Time‐Varying Lane‐Based Capacity Reversibility for Traffic Management

Abstract: This article presents a new bi‐level formulation for time‐varying lane‐based capacity reversibility problem for traffic management. The problem is formulated as a bi‐level program where the lower level is the cell‐transmission‐based user‐optimal dynamic traffic assignment (UODTA). Due to its Non‐deterministic Polynomial‐time hard (NP‐hard) complexity, the genetic algorithm (GA) with the simulation‐based UODTA is adopted to solve multiorigin multidestination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4 with a jam‐density factor parameter (JDF) employ time‐dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs best. The GA with the appropriate inclusion of problem‐specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.     

Modeling Vehicle Interactions during Lane‐Changing Behavior on Arterial Streets

Abstract: Lane‐changing algorithms have attracted increased attention during recent years in traffic modeling. However, little has been done to address the competition and cooperation of vehicles when changing lanes on urban streets. The main goal of this study is to quantify the vehicle interactions during a lane‐changing maneuver. Video data collected at a busy arterial street in Gainesville, Florida, were used to distinguish between free, forced, and competitive/cooperative lane changes. Models particularly for competitive/cooperative lane changes were developed, depending on whether the following vehicle cooperates with the subject vehicle or not. By referring to the “TCP/IP” protocol in computer network communications, a sequence of “hand‐shaking” negotiations were designed to handle the competition and cooperation among vehicles. The developed model was implemented and validated in the CORSIM microsimulator package, with the simulation capabilities compared against the original lane‐changing model in CORSIM. The results indicate that the new model better replicates the observed traffic under different levels of congestion.     

Passing Rates to Measure Relaxation and Impact of Lane‐Changing in Congestion

Abstract: Passing rate measurements of backward‐moving kinematic waves in congestion are applied to quantify two traffic features; a relaxation phenomenon of vehicle lane‐changing and impact of lane‐changing in traffic streams after the relaxation process is complete. The relaxation phenomenon occurs when either a lane‐changer or its immediate follower accepts a short spacing upon insertion and gradually resumes a larger spacing. A simple existing model describes this process with few observable parameters. In this study, the existing model is reformulated to estimate its parameter using passing rate measurements. Calibration results based on vehicle trajectories from two freeway locations indicate that the revised relaxation model matches the observation well. The results also indicate that the relaxation occurs in about 15 seconds and that the shoulder lane exhibits a longer relaxation duration. The passing rate measurements were also employed to quantify the postrelaxation impact of multiple lane‐changing maneuvers within a platoon of 10 or more vehicles in queued traffic stream. The analysis of the same data sets shows that lane‐changing activities do not induce a long‐term change in traffic states; traffic streams are perturbed temporarily by lane‐changing maneuvers but return to the initial states after relaxations.     

Agglomeration and firm‐level productivity: A Bayesian spatial approach

This paper estimates the impact of industrial agglomeration on firm‐level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model and use a Bayesian instrumental‐variable approach. We find that agglomeration of the same industry (i.e., localization) has a productivity‐boosting effect, but agglomeration of urban population (i.e., urbanization) has no such effect. In addition, the localization effect increases with the educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.     

“唤起”、“响应”与“家园”——重返吉大母校志感

<正>规则的合理使用并无规则可循。——康德没有一种哲学意义上的家园之感是能够自我强加的。——(拟)海德格尔自从1988年那个如哲学"成年礼"般的夏日离开长春,我只在2007年,也就是阔别19年之后的那个夏天,因为"公务"之需回到北国春城,回到我梦中的母校。记得当晚我就撇下同事,独自到了理化楼和鸣放宫"凭吊",并在深夜从东中华路搭出租到当年的斯大林大街和斯大林广场还有长春火车站转悠一大圈.第二天一早我又来到现     

A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

Determining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data‐driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on I‐5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.     

Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging-based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle-bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.     

Self‐calibrating Bayesian real‐time system identification

In this article, a novel Bayesian framework is proposed for real‐time system identification with calibratable model classes. This self‐calibrating scheme adaptively reconfigures the model classes to achieve reliable real‐time estimation for the system state and model parameters. At each time step, the plausibilities of the model classes are computed and they serve as the cue for calibration. Once calibration is triggered, all model classes will be reconfigured. Thereafter, identification will continue to propagate with the calibrated model classes until the next recalibration. Consequently, the model classes will evolve and their deficiencies can be corrected adaptively. This remarkable feature of the proposed framework stimulates the accessibility of reliable real‐time system identification. Examples are presented to demonstrate the efficacy of the proposed approach using noisy response measurement of linear and nonlinear time‐varying dynamical systems under stationary condition.     

城市高架道路下匝道车流插入机制的Markov链模型

通过分析城市高架道路下匝道车流与地面车流相互影响的规律,引入随机动态系统,建立了时间齐次的M arkov链模型,能对各车道的车辆“顺畅驶入”、“以某概率驶入”、“减速停车等待”作实时预报,揭示了地面车流与下匝道车流相互影响的机制。     

A Spatial‐Bayesian Technique for Imputing Pavement Network Repair Data

Abstract: Pavement construction and repair history is necessary for several pavement management functions such as developing pavement condition prediction models and developing maintenance and rehabilitation (M&R) trigger values based on past repair frequencies. It is often difficult to integrate M&R data with condition data since these data are often stored in disparate heterogeneous databases. This article provides a computational technique for estimating construction and M&R history of a pavement network from the spatiotemporal patterns of its condition data. The technique is founded on Bayesian and spatial statistics and searches pavement condition data in groups of adjacent pavement sections for evidence of repair. The developed technique was applied to a pavement network in Texas and has been found to have a 74% precision and a 95% accuracy in estimating repair history data.     

A stochastic combustion model for fire plumes

The paper presents a new model for turbulent mixing and combustion of fuel jets and buoyant plumes. It approximates the lateral distributions of velocity, density, and species concentrations with top-hat profiles. It calculates the downstream evolution of the mixture p.d.f. by use of a coalescence-dispersion concept. The model involves only two empirical turbulence coefficients: an entrainment coefficient and a mixing parameter. The relatively simple mixing model leads to integral solutions for the combustion rate, plume width, and flame radius versus downstream position. The model permits both fuel and oxidant to exist at each height with the combustion rate controlled by internal mixing. At this time the model has not been tested against experiment. It is also possible that detailed numerical evaluations of its predictions will lead to further simplification of approximations needed for engineering applications.     

A stochastic model of an ozonation reactor

Disinfection of some microorganisms is characterized by a lag-phase (a minimum required ozone exposure until disinfection occurs). This phenomenon is easy to model in laboratory batch reactors but not in continuous flow mixed reactors. This paper introduces a stochastic disinfection model where individual microorganisms are followed on their paths through full-scale reactors. Combining exponentially distributed transport processes with delayed exponential disinfection kinetics for large populations of microorganisms (up to 10,000 individuals) yields predictions which can be evaluated statistically. It could be shown that deterministic models work well for systems with good disinfection performance (more than 2 log units reduction of active microorganisms), for reactors with poor performance stochastic models have to be applied. It could be demonstrated for real reactors that Bacillus subtilis spores are poor surrogates for Cryptosporidium parvum oocysts. The differences between the two microorganisms are large for reactors that deviate significantly from plug-flow behaviour.     

A stochastic diagnostic model for subway stations

Performance of subway stations is a critical problem that faces public transit authorities worldwide. Although replacing subway stations is very expensive, the Société de Transport de Montreal (STM) and most transit authorities lack planning strategies because they do not have deterioration models for their infrastructure. The presented research in this paper assists in developing a stochastic Global Station Diagnosis Model (GSDM). The GSDM identifies and evaluates the weights of different functional condition criteria for subway stations. It also utilizes the Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) integrated with the Multi-Attribute Utility Theory (MAUT) and Monte Carlo simulation in order to determine a stochastic Global Diagnosis Index (GDI). Data were collected from experts through questionnaires and interviews. A case study of subway stations from the STM network is selected to implement the designed model. Results show that the GDI for the case study stations ranges from 5.6 to 7.8 with a 95% probability. Performing sensitivity analysis, the ‘Alarm and Security’ criterion is found to be the most effective criterion on the GDI. This research is relevant to industry practitioners and researchers since it provides a stochastic diagnostic tool for subway stations.     

Adaptive Markov chain Monte Carlo algorithms for Bayesian inference: recent advances and comparative study

Abstract

Condition assessments of structures require prediction models such as empirical model and numerical simulation model. Generally, these prediction models have model parameters to be estimated from experimental data. Bayesian inference is the formal statistical framework to estimate the model parameters and their uncertainties. As a result, uncertainties associated with the model and measurement can be accounted for decision making. Markov Chain Monte Carlo (MCMC) algorithms have been widely employed. However, there still remain some implementation issues from the inappropriate selection of the proposal mechanism in Markov chain. Since the posterior density for a given problem is often problem-dependent and unknown, users require a trial-and-error approach to select and tune optimal proposal mechanism. To relieve this difficulty, various adaptive MCMC algorithms have been recently appeared. Users must understand their mechanism and limitations before applying the algorithms to their problems. However, there is no comprehensive work to provide detailed exposition and their performance comparison together. This study aims to bring together different adaptive MCMC algorithms with the goal of providing their mechanisms and evaluating their performances through comparative study. Three algorithms are chosen as the representative proposal mechanism. From comparative studies, the discussions were drawn in terms of performances, simplicity and computational costs for less-experienced users.     

A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment

The investigation described in this article aims at developing a Bayesian‐based approach for probabilistic assessment of rail health condition using acoustic emission monitoring data. It comprises the following three phases: (i) formulation of a frequency‐domain structural health index (SHI), via a linear transformation method, tailored to damage‐sensitive frequency bandwidth; (ii) establishment of data‐driven reference models, using Bayesian regression about the real and imaginary parts of the SHI derived with monitoring data from the intact rail; and (iii) quantitative evaluation of discrimination between the new observations representative of current rail health condition and the baseline model predictions in terms of Bayes factor. If the deviation of the new observations from the predictions is within an acceptable tolerance, no damage is flagged, and the new data are further used to update and refine the reference models. If the observations deviate substantially from the model predictions in a probabilistic sense, damage is signaled, damage severity is quantified, and damage location determined. The proposed approach is examined by using field monitoring data acquired from an instrumented railway turnout, and the coincidence between the assessment results and the actual health conditions demonstrates its effectiveness in damage detection, localization, and quantification.     

“Objective” low informative priors for Bayesian inference from totally censored Gaussian data

Consider structural elements with random strength that after a suitable transformation has normal distribution with unknown mean μ and known or unknown standard deviation σ. By proof testing of n of these structural elements to a given load level it is observed that none of the elements fail. Given solely this test information the problem is that in order to state anything about either the value of μ when σ is known or about the values of μ and σ when both parameters are unknown, it is necessary to introduce some more information in the form of a suitable prior distribution of the parameters, that is, to use a Bayesian procedure with an informative prior. The paper considers the problem of defining such a prior in an axiomatic (“objective”) way without extending the information represented by the test results by more than very little extra information based on common physical sense. The solution suggested in the paper implies that the posterior distribution of the mean shifts towards larger values when the sample size n increases. However, convergence to a specific value is not obtained as long as no failures are observed among the tests. Moreover it turns out that the posterior distribution of the standard deviation is invariant to the sample size n, that is, no updating of the standard deviation is obtained as long as there are no failures among the tests.     

A Bayesian space‐time approach to identifying and interpreting regional convergence clubs in Europe

This study suggests a two‐step approach to identifying and interpreting regional convergence clubs in Europe. The first step calculates Bayesian probabilities for various assignments of regions to two clubs using a general stochastic space‐time dynamic panel relationship between growth rates and initial levels of income as well as endowments of physical, knowledge and human capital. The second step uses the club assignments in a dynamic space‐time panel data model to assess long‐run dynamic direct and spillover responses of regional income levels to changes in initial period endowments for clubs that were identified. We find different dynamic partial derivative responses to endowments by regions in the two clubs that appear consistent with low‐ and high‐income regions as clubs.     

Progressive collapse‐resisting capacity of modular mega‐frame buildings

In this study, the progressive collapse‐resisting capacity of modular mega‐frame structures consisting of a few identical subsystems was investigated based on column‐loss scenario. Four types of mega‐frame structures were designed as basic analysis model structures. According to pushdown analysis results, the mega‐frame structure with four corner columns did not satisfy the design guidelines for progressive collapse regardless of the number of subsystems when one of the first‐story mega‐columns was removed. To enhance the resistance against progressive collapse, we redesigned the basic model structure with four mega‐columns by adding additional floor trusses in the transfer floors, adding moment‐resisting frames at the perimeter and adding vertical mega‐bracing. The pushdown analysis results showed that the schemes with additional mega‐braces were most effective in increasing the progressive collapse‐resisting capacity of mega‐frame buildings. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting industrial employment figures in Southern California: A Bayesian vector autoregressive model

In this paper, we construct a Bayesian vector autoregressive model to forecast the industrial employment figures of the Southern California economy. The model includes both national and state variables. The root mean squared error (RMSE) and the Theil's U statistics are used in selecting the Bayesian prior. The out-of-sample forecasts derived from each model and prediction of the turning points show that the Bayesian VAR model outperforms the ARIMA and the unrestricted VAR models. At longer horizons the BVAR model appears to do relatively better than alternative models. A prior that becomes increasingly looser produces more accurate forecasts than a tighter prior in the BVAR estimations. Received: March 1999/Accepted: November 1999     

Slope reliability and back analysis of failure with geotechnical parameters estimated using Bayesian inference

A Bayesian approach is proposed for the inference of the geotechnical parameters used in slope design. The methodology involves the construction of posterior probability distributions that combine prior information on the parameter values with typical data from laboratory tests and site investigations used in design. The posterior distributions are often complex, multidimensional functions whose analysis requires the use of Markov chain Monte Carlo (MCMC) methods. These procedures are used to draw representative samples of the parameters investigated, providing information on their best estimate values, variability and correlations. The paper describes the methodology to define the posterior distributions of the input parameters for slope design and the use of these results for evaluation of the reliability of a slope with the first order reliability method (FORM). The reliability analysis corresponds to a forward stability analysis of the slope where the factor of safety (FS) is calculated with a surrogate model from the more likely values of the input parameters. The Bayesian model is also used to update the estimation of the input parameters based on the back analysis of slope failure. In this case, the condition FS = 1 is treated as a data point that is compared with the model prediction of FS. The analysis requires a sufficient number of observations of failure to outbalance the effect of the initial input parameters. The parameters are updated according to their uncertainty, which is determined by the amount of data supporting them. The methodology is illustrated with an example of a rock slope characterised with a Hoek-Brown rock mass strength. The example is used to highlight the advantages of using Bayesian methods for the slope reliability analysis and to show the effects of data support on the results of the updating process from back analysis of failure.