Technical,allocative and cost efficiencies of Chinese ports

This paper reports on empirical studies of the technical, allocative, and cost efficiencies (CEs) of Chinese ports based on the panel data of 16 listed port corporations from 1998 to 2011 by means of Bayesian Inference and Markov Chain Monte Carlo methods. An error terms approach is used to resolve the Greene Problem in the estimation of allocative efficiency. The results show that the technical efficiencies have tended to decline in most ports. Inputs to R&D and improving management level are insufficient to offset this decline. Seaports have higher CEs than river ports. Ports with higher container cargo proportion have higher CEs. Ports with more than 50% of shares owned by the State have higher CEs.     

Inferring left behind passengers in congested metro systems from automated data

With subway systems around the world experiencing increasing demand, measures such as passengers left behind are becoming increasingly important. This paper proposes a methodology for inferring the probability distribution of the number of times a passenger is left behind at stations in congested metro systems using automated data. Maximum likelihood estimation (MLE) and Bayesian inference methods are used to estimate the left behind probability mass function (LBPMF) for a given station and time period. The model is applied using actual and synthetic data. The results show that the model is able to estimate the probability of being left behind fairly accurately.     

Vehicle purchasing behaviors comparison in two-stage choice perspective before and after eco-car promotion policy in Japan

This paper compares the vehicle purchasing behaviors in Japan between before and after the eco-car (environmental friendly vehicle) promotion policy implemented. Consumer behaviors are modeled as a two-stage decision process: a consideration set formation stage and a choice-making stage. In the first stage, all available vehicle types are included in the choice set, and consumers are assumed to apply a conjunctive screening rule to construct consideration sets. In the second stage, consumers only evaluate the vehicles in the consideration set and choose the one with maximum utility. The applied Hierarchical Bayes model can avoid the issue of an indifferentiable and irregular likelihood surface caused by thresholds and discontinuities, and the data augmentation and Markov-Chain Monte Carlo estimation methods make it possible to estimate two stages simultaneously using only the information about the consumers’ actual choices. The estimations indicate that the change of consumer behavior during the formation of consideration sets after the policy implemented: more people preferred compact and hybrid vehicles because of their better fuel efficiency and more competitive prices under the tax reduction policy. The results show, however, that most of consumers who purchase hybrid vehicles after the policy implemented are only including hybrid vehicles in their consideration sets, and oil price and vehicle price still play important roles in the choice-making stage for these who consider both gasoline and hybrid vehicles.     

无信控人行横道多类型冲突严重程度模型

为减少无信控人行横道处多类型冲突及其带来的交通安全问题，本文采用交通冲突指标和回归分析模型研究交通冲突的严重程度和影响因素。提出考虑驾驶员视野障碍影响的冲突指标(TTZ)，结合后侵入时间(PET)和安全减速度(DST)冲突指标，量化交通冲突的严重程度；通过计算的冲突指标值，利用模糊C-均值聚类方法识别严重冲突和非严重冲突；将严重冲突和非严重冲突作为因变量，建立基于二元Logit模型的多类型交通冲突严重程度预测模型。结果表明，相较于单次冲突，多重威胁冲突的严重程度更高，其中，多重威胁冲突是严重冲突的占比为57.9%，单次冲突是严重冲突的占比为27.7%。相较于行人，非机动车的严重程度更高，其中，非机动车-机动车冲突是严重冲突的占比为45.7%，行人-机动车冲突是严重冲突的占比为35.4%。关于影响因素，机动车数量、过街等待时间、过街速度及侧面车辆合法屈服行为等因素对多重威胁冲突的严重程度具有显著影响；机动车数量、过街等待时间、过街速度及前方车辆屈服行为等因素对单次冲突的严重程度具有显著影响。     

Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice

In this paper, we review both the fundamentals and the expansion of computational Bayesian econometrics and statistics applied to transportation modeling problems in road safety analysis and travel behavior. Whereas for analyzing accident risk in transportation networks there has been a significant increase in the application of hierarchical Bayes methods, in transportation choice modeling, the use of Bayes estimators is rather scarce. We thus provide a general discussion of the benefits of using Bayesian Markov chain Monte Carlo methods to simulate answers to the problems of point and interval estimation and forecasting, including the use of the simulated posterior for building predictive distributions and constructing credible intervals for measures such as the value of time. Although there is the general idea that going Bayesian is just another way of finding an equivalent to frequentist results, in practice Bayes estimators have the potential of outperforming frequentist estimators and, at the same time, may offer more information. Additionally, Bayesian inference is particularly interesting for small samples and weakly identified models.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo

This study considers advanced statistical approaches for sequential data assimilation. These are explored in the context of nowcasting and forecasting using nonlinear differential equation based marine ecosystem models assimilating sparse and noisy non-Gaussian multivariate observations. The statistical framework uses a state space model with the goal of estimating the time evolving probability distribution of the ecosystem state. Assimilation of observations relies on stochastic dynamic prediction and Bayesian principles. In this study, a new sequential data assimilation approach is introduced based on Markov Chain Monte Carlo (MCMC). The ecosystem state is represented by an ensemble, or sample, from which distributional properties, or summary statistical measures, can be derived. The Metropolis-Hastings based MCMC approach is compared and contrasted with two other sequential data assimilation approaches: sequential importance resampling, and the (approximate) ensemble Kalman filter (including computational comparisons). A simple illustrative application is provided based on a 0-D nonlinear plankton ecosystem model with multivariate non-Gaussian observations of the ecosystem state from a coastal ocean observatory. The MCMC approach is shown to be straightforward to implement and to effectively characterize the non-Gaussian ecosystem state in both nowcast and forecast experiments. Results are reported which illustrate how non-Gaussian information originates, and how it can be used to characterize ecosystem properties.     

非稀疏欠定盲分离及其在语音分离中的应用

本文提出一种基于马尔科夫链蒙特卡洛方法(MCMC)的贝叶斯非稀疏盲源分离算法。用广义高斯分布(GGD)来拟合源信号的分布,通过MCMC抽样得到GGD参数和隐变量的估计,并由此得到源信号的最小均方误差估计(MMSE),解决了GGD参数估计容易陷入局部极值点、鲁棒性差的问题。根据语音信号的局部平稳性,提出基于非稀疏度评判准则的盲分离算法,用MCMC方法分离非稀疏区的语音信号,进一步提高了语音信号分离精度。仿真实验证明,本文方法改善了非稀疏信号和语音信号的分离效果,而且具有更好的鲁棒性。