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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.537
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Huangqin HUANG, Jianhua GUO, Xiangyu SHI, Leixiao SHEN
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Data Source Importance Evaluation for Highway Networks: A Complex Network-Based Approach

Authors:Huangqin HUANG, Jianhua GUO, Xiangyu SHI, Leixiao SHEN

Abstract

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Keywords:highway network operations, data source, complex network, centrality, entropy weight method

References

  1. [1] Federal Highway Administration. Traffic monitoring guide. Washington D.C, USA: U.S. Department of Transportation Federal Highway Administration; 2022. https://www.fhwa.dot.gov/policyinformation/tmguide/ [Accessed 18th June 2023].
  2. [2] Kamouch A, Chaoub A, Guennoun Z. Mobile big data in vehicular networks: The road to internet of vehicles. In: Skourletopoulos G, et al. (eds.) Mobile big data. Lecture Notes on Data Engineering and Communications Technologies. Cham, Switzerland: Springer; 2018. p. 129-143. DOI: 10.1007/978-3-319-67925-9_6.
  3. [3] Chan Y. Telecommunications-and information technology-inspired analyses: Review of an intelligent transportation systems experience. Transportation Research Record. 2017;2658(1):44-55. DOI: 10.3141/2658-06.
  4. [4] Huang Y, et al. Spatiotemporal approach for evaluating the vehicle restriction policy with multi-sensor data. Transportation Research Record. 2022;2676(8):724-736. DOI: 10.1177/03611981221085518.
  5. [5] Levenberg E, et al. Live road condition assessment with internal vehicle sensors. Transportation Research Record. 2021;2675(10):1442-1452. DOI: 10.1177/03611981211016852.
  6. [6] Seedah DPK, Sankaran B, O’Brien WJ. Approach to classifying freight data elements across multiple data sources. Transportation Research Record. 2015;2529(1):56-65. DOI: 10.3141/2529-06.
  7. [7] Robichaud K, Gordon M. Assessment of data-collection techniques for highway agencies. Transportation Research Record. 2003;1855(1):129-135. DOI: 10.3141/1855-16.
  8. [8] Hasnat MM, Bardaka E. Distribution of highway infrastructure cost responsibility and revenue contribution shares among highway users in North Carolina: Present conditions and future alternatives. Transportation Research Record. 2023;2677(2):1082-1102. DOI: 10.1177/03611981221112403.
  9. [9] Chen T, Ma J, Zhu Z, Guo X. Evaluation method for node importance of urban rail network considering traffic characteristics. Sustainability. 2023;15(4):3582. DOI: 10.3390/su15043582.
  10. [10] Liu S, Gao H. The structure entropy-based node importance ranking method for graph data. Entropy. 2023;25(6):941. DOI: 10.3390/e25060941.
  11. [11] Zhang Y, Lu Y, Yang G, Hang Z. Multi-attribute decision making method for node importance metric in complex network. Applied Sciences. 2022;12(4):1944-1944. DOI:10.3390/APP12041944.
  12. [12] Sotoodeh H, Falahrad M. Relative degree structural hole centrality, CRD-SH: a new centrality measure in complex networks. Journal of Systems Science & Complexity. 2019;32(05):1306-1323. DOI: 10.1007/s11424-018-7331-5.
  13. [13] Yu H, Cao X, Liu Z, Li Y. Identifying key nodes based on improved structural holes in complex networks. Physica A: Statistical Mechanics and its Applications. 2017;486(C):318-327. DOI: 10.1016/j.physa.2017.05.028.
  14. [14] Çalık A, Erdebilli B, Özdemir YS. Novel integrated hybrid multi-criteria decision-making approach for logistics performance index. Transportation Research Record. 2023;2677(2):1392-1400. DOI: 10.1177/03611981221113314.
  15. [15] Chen C, Zhang H. Evaluation of green development level of Mianyang agriculture, based on the entropy weight method. Sustainability. 2023;15(9):7589. DOI: 10.3390/SU15097589.
  16. [16] Hanson-DeFusco J. What data counts in policymaking and programming evaluation-relevant data sources for triangulation according to main epistemologies and philosophies within social science. Evaluation and Program Planning. 2023;97(3):102238. DOI: 10.1016/j.evalprogplan.2023.102238.
  17. [17] Park N, et al. Estimating node importance in knowledge graphs using graph neural networks. Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 4-8 Aug. 2019, Anchorage, USA. 2019. p. 596-606. DOI: 10.1145/3292500.3330855.
  18. [18] Narayan VV, et al. Evaluation of data sources and approaches for estimation of influenza-associated mortality in India. Influenza and Other Respiratory Viruses. 2018;12(1):72-80. DOI: 10.1111/irv.12493.
  19. [19] Price C, Burley RA. An evaluation of information sources for current awareness on occupational diseases. Journal of Information Science. 1986;12(5):247-255. DOI: 10.1177/016555158601200504.
  20. [20] Sorensen HT, Sabroe S, Olsen J. A framework for evaluation of secondary data sources for epidemiological research. International Journal of Epidemiology, 1996;25(2):435-442. DOI: 10.1093/ije/25.2.435.
  21. [21] Hjørland B. Evaluation of an information source illustrated by a case study: effect of screening for breast cancer. JASIST. 2011;62(10):1892-1898. DOI: 10.1002/asi.21606.
  22. [22] Hjørland B. Methods for evaluating information sources: an annotated catalogue. Journal of Information Science. 2012;38(3):258-268. DOI: 10.1177/0165551512439178.
  23. [23] Kaufmann D, Kraay A, Mastruzzi M. The worldwide governance indicators: methodology and analytical issues. Hague Journal on the Rule of Law. 2010;3(2):220-246. DOI: 10.1017/S1876404511200046.
  24. [24] Wood S, Regehr JD. Hierarchical methodology to evaluate the quality of disparate axle load data sources for pavement design. Journal of Traffic and Transportation Engineering (English Edition). 2022;9(2):261-279. DOI: 10.1016/J.JTTE.2021.02.005.
  25. [25] Broach J, et al. Evaluating the potential of crowdsourced data to estimate network-wide bicycle volumes. Transportation Research Record. 2024;2678(3):573-589. DOI: 10.1177/03611981231182388.
  26. [26] Jiang R, et al. Predicting bus travel time with hybrid incomplete data: A deep learning approach. Promet - Traffic & Transportation. 2022;34(5):673-685. DOI:10.7307/PTT.V34I5.4052.
  27. [27] Yang L, Maria ST, Breitfuss G. Data sources in data driven circular business models. New Business Models Conference Proceedings 2023. 21-23 Jun 2023, Maastricht, Netherlands. 2023.. DOI: 10.26481/mup.2302.21.
  28. [28] Khorashadizadeh H, Tiwari S, Groppe S. A survey on covid-19 knowledge graphs and their data sources. In: Mohanty SN, Diaz VG, Kumar GS. (eds.) Intelligent systems and machine learning. ICISML 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Cham, Switzerland: Springer; 2023. p. 142-152. DOI: 10.1007/978-3-031-35078-8_13.
  29. [29] Wan H, et al. Research on ship relation graph analysis driven by multi-source data, 2021 6th International Conference on Transportation Information and Safety (ICTIS), 22-24 Oct. 2021, Wuhan, China. 2021. p. 655-660. DOI: 10.1109/ICTIS54573.2021.9798661.
  30. [30] Kam KA, et al. Finding and exploring use of commodity-specific data sources for commodity flow modeling. Transportation Research Record. 2017;2646(1):77-83. DOI: 10.3141/2646-09.
  31. [31] Nguyen K, Cao J. Top-K data source selection for keyword queries over multiple XML data sources. Journal of Information Science. 2012;38(2):156-175. DOI: 10.1177/0165551511435875.
  32. [32] Tok AYC, et al. Online data repository for statewide freight planning and analysis. Transportation Research Record. 2011;2246(1):121-129. DOI: 10.3141/2246-15.
  33. [33] Tijssen R, Raan TV, Heiser W, Wachmann L. Integrating multiple sources of information in literature-based maps of science. Journal of Information Science. 1990;16(4):217-227. DOI: 10.1177/016555159001600402.
  34. [34] Wang W, et al. Factors affecting unmanned aerial vehicles’ unsafe behaviors and influence mechanism based on social network theory. Transportation Research Record. 2023;2677(5):1030-1045. DOI: 10.1177/03611981221138782.
  35. [35] Batista NA, et al. Dealing with data from multiple web sources. WebMedia ‘18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web. 16-19 Oct. 2018, Salvador, Brazil. 2018. p. 3-6. DOI: 10.1145/3243082.3264609.
  36. [36] Krogstie J. Evaluating data quality for integration of data sources. In: Grabis J, Kirikova M, Zdravkovic J, Stirna J. (eds.) The Practice of Enterprise Modeling. PoEM 2013. Lecture Notes in Business Information Processing. Berlin, Germany: Springer; 2013. p. 39-53. DOI: 10.1007/978-3-642-41641-5_4.
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