Land transportation remains one of the main contributors of noise and air pollution in urban areas. This is in addition to traffic congestion and accidents which result in the loss of productive activity. While there is a close relationship between traffic volumes and levels of noise and air pollution, transport authorities often assume that solving traffic congestion would reduce noise and air pollutant levels. Tight control over automobile ownership and use in Singapore has contributed in improving traffic flows, travel speeds and air quality. The adoption of internationally accepted standards on automobile emissions and gasoline have been effective in reducing air pollution from motor vehicles. Demand management measures have largely focused on controlling the source of traffic congestion, i.e. private automobile ownership and its use especially within the Central Business District during the day. This paper reviews and analyzes the effectiveness of two measures which are instrumental in controlling congestion and automobile ownership, i.e. road pricing and the vehicle quota scheme (VQS). While these measures have been successfAaual in achieving desired objectives, it has also led to the spreading of traffic externalitiesThe disproportionate growth of the number of vehicles compared to the available and even growing road infrastructure results in severe traffic congestion in metropolitan areas, causing tremendous tangible and consequential losses in all sectors, especially in fast developing Asian economies. Bangkok is one of the cities where traffic congestion is a crucial problem. Bangkok is an old city where high density residential areas are combined with ineffective road network. The Traffic Information Systems (TISs) can play a significant role towards improving traffic congestion problems. In this paper, we present the analytical results of our quantitative research which studies various aspects related to the knowledge-based TIS. The analysis of factors that affect Bangkok's traffic as perceived by drivers, or what we call influential factors (IF), along with the impact level of each IF are reported. In addition, the potential use of social networks for TIS is also discussed. This paper also highlights the success of using the social network to reach out the massive number of people who provide feedback and thus dramatically increase the usefulness of this information for the TIS. The reported results not only strongly confirm our selection of influential context attributes in our previous study, but also confirm the feasibility of using traffic-related data from social networks in Bangkok as context attributes for our framework. The Weight Mean Score method of influential factors presented in this paper can be further enhanced and become the metric to improve our proposed framework. Our analysis and result can also guide the design of the knowledge-based Traffic Information Systems. Although the research study was done based on Bangkok data, it can also be applicable to other cities that have similar road infrastructure problems as Bangkok to other roads in the network, loss in consumer welfare and rent seeking by automobile traders