Ssenger travel time along with the total 3-Hydroxybenzaldehyde web number of operating trains. Meanwhile, a option algorithm based on a genetic algorithm is proposed to resolve the model. Around the basis of prior research, this paper mainly focuses on schedule adjustment, optimization of a stop strategy and frequency below the overtaking situation, which can maximize the line capacity. A case of Jiangjin Line in Chongqing is utilized to show the reasonability and effectiveness with the proposed model and algorithm. The outcomes show that total travel time in E/L mode with all the overtaking situation is significantly decreased compared with AS mode and E/L mode devoid of the overtaking situation. Despite the fact that the number of trains in the optimal resolution is greater than other modes, the E/L mode using the overtaking situation continues to be improved than other modes around the whole. Escalating the station quit time can improve the superiority of E/L mode more than AS mode. The research results of this paper can offer a reference for the optimization investigation of skip-stop operation under overtaking conditions and supply evidence for urban rail transit operators and planners. You can find nonetheless some elements that can be extended in HBV| future work. Firstly, this paper assumes that passengers take the initial train to arrive at the station, regardless of whether it can be the express train or nearby train. In reality, the passenger’s option of train is often a probability dilemma, consequently the passenger route selection behaviorAppl. Sci. 2021, 11,16 ofconsidering the train congestion should be considered in future research. In addition, genetic algorithms possess the traits of getting partial optimal options in lieu of global optimal options. The optimization issue on the genetic algorithm for solving skip-stop operation optimization models is also a vital study tendency.Author Contributions: Each authors took element inside the discussion of your operate described in this paper. Writing–original draft preparation, J.X.; methodology, J.X.; writing–review and editing, Q.L.; data curation, X.H., L.W. All authors have study and agreed for the published version with the manuscript. Funding: This research received no external funding. Institutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The information presented within this study are offered on request in the corresponding author. Acknowledgments: The authors thank Songsong Li and Kuo Han, for their constructive comments and suggestions in 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 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 Workplace 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: With all the start off in the Fourth Industrial Revolution, World wide web of Factors (IoT), artificial intelligence (AI), and significant data technologies are attracting international focus. AI can obtain rapidly computational speed, and huge information makes it possible to store and use vast amounts of information. Furthermore, smartphones, that are IoT devices, are owned by most p.