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Recent Chinese economic and energy policies recognize the transportation sector as a key element in the nation's effort to meet its energy and air quality goals. The development of alternative fuel vehicle (AFV) has been considered as a particularly promising strategy. AFV-related policies can be traced back to the Eighth Five-Year Plan Period (i.e., 1991Period (i.e., -1995. All the work during the last twenty years has cumulatively prompted the transition of AFV development from policy-making to

Travelers today use technology that generates vast amounts of data at low cost. These data could supplement most outputs of regional travel demand models. New analysis tools could change how data and modeling are used in the assessment of travel demand. Recent work has shown how processed origin–destination trips, as developed by trip data providers, support travel analysis. Much less has been reported on how raw data from telecommunication providers can be processed to support such an analysis or to what extent the raw data can be treated to extract travel behavior. This paper discusses how cell phone data can be processed to inform a four-step transportation model, with a focus on the limitations and opportunities of such data. The illustrated data treatment approach uses only phone data and population density to generate trip matrices in two metropolitan areas: Boston, Massachusetts, and Rio de Janeiro, Brazil. How to label zones as home- and work-based according to frequency and time of day is detailed. By using the labels (home, work, or other) of consecutive stays, one can assign purposes to trips such as home-based work. The resulting trip pairs are expanded for the total population from census data. Comparable results with existing information reported in local surveys in Boston and existing origin–destination matrices in Rio de Janeiro are shown. The results detail a method for use of passively generated cellular data as a low-cost option for transportation planning.

A combination of open data tools and methods, facilitated by data format standardization, has started changing business-as-usual in the transit industry. The General Transit Feed Specification (GTFS) has become the de facto standard for releasing public transit route and schedule data. This paper analyzes this rapidly evolving transit information sector through the Mexico City experience. The case illustrates that even a mega-city with several different transit providers can create a fully-functional GTFS feed in a matter of weeks and obtain the benefits of work done elsewhere; thanks to the global open data ecosystem, a range of important free or low-cost applicationscustomer-facing applications and planning toolscan immediately capitalize on these data. However, the Mexico experience also reveals an important limitation of GTFS in its current form: its inability to easily accommodate semi-structured public transit services common in many developing world cities. An adaption to GTFS developed in Mexico City to address this limitation is described. Finally, the case reveals significant untapped potential to maximize the value of this open-data ecosystem, particularly for planning and regulatory tools. new forms of comparative assessment across public transportation systems (e.g., "benchmarking") and new service modeling possibilities (5). GTFS Goes Global The GTFS's simple file structure prompted rapid global adoption: as of November 2013, Google lists 229 public transit agencies around the world that release official GTFS feeds available for developers to use (6). If private transit companies are included, estimates range from 703 (7) or 1,048 (8). GTFS feeds range from covering all public transportation services for a particular region to a single provider. While concentrated in the Global North, GTFS experiences are also emerging in low-and middle-income cities (Table 1). This paper focuses on one such experience, the recent deployment of GTFS feeds in Mexico City. DATA COLLECTION AND GTFS FEED GENERATION IN MEXICO CITY Mexico City (the Federal District or DF) and its metropolitan area (MCMA) epitomize today's megalopolitan challenges. The DF, itself, represents essentially a single jurisdiction (one Mayor) with approximately 8.9 million persons, yet the broader MCMA encompasses some 40 additional local jurisdictions across two states and another 12 million people, posing institutional and operational challenges for transport and other sectors. This case focuses almost exclusively on services in the DF, where since 1975, the transportation secretariat (SETRAVI) has regulated both technical and non-technical aspects of public transportation planning and policy. SETRAVI oversees six relevant services in the DF (not including taxis); the government serves as operator (e.g., STE) or regulator (e.g., DGT) (Table 2). Except for a few lines of the Metro (STC), DF services do not extend into the broader MCMA. GTFS data collection included one metropolitan-scale transit service, the single line commuter rail (Tren Subu...

The work presented below was conducted as part of the World Bank's economic and sector work titled 'Urban Transport and Climate Change'. It is first a compendium of data-most of it collected as part of the 'China-GEF-World Bank Urban Transport Partnership Program'-and also provides a preliminary analysis of urban transport characteristics, energy use, and greenhouse gas (GHG) emissions for a diverse set of cities in China. This working paper is not in itself intended to be a strategy for urban transport and climate change in China. It is the view of the authors that this research could be an input toward the development of such a strategy in China and more broadly. Although transport in general, and urban transport in particular, is acknowledged to be an important and growing source of GHG emissions, work still needs to be done to develop robust and standardized datasets and frameworks to support a decision-making process. The paper is intended as a background document to support ongoing discussions about a climate change strategy and to establish a dataset to be made available as a platform for future studies and further refinement. It is hoped that others will take advantage of the dataset created for this study and use it as a basis for projections, comparative analysis, and to test their own hypothesis. Reviewers of this paper have also raised many specific possibilities and interesting ideas for further work, which are summarized in the conclusions. The authors would particularly like to thank AusAID (the Australian Government's overseas aid program), ESMAP (Energy Sector Management Assistance Program), and GEF (Global Environment Facility) who provided partial financing for this work.