A Mathematical Model of a Pelican Crossing

A simple mathematical model is set up to describe the mode ofoperation of a Pelican crossing. The distributions of timesduring which vehicles may and may not pass through the crossingare established, and the queues of pedestrians and vehicleswhich form are investigated. A limited amount of data has beencollected to estimate the numerical values of the parametersof the model and to check on the validity of the assumptionsmade. It is hoped at a later stage to report on similar investigationsat other types of pedestrian crossings, and to compare theireffectiveness with Pelican crossings.     

考虑右转机动车影响下的信号交叉口行人过街时间模型

为了弄清信号交叉口右转机动车对行人过街的影响机理,应用穿越间隙理论,分析行人穿越机动车的分布规律,并推导了行人过街时间模型公式,通过定量分析及模型算例,找出了行人过街时间的主要影响因素及其定量关系,模型精度基本满足要求,具有较高的理论和实用价值,为信号交叉口的信号设计,特别是行人专用信号相位的设置、分析和研究提供了新思路和新方法.     

Investigating pedestrian navigation in indoor open space environments using big data

Reliable empirical data are required to understand and model realistic pedestrian navigation behaviours at public crowd-gathering places such as public transportation hubs and shopping malls. Naturalistic walking big data could provide reliable information for investigating realistic pedestrian behaviours and to overcome critical shortcomings of the data collected through controlled laboratory experiments. In this work, we investigate pedestrian navigation in indoor open spaces using naturalistic walking big data collected through video recordings. The extracted data include 299,082 trajectories of individual pedestrians who navigated through the atrium of the Informatics Forum building of the University of Edinburgh. We compare several pedestrian vector fields by calibrating several cellular automaton (CA) models and we finally identify a generalized vector field for pedestrians who are walking in indoor open space environments under normal walking conditions.The output of this study could be useful in enhancing CA-based pedestrian simulation models by representing pedestrian navigation as well as route-choice behaviours more realistically in those models. Simulation tools based on such enhanced models can facilitate practitioners, such as public building designers, to optimize designs considering naturalistic pedestrian behaviours in open spaces.     

无信号控制路口行人过街行为特征及博弈模型分析

过城市实地行人问卷调查及过街行为分析,描述了行人过街重要行为特征,在此基础上,引入了交通工程相关结果,针对无信号控制路口行人过街问题,构建了行人先行和车辆先行的扩展式行人过街冲突博弈模型。纳什均衡分析结果揭示了行人过街冲突的机理,并给出了各种冲突情况下相应参数临界分析。预期结果对行人过街设施的管理与建设也具有意义。     

An algorithm to assign pedestrian groups dispersing at public gatherings based on pedestrian-traffic modelling

The minimization of weighted pedestrian and vehicle times for pedestrians dispersing at public gatherings is considered. An algorithm to solve this problem and to assign each pedestrian group to a unique selected path is developed. This algorithm considers general network where its arc values are dependent on pedestrian/traffic modelling and its nodes (intersections) have penalties.The algorithm is followed by a case study which exhibits possible pedestrian/traffic models. These experimental models yield some deterministic functional dependency between pedestrians' speed and sidewalk concentration, pedestrians' street concentration, and sidewalk concentration, and vehicles' speed and pedestrians' street concentration. If such and realistic models serve as an input to the algorithm it might contribute to reduce pedestrian/traffic conflicts and interference and hence to increase the overall safety of these two travel modes.     

Particle methods for multi-group pedestrian flow

We consider a multi-group microscopic model for pedestrian flow describing the behaviour of large groups. It is based on an interacting particle system coupled to an eikonal equation. Hydrodynamic multi-group models are derived from the underlying particle system as well as scalar multi-group models. The eikonal equation is used to compute optimal paths for the pedestrians. Particle methods are used to solve the equations on all levels of the hierarchy. Numerical test cases are investigated and the models and, in particular, the resulting evacuation times are compared for a wide range of different parameters.     

Dynamic navigation field in the social force model for pedestrian evacuation

This paper presents a generalized walking cost distribution to determine a dynamic navigation field in the social force model for pedestrian evacuation. The local walking cost per unit distance of movement includes the cost associated with travel time and other additional costs incurred by pedestrians to avoid colliding with obstacles in a dynamic environment. In the dynamic navigation field, pedestrians expect to choose an optimal path with the lowest walking cost to reach their target destination reactively based on available instantaneous information. The social force model with the dynamic navigation field is validated by comparing the simulation results with empirical observations. The fundamental diagrams for observations and simulation data agree well, which indicates the effectiveness of the model. Numerical results show that the model with the dynamic navigation field can reproduce typical stages of the dynamics of pedestrian evacuation, such as self-organized arching and queuing phenomena, and can capture the route choice and exit choice behaviors of pedestrians during the evacuation process. Compared to the model with the static navigation field, the model with the dynamic navigation field can reduce the total evacuation time of the room and save the required CPU time for a large group of pedestrians. Furthermore, the strong tendency to avoid local high-density regions (i.e., minimizing collisions) can also reduce the total evacuation time under the same conditions.     

不同行为风格下路段行人与机动车行为博弈分析

针对路段过街行人与机动车的博弈决策行为,考虑不同交通行为风格下行人的过街特征和驾驶人的驾驶行为习惯等影响因素,开展路段行人交通行为风格调查,掌握不同交通行为风格下路段行人的过街特征,构建非合作动态人车博弈模型.通过分析不同策略下行人与驾驶人的收益,求解纳什均衡,得到不同策略下行人与驾驶人的最优策略.为解决路段行人与机动车冲突提供新的思路.     

Estimating contact forces and pressure in a dense crowd: Microscopic and macroscopic models

This paper deals with the estimation of pressure at collisions times during the movement of a dense crowd. Through the non-smooth contact dynamics approach for rigid and deformable solids, proposed by Frémond and his collaborators, the value of pressure and contact forces at collisions points, generated through congestion or panic situation are estimated. Firstly, we propose a second-order microscopic model, in which the crowd is treated as a system of rigid solids. Contact forces are rigorously defined by taking into account multiple simultaneous contacts and the non-overlapping condition between pedestrians. We show that for a dense crowd, percussions can be seen as contact forces. Secondly, in order to overcome the restrictive hypothesis related to the geometric form adapted to model the pedestrian, a continuous equivalent approach is proposed where the crowd is modeled as a deformable solid, the pressure is then defined by the divergence of the stress tensor and calculated according to volume and surface constraints. This approach makes it possible to retain an admissible right-velocity, including both the non-local interactions between non-neighbor pedestrians and the choice of displacement strategy of each pedestrian. Finally, the comparison between the two proposed approaches and some other existing approaches are presented on several illustrative examples to estimate the contact forces between pedestrians.     

The three-factor model and artificial neural networks: predicting stock price movement in China

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China’s stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is significant, given the volatility of the Chinese stock markets and the added risk that arises from the Chinese legal and regulatory environment. In this paper we address this research gap by examining the predictive ability of several well-established forecasting models, including dynamic versions of a single-factor CAPM-based model and Fama and French’s three-factor model. In addition, we compare the forecasting ability of each of these models with that of an artificial neural network (ANN) model that contains the same predictor variables but relaxes the assumption of model linearity. Surprisingly, we find no statistical differences in the forecasting accuracy of the CAPM and three-factor model, a result that may reflect the emerging nature of the Chinese stock markets. We also find that each ANN model outperforms the corresponding linear model, indicating that neural networks may be a useful tool for stock price prediction in emerging markets.     

Non-local first-order modelling of crowd dynamics: A multidimensional framework with applications

In this work a physical modelling framework is presented, describing the intelligent, non-local, and anisotropic behaviour of pedestrians. Its phenomenological basics and constitutive elements are detailed, and a qualitative analysis is provided. Within this common framework, two first-order mathematical models, along with related numerical solution techniques, are derived. The models are oriented to specific real world applications: a one-dimensional model of crowd–structure interaction in footbridges and a two-dimensional model of pedestrian flow in an underground station with several obstacles and exits. The noticeable heterogeneity of the applications demonstrates the significance of the physical framework and its versatility in addressing different engineering problems. The results of the simulations point out the key role played by the physiological and psychological features of human perception on the overall crowd dynamics.     

Modelling pedestrian dynamics into a metro station by thermostatted kinetic theory methods

This paper deals with the modelling of pedestrian dynamics at the entry of a metro station by means of the thermostatted kinetic theory framework. Specifically, the model depicts the time evolution of the pedestrian dynamics at the turnstiles under no panic conditions. The modelling of the microscopic interactions is based on the stochastic game theory and reflects the decision dynamics of the turnstiles pursued by pedestrians. A qualitative analysis is addressed to the equilibrium solutions by means of the classical stability theory of perturbations. Numerical simulations aim at showing the emerging behaviours captured by the model. In particular the model validation is obtained by performing a sensitivity analysis on the parameters and on the initial conditions. Further refinements and research perspective, including the modelling under panic conditions, are discussed in the last section of the paper.     

THE ONE-DIMENSIONAL HUGHES MODEL FOR PEDESTRIAN FLOW: RIEMANN-TYPE SOLUTIONS

This paper deals with a coupled system consisting of a scalar conservation law and an eikonal equation,called the Hughes model.Introduced in [24],this model attempts to describe the motion of pedestria...     

Crossings as a side effect of dependency lengths

The syntactic structure of sentences exhibits a striking regularity: dependencies tend to not cross when drawn above the sentence. We investigate two competing explanations. The traditional hypothesis is that this trend arises from an independent principle of syntax that reduces crossings practically to zero. An alternative to this view is the hypothesis that crossings are a side effect of dependency lengths, that is, sentences with shorter dependency lengths should tend to have fewer crossings. We are able to reject the traditional view in the majority of languages considered. The alternative hypothesis can lead to a more parsimonious theory of language. © 2016 Wiley Periodicals, Inc. Complexity 21: 320–328, 2016     

A Lane-Based Optimization Method for Minimizing Delay at Isolated Signal-Controlled Junctions

This paper presents a lane-based optimization method for minimizing delay at isolated signal-controlled junctions. The method integrates the design of lane markings and signal settings, and considers both traffic and pedestrian movements in a unified framework. While the capacity maximization and cycle length minimization problems are formulated as Binary-Mix-Integer-Linear-Programs (BMILPs) that are solvable by standard branch-and-bound routines, the problem of delay minimization is formulated as a Binary-Mix-Integer-Non-Linear Program (BMINLP). A cutting plane algorithm and a heuristic line search algorithm are proposed to solve this difficult BMINLP problem. The integer variables include the permitted movements on traffic lanes and successor functions to govern the order of signal displays, whereas the continuous variables include the assigned lane flows, common flow multiplier, cycle length, and starts and durations of green for traffic movements, lanes and pedestrian crossings. A set of constraints is set up to ensure the feasibility and safety of the resultant optimized lane markings and signal settings. A numerical example is given to demonstrate the effectiveness of the proposed methodology. The heuristic line search algorithm is more cost-effective in terms of both optimality of solution and computing time requirement. This revised version was published online in July 2006 with corrections to the Cover Date.     

Macroscopic modeling and simulations of room evacuation

We analyze numerically two macroscopic models of crowd dynamics: the classical Hughes model and the second order model being an extension to pedestrian motion of the Payne–Whitham vehicular traffic model. The desired direction of motion is determined by solving an eikonal equation with density dependent running cost, which results in minimization of the travel time and avoidance of congested areas. We apply a mixed finite volume-finite element method to solve the problems and present error analysis for the eikonal solver, gradient computation and the second order model yielding a first order convergence. We show that Hughes’ model is incapable of reproducing complex crowd dynamics such as stop-and-go waves and clogging at bottlenecks. Finally, using the second order model, we study numerically the evacuation of pedestrians from a room through a narrow exit.     

Analysis of natural convection from a column of cold horizontal cylinders using Artificial Neural Network

Artificial Neural Networks (ANNs) offer an alternative way to tackle complex problems. They can learn from the examples and once trained can perform predictions and generalizations at high speed. They are particularly useful in behavior or system identification. According to the above advantages of ANN in the present paper ANN is used to predict natural convection heat transfer and fluid flow from a column of cold horizontal circular cylinders having uniform surface temperature. Governing equations are solved in a few specified cases by finite volume method to generate the database for training the ANN in the range of Rayleigh numbers of 10⁵–10⁸ and a range of cylinder spacing of 0.5, 1.0, and 1.5 diameters, thereafter a Multi-Layer Perceptron (MLP) network is used to capture the behavior of flow and temperature fields and then generalized this behavior to predict the flow and temperature fields for any other Rayleigh numbers. Different training algorithms are used and it is found that the resilient back-propagation algorithm is the best algorithm regarding the faster training procedure. To validate the accuracy of the trained network, comparison is performed among the ANN and available CFD results. It is observed that ANN can be used more efficiently to determine cold plume and thermal field in lesser computational time. Based on the generalized results from the ANN new correlations are developed to estimate natural convection from a column of cold horizontal cylinders with respect to a single horizontal cylinder.     

Four species CA model for facing pedestrian traffic at rush hour

A model for the facing pedestrian traffic on a passage with a partition line at rush hour is developed. The model is described by a bi-directional cellular automaton (CA) model with four species. The CA model is not stochastic but deterministic. If the passage is congested and the local density is superior to the threshold, walkers to the east and to the west try to move separately changing their lane as the traffic rule is imposed on pedestrians at a high density. Walkers move freely ignoring the partition line at a low density. The traffic-rule effect at rush hour is taken into account in addition to the excluded-volume effect and bi-directionality. The pedestrian behavior under the traffic rule is clarified.     

New approach to applying neural network in nonlinear dynamic model

In this work, radial basis function neural network (RBF-NN) is applied to emulate an extended Kalman filter (EKF) in a data assimilation scenario. The dynamical model studied here is based on the one-dimensional shallow water equation DYNAMO-1D. This code is simple when compared with an operational primitive equation models for numerical weather prediction. Although simple, the DYNAMO-1D is rich for representing some atmospheric motions, such as Rossby and gravity waves. It has been shown in the literature that the ability of the EKF to track nonlinear models depends on the frequency and accuracy of the observations and model errors. In some cases, just fourth-order moment EKF works well, but will be unwieldy when applied to high-dimensional state space. Artificial Neural Network (ANN) is an alternative solution for this computational complexity problem, once the ANN is trained offline with a high order Kalman filter, even though this Kalman filter has high computational cost (which is not a problem during ANN training phase). The results achieved in this work encourage us to apply this technique on operational model. However, it is not yet possible to assure convergence in high dimensional problems.     

Modelling of linguistic variables in multicriteria energy policy support

The climate change and the increasing complexity of the energy sector along with the prerequisite for sustainability have broadened the energy policy shaping field by bringing out new challenges. Decision support tools and methods, such as Multicriteria Decision Aid (MCDA), are necessary for energy policy, in the pursuit of appropriate approaches necessary to support the restructuring of the energy sector, concerning patterns of energy extraction, generation, transformation and use, from unsustainable to sustainable forms of development. Papers devoted to the investigation of MCDA models using linguistic variables for energy policy support seem to be not available in the international literature. The scope of this paper is to explore different linguistic representation and computational models in MCDA that are or can be applied to energy policy support and to establish a clear linkage between them. This paper argues that MCDA methodologies with direct computation on linguistic variables can support energy policy frameworks, bridging the gap between energy policy makers thinking, reasoning, representation and computing. Finally, current trends, open questions and prospects in this topic are pointed out.