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Vertex Importance Ranking Algorithm Based on Urban Traffic Network Design

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DOI: 10.23977/acss.2022.060408 | Downloads: 13 | Views: 585

Author(s)

Xiaobing Peng 1, Zundong Zhang 1

Affiliation(s)

1 North China University of Technology, Beijing, China

Corresponding Author

Xiaobing Peng

ABSTRACT

With the rapid development of social economy and the continuous expansion of urban scale, urban rail transit, as the backbone of urban public transportation system, has gradually become the main carrier connecting urban space. The purpose of this article is to study the peak importance ranking algorithm based on urban transportation network design, and to discuss the efficiency evaluation method according to the main characteristics of urban transportation system and the performance evaluation method of infrastructure network and dynamic transportation network. Sort peaks using the Peak Importance Index and the Peak Structure Difference Index. Vertex ordering in static and dynamic urban transportation networks is discussed. Static networks are represented by infrastructure networks (weighted in kilometers), dynamic networks are weighted by dynamic travel times (based on perceived traffic data), and infrastructure network models describe the design structural properties of all vertices. Finally, two traffic scenarios are provided for the top ranking of dynamic traffic networks. The peak sorting results show that the edges between nodes 3 and 4 and between nodes 6 and 10 are congested, so the importance of nodes 3, 6 and 10 is significantly affected.

KEYWORDS

Urban Transportation, Network Design, Vertex Importance, Sorting Algorithm

CITE THIS PAPER

Xiaobing Peng, Zundong Zhang, Vertex Importance Ranking Algorithm Based on Urban Traffic Network Design. Advances in Computer, Signals and Systems (2022) Vol. 6: 63-69. DOI: http://dx.doi.org/10.23977/acss.2022.060408.

REFERENCES

[1] Cosgrove S. Exploring usability and user-centered design through emergency management websites: advocating responsive web design[J]. Communication Design Quarterly Review, 2018, 6(2):93-102.
[2] Mcgowan D. The truth about Japanese web design[J]. Multilingual Computing & Technology, 2018, 29(6):22-27.
[3] Kropiwnicki J. A unified approach to the analysis of electric energy and fuel consumption of cars in city traffic[J]. Energy, 2019, 182(SEP.1):1045-1057.
[4] Abdel-Hafeez S., Gordon-Ross A., Abubaker S. A comparison-free sorting algorithm on CPUs and GPUs[J]. The Journal of Supercomputing, 2018, 74(11):6369-6400.
[5] Fletcher R. The impact of culture on web site content, design and structure : an international and a multicultural perspective[J]. Journal of Communication Management, 2018, 10(3):259-273.
[6] Merkisz, Jerzy, Fuc, et al. The Analysis of Exhaust Gas Thermal Energy Recovery Through a TEG Generator in City Traffic Conditions Reproduced on a Dynamic Engine Test Bed (vol 44, pg 1704, 2015)[J]. Journal of Electronic Materials, 2018, 47(5):3059-3059.
[7] Tezer M., Cimsir B T. The impact of using mobile-supported learning management systems in teaching web design on the academic success of students and their opinions on the course[J]. Interactive Learning Environments, 2018, 26(1-4):402-410.
[8] Hartono E., Holsapple C W. Website Visual Design Qualities: A Threefold Framework[J]. ACM Transactions on Management Information Systems, 2019, 10(1):1-21.
[9] Hartmann T., Bernt M., Middendorf M. An Exact Algorithm for Sorting by Weighted Preserving Genome Rearrangements[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2019, 16(1):52-62.
[10] Bahador, Eslamdoust. Pareto Optimal Calculation and Simulation of XLPE Power Cable Production Line by Elitist Nonsorting Genetic Algorithm (NSGA-II)[J]. Wire & cable technology international: Serving Manufacturers, Specifiers and Users of Wire and Cable, 2018, 46(4):124-131.
[11] Boulanouar' K., Hadjali A., Lagha M. Trends summarization of times series: a multi-objective genetic algorithm-based model[J]. Journal of Smart Environments and Green Computing, 2022, 2(1):19-33.
[12] Suresh S., Elango N., Venkatesan K., et al. Sustainable friction stir spot welding of 6061-T6 aluminium alloy using improved non-dominated sorting teaching learning algorithm[J]. Journal of Materials Research and Technology, 2020, 9(5):11650-11674.
[13] Jahanbakhshi A., Momeny M., Mahmoudi M., et al. Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks[J]. Energy Reports, 2021, 7(2):5248-5256.
[14] Bidlo M., Dobe M. Evolutionary Development of Growing Generic Sorting Networks by Means of Rewriting Systems[J]. IEEE Transactions on Evolutionary Computation, 2020, 24(2):232-244.
[15] Dimitrova Z., Dimitrov V., Borissova D., et al. Two-Stage Search-Based Approach for Determining and Sorting of Mountain Hiking Routes Using Directed Weighted Multigraph[J]. Cybernetics and Information Technologies, 2020, 20(6):28-39.
[16] Polednik B., Piotrowicz A. Pedestrian exposure to traffic-related particles along a city road in Lublin, Poland[J]. Atmospheric Pollution Research, 2020, 11( 4):686-692.
[17] Agudelo-Castaneda D., Paoli F D., Morgado-Gamero W B., et al. Assessment of the NO_2 distribution and relationship with traffic load in the Caribbean coastal city[J]. The Science of the Total Environment, 2020, 720(Jun.10):137675.1-137675.9.

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