Volumen 21 (2020): Heft 2 (April 2020)

The objective of this study was to investigate the carbon dioxide (CO₂) emissions of an airport, to determine if strategies are helping to achieve sustainability targets. Kansai International Airport was selected as the case study, and it is Japan’s third largest airport and there was readily available comprehensive data to enable a study to be undertaken. The airport has a dedicated environmental division and has implemented various initiatives over the past decade or so to reduce the airport’s impact on the surrounding environment, especially since it is in Osaka Bay. The research used an exploratory design, with an initial qualitative case study, followed by a quantitative longitudinal study, utilizing correlation to assess trends over time. Results showed statistically significant reductions in carbon dioxide (CO₂) emission from the three facets of airport operations, both in terms of the number of passengers and number of aircraft serviced by the airport. As a result, the initiatives undertaken at Kansai International Airport could be adapted and used by other airports to help reduce their carbon dioxide emissions.

Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. In the context of IoT in transportation, one feature is the smart junction. This research deals with this junction, where several cars approach the intersection from different directions, and a smart traffic light must decide regarding the time intervals of red and green light in each direction. Out novel approach is based not only on the number of vehicles in each lane, but also on the social characteristics of the passengers (e.g. a handicapped person, a driver with no previous traffic violations). These factors will be gleaned from IoT network sources on cars, traffic lights, individuals, municipality data, and more. In this paper, we suggest using a VCG (Vickrey-Clarke-Groves) auction mechanism for the intersection scheduling, combining the social characteristic with a benefit parameter that expresses the passenger’s subjective perception of the importance of crossing the intersection as soon as possible. Our simulation results show the efficiency of the suggested protocol and demonstrate how the intersection scheduling depends on the passengers’ preferences, as well as on their social priorities.

Short-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative.

This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.

Vehicular networks that deal with sharing of information among vehicles are gaining popularity among the automobile industry as well as the researchers. These networks are prevalent under the umbrella of Intelligent Transportation Systems (ITS) and deal with data that belongs to either the emergency category or the entertaining category. In case of emergency services, it is clear that - earlier the reception of information, lesser the commotion. The objective of this work thus has been the reduction of the end to end delay when video files are exchanged among vehicles during intersessions. The set objective is accomplished through the design and development of the technique “Instantly Decodable RaptorQ Inter-Sessions” (IDRQIS) for Vehicular Adhoc Networks and the results obtained show that this outperforms the existing popular techniques – the Network Coding and RaptorQ when applied independently to the same environment. This technique can also be applied to the upcoming unmanned vehicles.

Research is based on wholesale and distribution operations of real-life case company, and in this setting, the most critical part of company’s supply chain is the inventory replenishment to warehouse (Distribution Center) as well as fulfilling and delivering customers’ orders. Different Economic Order Quantity (EOQ)-based models have been considered (Reorder Point, Reorder Point with pipeline on order inventory, and “pulse train”). Simulation system evaluates annual total logistics costs. Results show that in an environment, where local warehouse inventory levels are rather high and replenishment order quantity is rather small, it is important have frequent shipments divided in suitable intervals. In simulation model, this could be done e.g. with the use of “pulse train” function or incorporating pipeline on order inventory in order decision. The research findings are valid for a small-scale supply chain servicing small and geographically limited markets with clients assuming high customer service levels (e.g. 24-hours lead time). For bigger markets, the cross-docking based supply chain models are worth considering in simulations.