Many places around the world periodically suffer from wildfires that threaten lives and disrupt normal traffic operations. Poor traffic performance during wildfires can inhibit the effectiveness of evacuations. Understanding traffic performance during a wildfire would therefore help transportation operators develop emergency traffic control plans. In this study, we developed a traffic speed and flow prediction model that uses support vector regression (SVR), for use during wildfire incidents. This was constructed using historical data for wildfires in California from 2010 to 2019, which were paired with records of the traffic speed and flow on adjacent highways and the prevailing weather conditions during the wildfire events. The results showed that traffic performance during a wildfire could be predicted using the SVR model. Based on our prediction results, we recommend that policies be implemented to encourage or mandate more detailed data collection of wildfire events, such as the fire’s boundary over time, to facilitate better prediction results in models like the one proposed in this paper. This paper should inspire further work on the topic to improve the model and provide a reliable prediction tool for transportation operators in the future.

This article is an analysis of general journal and article characteristics, content, and authorship for volumes 1 through 15 of the Journal of Employment Counseling, covering the years 1964 through 1978. It attempts to determine to what extent and in what ways the membership of the National Employment Counselors Association (NECA) is served by the Journal. The average length of the journal and its articles are noted as well as the number of authors and references per article. Each article is reviewed for content classification, authorship, and the author's institutional affiliation. The basic facts and overall publication trends are identified and discussed. It is noted that only a small proportion of published articles concerned Employment Service counseling techniques. The data in this article was originally prepared for the graduate course entitled “Psychology 411: Professional Problems in Psychology” at the University of Missouri‐Columbia.

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Uncertainty, a critical factor of causing congestion and extra travel costs in the commute, can be mitigated by providing information. This paper studies the welfare effects of accurate pretrip information on departure time and route choices in the morning commute under binary stochastic bottleneck capacity. We consider a classical two-route network. Each route has a single bottleneck where congestion occurs during the rush hours. The two routes' bottleneck capacities vary from day-to-day due to events such as bad weather, accidents, and temporary road closures. We derive all equilibrium solutions in consideration of the differences between routes in free-flow travel time, the shadow value of travel time, the severity of bottleneck capacity reductions, and the degree of correlation between two routes in travel conditions. Furthermore, we investigate the benefit changes from zero-information to full-information and prove that accurate pre-trip information about the bottleneck conditions is strictly welfare-improving. Finally, these theoretical results are supplemented by case studies that show examples of benefit gains from pre-trip information.INDEX TERMS Departure time choice, route choice, bottleneck, congestion, pre-trip information, uncertainty.

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