Understanding and Planning for Freight Movement in Cities: Practices and Challenges

ABSTRACT

Planning for freight presents a perpetual challenge for governments. Understanding freight flows has attracted increasing research attention, though such knowledge can be difficult to translate to address problems in planning practice. This paper critically assesses the challenges of understanding and planning for urban freight movement, especially with reference to achieving more sustainable outcomes. In doing so, we elucidate guidance for broader analytical and policy approaches suited to meeting diverse public interest objectives. We review the literature with specific reference to the nature of policy problems, while collating existing conceptual knowledge for broad urban land use types.     

Planning Life Tests Based on Progressively Type-I Grouped Censored Data from the Weibull Distribution

In this article, we apply the simulated annealing algorithm to determine optimally spaced inspection times for the two-parameter Weibull distribution for any given progressive Type-I grouped censoring plan. We examine how the asymptotic relative efficiencies of the estimates are affected by the position of the monitoring points and the number of monitoring points used. A comparison of different inspection plans is made that will enable the user to select a plan for a specified quality goal. Using the same algorithm, we can also determine an optimal progressive Type-I grouped censoring plan when the inspection times and the expected proportions of total failures in the experiment are pre-fixed. Finally, we discuss the sample size and the acceptance constant of the progressively Type-I grouped censored reliability sampling plan when the optimal inspection times are used.     

Investigation of disease-free survival as a surrogate endpoint for survival in cancer clinical trials

A stochastic model wiuh exponential components is used to describe our data collected from a phase III cancer clinical trial. Criteria which guarantee that disease-free survival (DFS) can be used as a surrogate for overall survival are explored under this model. We examine several colorectal adjuvant clinical trials and find that these conditions are not satisfied. The relationship between the hazard ratio of DFS for an active treatment versus a control treatment and the cumulative hazard ratio of survival for the same two treatments is then explored. An almost linear relationship is found such that a hazard ratio for DFS of less than a threshold R corresponds to a non-null treatment effect on survival The threshold value R is determined for our colorectal adjuvant trial data. Based on this relationship, a one-sided test of equal hazard rate of survival is equivalent to a test of hazard ratio of DFS small than R This approach assumes that recurrence information is unbiasedly and accurately assessed; an assumpion which is sometimes difficult to ensure for multicenter clinical trials, particularly for interim analyses.     

Evaluating the Link Between Perceived Income Adequacy and Financial Satisfaction: A Resource Deficit Hypothesis Approach

Data from an economically and racially diverse sample (N = 258) was used to determine (a) if an association between objectively measured income and perceived income adequacy exists, (b) how well individuals assess the adequacy of their income, and (c) if a bias exists, can these estimates be used to describe a person’s overall level of financial satisfaction? Duesenberry’s (Income, saving, and the theory of consumer behavior. Harvard University Press, Cambridge, 1949) relative income hypothesis and Kyrk’s (The family in the American economy. University of Chicago Press, Chicago, 1953) resource deficit hypothesis were adopted for use as the conceptual framework for this study. A positive but modest association between objective and perceived income adequacy was noted. It was also found that individuals do not do a particularly good job of accurately assessing their income adequacy. Finally, perceived income adequacy estimation bias was found to be associated with financial satisfaction. Those who perceived their income to be deficient were less satisfied financially. Policy and practitioner implications from the study are discussed as a means for improving financial satisfaction at the individual and household level.     

Nonparametric regression with responses missing at random

For the case of a complete sample of univariate predictors and responses, the modern nonparametric regression matches results known for parametric and semiparametric regressions. The situation changes dramatically if some values in a sample are missing. This paper develops the theory of nonparametric regression for the classical case of responses missing at random. The main conclusion is that an adaptive estimator, based on a complete-case subsample, is asymptotically sharp minimax over all possible oracle-estimators that know: an underlying sample with missing responses; probability of observing the response given the predictor; smoothness of an underlying regression function; design density of the predictor; scale function of the regression error.     

When are two multivariate random processes indistinguishable

Prediction error identification methods have been recently the objects of much study, and have wide applicability. The maximum likelihood (ML) identification methods for Gaussian models and the least squares prediction error method (LSPE) are special cases of the general approach. In this paper, we investigate conditions for distinguishability or identifiability of multivariate random processes, for both continuous and discrete observation time T. We consider stationary stochastic processes, for the ML and LSPE methods, and for large observation interval T, we resolve the identifiability question. Our analysis begins by considering stationary autoregressive moving average models, but the conclusions apply for general stationary, stable vector models. The limiting value for T → ∞ of the criterion function is evaluated, and it is viewed as a distance measure in the parameter space of the model. The main new result of this paper is to specify the equivalence classes of stationary models that achieve the global minimization of the above distance measure, and hence to determine precisely the classes of models that are not identifiable from each other. The new conclusions are useful for parameterizing multivariate stationary models in system identification problems. Relationships to previously discovered identifiability conditions are discussed.