This paper presents an analysis system to find the impact of architectural designs on pedestrian behavioural data. The system employs GDTW-P-SVMs which are capable of modelling sequential data with variable-length input series. We apply GDTW-P-SVMs to simulated pedestrian spatial behaviour data. The data include four types of behavioural characteristics: i) movement trajectories, ii) walking speed, iii) the angle α between the movement vector and the gaze vector and iv) its derivative. The analysis system learns a statistical model characterising three classes of spatial behaviour. The classes are formed based on pedestrians' reactions to visual attractions in a simulated environment. A separate data set that includes the crowd attraction effect is used to discuss the impact of social group formation on the classification result. Our experiments show that using the angle α and its derivative as input to the classifiers results in lower classification error rates compared to classification of trajectory and speed of movement data. We compare the classification accuracy of the GDTW-P-SVMs with other classification methods that are capable of handling data objects with variable-length input series. GDTW-P-SVMs showed promising results in classifying the simulated behavioural data.
2012 International Joint Conference on Neural Networks (IJCNN 2012). Proceedings of the 2012 International Joint Conference on Neural Networks (Brisbane, Qld 10-15 June, 2012)