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Ground holding policy (GHP) offers a potential solution to alleviate increasing congestion problems in the air transportation network. The policy proposes to hold an aircraft before takeoff so that when it arrives at its destination, it need not wait in the air before landing. This is motivated by the fact that airborne delays are costlier than ground delays. Many attempts have been made to solve the static GHP problem. However, aspects such as flight cancellations, transfers at hub airports, en route speeding, weather changes, and the interdependent nature of arrival and departure capacities suggest that updating ground holding decisions as time progresses is important. In this paper the dynamic instantaneous GHP problem, considering some of the above aspects, is formulated as an integer programming model. The presence of binary integer variables and discrete variables make the GHP problem complex and computationally difficult to solve using traditional algorithms. As an alternate approach, the use of genetic algorithms is demonstrated and analyzed in this paper. The observed program run time is reasonably small, indicating that genetic algorithms can be used for implementing GHP in real time for dynamic operating environments.

The U.S. airline industry is facing delays due to congestion problems in the air transportation network. These delays not only lead to increased costs, but they also have safety related implications. Some of the costs related to such delays could be minimized by holding aircraft on the ground at the originating airport when congestion-related delays are expected en route or at the destination airport. Aside from reducing operating costs, the ground holding policy (GHP) also has beneficial implications on some safety concerns, since it is generally felt that it is better to hold an aircraft on the ground than in the air. The GHP is formulated as an integer programming problem and solved using heuristic techniques. However, the presence of discrete and binary integer variables increases the complexity involved when solved using traditional algorithms. Genetic algorithms (GAs) offer a powerful alternative for efficiently solving such problems. A GA is a search and optimization technique based on natural genetics and selection. This paper presents a discussion of GAs and their applicability to evaluating the GHP. Specific examples are presented to illustrate such applications. Results obtained from the test problems are consistent with expectations, indicating that GA can be used as a solution technique. It is observed that the program run time was reasonably low.

Objective: Distinguished from the traditional perspectives in crash analyses, which examined the effects of geometric design features, traffic factors, and other relevant attributes on the crash frequencies of roadway entities, our study focuses on exploring the effects of highway safety laws, as well as sociocultural characteristics, on fatal crashes across states.Methods: Law and regulation related data were collected from the Insurance Institute for Highway Safety, State Highway Safety Offices, and Governors Highway Safety Association. A variety of sociodemographic characteristics were obtained from the U.S. Census Bureau. In addition, cultural factors and other attributes from a variety of resources are considered and incorporated in the modeling process. These data and fatal crash counts were collected for the 50 U.S. states and the District of Columbia and were analyzed using zero-truncated negative binomial (ZTNB) regression models.Results: The results show that, in law and regulation-related factors, the use of speed cameras, no handheld cell phone ban, limited handheld cell phone ban, and no text messaging ban are found to have significant effects on fatal crashes. Regarding sociocultural characteristics, married couples with both husband and wife in the labor force are found to be associated with lower crash frequencies, the ratios of workers traveling to work by carpool, those driving alone, workers working outside the county of residence, language other than English and limited English fluency, and the number of licensed drivers are found to be associated with higher crash frequencies.Conclusions: Through reviewing and modeling existing state highway safety laws and sociocultural characteristics, the results reveal new insights that could influence policy making. In addition, the results would benefit amending existing laws and regulations and provide testimony about highway safety issues before lawmakers consider new legislation. [ABSTRACT FROM AUTHOR]