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Purpose – This chapter introduces the basic strategy and practice for developing a sustainable transportation system in China, and puts forward problems and directions of improvement.

Methodology – To begin with, the chapter elaborates on the development background of the Chinese urban transportation system and on the challenges in terms of urbanization, mechanization, and resource constraints. The chapter then systematically summarizes the implementation strategy for developing a green transportation system in China, including the government's leading role, public transportation system resource integration, the combination of nonmotor traffic and public traffic, and traffic demand management policy. It illustrates these with specific examples of practical activities conducted.

Findings – Finally, in response to typical problems challenging China, this chapter puts forward directions of improvement for aspects of land utilization, planning, intelligent transportation, traffic demand management, and public participation.

Implications for China – During the critical period featuring rapid growth of private motor vehicle population and transformation of urban traffic development policy, this chapter contributes to building a consensus among the Chinese government and the civil society and to promoting the implementation and sound development of sustainable transportation system by adopting comprehensive measures.

The Formula 1 World Championship Japanese Grand Prix (denoted SUZUKA F1) has been held in Suzuka city in the Mie Prefecture of Japan every year since 2009. This event gathers a large number of motor racing fans around the circuit. The total number of attendees over three days amounts to more than 200,000. Reducing the traffic congestion around expressway interchanges (ICs) and decreasing the departure times of return traffic during peak hours are of critical importance not only for short-term transportation demand management (TDM) measures but also for sustainable development ­management in Suzuka city as a tourism destination. The chapter starts a brief review of previous studies on the TDM measures to identify the current trends in both their methodological and problem-oriented approaches and then introduces our approach called the area marketing and management approach (AMMA) relating to an issue on how we can pursue the sustainable development in tourism destinations. Based on the concept of the AMMA, a set of the Smart TDM measures are proposed involving the development of the application software that will be used as an interactive communication tool. The validity of the repeated applications of the Smart TDM measures is empirically examined by assessing the most recent experiences at the SUZUKA F1 until 2017. The limitations to what the current Smart TDM measures can do are finally discussed to improve the smartness of these TDM measures to contribute to the sustainable area development.

The purpose of this study is to use the optimization modeling method to explore whether there is an ideal arrangement of course enrollments that can yield optimal parking demand and supply on college campuses.

Using the University of Louisville as a case study, this study deploys a three-step analytical process to examine the correlation between parking demand and course enrollment, estimate parking demand based on course enrollment with regression analyses and embed this estimated relationship in an optimization model that minimizes on-campus parking demand and supply.

The correlation analyses suggest significant correlations between course enrollments and on-campus parking. The correlation patterns are different between students and university employees. The optimization results indicate that coupling parking supply and course scheduling decisions can reduce parking supply by 30%.

Voluminous studies on sustainable campus transportation have focused on transportation demand management strategies. The relationship between course-scheduling and parking demand was not explicitly accounted for in most studies. This study's results reveal that parking demand on campus depends on the number of courses offered across time. Thus, factoring and optimizing course schedules in campus parking decisions remains a viable and essential option to reduce on-campus parking demand.