近年来城市交通视频监控系统的发展使得海量交通视频的收集成为可能，通过视频数据对行人行为进行挖掘与理解在交通管理、城市规划等领域中具有广阔的应用前景。从视频中获取行人的轨迹，并对时空轨迹进行数据挖掘是理解行人行为的有效手段。

从几何学的角度来看，行人的时空轨迹是空间中一系列的点，而从语义的角度来看，行人的时空轨迹具有丰富的内涵。本文以从交通视频中提取的行人过街轨迹数据为研究对象，利用数据挖掘技术，通过定义停止、移动、进入/离开（过街区域）、加速、减速等轨迹语义信息对原始数据进行语义提取与标注。

行人的行为一般都会表现出某种模式或者规律，这些经过语义标注的轨迹，结合轨迹所在的环境并利用地理背景信息，可以有效地分析行人的行为。

本文利用简单滑动平均算法对轨迹数据进行预处理，并利用二叉空间分割算法对轨迹场景进行地理信息建模定义一系列感兴趣区域，然后通过判断轨迹点与兴趣区域的关系对轨迹信息进行空间维度信息的丰富，并选取适当的阈值对行人轨迹信息进行时间维度信息的丰富（加速、减速等）。

以往对于轨迹的语义标注研究利用GPS数据，从宏观的角度进行研究，通常局限于对于“停止”和“移动”行为的分析，而本文从微观分析与建模的角度，有效地解决了在过街行为论域中对行人行为进行轨迹语义挖掘与标注的问题。本文对“停止”行为在过街场景中的具体语义内涵作了解释，并通过引入新的语义概念，丰富了“移动”这个语义概念的内涵，尤其是针对行人穿越马路行为，更加细致地从速度的角度描述了“快速地走”、“慢速地跑”等行为。本文提出的四层框架对行人原始的时空轨迹数据进行了有效的高层次语义标注。该四层框架由以下部分组成：1）数据层：存储每个行人的原始时空轨迹数据；2）事件提取层：对行人的等待事件与速度选择事件进行标注；3）地理信息标注层：关联所有的事件与事件发生的地理区域；4）分析层：对行人整个过街行为进行分析与自然语言描述。此外，本文对于行人的速度数据也作了统计分析与说明。

本文基于PET(Post Enchrochment Time)指标，提出了改进的LPET(Lane-based Post Enchrochment Time)指标，定量地分析了过街过程中人车冲突在不同车道以及不同过街阶段的严重性。本文针对行人的过街速度也进行了详细的讨论，探讨了影响行人过街速度的因素，并对速度的分布进行了统计描述。

[1] Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D. (2008). Robust real-time unusual event detection using multiple fixed-location monitors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(3), 555-560

[2] Coifman, B., Beymer, D., McLauchlan, P., & Malik, J. (1998). A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies, 6(4), 271-288.

[3] Bissacco, A., Saisan, P., & Soatto, S. (2004, July). Gait recognition using dynamic affine invariants. In Proc. Int’l Symp. Math. Theory of Networks and Systems.

[4] Sumpter, N., & Bulpitt, A. (2000). Learning spatio-temporal patterns for predicting object behaviour. Image and Vision Computing, 18(9), 697-704.

[5] Fraile, R., & Maybank, S. J. (1998, September). Vehicle Trajectory Approximation and Classification. In BMVC (Vol. 98, pp. 832-840).

[6] Johnson, N., & Hogg, D. (1996). Learning the distribution of object trajectories for event recognition. Image and Vision computing, 14(8), 609-615.

[7] H?gerstraand, T. (1970). What about people in regional science?. Papers in regional science, 24(1), 7-24.

[8] Kondo, K., & Kitamura, R. (1987). Time-space constraints and the formation of trip chains. Regional Science and Urban Economics, 17(1), 49-65.

[9] Hornsby, Kathleen, Egenhofer, M.J., 2002. Modeling modeling objects over multiple granularities. Ann. Math. Artif. Intell. 36 (1–2), 177–194。

[10] Asakura, Y., & Iryo, T. (2007). Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument. Transportation Research Part A: Policy and Practice, 41(7), 684-690.

[11] Long, J. A., & Nelson, T. A. (2013). A review of quantitative methods for movement data. International Journal of Geographical Information Science, 27(2), 292-318.

[12] Cheng, Z., Caverlee, J., Lee, K., & Sui, D. Z. (2011). Exploring Millions of Footprints in Location Sharing Services. ICWSM, 2011, 81-88.

[13] Galata, A., Johnson, N., & Hogg, D. (2001). Learning variable-length Markov models of behavior. Computer Vision and Image Understanding, 81(3), 398-413. [14] Morris, B. T., & Trivedi, M. M. (2008). A survey of vision-based trajectory learning and analysis for surveillance. Circuits and Systems for Video Technology, IEEE Transactions on, 18(8), 1114-1127.

[15] Fontes, V. C., & Bogorny, V. (2013). Discovering semantic spatial and spatio-temporal outliers from moving object trajectories. arXiv preprint arXiv:1303.5132.

[16] Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., & Maybank, S. (2006). A system for learning statistical motion patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(9), 1450-1464.

[17] Grimson, E., Wang, X., Ng, G. W., & Ma, K. T. (2008). Trajectory analysis and semantic region modeling using a nonparametric bayesian model.

[18] Renso, C., Baglioni, M., de Macedo, J. A. F., Trasarti, R., & Wachowicz, M. (2013). How you move reveals who you are: understanding human behavior by analyzing trajectory data. Knowledge and information systems, 37(2), 331-362.

[19] Bogorny, V., Kuijpers, B., & Alvares, L. O. (2009). ST‐DMQL: a semantic trajectory data mining query language. International Journal of Geographical Information Science, 23(10), 1245-1276.

[20] Brakatsoulas, S., Pfoser, D., & Tryfona, N. (2004, July). Modeling, storing and mining moving object databases. In Database Engineering and Applications Symposium, 2004. IDEAS'04. Proceedings. International (pp. 68-77). IEEE.

[21] Schmid, F., Richter, K. F., & Laube, P. (2009). Semantic trajectory compression. In Advances in Spatial and Temporal Databases (pp. 411-416). Springer Berlin Heidelberg.

[22] Spaccapietra, S., Parent, C., Damiani, M. L., de Macedo, J. A., Porto, F., & Vangenot, C. (2008). A conceptual view on trajectories. Data & knowledge engineering, 65(1), 126-146.

[23] Gonzàlez, J., Rowe, D., Varona, J., & Roca, F. X. (2009). Understanding dynamic scenes based on human sequence evaluation. Image and Vision Computing, 27(10), 1433-1444.

[24] Gonzàlez, J., Rowe, D., Varona, J., & Roca, F. X. (2009). Understanding dynamic scenes based on human sequence evaluation. Image and Vision Computing, 27(10), 1433-1444.

[25] Brand, M., Oliver, N., & Pentland, A. (1997, June). Coupled hidden Markov models for complex action recognition. In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on (pp. 994-999). IEEE.

[26] Johnson, N., & Hogg, D. (1996). Learning the distribution of object trajectories for event recognition. Image and Vision computing, 14(8), 609-615.

[27] Fernyhough, J. H., Cohn, A. G., & Hogg, D. C. (1996). Generation of semantic regions from image sequences. In Computer Vision—ECCV'96 (pp. 475-484). Springer Berlin Heidelberg.

[28] SStauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. (Vol. 2). IEEE.

[29] Basharat, A., Gritai, A., & Shah, M. (2008, June). Learning object motion patterns for anomaly detection and improved object detection. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1-8). IEEE.

[30] Duong, T. V., Bui, H. H., Phung, D. Q., & Venkatesh, S. (2005, June). Activity recognition and abnormality detection with the switching hidden semi-markov model. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 838-845). IEEE.

[31] Galata, A., Johnson, N., & Hogg, D. (2001). Learning variable-length Markov models of behavior. Computer Vision and Image Understanding, 81(3), 398-413.

[32] Makris, D., & Ellis, T. (2005). Learning semantic scene models from observing activity in visual surveillance. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 35(3), 397-408.

[33] Giannotti, F., Pedreschi, D., & Theodoridis, Y. (2009, March). Geographic privacy-aware knowledge discovery and delivery. In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (pp. 1157-1158). ACM.

[34] Giannotti, F., & Pedreschi, D. (2008). Mobility, data mining and privacy: Geographic knowledge discovery. Springer Science & Business Media.

[35] EU Marie Curie Project N 295179, program PEOPLE IRSES 2011 scheme (2012–2015) SEEK - SEmantic Enrichment of trajectory Knowledge discovery (SEEK).URL http://www.seek-project.eu/.

[36] Andrienko, G., Andrienko, N., Bak, P., Keim, D., Kisilevich, S., & Wrobel, S. (2011). A conceptual framework and taxonomy of techniques for analyzing movement. Journal of Visual Languages & Computing, 22(3), 213-232.

[37] Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., & Aberer, K. (2011, March). SeMiTri: a framework for semantic annotation of heterogeneous trajectories. In Proceedings of the 14th international conference on extending database technology (pp. 259-270). ACM.

[38] Li, X., Han, J., Kim, S., & Gonzalez, H. (2007, April). ROAM: Rule-and Motif-Based Anomaly Detection in Massive Moving Object Data Sets. In SDM (Vol. 7, pp. 273-284).

[39] Andrienko, G., Andrienko, N., & Wrobel, S. (2007). Visual analytics tools for analysis of movement data. ACM SIGKDD Explorations Newsletter, 9(2), 38-46.

[40] Spinsanti, L., Celli, F., & Renso, C. (2010). Where you stop is who you are: understanding people's activities by places visited. Proceedings of BMI.

[41] Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., ... & Yan, Z. (2013). Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR), 45(4), 42.

[42] Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3-4), 240-252.

[34] Kalnis, P., Mamoulis, N., & Bakiras, S. (2005). On discovering moving clusters in spatio-temporal data.

[43] Laube, P., Imfeld, S., & Weibel, R. (2005). Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science, 19(6), 639-668.

[44] Andersson, M., Gudmundsson, J., Laube, P., & Wolle, T. (2007, March). Reporting leadership patterns among trajectories. In Proceedings of the 2007 ACM symposium on Applied computing (pp. 3-7). ACM.

[45] Meng, X., and Z. Ding (2003) “DSTTMOD: A Discrete Spatio-Temporal Trajectory Based Moving Object Databases System”, DEXA, LNCS 2736, Springer verlag, (pp444-453).

[46] Lee, J. G., Han, J., & Whang, K. Y. (2007, June). Trajectory clustering: a partition-and-group framework. In Proceedings of the 2007 ACM SIGMOD international conference on Management of data (pp. 593-604). ACM.

[47] Chen, L., ?zsu, M. T., & Oria, V. (2005, June). Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data (pp. 491-502). ACM.

[48] Zaki, M. J. (2001). SPADE: An efficient algorithm for mining frequent sequences. Machine learning, 42(1-2), 31-60.

[49] Sharma, P., & Balakrishna, G. (2011). PrefixSpan: Mining Sequential Patterns by Prefix-Projected Pattern. International Journal of Computer Science and Engineering Survey, 2(4).

[50] Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., ... & Hsu, M. C. (2004). Mining sequential patterns by pattern-growth: The prefixspan approach. Knowledge and Data Engineering, IEEE Transactions on, 16(11), 1424-1440.

[51] Cao, H., Mamoulis, N., & Cheung, D. W. (2007). Discovery of periodic patterns in spatiotemporal sequences. Knowledge and Data Engineering, IEEE Transactions on, 19(4), 453-467.

[52] Gidófalvi, G., & Pedersen, T. B. (2009). Mining long, sharable patterns in trajectories of moving objects. Geoinformatica, 13(1), 27-55.

[53] Han, J., Dong, G., & Yin, Y. (1999, March). Efficient mining of partial periodic patterns in time series database. In Data Engineering, 1999. Proceedings., 15th International Conference on (pp. 106-115). IEEE.

[54] Tsoukatos, I., & Gunopulos, D. (2001). Efficient mining of spatiotemporal patterns (pp. 425-442). Springer Berlin Heidelberg.

[55] Benkert, M., Gudmundsson, J., Hübner, F., & Wolle, T. (2008). Reporting flock patterns. Computational Geometry, 41(3), 111-125.

[56] Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3-4), 240-252.

[57] Gudmundsson, J., Laube, P., & Wolle, T. (2008). Movement Patterns in Spatio‐temporal Data. In Encyclopedia of GIS (pp. 726-732). Springer US.

[58] Sbalzarini, I. F., Theriot, J., & Koumoutsakos, P. (2002). Machine learning for biological trajectory classification applications.

[59] Bashir, F. I., Khokhar, A. A., & Schonfeld, D. (2007). Object trajectory-based activity classification and recognition using hidden Markov models. Image Processing, IEEE Transactions on, 16(7), 1912-1919.

[60] Jeung, H., Shen, H. T., & Zhou, X. (2007). Mining trajectory patterns using hidden Markov models. In Data Warehousing and Knowledge Discovery (pp. 470-480). Springer Berlin Heidelberg.

[61] Nascimento, J. C., Figueiredo, M. A., & Marques, J. S. (2010). Trajectory classification using switched dynamical hidden Markov models. Image Processing, IEEE Transactions on, 19(5), 1338-1348.

[62] Li, Z., Ji, M., Lee, J. G., Tang, L. A., Yu, Y., Han, J., & Kays, R. (2010, June). MoveMine: mining moving object databases. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 1203-1206). ACM.

[63] Wu, F., Lei, T. K. H., Li, Z., & Han, J. (2014). MoveMine 2.0: Mining object relationships from movement data. Proceedings of the VLDB Endowment, 7(13).

[64] de Lucca Siqueira, F., & Bogorny, V. (2011). Discovering chasing behavior in moving object trajectories. Transactions in GIS, 15(5), 667-688.

[65] Alvares, L. O., Loy, A. M., Renso, C., & Bogorny, V. (2011). An algorithm to identify avoidance behavior in moving object trajectories. Journal of the Brazilian Computer Society, 17(3), 193-203.

[66] Alvares, L. O., Bogorny, V., de Macedo, J. A. F., Moelans, B., & Spaccapietra, S. (2007, November). Dynamic modeling of trajectory patterns using data mining and reverse engineering. In Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling-Volume 83 (pp. 149-154). Australian Computer Society, Inc.

[67] Güting, R. H., B?hlen, M. H., Erwig, M., Jensen, C. S., Lorentzos, N. A., Schneider, M., & Vazirgiannis, M. (2000). A foundation for representing and querying moving objects. ACM Transactions on Database Systems (TODS), 25(1), 1-42.

[68] Güting, R. H., & Schneider, M. (2005). Moving objects databases. Elsevier.

[69] Wolfson, O., Xu, B., Chamberlain, S., & Jiang, L. (1998, July). Moving objects databases: Issues and solutions. In Scientific and Statistical Database Management, 1998. Proceedings. Tenth International Conference on (pp. 111-122). IEEE.

[70] Brakatsoulas, S., Pfoser, D., & Tryfona, N. (2004, July). Modeling, storing and mining moving object databases. In Database Engineering and Applications Symposium, 2004. IDEAS'04. Proceedings. International (pp. 68-77). IEEE.

[71] Zacks, J. M., & Tversky, B. (2001). Event structure in perception and conception. Psychological bulletin, 127(1), 3.

[72] Santer, R. D., Yamawaki, Y., Rind, F. C., & Simmons, P. J. (2005). Motor activity and trajectory control during escape jumping in the locust Locusta migratoria. Journal of Comparative Physiology A, 191(10), 965-975.

[73] Stopher, P., FitzGerald, C., & Zhang, J. (2008). Search for a global positioning system device to measure person travel. Transportation Research Part C: Emerging Technologies, 16(3), 350-369.

[74] Schüssler, N. and Axhausen, K.W., 2009. Processing Raw Data from Global Positioning Systems Without Additional Information. Transportation Research Record: Journal of the Transportation Research Board, 2105 (4), 28–36.

[75] Yan Z, Parent C, Spaccapietra S et al (2010) A hybrid model and computing platform for spatio-semantic trajectories. The semantic web: research and applications, lecture Notes in computer science, vol 6088,60–75.

[76] Palma AT, Bogorny V, Kuijpers B et al (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing (SAC’08), pp 863–868.

[77] Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5(2), 199–220 (1993)

[78] Liu, L., & Zsu, M. T. (2009). Encyclopedia of database systems. Springer Publishing Company, Incorporated.

[79] Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., & Ma, W. Y. (2008, November). Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (p. 34). ACM.

[80] Zheng, Y., Zhang, L., Xie, X., & Ma, W. Y. (2009, April). Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web (pp. 791-800). ACM.

[81] Cao, X., Cong, G., & Jensen, C. S. (2010). Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment, 3(1-2), 1009-1020.

[82] Ashbrook, D., and Starner, T. 2003. Using GPS to learn significant locations and predict movement across multiple users, Personal Ubiquitous Computing, 7, 275–286.

[83] Yan, Z., Patent, C., Spaccapietra S., and CHakraborty, D. 2010. A Hybrid Model and Computing Platform for Spatio-Semantic Trajectories. Proc. of the 7th Extended Semantic Web Conf. (ESWC’10), 60–75.

[84] Uddin, M.R., Ravishankar, C., Tsotras, V.J. 2011. Finding Regions of Interest from Trajectory Data, Proc. of the 12th IEEE Int. Conf. on Mobile Data Management (MDM), pages 39-48.

[85] Giannotti, F., Nanni, M., Pinelli, F., and PedreschiI, D. 2007. Trajectory pattern mining. In Proc. KDD’07, pp. 330–339. ACM.

[86] Manber, U. (1989). Introduction to algorithms: a creative approach (Vol. 4). Reading, MA: Addison-Wesley.

[87] Foley, J. D. (1990). A. vanDam, SK Feiner, and JF Hughes. Computer Graphics: Principles and Practice.

[88] Feito, F., Torres, J. C., & Urena, A. (1995). Orientation, simplicity, and inclusion test for planar polygons. Computers & Graphics, 19(4), 595-600.

[89] Feito, F. R., & Torres, J. C. (1997). Inclusion test for general polyhedra. Computers & Graphics, 21(1), 23-30.

[90] Preparata, F. P., Shamos, M. I., & Preparata, F. P. (1985). Computational geometry: an introduction (Vol. 5). New York: Springer-Verlag.

[91] Hormann, K., & Agathos, A. (2001). The point in polygon problem for arbitrary polygons. Computational Geometry, 20(3), 131-144.

[92] Taylor, G. (1994). Point in polygon test. Survey Review, 32(254), 479-484.

[93] Huang, C. W., & Shih, T. Y. (1997). On the complexity of point-in-polygon algorithms. Computers & Geosciences, 23(1), 109-118.

[94] ?alik, B., & Kolingerova, I. (2001). A cell-based point-in-polygon algorithm suitable for large sets of points. Computers & Geosciences, 27(10), 1135-1145.

[95] De Berg, M., Van Kreveld, M., Overmars, M., & Schwarzkopf, O. C. (2000). Computational geometry (pp. 1-17). Springer Berlin Heidelberg.

[96] Zalik B, Clapworthy GJ. A universal trapezoidation algorithm for planar polygons. Computers & Graphics 1999;23(3):353–63.

[97] Teillaud, M. (2000). Union and split operations on dynamic trapezoidal maps. Computational Geometry, 17(3), 153-163.

[98] Etzion, M., & Rappoport, A. (1997). On compatible star decompositions of simple polygons. Visualization and Computer Graphics, IEEE Transactions on, 3(1), 87-95.

[99] Skala, V. (1996). Line clipping in E2 with suboptimal complexity O (1). Computers & Graphics, 20(4), 523-530.

[100] Masoud, O., & Papanikolopoulos, N. P. (2007). Using geometric primitives to calibrate traffic scenes. Transportation Research Part C: Emerging Technologies, 15(6), 361-379.

[101]Tarawneh, M. S. (2001). Evaluation of pedestrian speed in Jordan with investigation of some contributing factors. Journal of Safety Research, 32(2), 229-236.

[102]Murray, S. J. (2006). The effects of simulated cellular phone conversation on road-crossing safety.

[103]Highway Design Manual: the road (Third edition) (2006), China Communications Press, P.R. China (in Chinese).

[104] Fang, Z., Li, Q., Li, Q., Han, L. D., & Shaw, S. L. (2013). A space–time efficiency model for optimizing intra-intersection vehicle–pedestrian evacuation movements. Transportation Research Part C: Emerging Technologies, 31, 112-130.

[105] Lam, W. H., and Cheung, C. Y. (2000). Pedestrian speed/flow relationships for walking facilities in Hong Kong. Journal of transportation engineering, 126(4), 343-349.

[106] Hatfield, J., and Murphy, S. (2007). “The effects of mobile phone use on pedestrian crossing behaviour at signalized and unsignalized intersections.” Accident Analysis & Prevention, 39(1), 197 –205.

[107] Shi, J., Chen, Y., Ren, F., and Rong, J. (2007), Research on Pedestrian Behaviour and Traffic Characteristics at Unsignalized Midblock Crosswalk: Case Study in Beijing, In Transportation Research Record: Journal of the Transportation Research Board, No. 2038, TRB, National Research Council, Washington, D.C.

[108] Rastogi, R., Chandra, S., Vamsheedhar, J., & Das, V. R. (2011). Parametric study of pedestrian speeds at midblock crossings. Journal of Urban Planning and Development, 137(4), 381-389.

[109] Goh, B. H., Subramaniam, K., Wai, Y. T., and Mohamed, A. A. (2012). PEDESTRIAN CROSSING SPEED: THE CASE OF MALAYSIA. International Journal for Traffic and Transport Engineering, 2(4).

[110]Chandra, S., and Bharti, A. K. (2013). Speed Distribution Curves for Pedestrians During Walking and Crossing. Procedia-Social and Behavioural Sciences, 104, 660-667.

[111] Li, P., Bian, Y., Rong, J., Zhao, L., and Shu, S. (2013). Pedestrian Crossing Behavior at Unsignalized Mid-block Crosswalks Around the Primary School. Procedia-Social and Behavioral Sciences, 96, 442-450.

[112] Zhuang, X., and Wu, C. (2011). Pedestrians’ crossing behaviors and safety at unmarked roadway in China. Accident Analysis & Prevention, 43(6), 1927-1936.

[113] Zhuang, X., and Wu, C. (2012). The safety margin and perceived safety of pedestrians at unmarked roadway. Transportation research part F: traffic psychology and behaviour, 15(2), 119-131.

[114] Sayed, T., Zaki, M. H., and Autey, J. (2013). Automated safety diagnosis of vehicle–bicycle interactions using computer vision analysis. Safety science, 59, 163-172.

[115] Zheng, L., Ismail, K., & Meng, X. (2014). Traffic conflict techniques for road safety analysis: open questions and some insights. Canadian journal of civil engineering, 41(7), 633-641.

[116] Parker Jr, M. R., & Zegeer, C. V. (1989). Traffic Conflict Techniques for Safety and Operations. Observers Manual (No. FHWA-IP-88-027).

[117] Amundsen and Hydén (1977) Proceedings of the 1st Workshop on Traffic Conflicts, Oslo, Norway.

[118] Zhang, Y., Yao, D., Qiu, T. Z., & Peng, L. (2014). Scene-based pedestrian safety performance model in mixed traffic situation. IET Intelligent Transport Systems, 8(3), 209-218.

[5] Li, B. (2013). A model of pedestrians’ intended waiting times for street crossings at signalized intersections. Transportation research part B: methodological, 51, 17-28.

[119] European Conference of Ministers of Transport (1998) Road safety vulnerable road users report on the safety of pedestrians. Available: http://www.internationaltransportforum.org/Pub/pdf/09CDsr/PDF_EN/18RecPedestrians_EN.pdf. Accessed: May 1998.

[120] Ministry of Public Security, Department of Traffic Management of China (2010). Annual Report of Road Traffic Accidents Statistics in P.R. China. Scientific Research Institute of Traffic Management, Ministry of Public Security. (In Chinese).

[121] Lu, M., Lu, Z., & Wang, J. (2011). Sustainable safe walking: the need for a pedestrian safety policy in PR China. In XII International Walk Conference on Walking and livable Communities, Vancouver, Canada.

[122] Liu, Y. C., & Tung, Y. C. (2014). Risk analysis of pedestrians’ road-crossing decisions: Effects of age, time gap, time of day, and vehicle speed. Safety Science, 63, 77-82.

[123] Yagil, D. (2000). Beliefs, motives and situational factors related to pedestrians’ self-reported behavior at signal-controlled crossings. Transportation Research Part F: Traffic Psychology and Behaviour, 3(1), 1-13.

[124] Tom, A., & Granié, M. A. (2011). Gender differences in pedestrian rule compliance and visual search at signalized and unsignalized crossroads. Accident Analysis & Prevention, 43(5), 1794-1801.

[125] Guozhu Cheng et al. (2013) Setting conditions of crosswalk signal on urban road sections in China. 2013 International Conference on Transportation (ICTR 2013): 96-105.DEStech Publications, Inc. ISBN: 978-1-60595-146-1.

[126] Himanen, V., & Kulmala, R. (1988). An application of logit models in analysing the behaviour of pedestrians and car drivers on pedestrian crossings. Accident Analysis & Prevention, 20(3), 187-197.

[127] Troutbeck, R. J., & Brilon, W. (1997). Unsignalized intersection theory. Traffic Flow Theory, TRB.

[128] Hydén, C. (1987). The development of a method for traffic safety evaluation: The Swedish Traffic Conflicts Technique. BULLETIN LUND INSTITUTE OF TECHNOLOGY, DEPARTMENT, (70).

[129] Varhelyi, A. (1998). Drivers' speed behaviour at a zebra crossing: a case study. Accident Analysis & Prevention, 30(6), 731-743.

[130] Hayward, J. C. (1971). Near misses as a measure of safety at urban intersections. Pennsylvania State University, Department of Civil Engineering.

[131] Archer, J. (2005). Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling: A study of urban and suburban intersections.

[132] Ismail, K., Sayed, T., & Saunier, N. (2011). Methodologies for aggregating indicators of traffic conflict. Transportation Research Record: Journal of the Transportation Research Board, 2237(1), 10-19.

[133] Malkhamah, S., Tight, M., & Montgomery, F. (2005). The development of an automatic method of safety monitoring at Pelican crossings. Accident Analysis & Prevention, 37(5), 938-946.

[134] Ismail, K. A. (2010). Application of computer vision techniques for automated road safety analysis and traffic data collection (Doctoral dissertation, UNIVERSITY OF BRITISH COLUMBIA (Vancouver).

[135] Peesapati, L. N., Hunter, M. P., & Rodgers, M. O. (2013). Evaluation of Postencroachment Time as Surrogate for Opposing Left-Turn Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2386(1), 42-51.

[136] Svensson, ?. (1998). A method for analysing the traffic process in a safety perspective (Doctoral dissertation, Lund University).

[137] Hydén, C. (1996). Traffic conflicts technique: state-of-the-art. Traffic Safety Work with Video-Processing, University Kaiserslautern, Transportation Department, Kaiserslautern, Germany.

[138] Zhuang, X., & Wu, C. (2011). Pedestrians’ crossing behaviors and safety at unmarked roadway in China. Accident Analysis & Prevention, 43(6), 1927-1936.

[139] Vallyon, C., Turner, S., & Hodgson, S. (2011). Reducing pedestrian delay at traffic signals (No. 440). NZ Transport Agency.

[140] Daamen, W., & Hoogendoorn, S. P. (2006, January). Free speed distributions for pedestrian traffic. In TRB-Annual Meeting, Washington.

[141]Martin, A. (2006). Factors influencing pedestrian safety: a literature review (No. PPR241). TRL.

[142]Li, B. (2014). A bilevel model for multivariate risk analysis of pedestrians’ crossing behavior at signalized intersections. Transportation research part B: methodological, 65, 18-30.

[143] Rastogi, R., Chandra, S., Vamsheedhar, J., & Das, V. R. (2011). Parametric study of pedestrian speeds at midblock crossings. Journal of urban planning and development, 137(4), 381-389.

[144] Laureshyn, A. (2010). Application of automated video analysis to road user behaviour (PhD Dissertation). Lund: Lund University Department of Technology and society Traffic Engineering.

[145] Ambros, J. (2011). Traffic conflict technique in the Czech Republic. In Proceedings of the 24th ICTCT Workshop in Warsaw 27.-28. Oct.

[146] Zajíc, P. (2012). Traffic Conflicts and Road Transport Safety – New Development. Electronical Technical Journal of Technology, Engineering and Logistics in Transport, 7(4), 174-183.

[147] Ambros, J., Turek, R. & Paukrt, J. (2014). Road Safety Evaluation Using Traffic Conflicts: Pilot Comparison of Micro-simulation and Observation. In International Conference on Traffic and Transport Engineering, Belgrade.

[148] Ariza, A. (2011). Validation of Road Safety Surrogate Measures as a Predictor of Crash Frequency Rates on a Large-Scale Microsimulation Network (Doctoral dissertation).

[149] Elvik, R., Vaa, T., Erke, A., & Sorensen, M. (Eds.). (2009). The handbook of road safety measures. Emerald Group