Abstract
Daily mobility patterns in highly populated urban environments rely on a well-functioning effective road network. Nevertheless, traffic bottlenecks are typical for urban environments with periodic traffic congestion . In this paper, we focus on the investigation of how traffic congestion is related with complicated crossings . First, we select an approach for the classification of the complexity of road partitions and the derivation of complicated crossings based on geodata from OpenStreetMap (OSM). Second, we calculate traffic congestions using Floating Taxi Data (FTD) from Shanghai in 2007. Then, we develop a matching technique to link the congestion and complicated crossings, and subsequently define the concept of traffic bottlenecks represented by polygons. The bottlenecks indicate locations where the transportation infrastructure is complex and traffic congestion appears periodically. Finally, we select suitable cartographic representations of traffic bottlenecks in potential thematic vehicle traffic maps .
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References
Andrienko, N., & Andrienko, G. (2007). Designing visual analytics methods for massive collections of movement data. Cartographica, 42(2), 117–138.
Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.
Andrienko, N., & Andrienko, G. (2013). Visual analytics of movement: An overview of methods, tools, and procedures. Information Visualization, 12(1), 3–24.
Andrienko, N., Andrienko, G., & Rinzivillo, S. (2015). Exploiting spatial abstraction in predictive analytics of vehicle traffic. ISPRS International Journal of Geo-Information, 4(2), 591–606.
Andrienko, N., Andrienko, G., & Rinzivillo, S. (2016). Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics. Information Systems, 57, 172–194.
Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. In ACM SIGMOD International Conference on Management of data (pp. 49–60). ACM Press.
Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1–3), 1–101.
Brakatsoulas, S., Pfoser, D., & Tryfona, N. (2004). Modeling, storing and mining moving object databases. Proceedings of the International Database Engineering and Applications Symposium 2004 (IDEAS ’04) (pp. 68–77).
Chen, W., Guo, F., & Wang, F. Y. (2015). A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 16(6), 2970–2984.
Costa, G., Manco, G., & Masciari, E. (2014). Dealing with trajectory streams by clustering and mathematical transforms. Journal of Intelligent Information Systems, 42, 155–177.
Dewulf, B., Neutens, T., Vanlommel, M., Logghe, S., De Maeyer, P., Witlox, F., et al. (2015). Examining commuting patterns using floating car data and circular statistics: Exploring the use of new methods and visualizations to study travel times. Journal of Transport Geography, 48, 41–51.
Duan, Z., Liu, L., & Sun, W. (2009). Traffic congestion analysis of Shanghai road network based on floating car data. International Conference on Transportation Engineering 2009 (pp. 2731–2736).
Gühnemann, A., Schäfer, R.-P., Thiessenhusen, K.-U., & Wagner, P. (2004). monitoring traffic and emissions by floating car data. WORKING PAPER ITS-WP-04-07.
Haworth, J., Shawe-Taylor, J., Cheng, T., & Wang, J. (2014). Local online kernel ridge regression for forecasting of urban travel times. Transportation Research Part C: Emerging Technologies, 46, 151–178.
Jarvis, R. A. (1973). On the identification of the convex hull of a finite set of points in the plane. Information Processing Letters, 2, 18–21.
Kang, J. H., Welbourne, W., Stewart, B., & Borriello, G. (2004). Extracting places from traces of locations. In Proceedings of the 2nd ACM International Workshop on Wireless Mobile Applications and Services on WLAN hotspots (WMASH ‘04) (pp. 110–118).
Keler, A., & Krisp, J. M. (2016). Visual analysis of floating taxi data based on interconnected and timestamped area selections. In G. Gartner, M. Jobst, & H. Huang (Eds.), Progress in cartography, EuroCarto 2015 (pp. 115–131). Cham: Springer International Publishing.
Keler, A., Ding, L., & Krisp, J. M. (2016). Visualization of traffic congestion based on floating taxi data. Kartographische Nachrichten, 66(1), 7–13.
Körner, M. (2011). Nutzungsmöglichkeiten von Floating Car Data zur Verkehrsflussoptimierung. In J. Strobl, T. Blaschke, G Griesebner (Eds.), Angewandte Geoinformatik 2011, Beiträge zum 23. AGIT-Symposium Salzburg (pp. 381–386). Berlin/Offenbach: Wichmann.
Krisp, J. M., & Keler, A. (2015). Car navigation—computing routes that avoid complicated crossings. International Journal of Geographical Information Science, 29(11), 1988–2000.
Lan, J., Long, C., Wong, R. C.-W., Chen, Y., Fu, Y., Guo, D., Liu, S., Ge, Y., Zhou, Y., & Li, J. (2014). A new framework for traffic anomaly detection. In Proceedings of the 2014 SIAM International Conference on DATA MINING (pp. 875–883).
Li, J., & Zuo, L. (2004). Shanghai urban elevated roads and metropolis traffic. Urban Roads Bridges & Flood Control, 21(6), 14–17.
Li, D., Fu, B., Wang, Y., Lu, G., Berezin, Y., Stanley, H. E., et al. (2015a). Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proceedings of the National Academy of Sciences, 112(3), 669–672.
Li, H., Kulik, L., & Ramamohanarao, K. (2015b). Robust inferences of travel paths from GPS trajectories. International Journal of Geographical Information Science, 29(12), 2194–2222.
Liu, X., & Ban, Y. (2013). Uncovering spatio-temporal cluster patterns using massive floating car data. ISPRS International Journal of Geo-Information, 2, 371–384.
Long, J. C., Gao, Z. Y., Ren, H. L., & Lian, A. P. (2008). Urban traffic congestion propagation and bottleneck identification. Science in China Series F: Information Sciences, 51(7), 948–964.
Protschky, V., Ruhhammer, C., & Feit, S. (2015). Learning traffic light parameters with floating car data. In IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015) (pp. 2438–2443).
Ranacher, P., Brunauer, R., Van der Spek, S. C., & Reich, S. (2016). A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic. Computers, Environment and Urban Systems, 59, 152–163.
Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., & Andrienko, G. (2008). Visually driven analysis of movement data by progressive clustering. Information Visualization, 7(3–4), 225–239.
Robinson, R. (1984). Problems in the urban environment: traffic congestion and its effects. In Wollongong studies in geography (Vol. 14). Department of Geography, University of Wollongong: NSW, Australia.
Sohr, A., Brockfeld, E., & Krieg, S. (2010). Quality of floating car data. In Conference Proceedings, paper nr 02392, 12th World Conference on Transport Research (WCTR). Lisbon, Portugal. Retrieved July, 11–15, 2010 from www.digitalpapers.org.
Stanica, R., Fiore, M., & Malandrino, F. (2013). Offloading floating car data. Proceedings of the 2013 IEEE 14th International Symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) (pp. 1–9).
Wen, H., Sun, J., & Zhang, X. (2014). Study on traffic congestion patterns of large city in China taking Beijing as an example. Procedia—Social and Behavioral Sciences, 138, 482–491.
Xu, X., Li, X., Hu, Y., & Peng, Z. (2012). A novel algorithm to identifying vehicle travel path in elevated road area based on GPS trajectory data. Frontiers of Earth Science, 6(4), 354–363.
Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., & Aberer, K. (2013). Semantic trajectories: Mobility data computation and annotation. In ACM transactions on intelligent systems and technology (TIST)—Special sections on paraphrasing; intelligent systems for socially aware computing; social computing, behavioral-cultural modeling, and prediction, 4(3), 1–38.
Yuan, Q., Liu, Z., Li, J., Zhang, J., & Yang, F. (2014). A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks. Transportation Research Part C: Emerging Technologies, 47(2), 114–127.
Zhang, Y. M. (2004). The thinking of relieving the congestion of elevated road system. Traffic and Transportation, 5, 37–38.
Acknowledgements
The described taxi Floating Car Data set of Shanghai (‘SUVnet-Trace Data,’ http://wirelesslab.sjtu.edu.cn/taxi_trace_data.html) was obtained from the Wireless and Sensor networks Lab (WnSN) at Shanghai Jiao Tong University. We would like to thank the Laboratory for Wireless and Sensor Networks at Shanghai Jiao Tong University, especially Prof. Min-You Wu and Jia Peng, for providing access to this data.
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Keler, A., Krisp, J.M., Ding, L. (2017). Visualization of Traffic Bottlenecks: Combining Traffic Congestion with Complicated Crossings. In: Peterson, M. (eds) Advances in Cartography and GIScience. ICACI 2017. Lecture Notes in Geoinformation and Cartography(). Springer, Cham. https://doi.org/10.1007/978-3-319-57336-6_34
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DOI: https://doi.org/10.1007/978-3-319-57336-6_34
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