Ssenger travel time plus the total number of operating trains. Meanwhile, a option algorithm primarily based on a genetic algorithm is proposed to solve the model. On the basis of preceding study, this paper primarily focuses on schedule adjustment, optimization of a cease strategy and frequency under the overtaking situation, which can maximize the line capacity. A case of Jiangjin Line in Chongqing is used to show the reasonability and effectiveness of the proposed model and algorithm. The outcomes show that total travel time in E/L mode with all the overtaking situation is considerably lowered compared with AS mode and E/L mode without having the overtaking situation. Even though the amount of trains in the optimal remedy is more than other modes, the E/L mode with all the overtaking condition continues to be greater than other modes around the complete. Rising the station stop time can enhance the superiority of E/L mode more than AS mode. The investigation benefits of this paper can (-)-Cedrene supplier deliver a reference for the optimization study of skip-stop operation under overtaking situations and deliver proof for urban rail transit operators and planners. You will discover still some aspects that can be extended in future perform. Firstly, this paper assumes that passengers take the very first train to arrive in the station, regardless of whether it truly is the express train or nearby train. In reality, the passenger’s option of train can be a probability trouble, as a result the passenger route option behaviorAppl. Sci. 2021, 11,16 ofconsidering the train congestion really should be regarded in future research. Furthermore, genetic algorithms possess the traits of obtaining partial optimal options as an alternative to international optimal options. The optimization dilemma in the genetic algorithm for solving skip-stop operation optimization models can also be a vital analysis tendency.Author Contributions: Each authors took component within the discussion of your work described in this paper. Writing–original draft preparation, J.X.; methodology, J.X.; writing–review and editing, Q.L.; information curation, X.H., L.W. All authors have read and agreed for the published version from the manuscript. Funding: This analysis received no external funding. Institutional Critique Board Statement: Not applicable. Informed Consent Statement: Not applicable. Information Availability Statement: The information presented in this study are readily available on request in the corresponding author. Acknowledgments: The authors thank Songsong Li and Kuo Han, for their constructive comments and suggestions within this study. Conflicts of Interest: The authors declare no conflict of Interest.
applied sciencesArticleWiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm OptimizationSung Hyun Oh and Jeong Gon Kim Department of Electronic Engineering, Korea Polytechnic University, Siheung-si 15297, Korea; [email protected] Correspondence: [email protected]; Tel.: +82-10-9530-Citation: Oh, S.H.; Kim, J.G. WiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm Optimization. Appl. Sci. 2021, 11, 9522. https://doi.org/10.3390/ app11209522 Academic Editor: Jaehyuk Choi Received: 1 September 2021 Accepted: 10 October 2021 Published: 13 OctoberAbstract: With all the start out of the Fourth Industrial Revolution, Internet of Issues (IoT), artificial intelligence (AI), and major data technologies are attracting worldwide attention. AI can reach speedy computational speed, and huge information makes it probable to retailer and use vast amounts of information. Also, smartphones, that are IoT devices, are owned by most p.