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Physica

North-Holland

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    Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights

    Xing, JipingWu, WeiCheng, QixiuLiu, Ronghui...
    25页
    查看更多>>摘要：Accurate traffic state (i.e., flow, speed, density, etc.) on an urban road network is important information for urban traffic control and management strategies. However, due to the limitation of detector installation cost, it is difficult to obtain accurate traffic states through detectors in the whole urban road network with limited detector equipment. In this paper, we review the studies that focus on the missing traffic state estimation problem, especially for the traffic state estimation on the segments without detectors. We provide a way to summarize for readers who have an interest in the different modelling and application of missing traffic state estimation. We first divide the existing studies into three categories: estimation under different missing scenarios, estimation with multi-source data, estimation by fusing different detector types. Then, we summary some existing challenges by the different missing scenarios, data applications, and methodologies. Finally, this work also discusses some future research directions.
      原文链接:
    • NSTL
    • Elsevier

    An algorithm for network community structure determination by surprise

    Gamermann, DanielPellizzaro, Jose Antonio
    22页
    查看更多>>摘要：A community in a network is an intuitive idea for which there is no consensus on its objective mathematical definition. Therefore, different algorithms and metrics have been suggested in order to identify these structures in graphs. In this work, we propose a new benchmark and a new approach based on a metric known as surprise. We compare our approach to several others in the literature, in different kinds of benchmarks, including our own (that tackles separately the different ways in which one may degrade a network's community structure) and discuss the different biases we identify for each algorithm and benchmark. In particular, we identify a possible flaw in the way the LFR benchmark constructs its communities and that algorithms suffering from bad resolution are biased towards identifying communities with similar sizes. We show that the surprise based approaches perform better than the modularity based ones, specially for heterogeneous graphs (with very different community sizes coexisting). (C) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    Mathematical modeling of probability distribution of money by means of potential formation

    Aktaev, Nurken E.Bannova, K. A.
    7页
    查看更多>>摘要：The work is devoted to the development of a mathematical model for studying the probability distribution of money of an agent. The model is based on the Fokker-Planck equation. To calculate the diffusion term, we used the quadratic dependence of the money balance of an agent in the Yakovenko model. To calculate the drift term, we propose to use a function (potential) that takes into account the income (i.e. the influx of money) and expenditures (i.e. the outflow of money) for an agent. For an analytical description of the income of an agent, a linear dependence on money balance was used. Expenditures were characterized by the demand for essential goods, long-term and luxury goods. Tornquist functions were used to describe the demand functions. The construction of the potential made it possible to identify atypical conditions for the formation of the probability distribution of money. (C) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    A two-stage causality method for time series prediction based on feature selection and momentary conditional independence

    Ma, DeweiRen, WeijieHan, Min
    17页
    查看更多>>摘要：Since the actual time series contain a lot of variables and the relations among them are complex. Hence, it is difficult to accurately judge cause and effect by conventional causality methods. Aiming at the problem, a two-stage causal network learning method, the feature selection stage and the conditional independence test stage, is proposed to reveal the causalities between variables and construct an accurate prediction model. In the first stage, there are two steps to perform. Firstly, a feature selection method is utilized to reduce data dimensionality by removing irrelevant and redundant variables. These variables are not only increase computational complexity, but also cover up part of the effective information, which may result in insufficient accuracy of the constructed model. Then, a global redundancy minimization (GRM) scheme is used to further refine the result of the previous step from a global perspective. In the second stage, a momentary conditional independence (MCI) test is performed to test the causalities between variables, which can accurately detect the causal network structure. Finally, an accuracy causal network and subsequent prediction model can be established based on the output of the two-stage model. In this simulations, two benchmark datasets, a coupled Lorenz system and two actual datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method can effectively analyze the causalities between variables and construct an accuracy prediction model.(c) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods

    Yang, YangHe, KunWang, Yun-pengYuan, Zhen-zhou...
    26页
    查看更多>>摘要：Freeway traffic safety should be given great attention due to the frequent and serious consequences that arise from freeway traffic crashes. With the possibility of obtaining high resolution traffic big data, traditional freeway safety promotion methods are gradually replaced by the emerging technology of active safety control based on the real-time traffic data. However, there is still a lack of cross-area pertinence towards dynamic traffic safety currently. To overcome the defects of existing dynamic traffic crash precursors identification studies and provide precise theoretical basis for freeway dynamic safety control, this research took cross-area freeways as the research object. Firstly, based on the LOS (level of service) A-F theory and the consideration of area types, six units to be evaluated were generated (two dimensions totally: saturated/unsaturated flow, urban/suburban/mountainous). Then, conditional logistic regression based on Markov chain Monte Carlo method was adopted to quantitatively evaluate the dynamic risk of cross-area freeways. Finally, 20 related traffic flow variables were extracted from five dimensions which can reflect the dynamic traffic flow characteristics, and the machine learning approaches including random forest algorithm and Bayesian logistics regression were applied for analysis and modeling; then, crash precursors were identified for each type of freeway area, and the statistical relationship between traffic flow variables and crash risk was established via the statistical models. The results show the area types and traffic conditions of freeway are significantly correlated with dynamic traffic safety, and the crash risk in the urban area/saturated flow condition is the highest, which is 29.6 times of that in the condition of suburban area/unsaturated flow. When the traffic operates at the freeway of urban/unsaturated flow, the formation of small fleets and the lane changing behavior play a dominant role influencing crash risk; when operating at urban/saturated flow, frequent acceleration or deceleration in the fleet and frequent lane changes during the formation of the fleet have great impact towards crash risk. When traffic operates at suburban/unsaturated flow, the main crash precursors are the sudden change of occupancy and volume in a short period, as well as the formation of a small fleet. When operating at suburban/saturated flow, the crash-prone variables are the sudden increase of speed and occupancy in a short period and the change of volume. When traffic operates at mountainous/unsaturated flow, the changes of speed and occupancy in a short period and the formation of small fleets are the main factors influencing crash. When operating at mountainous/saturated flow, lane changing behavior, the sudden increase of volume, and the speed mutation of some vehicles in saturated flow are the significant precursors. The results indicate every area type of freeway has a different mechanism of traffic crash. Moreover, with the consideration of freeway area types and traffic state differences, the machine learning and statistical com-bination models proposed in this research can identify the relationship and mechanism between dynamic traffic flow characteristics and traffic safety more comprehensively and accurately, and the identification results in this research can refer to the further dynamic crash prediction work for cross-area freeway.(c) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    Influence of individual factors on fundamental diagrams of pedestrians

    Paetzke, SarahBoltes, MaikSeyfried, Armin
    12页
    查看更多>>摘要：In recent years, numerous studies have been published dealing with the effect of individual characteristics of pedestrians on the fundamental diagram. These studies compared cumulative data on individuals in a group homogeneous in terms of one human factor such as age but heterogeneous in terms of other factors for instance gender. In order to examine the effect of all determined as well as undetermined human factors, individual fundamental diagrams are introduced and analyzed using multiple linear regression. A single-file school experiment with students of different age, gender, and height is therefore considered. Single individuals appearing in different runs are analyzed to study the effect of human factors such as height, age and gender and all other unknown individual effects such as motivation or attention to the individual speed. The analysis shows that for students age and height are strongly correlated and, consequently, age can be ignored. Furthermore, the study shows that gender has a weak effect and other nonmeasurable individual characteristics have a stronger effect than height. In a further step, a mixed model is used as well as the multiple linear model. Here, it is shown that the mixed model that considers all other unknown individual effects of each person as a random factor is preferable to the model where the individual speed only depends on the variables of headway, height, and all other unknown individual effects as fixed factors. (C) 2022 The Authors. Published by Elsevier B.V.
      原文链接:
    • NSTL
    • Elsevier

    Method of variable separation for investigating exact solutions and dynamical properties of the time-fractional Fokker-Planck equation

    Rui, WeiguoYang, XinsongChen, Fen
    16页
    查看更多>>摘要：In this paper, the traditional separation method of variables is improved by fixing a part of the variables called the separation method of semi-fixed variables. By using this improved method, the time-fractional Fokker-Planck equation with external force field is studied. Some new exact solutions and dynamical properties of the equation are investigated in various external potential functions such as linear potential, harmonic potential, logarithmic potential, exponential potential, and quartic potential. Some interesting dynamical behaviors and phenomena are discovered. The profiles of several representative exact solutions are illustrated by 3D-graphs and 2D-graphs(C) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    The effect of media on opinion formation

    Lee, WoosubYang, Seong-GyuKim, Beom Jun
    8页
    查看更多>>摘要：Our opinions on a social issue can be affected by others' opinions in social networks and also by various media we are acquainted with. In a modern society, there are many different media we can choose, and we often choose the one that is close to our own political and cultural tastes. We introduce a simple model in which the opinion of an agent is affected not only by other agents in the system, but also by the media. The effect by the media is tuned by a parameter, the media field F in our model, which can either strengthen (for F > 0) or weaken (for F < 0) the opinion of the agent. As F is varied, we find that our model exhibits three different states: neutral state, consensus state, and polarized state. We observe that a discontinuous transition occurs between the neutral and consensus states, and examine how the finiteness of the system size affects the transition between the consensus and polarized states.(c) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    Short term traffic flow prediction of expressway service area based on STL-OMS

    Zhao, JiandongYu, ZhixinYang, XinGao, Ziyou...
    15页
    查看更多>>摘要：To improve the management ability of expressway service area and formulate strategies for traffic flow changes in time, a short-term traffic flow prediction model is pro-posed. Firstly, cleaning the extracted data according to the rules and constructing four kinds of features (temporal, spatial, statistical and external factors). Then, a short-term traffic flow prediction model WADNN (wide attention and deep neural networks) is constructed. In the model, LSTM (long and short-term memory neural network), CNN (convolution neural network) and self-attention mechanism are used to extract different features respectively. In addition, the STL (Seasonal-Trend decomposition procedure based on LOESS) algorithm is used to decompose the traffic flow to fit the trend better. For the three decomposed components, the OMS (optimal model selection) operation is carried out, the prediction of each component is added to obtain the final predicted value, and the model effect is measured according to the RMSE (root mean square error), MAE (mean absolute error), MAPE (Mean Absolute Percentage Error) and R2 coefficient. Finally, taking an expressway service area as an example, the proposed model is compared with some common models. The results show that the prediction effect of WADNN is better and STL-OMS can further improve the accuracy. (C) 2022 Elsevier B.V. All rights reserved.
      原文链接:
    • NSTL
    • Elsevier

    Development of a railway out-of-gauge freight transport routing optimal method

    Zhang, YingguiGuo, JingyiAn, Min
    15页
    查看更多>>摘要：Railway out-of-gauge freight (ROF) is beyond railway gauges in its dimensions, which could be risks leading to serious railway accidents. This article presents a new method-ology to search for a safest and more economic route of ROF by taking safety and cost objectives into consideration. In this method, a mathematical model is proposed based on the transport costs as optimization objectives with the constraints of flow balance and safety gap clearance in which ROF transport routing generation, loading outline and railway gauge double-checking algorithms are established, and then a ROF transport routing model combining transportation costs, loading outlines and railway gauges are developed. The proposed method can be used to determine the safest and more economic ROF route. A case study is used to demonstrate the application of the proposed method. (C) 2022 Elsevier B.V.
      原文链接:
    • NSTL
    • Elsevier