Ssenger travel time as well as the total variety of operating trains. Meanwhile, a option algorithm based on a genetic algorithm is proposed to solve the model. On the basis of earlier analysis, this paper mostly focuses on schedule adjustment, optimization of a quit plan and frequency below the overtaking condition, which can maximize the line capacity. A case of Jiangjin Line in Chongqing is utilized to show the reasonability and effectiveness in the proposed model and algorithm. The outcomes show that total travel time in E/L mode with the overtaking condition is substantially lowered Fluticasone furoate References compared with AS mode and E/L mode without the need of the overtaking condition. Even though the number of trains in the optimal resolution is more than other modes, the E/L mode together with the overtaking situation is still much better than other modes around the whole. Growing the station quit time can improve the superiority of E/L mode over AS mode. The study benefits of this paper can deliver a reference for the optimization study of skip-stop operation below overtaking situations and supply proof for urban rail transit operators and planners. You’ll find nevertheless some aspects that could be extended in future work. Firstly, this paper assumes that Clindamycin palmitate (hydrochloride) Bacterial passengers take the very first train to arrive at the station, no matter whether it’s the express train or neighborhood train. In reality, the passenger’s option of train can be a probability challenge, for that reason the passenger route decision behaviorAppl. Sci. 2021, 11,16 ofconsidering the train congestion ought to be deemed in future research. In addition, genetic algorithms possess the characteristics of obtaining partial optimal options in lieu of international optimal options. The optimization trouble of the genetic algorithm for solving skip-stop operation optimization models can also be an essential study tendency.Author Contributions: Both authors took component within the discussion from the function 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 to the published version on the manuscript. Funding: This study received no external funding. Institutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The information presented in this study are out there on request in the corresponding author. Acknowledgments: The authors thank Songsong Li and Kuo Han, for their constructive comments and ideas in this study. Conflicts of Interest: The authors declare no conflict of interest.
applied sciencesArticleWiFi Positioning in 3GPP Indoor Workplace with Modified Particle Swarm OptimizationSung Hyun Oh and Jeong Gon Kim Division 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: ten October 2021 Published: 13 OctoberAbstract: Together with the commence in the Fourth Industrial Revolution, Web of Issues (IoT), artificial intelligence (AI), and major information technologies are attracting global focus. AI can achieve rapid computational speed, and big data tends to make it achievable to shop and use vast amounts of data. Moreover, smartphones, that are IoT devices, are owned by most p.