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Jinxian Weng - One of the best experts on this subject based on the ideXlab platform.
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an improved cellular automata model for heterogeneous work zone traffic
Transportation Research Part C-emerging Technologies, 2011Co-Authors: Qiang Meng, Jinxian WengAbstract:Abstract This paper aims to develop an improved cellular automata (ICA) model for simulating heterogeneous traffic in work zone. The proposed ICA model includes the forwarding rules to update longitudinal speeds and positions of work zone vehicles. The randomization Probability Parameter used by the ICA is formulated as a function of the activity length, the transition length and the volumes of different types of vehicles traveling across work zone. Compared to the existing cellular automata models, the ICA model possesses a novel and realistic lateral speed and position updating rule so that the simulation of vehicle’s lateral movement in work zone is close to the reality. The ICA model is calibrated and validated microscopically and macroscopically by using the real work zone data. Comparisons of field data and ICA for trajectories, speed and speed–flow relationship in work zone show very close agreement. Finally, the proposed ICA model is applied to estimate traffic delay occurred in work zone.
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cellular automata model for work zone traffic
Transportation Research Record, 2010Co-Authors: Qiang Meng, Jinxian WengAbstract:This paper proposes a cellular automata (CA) model incorporating work zone configuration to model work zone traffic. The randomization Probability Parameter of the proposed CA model is able to characterize driver acceleration-deceleration behavior. The randomization Probability should be a function of traffic flow and work zone configuration that comprises the activity length and transition length; however, past studies have assigned a hypothetical constant value for the randomization Probability. This paper calibrates the randomization Probability from field data, which is determined by minimizing the square error between simulated travel time and observed travel time by using a trial-and-error method. A polynomial regression method is employed to formulate the randomization Probability functions in and outside the work zone. A case study was performed to test the proposed CA model dependent on work zone configuration. Comparison of field data and the proposed CA model for travel time and traffic delay s...
Qiang Meng - One of the best experts on this subject based on the ideXlab platform.
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an improved cellular automata model for heterogeneous work zone traffic
Transportation Research Part C-emerging Technologies, 2011Co-Authors: Qiang Meng, Jinxian WengAbstract:Abstract This paper aims to develop an improved cellular automata (ICA) model for simulating heterogeneous traffic in work zone. The proposed ICA model includes the forwarding rules to update longitudinal speeds and positions of work zone vehicles. The randomization Probability Parameter used by the ICA is formulated as a function of the activity length, the transition length and the volumes of different types of vehicles traveling across work zone. Compared to the existing cellular automata models, the ICA model possesses a novel and realistic lateral speed and position updating rule so that the simulation of vehicle’s lateral movement in work zone is close to the reality. The ICA model is calibrated and validated microscopically and macroscopically by using the real work zone data. Comparisons of field data and ICA for trajectories, speed and speed–flow relationship in work zone show very close agreement. Finally, the proposed ICA model is applied to estimate traffic delay occurred in work zone.
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cellular automata model for work zone traffic
Transportation Research Record, 2010Co-Authors: Qiang Meng, Jinxian WengAbstract:This paper proposes a cellular automata (CA) model incorporating work zone configuration to model work zone traffic. The randomization Probability Parameter of the proposed CA model is able to characterize driver acceleration-deceleration behavior. The randomization Probability should be a function of traffic flow and work zone configuration that comprises the activity length and transition length; however, past studies have assigned a hypothetical constant value for the randomization Probability. This paper calibrates the randomization Probability from field data, which is determined by minimizing the square error between simulated travel time and observed travel time by using a trial-and-error method. A polynomial regression method is employed to formulate the randomization Probability functions in and outside the work zone. A case study was performed to test the proposed CA model dependent on work zone configuration. Comparison of field data and the proposed CA model for travel time and traffic delay s...
Sergio R Souza - One of the best experts on this subject based on the ideXlab platform.
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quasispecies dynamics on a network of interacting genotypes and idiotypes formulation of the model
Journal of Statistical Mechanics: Theory and Experiment, 2015Co-Authors: Valmir C Barbosa, R Donangelo, Sergio R SouzaAbstract:A quasispecies is the stationary state of a set of interrelated genotypes that evolve according to the usual principles of selection and mutation. Quasispecies studies have for the most part concentrated on the possibility of errors during genotype replication and their role in promoting either the survival or the demise of the quasispecies. In a previous work, we introduced a network model of quasispecies dynamics, based on a single Probability Parameter (p) and capable of addressing several plausibility issues of previous models. Here we extend that model by pairing its network with another one aimed at modeling the dynamics of the immune system when confronted with the quasispecies. The new network is based on the idiotypic-network model of immunity and, together with the previous one, constitutes a network model of interacting genotypes and idiotypes. The resulting model requires further Parameters and as a consequence leads to a vast phase space. We have focused on a particular niche in which it is possible to observe the trade-offs involved in the quasispecies' survival or destruction. Within this niche, we give simulation results that highlight some key preconditions for quasispecies survival. These include a minimum initial abundance of genotypes relative to that of the idiotypes and a minimum value of p. The latter, in particular, is to be contrasted with the stand-alone quasispecies network of our previous work, in which arbitrarily low values of p constitute a guarantee of quasispecies survival.
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quasispecies dynamics on a network of interacting genotypes and idiotypes formulation of the model
arXiv: Populations and Evolution, 2013Co-Authors: Valmir C Barbosa, R Donangelo, Sergio R SouzaAbstract:A quasispecies is the stationary state of a set of interrelated genotypes that evolve according to the usual principles of selection and mutation. Quasispecies studies have invariably concentrated on the possibility of errors during genotype replication and their role in promoting either the survival or the demise of the quasispecies. In a previous work [V. C. Barbosa, R. Donangelo, and S. R. Souza, J. Theor. Biol. 312, 114 (2012)], we introduced a network model of quasispecies dynamics, based on a single Probability Parameter ($p$) and capable of addressing several plausibility issues of previous models. Here we extend that model by pairing its network with another one aimed at modeling the dynamics of the immune system when confronted with the quasispecies. The new network is based on the idiotypic-network model of immunity and, together with the previous one, constitutes a network model of interacting genotypes and idiotypes. The resulting model requires further Parameters and as a consequence leads to a vast phase space. We have focused on a particular niche in which it is possible to observe the trade-offs involved in the quasispecies' survival or destruction. Within this niche, we give simulation results that highlight some key preconditions for quasispecies survival. These include a minimum initial abundance of genotypes relative to that of the idiotypes and a minimum value of $p$. The latter, in particular, is to be contrasted with the stand-alone quasispecies network of our previous work, in which arbitrarily low values of $p$ constitute a guarantee of quasispecies survival.
G I Shamir - One of the best experts on this subject based on the ideXlab platform.
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universal lossless compression with unknown alphabets the average case
IEEE Transactions on Information Theory, 2006Co-Authors: G I ShamirAbstract:Universal compression of patterns of sequences generated by independent and identically distributed (i.i.d.) sources with unknown, possibly large, alphabets is investigated. A pattern is a sequence of indices that contains all consecutive indices in increasing order of first occurrence. If the alphabet of a source that generated a sequence is unknown, the inevitable cost of coding the unknown alphabet symbols can be exploited to create the pattern of the sequence. This pattern can in turn be compressed by itself. It is shown that if the alphabet size k is essentially small, then the average minimax and maximin redundancies as well as the redundancy of every code for almost every source, when compressing a pattern, consist of at least 0.5log(n/k3) bits per each unknown Probability Parameter, and if all alphabet letters are likely to occur, there exist codes whose redundancy is at most 0.5log(n/k2) bits per each unknown Probability Parameter, where n is the length of the data sequences. Otherwise, if the alphabet is large, these redundancies are essentially at least Theta(n-2/3 ) bits per symbol, and there exist codes that achieve redundancy of O(n-1/2) bits per symbol. Two suboptimal low-complexity sequential algorithms for compression of patterns are presented and their description lengths analyzed, also pointing out that the pattern average universal description length can decrease below the underlying i.i.d. entropy for large enough alphabets
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universal lossless compression with unknown alphabets the average case
arXiv: Information Theory, 2006Co-Authors: G I ShamirAbstract:Universal compression of patterns of sequences generated by independently identically distributed (i.i.d.) sources with unknown, possibly large, alphabets is investigated. A pattern is a sequence of indices that contains all consecutive indices in increasing order of first occurrence. If the alphabet of a source that generated a sequence is unknown, the inevitable cost of coding the unknown alphabet symbols can be exploited to create the pattern of the sequence. This pattern can in turn be compressed by itself. It is shown that if the alphabet size $k$ is essentially small, then the average minimax and maximin redundancies as well as the redundancy of every code for almost every source, when compressing a pattern, consist of at least 0.5 log(n/k^3) bits per each unknown Probability Parameter, and if all alphabet letters are likely to occur, there exist codes whose redundancy is at most 0.5 log(n/k^2) bits per each unknown Probability Parameter, where n is the length of the data sequences. Otherwise, if the alphabet is large, these redundancies are essentially at least O(n^{-2/3}) bits per symbol, and there exist codes that achieve redundancy of essentially O(n^{-1/2}) bits per symbol. Two sub-optimal low-complexity sequential algorithms for compression of patterns are presented and their description lengths analyzed, also pointing out that the pattern average universal description length can decrease below the underlying i.i.d.\ entropy for large enough alphabets.
Jose Miguel Ponciano - One of the best experts on this subject based on the ideXlab platform.
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an efficient extension of n mixture models for multi species abundance estimation
Methods in Ecology and Evolution, 2017Co-Authors: J P Gomez, Scott K Robinson, Jason K Blackburn, Jose Miguel PoncianoAbstract:1.In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species’ estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species. 2.We evaluate the proposed methods using an assumption of 50 m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities are drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection Probability Parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood. 3.We compared our multi-species approach with previously proposed multispecies N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100 hectares. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation. 4.We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities. This article is protected by copyright. All rights reserved.
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an efficient extension of n mixture models for multi species abundance estimation
bioRxiv, 2016Co-Authors: J P Gomez, Scott K Robinson, Jason K Blackburn, Jose Miguel PoncianoAbstract:1. In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical size for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species' estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species. 2. We evaluate the proposed methods using an assumption of 50-m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities of a set of species are all drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection Probability Parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood. 3. We compared our multi-species approach with previously proposed multi-species N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100-ha. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation. 4. We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities.