Visualizing road connectivity under traffic influence: a CiteSpace-based method

References

• Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, 22, 207–216.
   Google Scholar
• Akbarzadeh, M., Memarmontazerin, S., Derrible, S., & Salehi Reihani, S. F. (2019). The role of travel demand and network centrality on the connectivity and resilience of an urban street system. Transportation, 46(4), 1127–1141. https://doi.org/10.1007/s11116-017-9814-y
   Web of Science ®Google Scholar
• Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
   Web of Science ®Google Scholar
• Chen, C., & Morris, S. (2003). Visualizing evolving networks: Minimum spanning trees versus pathfinder networks. In IEEE Symposium on Information Visualization 2003 (IEEE Cat. No. 03TH8714) (pp. 67–74). IEEE. https://doi.org/10.1109/infvis.2003.1249010
   Google Scholar
• Chen, W., Guo, F., & Wang, F.-Y. (2015). A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 16(6), 2970–2984. https://doi.org/10.1109/TITS.2015.2436897
   Web of Science ®Google Scholar
• Cheng, T., Haworth, J., & Wang, J. (2012). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14(4), 389–413. https://doi.org/10.1007/s10109-011-0149-5
   Web of Science ®Google Scholar
• Cheng, T., Wang, J., Haworth, J., Heydecker, B., & Chow, A. (2014). A dynamic spatial weight matrix and localized space-time autoregressive integrated moving average for network modeling. Geographical Analysis, 46(1), 75–97. https://doi.org/10.1111/gean.12026
   Web of Science ®Google Scholar
• Ding, Q. Y., Wang, X. F., Zhang, X. Y., & Sun, Z. Q. (2011). Forecasting traffic volume with space-time arima model. Advanced Materials Research, 156–157, 979–983. https://doi.org/10.4028/www.scientific.net/AMR.156-157.979
   Google Scholar
• Erath, A., Löchl, M., & Axhausen, K. W. (2009). Graph-theoretical analysis of the Swiss road and railway networks over time. Networks and Spatial Economics, 9(3), 379–400. https://doi.org/10.1007/s11067-008-9074-7
   Web of Science ®Google Scholar
• Fang, Z., Shaw, S. L., Tu, W., Li, Q., & Li, Y. (2012). Spatiotemporal analysis of critical transportation links based on time geographic concepts: A case study of critical bridges in Wuhan, China. Journal of Transport Geography, 23, 44–59. https://doi.org/10.1016/j.jtrangeo.2012.03.018
   Web of Science ®Google Scholar
• Feng, Z., Li, H., Zeng, W., Yang, S. H., & Qu, H. (2021). Topology density map for urban data visualization and analysis. IEEE Transactions on Visualization and Computer Graphics, 27(2), 828–838. https://doi.org/10.1109/TVCG.2020.3030469
   PubMed Web of Science ®Google Scholar
• Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
   Web of Science ®Google Scholar
• Hu, M.-B., Jiang, R., Wu, Y.-H., Wang, W.-X., & Wu, Q.-S. (2008). Urban traffic from the perspective of dual graph. The European Physical Journal B, 63(1), 127–133. https://doi.org/10.1140/epjb/e2008-00219-5
   Web of Science ®Google Scholar
• Kamarianakis, Y., & Prastacos, P. (2005). Space-time modeling of traffic flow. Computers and Geosciences, 31(2), 119–133. https://doi.org/10.1016/j.cageo.2004.05.012
   Web of Science ®Google Scholar
• Kohan, M., & Ale, J. M. (2020). Discovering traffic congestion through traffic flow patterns generated by moving object trajectories. Computers, Environment and Urban Systems, 80, 101426. https://doi.org/10.1016/j.compenvurbsys.2019.101426
   Web of Science ®Google Scholar
• Liu, B., Long, J., Deng, M., Tang, J., & Huang, J. (2022). Revealing spatiotemporal correlation of urban roads via traffic perturbation simulation. Sustainable Cities and Society, 77, 103545. https://doi.org/10.1016/j.scs.2021.103545
   Web of Science ®Google Scholar
• Lu, F., Liu, K., Duan, Y., Cheng, S., & Du, F. (2018). Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach. Physica A: Statistical Mechanics and Its Applications, 501, 227–237. https://doi.org/10.1016/j.physa.2018.02.062
   Web of Science ®Google Scholar
• Min, W., & Wynter, L. (2011). Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies, 19(4), 606–616. https://doi.org/10.1016/j.trc.2010.10.002
   Web of Science ®Google Scholar
• Min, X., Hu, J., Chen, Q., Zhang, T., & Zhang, Y. (2009). Short-term traffic flow forecasting of urban network based on dynamic STARIMA model. In 2009 12th International IEEE Conference on Intelligent Transportation Systems. IEEE Publications. https://doi.org/10.1109/ITSC.2009.5309741.
   Google Scholar
• Ministry of Public Security. (2020). Evaluation methods for road traffic congestion levels (GA/T 115). Ministry of Public Security.
   Google Scholar
• Scheepens, R., Hurter, C., Van De Wetering, H., & Van Wijk, J. J. (2016). Visualization, selection, and analysis of traffic flows. IEEE Transactions on Visualization and Computer Graphics, 22(1), 379–388. https://doi.org/10.1109/TVCG.2015.2467112
   PubMed Web of Science ®Google Scholar
• Shen, Y., Zhao, L., & Fan, J. (2015). Analysis and visualization for hot spot based route recommendation using short-dated taxi GPS traces. Information, 6(2), 134–151. https://doi.org/10.3390/info6020134
   Google Scholar
• Slingsby, A., Dykes, J., & Wood, J. (2008). Using treemaps for variable selection in spatio-temporal visualisation. Information Visualization, 7(3–4), 210–224. https://doi.org/10.1057/PALGRAVE.IVS.9500185
   Web of Science ®Google Scholar
• Sreelekha, M. G., Krishnamurthy, K., & Anjaneyulu, M. V. L. R. (2016). Interaction between road network connectivity and spatial pattern. Procedia Technology, 24, 131–139. https://doi.org/10.1016/j.protcy.2016.05.019
   Google Scholar
• Su, F., Dong, H., Jia, L., Tian, Z., & Sun, X. (2017). Space–time correlation analysis of traffic flow on road network. International Journal of Modern Physics B, 31(5), 1750027. https://doi.org/10.1142/S0217979217500278
   Web of Science ®Google Scholar
• Tolone, W. J. (2009). Interactive visualizations for critical infrastructure analysis. International Journal of Critical Infrastructure Protection, 2(3), 124–134. https://doi.org/10.1016/j.ijcip.2009.07.004
   Web of Science ®Google Scholar
• Von Landesberger, T., Brodkorb, F., Roskosch, P., Andrienko, N., Andrienko, G., & Kerren, A. (2016). MobilityGraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Transactions on Visualization and Computer Graphics, 22(1), 11–20. https://doi.org/10.1109/TVCG.2015.2468111
   PubMed Web of Science ®Google Scholar
• Wang, J., Mo, H., Wang, F., & Jin, F. (2011). Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach. Journal of Transport Geography, 19(4), 712–721. https://doi.org/10.1016/j.jtrangeo.2010.08.012
   Web of Science ®Google Scholar
• Yang, S., Wu, J., Xu, Y., & Yang, T. (2019). Revealing heterogeneous spatiotemporal traffic flow patterns of urban road network via tensor decomposition-based clustering approach. Physica A: Statistical Mechanics and Its Applications, 526, 120688. https://doi.org/10.1016/j.physa.2019.03.053
   Web of Science ®Google Scholar
• Yu, W., Zhang, Y., Ai, T., Guan, Q., Chen, Z., & Li, H. (2020). Road network generalization considering traffic flow patterns. International Journal of Geographical Information Science, 34(1), 119–149. https://doi.org/10.1080/13658816.2019.1650936
   Web of Science ®Google Scholar
• Zhang, Y., Wang, X., Zeng, P., & Chen, X. (2011). Centrality characteristics of road network patterns of traffic analysis zones. Transportation Research Record(1), 16–24. https://doi.org/10.3141/2256-03
   Google Scholar
• Zhang, Z., He, Q., Tong, H., Gou, J., & Li, X. (2016). Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network. Transportation Research Part C: Emerging Technologies, 71, 284–302. https://doi.org/10.1016/j.trc.2016.08.006
   Web of Science ®Google Scholar
• Zhao, S., Zhao, P., & Cui, Y. (2017). A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China. Physica A: Statistical Mechanics and Its Applications, 478, 143–157. https://doi.org/10.1016/j.physa.2017.02.069
   Web of Science ®Google Scholar
• Zheng, L., Feng, Q., Liu, W., & Zhao, X. (2016). Discovering trip hot routes using large scale taxi trajectory data. In International Conference on Advanced Data Mining and Applications. Springer. https://doi.org/10.1007/978-3-319-49586-6_37
   Google Scholar
• Zou, H., Yue, Y., Li, Q., & Yeh, A. G. O. (2012). An improved distance metric for the interpolation of link-based traffic data using kriging: A case study of a large-scale urban road network. International Journal of Geographical Information Science, 26(4), 667–689. https://doi.org/10.1080/13658816.2011.609488
   Web of Science ®Google Scholar
• Zou, H., Yue, Y., & Li, Q. Q. (2014). Urban traffic state explained by road networks and spatial variance: Approach using floating car data. Transportation Research Record, 2467(1), 40–48. https://doi.org/10.3141/2467-05
   Google Scholar