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Developed at the request of the Mexican G20 Presidency for consideration by the Finance Ministers and Central Bank Governors at the G20 Leaders' Summit in Mexico, and jointly prepared with the Asian Development Bank, this policy paper positioned green transport in the context of cities development. Urban transport determines the shape of a city and its ecological footprint. Many cities in low and middle income countries are at a crossroads. Policy decisions taken now, while car use is still relatively low and cities retain a relatively transit friendly, compact urban form, will affect how people will live in their cities for many decades into the future. A new paradigm of urban transport can be part of the solution to reversing the deteriorating situation in some cities of developing countries, and supporting others to embark on a sustainable, low carbon, green growth path: developing a city for people rather than cars, and including public and mass transport as a major component of the modal structure. Implementing such a new paradigm can be truly transformational. This joint World Bank and Asian Development Bank paper lays out six aspects, which are most difficult to align, yet, are critical to ensure the sustainability of urban transport systems, visionary leadership, integrated strategy for land use and urban transport, coordination among agencies, domestic capacity, adequate cost recovery, and private participation in the operation and construction of urban transport systems. The paper proposes a set of new initiatives for G20 leaders' consideration, including the development of an umbrella toolkit to guide policy makers in charge of urban planning to make transport decisions best suited to their local contexts.

Thailand needs to avoid the high-carbon growth path of many developed countries and, instead, take a low-carbon growth path. A green low-carbon growth path is in Thailand's own interest as it can simultaneously tackle local environmental degradation, global climate change, and energy security challenges. It can also position Thailand as a regional leader in green, sustainable growth. Green low-carbon growth in Thailand could focus on the following four pillars: 1) maintaining rapid economic growth while adjusting the country's economic structure toward a less energy, and carbon-intensive economy; 2) achieving greater urbanization while shifting toward green livable low-carbon cities; 3) meeting the huge thirst for energy while transforming the energy sector toward one of high energy efficiency and widespread diffusion of low-carbon technologies; and 4) improving quality of life while shifting toward a resource-efficient and sustainable lifestyle.

Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions. � The Institution of Engineering and Technology 2008.