A Framework on New Travel Demand Model Based on Potential Travelers and Surrounding Land Uses for Rapid Transit

One of the public transports is rapid transit, which provides the highest performance mode of urban transportation. Currently, existing rapid transit travel demand analysis from the service provider is based on ticketing data that contain information such as time travel, origin and destination; which is using trip based method. This method has its limitation such as the demand is for trip making rather than for activities as well as having spatial, temporal and demographic aggregation errors. It also failed to predict the travel demand when there is future development or growth in the surrounding area. Therefore, new method for modeling travel demand is needed. This paper proposes a framework of new model and analytics for travel demands of rapid transits based on big data of potential travelers and surrounding land uses. Land uses and transportation are interdependent. With this proposed concept, the accurate travel demand for rapid transit in the future will be met. Therefore, the rapid transit service will have excellence operation, which includes optimum frequency, punctuality and reliable service

An Analysis of Human Silhouettes with Normalised Silhouettes Images and Shape Fourier Descriptors

This paper aims to investigate the human silhouettes in video frames, which involves normalizing the silhouettes and describing the shape of the region in video frames using Shape Fourier Descriptors. Shape Fourier Descriptor describes the shape of an object by considering its boundaries, which are the shape centroid and calculated by a particular formula through all the video frames after normalized the videos. This shows the changes of the objects with various actions and can be recognized and characterized human or non-human in the video frames. Normalized Silhouette Image is significant before the videos are being processed to describe in descriptors. It focuses on the region based on the object’s ratio in images of the shape of the object and silhouette images are centred after action segmentation. This reduces the burden of the process of extract unnecessary part in whole videos. Various human action videos and animal videos are used for the training and testing in this study to make sure the system performed better

Automated Bus Crew Rescheduling for Late for Sign-On (LFSO) Event using Multi-Agent System

Unpredictable events (UE) are major factors that cause crew rescheduling to be performed. One of the UE is when a crew is late for duty. In this research, it is termed as Late for Sign-On (LFSO). When LFSO occurred, the reschedule is needed to make sure available crew take the duty. Currently, there is no automated mechanism to handle the LFSO. Real time rescheduling approaches mostly are not supported due to static schedules constraint. Mathematical approaches require extensive computational power therefore delayed the real-time results. Meanwhile, manual rescheduling is prone to error and not optimum. This research objective is to develop a new approach in automating the crew rescheduling process using multiagent system. The agents dynamically adapt their behaviour to changing environments quickly and find solutions via negotiations and cooperation between them. Experiment is conducted using AgentPower simulation tool. The result concluded that the proposed technique is capable to reschedule quickly. The distribution of a duty also plays a major role in achieving rescheduling success