Suchergebnisse

Chinese cities have experienced diverse urbanization and motorization trends that present distinct challenges for municipal transportation policymaking. However, there is no systematic understanding of the unique motorization and urbanization trends of Chinese cities and how physical characteristics map to their transportation policy priorities. We adopt a mixed-method approach to address this knowledge gap. We conduct a time-series clustering of 287 Chinese cities using eight indicators of urbanization and motorization from 2001 to 2014, identifying four distinct city clusters. We compile a policy matrix of 21 policy types from 44 representative cities and conduct a qualitative comparison of transportation policies across the four city clusters. We find clear patterns among policies adopted within city clusters and differences across clusters. Wealthy megacities (Cluster 1) are leveraging their existing urban rail with multimodal integration and transit-oriented development, while more car-oriented wealthy cities (Cluster 2) are building urban rail and discounting public transport. Sprawling, medium-wealth cities (Cluster 3) are opting for electric buses and the poorest, dense cities with low mobility levels (Cluster 4) have policies focused on road-building to connect urban cores to rural areas. Transportation policies among Chinese cities are at least partially reflective of urbanization and motorization trends and policy learning needs to account for these distinct patterns in both physical conditions and policy priorities. Our mixed-method approach (involving time-series clustering and qualitative policy profiling) provides a way for government officials to identify peer cities as role models or collaborators in forming more targeted, context-specific, and visionary transportation policies.

Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.