Do people consider an acceptable travel time? Evidence from Berkeley,CA

This paper explores the extent to which people identify an acceptable travel time for each trip they want to make and investigates the primary characteristics of this behavioural threshold. We assume that an acceptable travel time is defined through utilitarian processes that are related to cognitive and affective evaluations of travel that develop throughout a person’s lifetime. We apply a mixed methods approach to explore our hypothesis, based on semi-structured in-depth interviews with a stratified, according to commuting mode, random sample of 20 individuals living in Berkeley, CA. Our results support the validity of the acceptable travel time concept and offer (a) initial evidence on its variation to the travel mode and sociodemographic characteristics and (b) insights into people’s perceptions, attitudes, feelings and experiences about their travel. Thus, our results can contribute to a better understanding of individuals’ travel decisions and valuation of travel time and can inform time–space and accessibility research.     

Updating Input–Output Tables with Benchmark Table Series

Numerous methods have been proposed to update input–output (I–O) tables. They rely on the assumption that the economic structure will not change significantly during the interpolation period. However, this assumption may not always hold, particularly for countries experiencing rapid development. This study attempts to combine forecasting with a matrix transformation technique (MTT) to provide a new perspective on updating I–O tables. Under the assumption that changes in the trend of an economic structure are statistically significant, the method extrapolates I–O tables by combining time series models with an MTT and proceeds with only the total value added during the target years. A simulation study and empirical analysis are conducted to compare the forecasting performance of the MTT to the Generalized RAS (GRAS) and Kuroda methods. The results show that the comprehensive performance of the MTT is better than the performance of the GRAS and Kuroda methods, as measured by the Standardized Total Percentage Error, Theil's U and Mean Absolute Percentage Error indices.     

Resource conflict detection and removal strategy for nondeterministic emergency response processes using Petri nets

Correctness of an emergency response process specification is critical to emergency mission success. Therefore, errors in the specification should be detected and corrected at build-time. In this paper, we propose a resource conflict detection approach and removal strategy for emergency response processes constrained by resources and time. In this kind of emergency response process, there are two timing functions representing the minimum and maximum execution time for each activity, respectively, and many activities require resources to be executed. Based on the RT_ERP_Net, the earliest time to start each activity and the ideal execution time of the process can be obtained. To detect and remove the resource conflicts in the process, the conflict detection algorithms and a priority-activity-first resolution strategy are given. In this way, real execution time for each activity is obtained and a conflict-free RT_ERP_Net is constructed by adding virtual activities. By experiments, it is proved that the resolution strategy proposed can shorten the execution time of the whole process to a great degree.     

基于时间维度的组织学习理论——组织从过去经历中学习的模型

从经历中学习是组织学习的重要方面。从科学、严谨的研究范式出发,结合东方传统的"阴阳"哲学思想,基于时间维度提出了包含学习主体、经历样本、成功失败经历集、学习主体特征、"知"和"行"等要素的组织从过去经历中学习的模型,并对相关问题进行了探究。具体包括:学习主体从经历中学习的认知特性;不同类型经历对应的不同学习方法,成功经历和失败经历对学习效果的差异性影响,经历集影响"知"和"行"的7种自动的和有意的学习方式;"知"与"行"相互转化及其触发机制;客观世界从时间和空间两个维度客观展开的观点等。该系统模型为未来研究提供了理论框架和实证检验方向。     

Post encroachment time threshold identification for right-turn related crashes at unsignalized intersections on intercity highways under mixed traffic

Abstract

Unsignalized intersections in developing countries experience many serious conflicts between cross-traffic due to indiscipline traffic manoeuvrability. Historically, Post Encroachment Time (PET) has gained attention as a proximal indicator to analyze crossing conflicts. However, identifying an appropriate PET threshold to classify critical conflicts for highly heterogeneous traffic scenario is still an unexplored area. Consequently, this study proposes a novel approach of PET threshold identification with proof of application by collecting data from ten intersections located on four-lane intercity highways in the National Capital Region (NCR), India. Both crossing conflicts and right-turn related crash data (for the left-hand drive) are collected. Their correlations are thoroughly studied for each PET threshold using a quantitative technique considering all and individual vehicle categories. Finally, a qualitative analysis is done by ranking the sites based on cumulative PET and related crashes to verify the proposed quantitative technique. A PET threshold of 1?s is obtained from both the techniques which can be used to identify critical conflicts for unsignalized intersections located on four-lane intercity highways. The proposed methodology will serve as an alternative, faster and effective tool to evaluate the hazardousness of unsignalized intersections located on intercity highways under highly heterogeneous traffic condition.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Information congruity in scarcity appeal: A structural equation modeling study of time-limited promotions

Although market scarcity such as time restriction is widely used in retail advertisements, its impact on product message processing and product evaluation is still subject to controversy. In this study, analyses through structural equation modeling indicated that (i) scarcity had no impact on product message processing, (ii) scarcity had a direct impact on product evaluation as a heuristic cue when the value inferred from scarcity was congruous with the worth derived from product message, and (iii) scarcity lost its impact on product evaluation when the value inferred from scarcity was incongruous with the worth derived from product message, and the incongruity prompted scrutiny of product message, resulting in a mediated impact of product message on product evaluation through product message processing. In conclusion, the results supported an information congruity theory of market scarcity.     

Is There a Causal Effect of Working Part‐Time on Current and Future Wages?

In this paper, I study the causal effects of part‐time work on current and future wages. To estimate these effects, I use a random effects model with a wage equation capturing the employment history and a dynamic multinomial probit component for the choice of employment status. Exclusion restrictions from the institutional context are exploited to support identification. The results suggest that working part‐time with few hours has a large causal effect on current wages, but more extensive part‐time work does not reduce current wages. However, both types of part‐time work lead to negative long‐term wage effects.