How can we avoid traffic jams? : design of on-demand traffic guidance system

• Main Author: Menoth Mohan Dhanya.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Theses and Dissertations
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/54723
• Institution: Nanyang Technological University

The term Intelligent Transportation System (ITS) refers to information and communication technology (applied to transport infrastructure and communication technology) that improves transport outcomes such as transport safety, transport productivity, travel reliability, informed travel choices, social equity, environmental performance and network operation resilience (as given by Wikipedia). With the advancement of wireless communication technology, increased traffic congestion has led us to think about the serious traffic condition we have named 'traffic jam'. In a city like Singapore, traffic jam is a common phenomenon, especially during the peak hours. Hence the necessity of a real-time traffic guidance with the help of which the traffic jam can potentially be reduced. Proper utilization of the available resources can improve the traffic condition and reduce the travelling time. The topic, 'On-demand Traffic Guidance System' deals with developing algorithm (using MATLAB software) at various stages like data acquisition/segmentation, traffic prediction and network optimization. But this thesis addresses only the issues of "Missing data imputation" which comes under the first phase and provides an overview of the other phases which are related to the topic under study. In all these phases, the raw data (the traffic data which is not yet subjected to analysis) cannot be used directly because it has to be processed well to fulfill the basic requirement of data needed by each phase of the work. The major problem we noticed is 'missing data' in the raw traffic data. So we made a strong research in those missing data problems and the experiments and findings are explained in various chapters. The thesis examines the reliability and robustness of the proposed method by comparing the same with the traditional technique. All the testing is implemented using MATLAB software and plot for the works are included in Chapter 5.