected automated vehicles (CAVs) dedicated lanes based on a flexible and efficient lane management strategy for CAVs. First, two lane management strategies of CAVs dedicated-HDVs lane strategy (i.e., C-H strategy) and CAVs dedicated-Mixed lane strategy (i.e., C-M strategy) are introduced. The more efficient C-M strategy is chosen based on the characteristics of the two strategies, and the five stable states under this strategy are analyzed. Second, different car-following modes in mixed traffic flows are analyzed, and general fundamental diagrams for homogeneous and mixed traffic flows are derived based on car-following models. Then, the stabilized traffic conditions are derived based on the traffic flow characteristics in the five stable states. A fundamental diagram model for the multi-lane road segment with CAVs dedicated lanes is developed based on the general fundamental diagram, and some properties of the fundamental diagram are derived. Finally, the impacts of different numbers of dedicated lanes and traffic conditions on the traffic volume and capacity are analyzed based on the fundamental diagram, and the optimal settings of dedicated lanes for CAVs under different lane number scenarios are given. Numerical simulation results showed that: (1) there are five stable states for the traffic flow on the road segment under the C-M strategy; (2) properly setting up CAVs dedicated lanes can effectively improve the efficiency of road segments; (3) under the C-M strategy, for any CAVs dedicated lane setting scenario, the traffic capacity of the road segment shows a gradually increasing trend as the penetration rate of CAVs increases. (4) compared to the C-H strategy, the capacity of the road segment under the C-M strategy will be higher than the C-H strategy when the CAVs penetration rate exceeds a certain threshold. Moreover, as CAVs penetration rate increases further, the advantages of the C-M strategy in terms of traffic capacity become more and more significant. [ABSTRACT FROM AUTHOR]

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption. • Integrate Lagrangian and Eulerian observations to reconstruct trajectories. • Calibrate time-varying short-term shockwave speeds using the two types of data. • Reconstruct trajectories for non-connected vehicles based on shockwave theory. • Optimize trajectories by adding driver dynamics for better energy estimation. • Evaluation on real-world datasets shows excellent performances from several aspects. [ABSTRACT FROM AUTHOR]

Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists' distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists' decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions. [ABSTRACT FROM AUTHOR]

• Informed fundamental diagram knowledge into a newly proposed data-driven model. • Applied and evaluated the proposed PI-SGTGCN model through real-world dataset. • Investigated the physical interpretation of the dynamic graph adjacency matrix. Accurate and fine-grained traffic state prediction has always been an important research field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature evolution patterns are deeply recessive, posing challenges in effectively characterizing and modeling them. This paper proposed a novel spatial–temporal graph convolution network model with traffic Fundamental Diagram (FD) information informed. The model decouples the high-dimensional spatiotemporal relationships in the transportation network and fully leverages the physical evolution laws of traffic states. First, the Graph Convolutional Network (GCN) with a spatial attention mechanism was proposed to capture spatial relations of the road network. The mechanism can better represent the spatial dynamics of the graph adjacency matrix in GCN. Second, this study injected prior physical knowledge into the graph adjacency matrix. This process was achieved by embedding characteristics of FDs from historical traffic data on the diagonal of the matrix, by which the propagation pattern of traffic states in road network could be considered. Third, to further catch the time dependence of the road network, the Gated Recurrent Unit (GRU) structure and the Transformer encoding structure were employed to locally and globally reform traffic state time sequences. Finally, experiments on a revised traffic dataset demonstrated that the proposed method consistently outperforms other baselines regarding Mean Absolute Error and Root Mean Square Error across all cases. Moreover, it achieved the optimal Mean Absolute Percentage Error in the 30- and 60-minute prediction tasks. This study shows a novel solution to inform traffic physical laws into data-driven state prediction models, and the reliability of the proposed method in long-term prediction offers valuable support for improving traffic management and alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

• We introduce the PIDL method to traffic state estimation, enabling accurate estimation results even with sparse data. • The computational graph method is applied to determine the key parameters of the fundamental diagram of traffic flow. • PIDLʹs versatility is showcased through two different traffic data scenarios: loop detectors and probe vehicles. • The research highlights the significance of integrating the physical models into the deep learning framework. Traffic state estimation (TSE) is a critical task for intelligent transportation systems. However, it is extremely challenging because the traffic data quality is often affected by the installation position of devices, data collection frequency, interference during the transmission process, etc., thus causing the problem of data sparsity or data missing. To address the issue of traffic state estimation under the scenario of data sparsity, we propose a TSE model that combines the computational graph with physics-informed deep learning (PIDL) methods. Firstly, we apply the computational graph method to determine the parameters of the traffic fundamental diagram. These parameters are embedded into the computational graph framework, and their values are determined through the forward propagation of variables and the backward propagation of errors. Next, we employ the PIDL method to realize TSE (taking the LWR model based on the Greenshields fundamental diagram as an example). The PIDL leverages the advantages of data-driven and model-driven approaches to achieve accurate traffic state estimation. Case studies are conducted using the NGSIM dataset under two sparse data scenarios: loop detectors and probe vehicles. Experimental results demonstrate that PIDL can accurately reconstruct the traffic state of the entire road segment based on partially observed data. Furthermore, compared to pure deep learning methods and other baseline models, PIDL performs better in situations with sparse data, thereby proving the feasibility of integrating domain knowledge with deep learning frameworks. This paper fully acknowledges the issue of data sparsity in TSE and effectively addresses it by applying the PIDL method to achieve precise TSE, which holds significant implications for the control and management of real traffic flow. [ABSTRACT FROM AUTHOR]

• Answered the question: Why does the dynamism of the crowd persist? • Cross channel formation phenomenon. • Disparities of distinct spatial measurement methods have been unveiled. • Within dynamic crowds, two crossing patterns were uncovered. Crowd studies have gained increasing relevance due to the recurring incidents of crowd crush accidents. In addressing the issue of the crowd's persistent dynamism, this paper explored the macroscopic and microscopic features of pedestrians crossing in static and dynamic contexts, employing a series of systematic experiments. Firstly, empirical evidence has confirmed the existence of crowd's persistent dynamism. Subsequently, the research delves into two aspects, qualitative and quantitative, to address the following questions: (1) Cross pedestrians tend to avoid high-density areas when crossing static crowds and particularly evade pedestrians in front to avoid deceleration, thus inducing the formation of cross-channels, a self-organization phenomenon. (2) In dynamic crowds, when pedestrian suffers spatial constrained, two patterns emerge: decelerate or detour. Research results indicate the differences in pedestrian crossing behaviors between static and dynamic crowds, such as the formation of crossing channels, backward detours, and spiral turning. However, the strategy of pedestrian crossing remains consistent: utilizing detours to overcome spatial constraints. Finally, the empirical results of this study address the final question: pedestrians detouring causes crowds' persistent collective dynamism. These findings contribute to an enhanced understanding of pedestrian dynamics in extreme conditions and provide empirical support for research on individual movement patterns and crowd behavior prediction. [ABSTRACT FROM AUTHOR]

Within the realm of pedestrian research numerous measures have been proposed to estimate the level of crowdedness experienced by pedestrians. However, within the field of pedestrian traffic flow modelling there does not seem to be consensus on the question which of these measures performs best. This paper shows that the shape and scatter within the resulting fundamental diagrams differs a lot depending on the measure of crowdedness used. The main aim of the paper is to establish the advantages and disadvantages of the currently existing measures to quantify crowdedness in order to evaluate which measures provide both accurate and consistent results. The assessment is not only based on the theoretical differences, but also on the qualitative and quantitative differences between the resulting fundamental diagrams computed using the crowdedness measures on one and the same data set. The qualitative and quantitative functioning of the classical Grid-based measure is compared to with the X-T measure, an Exponentially Weighted Distance measure, and a Voronoi-Diagram measure. The consistency of relating these measures for crowdedness to the two macroscopic flow variables velocity and flow, the computational efficiency and the amount of scatter present within the fundamental diagrams produced by the implementation of the different measures are reviewed. It is found that the Voronoi-Diagram and X-T measure are the most efficient and consistent measures for crowdedness. [ABSTRACT FROM AUTHOR]

• Formulation of a novel, fast flow model for speed heterogeneous bicycle traffic. • The model implements continuous, individual-specific desired speeds and headways. • Parameters estimated based on traces from video measurements of real-life traffic. • Sensible speed-flow relationships demonstrated through simulation experiments. • Computational efficiency of the model allows for large-scale traffic assignment. Copenhagen is world-known for its large proportion of cyclists, forming a diverse group with a large variation of equipment and physical abilities. This leads to a considerable speed heterogeneity which needs to be taken into account when modelling the traffic on dedicated bicycle paths. Nevertheless, existing studies on bicycle traffic simulation have either neglected such speed heterogeneity altogether or modelled it by dividing cyclists into a few discrete classes ignoring the entirety of the speed distribution. This paper proposes an efficient bicycle traffic simulation model with continuously speed heterogeneous cyclists and corresponding congestion effects. Based on individual-specific desired speeds and headway distance preferences, the model shows realistic speed-flow relationships validated with on-site observations while being capable of delaying rapid cyclists more often than slower ones in moderate traffic flows. The scalability of the model allows it to be large-scale applicable for network loading purposes, and thus suitable for evaluating impacts of cycling related infrastructure investments. [ABSTRACT FROM AUTHOR]

The single-file pedestrian flow experiment is a simplified system for investigating the fundamental walking characteristics of pedestrians and their dependence on various individual factors. Despite numerous experimental studies emerging in recent years, a comprehensive review and meta-analysis of the literature is lacking. Abundant research results and findings have not been thoroughly compared and examined in a unified manner. To address this gap, this paper comprehensively gathers existing experimental studies and compared the basic characteristics of pedestrian walking in a single-file flow, highlighting consistencies, inconsistencies, and controversies across different studies. Furthermore, this review emphasizes the need for future investigations to address theoretical inconsistencies and controversies. Specifically, we suggest more efforts in single-file flow experiments should be made for the open-boundary scenarios and the high-density conditions for normal and emergency conditions. Additionally, we recommend that future studies should also consider the effects of distractions, diverse age composition and disabilities on pedestrian walking characteristics as these factors are less examined in existing single-file flow experiments. Furthermore, we suggest that future studies should continue to reveal the underlying mechanism that governs the speed-headway relations of pedestrians and develop a universal standard form of the fundamental diagram. Finally, we propose the development of a hybrid model in the future that integrates behavioural rules, motion dynamics, and stepping locomotion to simulate single-file pedestrian movement from a microscopic to a macroscopic level. Addressing these gaps will lead to a more comprehensive understanding of single-file pedestrian flow and improve the design and management of pedestrian-traffic environments, ultimately enhancing pedestrian mobility and safety. [ABSTRACT FROM AUTHOR]

Sloping walkways are common pedestrian traffic facilities in modern cities, especially, in some cities located in mountainous terrain, such as Hong Kong, and Zurich. Compared with level walkways, sloping walkways may lead to the decrease in pedestrian flow efficiency, and are more prone to trigger crowd disasters at extreme crowd densities, for example, recent crowd stampede event in Itaewon, South Korea, did happen on a narrow ramp with an extreme crowd density. Therefore, it is important to study pedestrian flow dynamics on ramps. In this paper, we conducted single-file pedestrian flow experiments in 0°, 3°, 5°, 7°, 9°, 12°, 17°, 22°, 27° uphill and downhill conditions, and analyzed contrastively the influence of walkway slope on pedestrian flow dynamics. Our main findings are as follows. (i) Pedestrian flow efficiency is the highest in 7° downhill condition, indicating that a small downhill slope is advantageous to pedestrian flowing. (ii) Whether it is uphill or downhill, the ramp steeper than 7° will significantly decrease pedestrian flow efficiency, therefore is disadvantageous to pedestrian flowing. (iii) For a ramp, uphill and downhill pedestrian flow efficiencies are not equal. Downhill pedestrian flow efficiency is always larger than uphill pedestrian flow efficiency. These findings provide important guidance for the design of sloping walkways and the safety management of pedestrian crowds on sloping walkways. • The influence of walkway slope on pedestrian flow is studied. • A slope pedestrian flow experiment is conducted. • A small downhill slope around 7°is most advantageous to pedestrian flowing. [ABSTRACT FROM AUTHOR]

Expressway Traffic forecasting is a crucial issue in Intelligent Transportation System (ITS), and extensive studies have been conducted in this field. However, most existing models do not distinguish between expressway and urban road networks in traffic forecasting, which limits the performance improvement of traffic forecasting in expressways. Moreover, how auxiliary features contribute to prediction models are also hardly discussed in current studies. To deal with the above shortages, we put forward a Fundamental Diagram based Spatiotemporal Graph Convolutional Network (FDST-GCN) in this paper, which incorporates the prior knowledge in Fundamental Diagrams (FD) of the expressway and is dedicated to expressway traffic forecasting. FDST-GCN takes the Sequence to Sequence (Seq2Seq) structure as the backbone and assigns tasks to the encoder and decoder. The encoder aims to extract historical information; note the relations between speed, density, and capacity in FD; FDST-GCN uses two GCNs in the encoder to extract the spatial dependencies of speed and volume simultaneously. Decoder devotes to predicting future traffic status, which integrates the Time Feature , Weather Feature , and FD Feature into the model. In experiments, we validate the performance of FDST-GCN by comparing the prediction accuracy with baselines on two real-world traffic datasets; the results show that FDST-GCN improves 8.23% and 7.49% in RMSE compared to the best baselines in the above two datasets, respectively; 2.94% and 10.41% in MAE, respectively. Besides, we also illustrate the characteristics of auxiliary features and how those features boost the prediction accuracy. • A Fundamental Diagram based Spatiotemporal Graph Convolutional Network (FDST-GCN) is proposed for expressway traffic forecasting. • The spatial dependencies of speed and volume are extracted simultaneously to track the sharp change of traffic flow. • The FD Feature , Time Feature , and Weather Feature are fused in FDST-GCN to improve the prediction accuracy. • The performance of FDST-GCN is validated on two real-world traffic datasets. • The contributions of volume and auxiliary features are illustrated in the experiments. [ABSTRACT FROM AUTHOR]

To investigate the impact of connected automated vehicles (CAVs) on the characteristics of mixed traffic flow this paper proposed a fundamental diagram to consider time lags, platooning intensity, and the degradation of CAVs. First, the cooperative adaptive cruise control (CACC), adaptive cruise control (ACC), and intelligent driver model (IDM) are used to describe the car-following behavior characteristics of CAVs, and degraded CAVs (DCAVs), and HDVs, respectively. Second, a fundamental diagram model of mixed traffic flow under different penetration rates of CAVs is derived. The model is related to CAVs degradation, time lags, and platooning intensity. Then, a sensitivity analysis is investigated, including platooning intensity, free-flow speed, and minimum safety distance. Finally, the proposed model is verified by a simulation experiment designed by SUMO. The results show that the free-flow speed, penetration rate, and platooning intensity of CAVs positively affect the traffic capacity and optimal traffic density of mixed traffic flow. On the contrary, the time lags and minimum safety distance harm the traffic capacity and optimal traffic density of mixed traffic flow. • A fundamental diagram of mixed traffic flow is derived to capture CAVs degradation, time lags, and platooning intensity. • A sensitivity analysis including platooning intensity, free-flow speed, and minimum safety distance is investigated. • A simulation experiment designed by SUMO to verify the proposed model. [ABSTRACT FROM AUTHOR]

With cooperative adaptive cruise control vehicles (CACC) development, the heterogeneous traffic flow of human-driven vehicles (HV) and CACC will appear on the same road. Although numerous studies have carefully analyzed the characteristic of the heterogeneous traffic flow, none of them considered the effect of the differences in acceptance of CACC by HV drivers on the heterogeneous traffic flow. In terms of the differences in car-following behaviors of HV drivers with diverse CACC acceptances, this paper deduces the capacity and stability of the heterogeneous traffic flow under different CACC penetration rates. Firstly, the HV driver's car-following behavior model with different CACC acceptability is calibrated through a questionnaire. Then, the analytical framework of the fundamental diagram and stability of the heterogeneous traffic flow is derived, according to the differences of HV drivers' acceptance of CACC. Finally, the paper analyzes the influence factors and effects of the heterogeneous traffic flow capacity and stability. The results indicate under different CACC penetration rates, the improvement of HV drivers' acceptance of CACC increases the capacity and stability of the heterogeneous traffic flow. When CACC penetration rate is lower than 96.35% and HV drivers' acceptance level of CACC is lower than the critical ratio, the popularity of CACC will decrease the stability of traffic flow. When the penetration rate of CACC is less than 34.76%, and HV drivers' acceptance level of CACC is lower than the critical proportion, the popularization of CACC will decrease the capacity of heterogeneous traffic. • Current driver acceptance of CACC and HV drivers' driving behavior in the heterogeneous traffic flow are investigated. • The fundamental diagram and stability analysis framework of the heterogeneous traffic flow considering HV drivers' CACC acceptance are derived. • The influence of HV drivers' CACC acceptance on the heterogeneous traffic flow capacity and stability under different CACC penetration rates are studied. • The critical HV drivers' CACC acceptance and CACC penetration rates to avoid the deterioration of the heterogeneous traffic flow operational due to the prevalence of CACC are extracted. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a very basic experiment of single-file bicycles moving in a circle passage was conducted. A video processing method was used to extract the relevant data such as trajectories, velocity, density, flow rate, time headway and so on. Afterwards, the relations between them were explained in detail. The relation between the flow rate and the density indicated that the flow rate is in proportion with the density before the congestion appears. It is also found that the interval between two bicycles is long but the time headway decreased as the density increased when the bicycles can move freely, while the situation is opposite when congestion occurs. Some of the results in this paper consist with those of other researchers. The purpose of this experiment is to provide basic experimental data and fundamental diagrams for single-file bicycles’ following movement. Results provided in this paper might be useful for validating related models and designing facility. [Copyright &y& Elsevier]

Abstract: According to Banks [Investigation of some characteristics of congested flow. Transportation Research Record, 1999], traffic heterogeneity explains the data scattering on the flow–density plane and positive transferences within the congested phase (a transference is a line connecting adjacent points in the time series). This heterogeneity results from a traffic mixture, made up of various vehicles and drivers, or different traffic conditions such as meteorological conditions. This paper only deals with traffic mixture and more particularly with vehicle classes such as passenger car and truck, which are correlated to the vehicle length. When considering a macroscopic model, the mean vehicle length, which is measured by sensors, is associated with the truck percentage. Then the Generic Second Order Model (GSOM) by Lebacque [Lebacque, J.P., Mammar, S., Haj-Salem, H., 2007a. Generic second-order traffic flow modeling. In: Proceedings of the 17th International Symposium on Transportation and Traffic Theory, London, 23–25 July 2007, 749–770.] provides a rigorous mathematical framework for traffic heterogeneity modeling. The added value in this paper is that admissible invariants which characterize generic fundamental diagrams, possibly depending on the mean vehicle length, are interpreted and debated. Aw–Rascle–Zhang’s [Aw, A., Rascle, M., 2000. Resurrection of second-order models of traffic flow. SIAM Journal of Applied Mathematics, 60 (3), 916–938; Zhang, H.M., 2002. A non equilibrium traffic model devoid of gas-like behavior. Transportation Research Part B, 36, 275–290.] and Colombo’s [Colombo, R.M., 2002. A 2×2 hyperbolic traffic flow model. Mathematical and Computer Modeling, 35, 683–688.] anisotropic models are deeply analyzed from a traffic point of view. At last an extended GSOM equation system provides a full parameterization of fundamental diagrams which is needed to traffic heterogeneity modeling. [Copyright &y& Elsevier]

• A SSDPR estimation method is proposed to unbiasedly estimate the CV penetration rate. • The proposed SSDPR estimation method is simple, analytical and non-parametric. • The numbers of CVs and vehicles in front of the last CV are the only required inputs. • A BQ algorithm is proposed to handle cases of overflow or oversaturation. • A case study of fundamental diagram estimation based on CV data only is presented. With more connected vehicles (CVs) in the networks, the big data era leads to the availability of abundant data from CVs. CV penetration rate is the fundamental building block of tremendous applications, such as traffic data estimation, CV-based adaptive signal control and origin-destination estimation. While CV penetration rate is a random variable unknown in nature, the current estimation method of penetration rate mainly relies on two sources of data —detector and CV data. Penetration rate across the link is computed as CV flow divided by all traffic flow over a certain period of time. However, the current method is constrained by availability and quality of detector data. This paper proposes a simple, analytical, non-parametric, and most importantly, unbiased single-source data penetration rate (SSDPR) estimation method for estimating penetration rate solely based on CV data. It subtly and simultaneously fuses two estimation mechanisms—(1) the measurement of the probability of the first vehicle in a queue being a CV and (2) the direct estimation of the penetration rate of a sample queue—to constitute a single estimator to handle all the possible sample queue patterns. Applicability of the proposed method is not confined to a specific arrival pattern. It solely utilizes the number of the observed CVs and the number of vehicles before the last observed CV in a sample queue. Combining with bridging the queue algorithm, the proposed SSDPR estimation method is extended to overflow or oversaturated conditions. Simulation results show that the proposed method is able to accurately estimate penetration rate as low as 0.1% for all the situations considered. To illustrate the applicability of the proposed method, a case study of fundamental diagram estimation of a link without being installed with any detector is presented. [ABSTRACT FROM AUTHOR]

Heavy casualties in stampede accidents in recent years have brought new challenge to crowd management and public safety as well. Therefore, a series of controlled experiments were conducted to investigate traffic dynamics of pedestrians in this paper. The fundamental diagrams for unidirectional flow, bidirectional counter flow, bidirectional cross flow and four-directional cross flow are surveyed by using the improved Voronoi diagram measurement method. The maximum specific flow and the optimal density are obtained from the density–flow relation. Furthermore, difference of fundamental diagrams for different pedestrian flows is also discussed. Evolution of individual density and individual velocity in the Voronoi cell is explored and the dangerous area for pedestrians is identified. Movement efficiency under different densities is investigated when passing through the intersection. The study is helpful to develop a modular approach that serves as a basis for the planning and evaluation of large scale public events. • An improved Voronoi diagram method where moving back is considered as negative contribution to flow is proposed. • Fundamental diagrams for different flows under all states and steady state are discussed. • The dangerous area is identified through the individual Voronoi cell. [ABSTRACT FROM AUTHOR]

Exploring mice movement characteristics is a basic issue of using mice to study crowd behavior in evacuation due to cost and ethical consideration of pedestrian evacuation experiments. In this paper, single-file experiment was performed in a 2m-long passageway with trained mice. Mice movement characteristics were studied by fundamental diagrams in macroscopic view and the velocity–distance headway relation in microscopic analysis. The results show the decaying velocity with density at low densities, and show the non-strict relation of velocity and density at high densities. From microscopic perspective, random pause was observed, and mice can move forward together when overlap occurs. Further, the numerical simulation validates the scattered effect of the random pauses on the velocity–distance headway diagram, and indicates that mice velocity increase with distance headway sharply and then keeps stable. This study provides insights into mice movement as well as the similarities and differences between mice and humans in movement, and it may contribute to crowd evacuation studies. • The single-movement experiment was conducted with trained mice. • Fundamental diagrams under the experiment condition are shown. • The velocity–distance headway relation under the experiment condition is shown. • Mice special interactions of random pauses and overlap are analyzed. [ABSTRACT FROM AUTHOR]

Highlights • Pedestrian dynamics with background music is investigated. • Fundamental diagrams with rhythm and without rhythm are compared. • Coexistence of two phases with rhythm is analyzed. Abstract Although crowding is often observed in large cities, it is still not known how to control it. Rhythm is a potential method to improve pedestrian flow. This paper investigates the effect of background music on pedestrian movement. Six experiments with different background rhythms and an experiment under normal condition (without any rhythm) are conducted to study the issue. Fundamental diagrams show that velocity and flow with background music at medium and high densities will be smaller than those under normal conditions. With music, stop-and-go behavior is more frequently observed due to a higher stopping headway and a higher restarting headway. Stopping duration analysis shows that pedestrians stop longer with music. Therefore it has to be considered as another factor for jam formation in pedestrian traffic besides density and inhomogeneities. Because of the weaker dynamic coordinate behavior (slower response to the motion of the frontal pedestrians), pedestrians need more time to prevent conflicts and step consecutively at high densities in the presence of music. Decreasing step frequency with rhythm at high density also results in stopping more frequently. Our study will be helpful for understanding the effect of music, especially background music, on pedestrian movement. [ABSTRACT FROM AUTHOR]

Abstract: The fundamental diagram, as the graphical representation of the relationships among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering. Seventy-five years after the seminal Greenshields model, a variety of models have been proposed to mathematically represent the speed–density relationship which underlies the fundamental diagram. Observed in these models was a clear path toward two competing goals: mathematical elegance and empirical accuracy. As the latest development of such a pursuit, this paper presents a family of speed–density models with varying numbers of parameters. All of these models perform satisfactorily and have physically meaningful parameters. In addition, speed variation with traffic density is accounted for; this enables statistical approaches to traffic flow analysis. The results of this paper not only improve our understanding of traffic flow but also provide a sound basis for transportation engineering studies. [Copyright &y& Elsevier]

Abstract: In this paper, we incorporate a limitation on the interaction range between neighboring vehicles into the cellular automaton model proposed by Gao and Jiang et al. [K. Gao, R. Jiang, S. X. Hu, B. H. Wang and Q. S. Wu, Phys. Rev. E 76 (2007) 026105], which was established within the framework of Kerner’s three-phase traffic theory and has been shown to be able to reproduce the three-phase traffic flow. This modification eliminates an unrealistic phenomenon found in the previous model, where the velocity-adaptation effect between neighboring vehicles can exist even if those vehicles are infinitely far away from each other. Therefore, in the improved model, we regulate that such interactions can only occur within a finite distance. For simplicity, we suppose a constant value to describe this distance in this paper. As a result, when compared to the previous model, the improved model mainly simulates the following results which are believed to be an improvement. (1) The improved model successfully reproduces the expected discontinuous transition from free flow to synchronized flow and the related “moving synchronized flow pattern”, which are both absent in the original model but have been observed in real traffic. (2) The improved model simulates the correlation functions, time headway distributions and optimal velocity functions which are all more consistent with the empirical data than the previous model and most of the other models published before. (3) Together with the previous two models considering the velocity-difference effect, this model finally accomplishes a significative process of developing traffic flow models from the traditional “fundamental diagram approach” to the three-phase traffic theory. This process should be helpful for us to understand the traffic dynamics and mechanics further and deeper. [Copyright &y& Elsevier]

Motivated by the similarity between the fundamental diagram of vehicular traffic and the Maxwell–Boltzmann distribution of ideal gases, this paper proposed a methodology to model the fundamental diagram as a stochastic process which also applies to other real-world systems with similar nature. A concrete example is provided to illustrate the application of the methodology where the fundamental diagram of vehicular traffic is modeled as a stochastic process to capture the scattering effect in flow–density relationship. A verification study was conducted on the model using empirical data and the statistical analysis shows that the overall quality of the fitted stochastic process is acceptable. Related existing efforts are referenced to the proposed stochastic fundamental diagram where their similarities and differences are elaborated. Further discussion is carried out on the significance of the stochastic fundamental diagram as well as the proposed methodology with an additional real-world example to illustrate its applications. [ABSTRACT FROM AUTHOR]

This paper reveals and explores the flow characteristics of airport surface network on both mesoscopic and macroscopic levels. We propose an efficient modeling approach based on the cell transmission model for simulating the spatio-temporal evolution of flow and congestion on taxiway and apron networks. The existence of link-based fundamental diagram that expresses the functional relationship between link density and flow is demonstrated using empirical data collected in Guangzhou Baiyun airport. The proposed CTM-based network model is shown to be an efficient and accurate method capable of supporting air traffic prediction and decision support. In addition, using both CTM-based simulation and empirical data, we further reveal the existence of an aggregate relationship between traffic density and runway throughput, which is referred to as macroscopic fundamental diagram (MFD) in the literature of road traffic. The MFD on the airport surface is analyzed in depth, and utilized to devise several robust off-block control strategies under uncertainties, which are shown to significantly outperform existing off-block control methods. [ABSTRACT FROM AUTHOR]

In this research we performed new bicycle and pedestrian experiments to supplement data extracted from existing follow-the-leader experiments in vehicles, bicycles and pedestrians, and studied their spacetime trajectories and flow-density (or spacing-velocity) phase diagrams. The strong similarities in the spacetime trajectories and the bi-variate phase plots as well as the relative consistence of the estimated proportionality parameter across all three types of traffic, suggest that a unified behavioral mechanism is at play in human-driven traffic. It is suggested that this mechanism is essentially a safety-driven behavior that vehicles, bicycles or pedestrians adopt a safe speed for a given spacing between them. This behavior is well described by a well-known model in vehicular traffic and it is shown in this paper that a scaled version of this model applies to all three types of traffic. A unified relaxation-driven social force traffic model is then proposed to incorporate this behavior mechanism. Simulations with the same setup as the real-life experiments were carried out for vehicle, bicycle, and pedestrian traffic using the unified traffic model and the simulated spacetime trajectories and fundamental diagrams show remarkable consistence with the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, the impact of vision on the uni- and bi-directional flow has been investigated via experiment and modeling. In the experiments, pedestrians are asked to walk clockwise/anti-clockwise in a ring-shaped corridor under view-limited condition and normal view condition. As expected, the flow rate under the view-limited condition decreases comparing with that under the normal view condition, no matter in uni- or bi-directional flow. In bidirectional flow, pedestrians segregate into two opposite moving streams very quickly under the normal view condition, and clockwise/anti-clockwise walking pedestrians are always in the inner/outer ring due to right-walking preference. In the first set of experiment, spontaneous lane formation has not occurred under the view-limited condition. Pedestrian flow does not evolve into stationary state. Local congestion occurs and dissipates from time to time. However, in the later sets of experiments, spontaneous lane formation has re-occurred. This is because participants learned from the experience and adapted right-walking preference to avoid collision. To model the flow dynamics, an improved force-based model has been proposed. The driving force has been modified. The right-walking preference has been taken into account. The fact that pedestrians cannot judge the moving direction accurately under limited-view condition has been considered. Simulation results are in good agreement with the experimental ones. [ABSTRACT FROM AUTHOR]

Properties of pedestrian may change along their moving path, for example, as a result of fatigue or injury, which has never been properly investigated in the past research. The paper attempts to study the speed matching effect (a pedestrian adjusts his velocity constantly to the average velocity of his neighbors) and its influence on the density–velocity relationship (a pedestrian adjust his velocity to the surrounding density), known as the fundamental diagram of the pedestrian flow. By the means of the cellular automaton, the simulation results fit well with the empirical data, indicating the great advance of the discrete model for pedestrian dynamics. The results suggest that the system velocity and flow rate increase obviously under a big noise, i.e., a diverse composition of pedestrian crowd, especially in the region of middle or high density. Because of the temporary effect, the speed matching has little influence on the fundamental diagram. Along the entire density, the relationship between the step length and the average pedestrian velocity is a piecewise function combined two linear functions. The number of conflicts reaches the maximum with the pedestrian density of 2.5 m − 2 , while decreases by 5.1% with the speed matching. [ABSTRACT FROM AUTHOR]

Being one of the most classic concepts in the traffic flow theory, the fundamental diagram (FD) describes the relationship between average flow and average density of link-level traffic flow dynamics. Inductive loop detectors or closed-circuit television are commonly used for FD estimations and they are known to have cost-effective and accuracy issues. Thanks to the GPS-enabled smartphones and GPS-equipped probe vehicles, high temporal and spatial resolution traffic data are available which enable traffic condition inference over time and space continuously. Several existing studies have explored FD estimation algorithms on freeways where flow is generally uninterrupted and uni-modal, based on GPS trajectory data. These developments motivate this study, where the objective is to extend the application to multi-modal and interrupted environment, i.e., urban signalized areas. In this paper, an estimation method is developed to capture the FD of multi-modal traffic streams on signalized urban links. The proposed algorithm is empirically tested using real-world GPS datasets collected on a signalized arterial road in Shenzhen City. Promising results show that the proposed algorithm is capable to estimate the FD under such condition. Furthermore, impacts of multi-modal traffic and signal operations on the FD estimation are analyzed and discussed. • A fundamental diagram estimation approach using limited probe data from interrupted environments is proposed. • Multi-modal traffic mixture in a heterogeneous traffic pattern is considered for fundamental diagram estimation. • The impacts of different transport modes and signal operations on fundamental diagrams are analyzed. • Empirically tested with real-world GPS datasets collected on a signalized arterial road. [ABSTRACT FROM AUTHOR]

The connected automated vehicle platoon is a critical organizational form of the intelligent transportation system in the future. Considering the platoon size and intensity of connected automated vehicles (CAVs), this paper studies the fundamental diagram and stability of mixed traffic flow. Firstly, the spatial distribution of vehicles in the mixed traffic flow is analyzed. Based on the platoon intensity and Markov chain, the proportion of different types of vehicles in the mixed traffic flow is deduced. Then, the general model framework of the fundamental diagram of the mixed traffic flow is established. The influence factors of the fundamental diagram under the critical scenarios are discussed. Finally, the general condition of mixed traffic flow stability considering the platoon size and intensity of CAVs is developed. Numerical experiments are adopted to analyze the influence of the characteristics of the CAVs platoon on stability. The results show that: (1) a greater platoon size leads to the increase of traffic capacity while it is harmful to the maintenance of traffic flow stability; (2) the platoon size is recommended to be set at 4 to 6 to balance the relationship between traffic capacity and stability; (3) a more significant platoon intensity can help improve the traffic capacity and stability; (4) the penetration rate of CAVs has a positive effect on the traffic flow stability until it increases to a certain degree. • Analyzed the spatial distribution of vehicles in mixed traffic flow with a CAVs platoon. • Derived the fundamental diagram of mixed traffic flow considering the platoon size and intensity. • Derived the stability condition of mixed traffic flow considering the platoon size and intensity. • Discussed the traffic volume and stability of mixed traffic flow under different penetration rates of CAVs, platoon sizes and intensities. [ABSTRACT FROM AUTHOR]

Macroscopic traffic flow theory is widely used to analyze the performance and congestion in transportation systems and urban traffic. Currently, the main area of application is road traffic, where fundamental diagrams depicting the relationship between performance parameters such as traffic density, traffic flow and vehicle speed are used to represent traffic regimes and system settings. Research on the applicability for rail traffic systems is only starting to emerge and has mainly focused on mass transit systems and moving block signaling systems, so far. The current paper provides a theoretical analysis of traffic flow properties on unidirectional railway lines. Macroscopic fundamental diagrams are established and used to analyze similarities and differences of traffic flow behavior in railway systems. It is shown how fundamental diagrams allow to pinpoint flow regimes and different phases in train traffic, which can be useful information for system design and operation planning. Railway systems are studied both in a deterministic setting allowing to analyze peak efficiency and in a stochastic setting with perturbations. • Macroscopic fundamental diagrams of rail traffic on commuter lines are studied. • Both closed and open systems are studied using discrete-event simulation. • Different traffic phases are identified and corresponding explanation is provided. • Potential use for bottleneck identification and robustness improvement is discussed. [ABSTRACT FROM AUTHOR]

Car-following models are always of great interest of traffic engineers and researchers. In the age of mass data, this paper proposes a nonparametric car-following model driven by field data. Different from most of the existing car-following models, neither driver’s behaviour parameters nor fundamental diagrams are assumed in the data-driven model. The model is proposed based on the simple k -nearest neighbour, which outputs the average of the most similar cases, i.e., the most likely driving behaviour under the current circumstance. The inputs and outputs are selected, and the determination of the only parameter k is introduced. Three simulation scenarios are conducted to test the model. The first scenario is to simulate platoons following real leaders, where traffic waves with constant speed and the detailed trajectories are observed to be consistent with the empirical data. Driver’s rubbernecking behaviour and driving errors are simulated in the second and third scenarios, respectively. The time–space diagrams of the simulated trajectories are presented and explicitly analysed. It is demonstrated that the model is able to well replicate periodic traffic oscillations from the precursor stage to the decay stage. Without making any assumption, the fundamental diagrams for the simulated scenario coincide with the empirical fundamental diagrams. These all validate that the model can well reproduce the traffic characteristics contained by the field data. The nonparametric car-following model exhibits traffic dynamics in a simple and parsimonious manner. [ABSTRACT FROM AUTHOR]

Single-file experiments are widely used to investigate pedestrian movement properties. However, most single-file experiments were conducted in a circular channel rather than complex structures. In this paper, we designed single-file merging experiments with different merging angles θ and inflow rates at the entrances of the branch and upstream. We found fundamental diagrams were not affected by the merging angle, but the fundamental diagram after merging (downstream) was different from that before merging (upstream and the branch) at large densities. The flow before merging had a peak value 1.2 ped/s at the density of 0.9 ped/m and the flow after merging increased with the density up to 1.6 ped/m. The function proposed by Weidmann was extended to describe the density–velocity​ relation before merging. Moreover, we found when the branch and upstream inflows at the entrances were large enough, the branch and upstream flows in the corridors would reach a balanced state and were almost equal, and putting a proper frequency of metronome program to control the inflow rate at the entrances can increase the efficiency of the merging flow. The flow in scenario with θ = 120 ° was most efficient considering both Shannon entropy and average time headway. The results can be used to calibrate pedestrian simulation models. • Fundamental diagrams of pedestrian single-file merging flow are investigated and are fitted by an exponential function. [ABSTRACT FROM AUTHOR]

The speed-density or flow-density relationship has been considered as the foundation of traffic flow theory. Existing single-regime models calibrated by the least square method (LSM) could not fit the empirical data consistently well both in light-traffic/free-flow conditions and congested/jam conditions. In this paper, first, we point out that the inaccuracy of single-regime models is not caused solely by their functional forms, but also by the sample selection bias. Second, we apply a weighted least square method (WLSM) that addresses the sample selection bias problem. The calibration results for six well-known single-regime models using the WLSM fit the empirical data reasonably well both in light-traffic/free-flow conditions and congested/jam conditions. Third, we conduct a theoretical investigation that reveals the deficiency associated with the LSM is because the expected value of speed (or a function of it) is nonlinear with regard to the density (or a function of it). [ABSTRACT FROM AUTHOR]

Experimental measurements on commercial adaptive cruise control (ACC) vehicles are becoming increasingly available from around the world, providing an unprecedented opportunity to study the traffic flow characteristics that arise from this technology. This paper adds new experimental evidence to this knowledge base and presents a comprehensive empirical study on the ACC equilibrium behaviors via the resulting fundamental diagrams. We find that, like human-driven vehicles, ACC systems display a linear equilibrium spacing-speed relationship (within the range of available data) but the key parameters of these relationships can differ significantly from human-driven traffic depending on input settings. At the minimum ACC headway setting, equilibrium capacities in excess of 3500 vehicles per hour are observed, together with an extremely fast equilibrium wave speed of 100 kilometers per hour. These fast waves are unfamiliar to human drivers, and may pose a safety risk. The results also suggest that ACC jam spacing can be much larger than in human traffic, which reduces the network storage capacity. • A comprehensive study on ACC fundamental diagrams based on worldwide experiments. • ACC has a linear speed-spacing relationship in the available data range. • ACC can produce high capacities excessing 3500 vph with fast congested wave speed. • ACC jam spacing is much larger than human, which reduces network storage capacity. [ABSTRACT FROM AUTHOR]

Abstract: Many tragic crowd disasters have happened across the world in recent years, such as the Phnom Penh stampede in Cambodia, crowd disaster in Mina/Makkah, and the Love Parade disaster in Germany, showing that management of mass events is a tough task for organizers. The study of unidirectional flow, one of the most common forms of motion in mass activities, is essential for safe organization of such events. In this paper, the properties of unidirectional flow in a crowded street during a real mass event in China are quantitatively investigated with sophisticated active infrared counters and an image processing method. A complete dataset of flow rates during the whole celebration is recorded, and a time series analysis gives new insight into such activities. The spatial analysis shows that the velocity and density of the crowd are inhomogeneous due to the boundary effect, whereas the flux is uniform. The estimated capacity of the street indicates that the maximum flow rate under normal condition should be between 1.73 and 1.98 /m/s, which is in good agreement with several field studies available in the existing literature. In consideration of the significant deviation among different studies, fundamental diagrams of dense crowds are also re-verified, and the results here are consistent with those from other field studies of unidirectional flow, but different from the bidirectional and experimental results. It is suggested that the data from multidirectional flow and experiments cannot be directly applied to unidirectional dense flow in a real mass event. The results also imply that the density of a similar unidirectional marching crowd should be controlled to be under 5 /m2, which can produce optimal efficiency and have more possibility to ensure safety. The field study data given here provide a good example of a database for crowd studies. [Copyright &y& Elsevier]

Abstract: People die or get injured at mass events when the crowd gets out of control. Urbanization and the increasing popularity of mass events, from soccer games to religious celebrations, enforce this trend. Thus, there is a strong need to better control crowd behavior. Here, simulation of pedestrian streams can be very helpful: Simulations allow a user to run through a number of scenarios in a critical situation and thereby to investigate adequate measures to improve security. In order to make realistic, reliable predictions, a model must be able to reproduce the data known from experiments quantitatively. Therefore, automatic and fast calibration methods are needed that can easily adapt model parameters to different scenarios. Also, the model must be robust. Small changes or measurement errors in the crucial input parameters must not lead to disproportionally large changes in the simulation outcome and thus potentially useless results. In this paper we present two methods to automatically calibrate pedestrian simulations to the socio-cultural parameters captured through measured fundamental diagrams. We then introduce a concept of robustness to compare the two methods. In particular, we propose a quantitative estimation of parameter quality and a method of parameter selection based on a criterion for robustness. We discuss the results of our test scenarios and, based on our experience, propose further steps. [Copyright &y& Elsevier]

Abstract: In this paper, the delay probability of the original Nagel and Schreckenberg model is modified to simulate one-lane traffic flow. The delay probability of a vehicle depends on its corresponding gap. According to simulation results, it has been found that the structure of the fundamental diagram of the new model is sensitively dependent on the values of the delay probability. In comparison with the NS model, one notes that the fundamental diagram of the new model is more consistent with the results measured in the real traffic, and the velocity distributions of the new model are relatively reasonable. [Copyright &y& Elsevier]

Abstract: In order to describe car following behavior in real world, this paper presents a non-lane-based car following model by incorporating the effects of the lane width in traffic. The stability condition of the model is obtained by using the linear stability theory. And numerical simulation is carried out to validate the analytic results. The property of the model is investigated, and it is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion. The results implied that incorporating the lane width effects in car following model not only stabilize traffic flow and suppress the traffic jam, but also lower critical headway and increase capacity. Thus, the lateral separation effects greatly enhance the realism of car following models. [ABSTRACT FROM AUTHOR]

Abstract: Frequent lane-changes in highway merging, diverging, and weaving areas could disrupt traffic flow and, even worse, lead to accidents. In this paper, we propose a simple model for studying bottleneck effects of lane-changing traffic and aggregate traffic dynamics of a roadway with lane-changing areas. Based on the observation that, when changing its lane, a vehicle affects traffic on both its current and target lanes, we propose to capture such lateral interactions by introducing a new lane-changing intensity variable. With a modified fundamental diagram, we are able to study the impacts of lane-changing traffic on overall traffic flow. In addition, the corresponding traffic dynamics can be described with a simple kinematic wave model. For a location-dependent lane-changing intensity variable, we discuss kinematic wave solutions of the Riemann problem of the new model and introduce a supply–demand method for its numerical solutions. With both theoretical and empirical analysis, we demonstrate that lane-changes could have significant bottleneck effects on overall traffic flow. In the future, we will be interested in studying lane-changing intensities for different road geometries, locations, on-ramp/off-ramp flows, as well as traffic conditions. The new modeling framework could be helpful for developing ramp-metering and other lane management strategies to mitigate the bottleneck effects of lane-changes. [Copyright &y& Elsevier]

Abstract: The modeling of vehicular traffic flow is developed by methods of the discrete mathematical kinetic theory for active particles. The discretization refers to the velocity variable in the case of spatially homogeneity. The discretization overcomes, at least in part, some technical difficulties related to the selection of the correct representation scale. Moreover, the modeling approach includes in the state equation of the vehicle an activity variable suitable to model the quality (low or high) of the vehicle–driver system. This paper aims to be the first of a project concerning traffic flow by active particles methods. [Copyright &y& Elsevier]

As Connected and Automated Vehicles (CAVs) gradually occupy more market share, the mixed traffic flow composed of MVs and CAVs will exist for a long time. This paper mainly explores the impact of CAV platoon management and control mode degradation on the fundamental diagram, and further studies how they affect fuel consumption and pollutant emissions. First, in terms of platoon management, the mixed traffic flow configurations and distribution characteristics of four types of vehicles were proposed. Second, numerical analyses were conducted, and a fundamental diagram considering CAV platoons and degradation of the Cooperative Adaptive Cruise Control (CACC) system was proposed. To validate this fundamental diagram, the Intelligent Driver Model (IDM) and the Connected and Automated Vehicle (CAV) and CACC models proposed by the PATH lab were applied for simulations. Third, the influence of CAV platoons on the traffic environment was explored via micro simulations as well. Conclusions were drawn from the results of the simulations: The mixed traffic can obtain significant capacity gain due to the existence of CAV platoons, especially with high Market Penetration Rate (MPR). Moreover, capacity break-downs occurred in the diagram due to the adjustment of information flow topology, and the break-downs were more prominent with higher MPR. Additionally, CAV platoons can significantly reduce fuel consumption and emissions by 34.7% and 41.4% respectively. The first degradation raises fuel consumption and emissions by 44.5% and 60.1%, while the second one by 12.0% and 16.1%, with the initial vehicle speed set at 33 m/s and MPR set at 1.0. Finally, according to the sensitivity analysis, an optimal platoon size of 5 vehicles was suggested for CAV platoon management. • A fundamental diagram of mixed traffic flow is built considering platoon management. • The impact of control mode degradations on the fundamental diagram is pointed out. • The application of platoon management could benefit eco-driving. • The impact of degradations on emissions and fuel consumption is explored. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to explore evacuees' behaviour during an unannounced fire drill using data collected in two office buildings located at CERN, in Switzerland. Overall, a total of 142 pre-evacuation time measurements, 121 evacuee walking speed measurements in staircase configurations, and 336 evacuee walking speed measurements on floor configurations are included in the dataset. These data were compared with the existing evacuation data for office buildings. This comparison revealed that the pre-evacuation times measured in the present study are significantly lower compared to existing data from the literature for the same type of occupancy. Walking speed data collected in corridors in the present study is within the range of the values reported in previous studies. Further, walking speeds on descending stairways measured in the present study were significantly higher than those available in the literature. However, the novel dataset presented in this study is in accordance with the values recommended in well-known guidelines (such as the SFPE Handbook) and could be used in the future to simulate evacuations of office buildings. [ABSTRACT FROM AUTHOR]

Abstract: The paper characterizes the behavior of the cell transmission model of a freeway, divided into N sections or cells, each with one on-ramp and one off-ramp. The state of the dynamical system is the N-dimensional vector n of vehicle densities in the N sections. A feasible stationary demand pattern induces a unique equilibrium flow in each section. However, there is an infinite set—in fact a continuum—of equilibrium states, including a unique uncongested equilibrium n u in which free flow speed prevails in all sections, and a unique most congested equilibrium n con. In every other equilibrium n e one or more sections are congested, and n u ⩽ n e ⩽ n con. Every equilibrium is stable and every trajectory converges to some equilibrium state. Two implications for ramp metering are explored. First, if the demand exceeds capacity and the ramps are not metered, every trajectory converges to the most congested equilibrium. Moreover, there is a ramp metering strategy that increases discharge flows and reduces total travel time compared with the no-metering strategy. Second, even when the demand is feasible but the freeway is initially congested, there is a ramp metering strategy that moves the system to the uncongested equilibrium and reduces total travel time. The two conclusions show that congestion invariably indicates wastefulness of freeway resources that ramp metering can eliminate. [Copyright &y& Elsevier]