Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence. 相似文献
Although the improvement of well-being is often an implicitly-assumed goal of many, if not most, public policies, the study of subjective well-being (SWB) and travel has so far been confined to a relatively small segment of the travel behavior community. Accordingly, one main purpose of this paper is to introduce a larger share of the community to some fundamental SWB-related concepts and their application in transportation research, with the goal of attracting others to this rewarding area of study. At the same time, however, I also hope to offer some useful reflections to those already working in this field. After discussing some basic issues of terminology and measurement of SWB, I present from the literature four conceptual models relating travel and subjective well-being. Following one of those models, I review five ways in which travel can influence well-being. I conclude by examining some challenges associated with assessing the impacts of travel on well-being, as well as challenges associated with applying what we learn to policy.
Failure mode and effects analysis are performed for a dual levelling valve pneumatic suspension to determine the effect of suspension failure on tractor–semi-trailer dynamics, using a detailed model of suspension pneumatics coupled with a truck dynamic model. A key element of failure analysis in suspensions with one or two levelling valves is determining the effect on the vehicle body roll when one or more failures occur. The failure modes considered are mainly the suspension pneumatic components, including clogged levelling valve, bent control rod, disabled lever arm, and punctured or leaking connectors and pipes. The pneumatic suspension is modelled in AMESim, with critical parameters established through component testing. Upon validating the AMESim component model experimentally, the pneumatic suspension model is integrated into TruckSim for studying the consequences of suspension failure on truck dynamics. The simulation results indicate that the second levelling valve in a dual-valve arrangement brings a certain amount of failure redundancy to the system, in the sense that when one side fails, the other side can compensate for the failure. Equipping the trailer with dual levelling valves brings an additional stabilising effect to the vehicle in the event of tractor suspension failure. 相似文献