Core-selection is a crucial property of rules in the literature of resource allocation. It is also desirable, from the perspective of mechanism design, to address the incentive of agents to cheat by misreporting their preferences. This paper investigates the exchange problem where (i) each agent is initially endowed with (possibly multiple) indivisible goods, (ii) agents' preferences are assumed to be conditionally lexicographic, and (iii) side payments are prohibited. We propose an exchange rule called augmented top-trading-cycles (ATTC), based on the original TTC procedure. We first show that ATTC is core-selecting and runs in polynomial time with respect to the number of goods. We then show that finding a beneficial misreport under ATTC is NP-hard. We finally clarify relationship of misreporting with splitting and hiding, two different types of manipulations, under ATTC.

Individual rationality, Pareto efficiency, and strategy- proofness are crucial properties of decision making functions, or mechanisms, in social choice literatures. In this paper we investigate mechanisms for exchange models where each agent is initially endowed with a set of goods and may have indifferences on distinct bundles of goods, and monetary transfers are not allowed. Sonmez (1999) showed that in such models, those three properties are not compatible in general. The impossibility, however, only holds under an assumption on preference domains. The main purpose of this paper is to discuss the compatibility of those three properties when the assumption does not hold. We first establish a preference domain called top-only preferences, which violates the assumption, and develop a class of exchange mechanisms that satisfy all those properties. Each mechanism in the class utilizes one instance of the mechanisms introduced by Saban and Sethuraman (2013). We also find a class of preference domains called m-chotomous preferences, where the assumption fails and these properties are incompatible.

Around airports having multiple runways, aircraft noise affected areas vary depending on the runway operation because the flight path changes by the operation. Therefore, it is important to figure out exactly which runway was used for analyzing the noise impact on the surrounding area. For this purpose, runway information included in flight data provided by the airport operator can be used in most commercial airports. However, for some airports, the data is unavailable or not always accurate. We developed "Takeoff/ landing runway determination system; DL-TLS" for determining runway operations and have been utilizing it to aircraft noise analysis. However, that system requires to place measurement stations at the end of each runway to ensure the high level of accuracy. To solve this issue, we focused on difference in acquisition time of the same transponder signal at two measurement stations. This new approach achieved satisfactory level of runway determination accuracy, by matching the time difference changes with template for each runway. We proved that this method can be used in combination with the current DL-TLS with even higher accuracy. In addition, we showed the possibility of reducing number of measurement stations and eliminating the measurement location constraint.

There are three airports in the Osaka area and each of them has been analyzing and evaluating aircraft noise and publishing its results. However, since these three airports are adjacent to each other, the airspace is complicated, and the monitoring range is wide, which caused so much manual work for data review before obtaining an accurate noise impact assessment. This paper introduces a new aircraft noise monitoring system that has been constructed to reduce the human workload and accurately and integrally monitor aircraft noise at the three airports with flight-track measurement. The new system reduces the time for noise analysis and evaluation using artificial intelligence, and it has become possible to publish accurate noise conditions more quickly and at a lower cost. In addition, since it is difficult for an airport operator to obtain air traffic control data, we constructed a system that can monitor the position coordinates of aircraft as recent as 15 seconds ago. By operating this system integrally with the noise system, we can now visually confirm the noise level at the measurement point and the position of aircraft on a map, and publish the real-time noise data online for residents living near the airports.

In recent aircraft noise survey in Japan, noise data is associated with each aircraft by flight log or by radio information including transponder signals. Especially, above Tokyo metropolitan area, flight tracks are tangled extremely each other, therefore assessments from various perspectives such as departure / arrival airport, used runway, aircraft model, and operator have been demanded for determining noise policies. However, for military aircrafts, it is not easy to identify their information with the same way as commercial aircrafts, because their flight logs are not disclosed and many of them do not emit transponder signals like commercial aircrafts. Therefore, manned 24 hours survey around air bases have been necessary to obtain flight information of military aircrafts. In this paper, we propose an AI-based analysis using captured aircraft images for obtaining actual flight data of military aircrafts. In the past trials, we could determine the takeoff/landing time and the aircraft model by the above method. Associating these information and noise data measured at monitoring stations, details of noise characteristics around the air base can be clearly grasped. Advanced analysis of the causes of noise impact will lead effective and concrete countermeasures.