Analysis of interrelationship between pedestrian flow parameters using artificial neural network

Pedestrian flow parameters are analysed in this study considering linear and non-linear relationships between stream flow parameters using conventional and soft computing approach. Speed-density relationship serves as a fundamental relationship, Single-regime con- cepts and deterministic models like Greenshield and Underwood were applied in the study to describe bidirec- tional flow characteristics on sidewalks and carriageways around transport terminals in India. Artificial Neural Net- work （ANN） approach is also used for traffic flow mod- elling to build a relationship between different pedestrian flow parameters. A non-linear model based on ANN is suggested and compared with the other deterministic models. Out of the aforesaid models, ANN model demonstrated good results based on accuracy measure- ment. Also these ANN models have an advantage in terms of their self-processing and intelligent behaviour. Flow parameters are estimated by ANN model using MFD （Macroscopic Fundamental Diagram）. Estimated mean absolute error （MAE） and root mean square error （RMSE）values for the best fitted ANN model are 3.83 and 4.73 m/ min, respectively, less than those for the other models for sidewalk movement. Further estimated MAE and RMSE values of ANN model for carriageway movement are 4.02 and 4.98 m/min, respectively, which are comparatively less than those of the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters. Also when using linear regression model between observed and estimated values for speed and flow parameters, performance of ANN model gives better fitness to predict data as compared to deterministic model. R value for speed data prediction is 0.756 and for flow data pre- diction is 0.997 using ANN model at sidewalk movement around transport terminal.

A macroscopic traffic model based on weather conditions

A traffic model based on the road surface conditions during adverse weather is presented. The surface of a road is affected by snow, compacted snow, and ice, which affects the traffic behavior. In this paper, a new macroscopic traffic flow model based on the transition velocity distribution is proposed which characterizes traffic alignment under adverse weather conditions. Two examples are considered to illustrate the effect of the transition velocity behavior on traffic velocity and density. Simulation results are presented which show that this model provides a more accurate characterization of traffic flow behavior than the well known Payne-Whitham model. The proposed model can be used to reduce accidents and improve road safety during adverse weather conditions.

Wave propagation of the traffic flow dynamic model based on wavefront expansion

This paper discusses propagation of perturbations along traffic flow modeled by a modified second-order macroscopic model through the wavefront expansion technique. The coefficients in this expansion satisfy a sequence of transport equations that can be solved analytically. One of these analytic solutions yields information about wavefront shock. Numerical simulations based on a Padé approximation of this expansion were done at the end of this paper and results showed that propagation of perturbations at traffic flow speed conforms to the theoretical analysis results.

Mechanical stirring for highly efficient gas injection refining

In gas injection refining processes,wide dispersion of small bubbles in the bath is indispensable for high refining efficiency.Eccentric mechanical stirring with unidirectional impeller rotation was tested using a water model for pursuing better bubble disintegration and dispersion.Effects of various factors on bubble disintegration and dispersion were investigated.These factors were stirring mode,eccentricity and rotation speed,nozzle structure,nozzle immersion depth,and gas flow rate.Gas injection from a nozzle at the end of the impeller shaft and from an immersed lance was studied.Under eccentric stirring,a vortex was formed away from the shaft.Small bubbles were produced in the strong turbulence or high shear stress field near the rotating impeller and moved in the direction to the vortex keeping up with the macroscopic flow induced by the mechanical stirring.Thus small bubbles could disperse widely in the bath under eccentric stirring with unidirectional rotation.

Iterative learning control approach for ramp metering

An iterative learning control scheme is developed to the traffic densitycontrol in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligentcontrol scheme guarantees the asymptotic convergence of the traffic density to the desired one. Thecontrol scheme is applied to a freeway model, and simulation results confirm the efficacy of theproposed approach.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

Continuum modeling of freeway traffic flows:State-of-the-art,challenges and future directions in the era of connected and automated vehicles

Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives(e.g.,traffic efficiency and safety),there are uncertainties about the flow dynamics of CAVs in real-world traffic.The uncertainties are especially amplified for mixed traffic flows,consisting of CAVs and human-driven vehicles,where the implications can be significant from the continuum-modeling perspective,which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations.This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs.We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework.Wherever applicable,we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature.The paper then discusses some major problems inherent to continuum modeling of real-world(mixed)CAV traffic flows modeling by distinguishing between two major research directions:(a)modeling for explaining purposes,where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest,and(b)modeling for practical purposes,in which the focus is on the reliable predictions for operation and control.The paper proposes some potential solutions in each research direction and recommends some future research topics.

A Piecewise Switched Linear Approach for Traffic Flow Modeling

Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffic flow as a hybrid dynamic system and proposes a piecewise switched linear traffic model. The latter allows an accurate modeling of the traffic flow in a given section by considering its geometry. On the other hand, the piecewise switched linear traffic model handles more than one congestion wave and has the advantage to be modular. The measurements at upstream and downstream boundaries are also used in this model in order to decouple the traffic flow dynamics of successive road portions. Finally, real magnetic sensor data, provided by the performance measurement system on a portion of the Californian SR60-E highway are used to validate the proposed model.

Incident indicators for freeway traffic flow models

Developed in this paper is a traffic flow model parametrised to describe abnormal traffic behaviour.In large traffic networks,the immediate detection and categorisation of traffic incidents/accidents is of capital importance to avoid breakdowns,further accidents.First,this claims for traffic flow models capable to capture abnormal traffic condition like accidents.Second,by means of proper real-time estimation technique,observing accident related parameters,one may even categorize the severity of accidents.Hence,in this paper,we suggest to modify the nominal Aw-Rascle(AR)traffic model by a proper incident related parametrisation.The proposed Incident Traffic Flow(ITF)model is defined by introducing the incident parameters modifying the anticipation and the dynamic speed relaxation terms in the speed equation of the AR model.These modifications are proven to have physical meaning.Furthermore,the characteristic properties of the ITF model is discussed in the paper.A multi stage numerical scheme is suggested to discretise in space and time the resulting non-homogeneous system of PDEs.The resulting systems of ODE is then combined with receding horizon estimation methods to reconstruct the incident parameters.Finally,the viability of the suggested incident parametrisation is validated in a simulation environment.