Modelling, calibrating, and validating car following and lane changing behaviour

Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behaviour under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety; this realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modelling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few. Thus, this special issue will focus on latest developments in modelling, calibrating, and validating two primary vehicular interactions observed in traffic flow: CF and LC.

Middle-aged and elderly Finnish households considering moving, their preferences, and potential downsizing amidst changing life course and housing careers

We examine the moving and housing preferences of middle-aged and older in Finland, a country where population composition and movement through the life course are changing. A logistic regression reveals that middle-aged, moderate income residents, renters, those who have lived in their houses only a short time, and residents who are generally dissatisfied are most likely to consider moving. Downsizing appeals to residents with lower incomes who live alone, and who have been in their current houses longer. All potential movers agree on the importance of transportation access and a neighborhood grocery store; however, those preferring to downsize are also interested in house and neighborhood design as well as services that will allow aging in place. Income limitations may create affordability problems for some potential movers.

Testing against changing correlation

A test for time-varying correlation is developed within the framework of a dynamic conditional score (DCS) model for both Gaussian and Student t-distributions. The test may be interpreted as a Lagrange multiplier test and modified to allow for the estimation of models for time-varying volatility in the individual series. Unlike standard moment-based tests, the score-based test statistic includes information on the level of correlation under the null hypothesis and local power arguments indicate the benefits of doing so. A simulation study shows that the performance of the score-based test is strong relative to existing tests across a range of data generating processes. An application to the Hong Kong and South Korean equity markets shows that the new test reveals changes in correlation that are not detected by the standard moment-based test.