基于时间序列分析的预测控制算法

在分析Turbo码的译码结构以及外部信息传递特性的基础上,从时间序列的角度分析分量译码器外部信息的传递规律,基于预测算法对Turbo码的迭代译码结构进行了改进,提出了单次预测和复次预测的改进算法。建立分量译码器的n阶多项式线性模型,将前n次迭代信息保存至线性模型中,采用多项式曲线拟合或指数拟合算法预测分量译码器第n+1次迭代的外部信息值。利用预测的外部信息值代替分量译码器第n+1次迭代的译码过程。在保证外部信息准确预测、误码率性能相近的前提下,减少了迭代次数和迭代时延,简化了译码结构。     

频率跟踪移相PWM控制下串联谐振式逆变器运行模式分析

结合DBD型臭氧发生器负载的特性,详细分析逆变器在各种运行模式下的工作过程。定性地分析了逆变器在各种工作模式中功率器件所受的电磁应力与开关损耗,由此得出适合于DBD型臭氧发生器负载和IGBT功率开关的逆变器运行模式,为确定DBD型臭氧发生器电源的运行方式提供了理论依据。     

Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach

After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity.     

Coherent approach for modeling and nowcasting hourly near-road Black Carbon concentrations in Seattle,Washington

With a growing awareness of the importance of near-road air pollution and an increasing population of near-road pedestrians, it is imperative to “nowcast” near-road air quality conditions to the general public. This necessitates the building hourly predictive models that are both accurate and easy to use. This study demonstrates an approach to model the hourly near-road Black Carbon (BC) concentrations given on-road traffic information and current meteorological conditions using datasets from two urban sites in Seattle, Washington. The optimal set of prediction variables is determined with a Bayesian Model Averaging (BMA) method and three different model structures are further developed and compared by goodness-of-fit. An innovative approach is proposed to translate wind direction from numerical values to categorical variables with statistical significance. By modeling the autocorrelation within the BC time series using an AR(1) component, the model achieves a satisfactory prediction accuracy. The conditional heteroscedasticity and heavy-tailed distribution of the model residuals are successfully identified and modeled by the General Auto Regressive Conditional Heteroscedasticity (GARCH) model, which provides valuable insights to the interpretation of prediction results. The methodological procedure demonstrated in selecting and fine-tuning the model is computationally efficient and valuable for further implementation onto online platforms for near-road BC nowcasting. A comparison between the two sites also reveals the effectiveness of local freight regulation for mitigating the environmental impacts from a heavy truck fleet.     

Estimation of bus dwell time using univariate time series models

A significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non‐CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non‐CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.     

How to mix per-flight and per-passenger based airport charges

This paper investigates the questions of why carriers advocate for higher per-passenger airport charges and lower per-flight charges, and whether and when this proposal is welfare-enhancing. Specifically, the paper compares the optimal mix of per-flight and per-passenger based airport charges from both a monopoly carriers’ and the social viewpoints conditional on airport cost recovery. It focuses on the trade-off between price and frequency (i.e., schedule delays) when time valuations are uniform, or differ, between business and leisure passengers. We identify an easy test for the evaluation of the mix of per-passenger and per-flight based airport charges by policy makers, which is simply to check whether the carrier’s preferred per-flight charge is zero. Our analysis suggests that there is no need for immediate regulatory corrections of the current trend towards the strong use of per-passenger based airport charges.     

Predictive usage mining for life cycle assessment

The usage modeling in life cycle assessment (LCA) is rarely discussed despite the magnitude of environmental impact from the usage stage. In this paper, the usage modeling technique, predictive usage mining for life cycle assessment (PUMLCA) algorithm, is proposed as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, and formulate predictive LCA equations for current and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations.     

The design of electric vehicle charging network

The promotion of Electric Vehicles (EVs) has become a key measure of the governments in their attempt to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To determine the locations of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain coverage goal. Instead of using discrete points, we use geometric objects to represent charging demands. Importantly, to resolve the partial coverage problem (PCP) for networks, we extend the polygon overlay method to split the demands on the road network. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that the proposed models are practical and effective in determining the locations of charging stations. Moreover, they can eliminate PCP and provide much more accurate results than the complementary partial coverage method (CP).     

How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?

The entry of various plug-in electric vehicles (PEVs) into the passenger vehicle sector provides novel opportunities to learn about the potential for future PEV markets. However, early PEV buyers (or “Pioneers”) can substantially differ from present conventional vehicle owners that have interest in purchasing PEVs in the future (or the “Potential Early Mainstream buyers”). To compare the characteristics, preferences, and motivations of Pioneers and Potential Early Mainstream buyers, we draw data from the Canadian Plug-in Electric Vehicle Study, a three-part mixed-mode survey with samples of PEV owners (n = 94) and conventional new vehicle buyers (n = 1754). We identify several significant differences in household characteristics, including income, education, and recharge access. In terms of preferences, Pioneers express extremely high valuation of PEVs and prefer pure battery electric vehicle (BEV) designs over plug-in hybrid electric (PHEV) designs. In contrast, Potential Early Mainstream respondents prefer PHEVs. Both Pioneer and Potential Early Mainstream respondents are similarly cautious about controlled charging programs, but Pioneers place five times as much value on using electricity generated from renewable sources than the Potential Early Mainstream. Pioneers also tend to have different motivations, including significantly higher levels of environmental concern, and higher engagement in environment- and technology-oriented lifestyles. Policymakers, automakers, and electric utilities that anticipate a transition to electric mobility ought to consider how potential future PEV buyers may differ in their vehicle preferences, usage and motivations relative to current PEV owners.     

A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement

Road segmentation is one of the most important steps in identification of high accident-proneness segments of a road. Based on the ratio of the Potential to Safety Improvement (PSI) along the road, the objective of the paper is to propose a novel dynamic road segmentation model. According to the fundamental model assumption, the determined segments must have the same pattern of PSI. Experimental results obtained from implementation of the proposed method took four Performance Measures (PMs) into consideration; namely, Crash Frequency, Crash Rate, Equivalent Property Damage Only, and Expected Average Crash Frequency with Empirical Bayes adjustment into the accident data obtained from Highway 37 located between two cities in Iran. Results indicated the low sensitivity of the method to PMs. In comparison with the real high accident-proneness segments, identified High Crash Road Segments (HCRS) obtained from the model, demonstrated the potential of the method to recognize the position and length of high accident-proneness segments accurately. Based on the road repair and maintenance costs limitation index for safety improvement, in an attempt to compare the proposed method of road segmentation with conventional ones, results demonstrated the efficient performance of the proposed method. So as to identify 20 percent HCRS located on a read, the proposed method showed an improvement of 38 and 57 percent in comparison with the best and worst outcomes derived from conventional road segmentation methods.