scholarly journals Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 360 ◽  
Author(s):  
Ivana Hartmann Tolić ◽  
Emmanuel Karlo Nyarko ◽  
Avishai (Avi) Ceder
Keyword(s):  
Particle Swarm Optimization ◽  
Waiting Time ◽  
Public Transportation ◽  
Waiting Times ◽  
Search Space ◽  
Complex Problem ◽  
Swarm Optimization ◽  
Transport Services ◽  
Multiobjective Particle Swarm Optimization ◽  
Public Transport Services

Determining the best timetable for vehicles in a public transportation (PT) network is a complex problem, especially because it is just necessary to consider the requirements and satisfaction of passengers as the requirements of transportation companies. In this paper, a model of the PT timetabling problem which takes into consideration the passenger waiting time (PWT) at a station and the vehicle occupancy ratio (VOR) is proposed. The solution aims to minimize PWT and maximize VOR. Due to the large search space of the problem, we use a multiobjective particle swarm optimization (MOPSO) algorithm to arrive at the solution of the problem. The results of the proposed method are compared with similar results from the existing literature.

scholarly journals PERFORMANCE ANALYSIS AND MODELING OF PASSENGER PUBLIC TRANSPORT SERVICES IN SALATIGA CITY

ASTONJADRO ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 61
Author(s):  
Mudjiastuti Handajani ◽  
Ferry Firmawan ◽  
Harmini Harmini
Keyword(s):  
Performance Analysis ◽  
Public Transport ◽  
Public Transportation ◽  
Waiting Times ◽  
Load Factor ◽  
Transportation Services ◽  
Transport Services ◽  
Research Activities ◽  
Directorate General ◽  
Public Transport Services

<p>The performance condition of public transportation services in Salatiga City does have an average rating of still good, but it still has classic problems such as there is no passenger stop, public transportation takes a long time to catch up on deposits, and passengers do not know the fare per kilometer that must be paid. These conditions encourage research activities in the form of an analysis of the performance of public transport passengers in Salatiga City (Case Study of the Tamansari – Blotongan Route). The long-term goal of this research is that the resulting analysis can contribute to stakeholders in Salatiga City, especially in terms of (public transport) towards smart transportation. The specific target to be achieved from the research is the analysis and modeling of the performance of passenger public transport services that will be carried out in Salatiga City, in the form of field surveys and secondary data collection, namely: number of vehicles, number of passengers and data in the form of public transport routes: routes, schedules, speeds and Primary forms are: boarding alighting (up and down passengers) and headway (distance between two public transport vehicles). The analytical method used is the analysis and modeling of the performance of passenger public transportation services in accordance with Government Regulation No. 10 of 2012 Directorate General of Land Transportation concerning Minimum Service Standards for Road-Based Mass Transportation. The results showed that the physical condition of the passenger fleet of Salatiga City on the Tamansari - Blotongan PP route was classified as suitable for use. There are a total of 90 fleets that are sufficient for the needs of Salatiga City public transport passengers who want to travel by means of transportation. The results of the performance analysis based on various indicators show that the performance quality of public transportation services in Salatiga City has met the standards set by the Directorate General of Land Transportation. It can be seen from the results of the load factor analysis with a value of 0.72, the value is included in category A, namely &gt; 0.8. For the level of satisfaction and level of performance, most of the indicators have met the satisfaction of public transport passengers in Salatiga City, so it is sufficient to maintain it. However, there are indicators of waiting times for public transportation that need to be improved.</p>

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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