There are metrics of reliability that have been recommended. Here we investigate the variation of travel time distributions by time of day at fine temporal aggregation levels, the sensitivity of reliability metrics to these variations, the effect of the aggregation on the calculated metrics, and the amount of data required to estimate stable values of the reliability metrics. The results show that the parameters of travel time distributions vary among periods reflecting the effects of the traffic congestion, traffic flow dynamics, and the contribution of non-recurrent factors such as incidents to the unreliability of travel time on the investigated facility.
Historically, the construction industry has presented high accident rates, and in order to get a deeper understanding and making right decisions, it is interesting to consider the risks from a worker's point of view. This research addresses the perception of risks by construction workers from a psychometric paradigm and considering some sociodemographic variables. The study considered a sample consisted of a group of workers who belong to the Chilean construction industry, particularly from the building construction sector. Relevant risks associated with a high accident rate were identified through an extensive literature review. In addition, the relative risk related to physical overexertion and that related to the exposure to physical and chemical agents are considered. Based on a psychometric approach for the evaluation of qualitative attributes, a measurement instrument was applied; results were then statistically analyzed. Additionally, the incidence of sociodemographic variables was evaluated: age, profession, experience and educational level in relation to the perceived risk level. Statistically significant differences of the perceived risk associated to noise exposure, depending on the age of the workers were obtained. On the other hand, it was determined that workers with the most experience consider that those jobs that involve uncomfortable or forced postures constitute a relevant physical risk. Meanwhile, jobs such as: rebar workers, bricklayers and concrete workers, perceive the gravity and immediacy of the effect associated to risk significantly more in activities that involve repetitive movements.
Collecting data for traffic simulation is expensive, particularly for large simulated systems. When traditional methods are used, data are normally collected for only one day or a few days and may not represent variations in traffic demands and conditions throughout the year. Collected data usually are imperfect, and additional efforts are needed to compensate for missing and erroneous data and to resolve data inconsistencies. In recent years, agencies have started archiving data collected by intelligent transportation systems (ITS). The ITS data archives can provide cost-effective, detailed information for the development and calibration of simulation tools if procedures are used to ensure data quality and to allow optimal categorization and use of the archived ITS data. An effort to develop a series of data manipulation procedures for the use of ITS data archives in support of simulation modeling is discussed. These procedures allow the extraction of collected volume data from ITS data archives, automatic identification of temporal patterns in the data, automatic segmentation of daily demands into dynamically captured subperiods to best fit variations in demand, resolution of possible spatial inconsistencies in the data, and estimation of missing volumes. The developed procedures have been implemented as an automated tool for populating simulation models. The procedures and the developed tool can easily be adapted by other traffic agencies to interface with their ITS data archives.
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