A model is presented that describes the life of large transportation projects. The model has six stages, each one devoted to a different part in the life of the project: prehistory of the project, project development, procurement, implementation, operations and maintenance, and long-range impacts and economic restructuring. This six-stage model should help planners and project promoters go through the task of developing and implementing large transportation projects. The model describes the different issues in each stage and embedded in the discussion are ways for addressing them. The political, institutional, as well as technical aspects are addressed, and they are linked to show how they affect the evolution of the project. The analysis presented in the six-stage model can be inter preted as a call for planners and project promoters to acknowledge that large transportation projects are very complex and that planning and implementing them requires planners and promoters to behave in a strategic way that includes intertwining technical and political issues.
As public transit agencies install new technology systems, these agencies are gaining increasing amounts of data. These data have the potential to change how transit agencies operate by generating better information for decision making. Deriving value from these data and applying it to improve service requires changing the institutional processes that developed when agencies had little reliable information about their systems and customers. This research used the Massachusetts Bay Transportation Authority as a case study. The research assessed how the agency measured performance and then redesigned and advanced the agency's daily performance reports for rapid transit through a collaborative and iterative process with the operations control center staff. These reports were used to identify poor performance, to implement pilot projects to address the causes of poor performance, and to evaluate the effects of these pilots. Through the case study, this research found that service controllers’ trust and interpretation of performance information determined the impact the information had on operations. The results showed that new data would be most effective in producing service improvements if measurements accurately reflected human experience and were developed in conjunction with their intended users. Developing small pilot projects during this collaborative process would also enable new performance information and results in sustainable service improvements.
This paper describes analysis of whether the public transit sector suffers from Baumol’s cost disease. The evolution of labor productivity and average labor costs across transit agencies in the United States was assessed compared with other industries. It was found that ( a) labor productivity in the transit sector was mostly stagnant over the period 1997 to 2013, more so in bus operations than in rail operations (0.0% and 0.7% average labor productivity growth rates, respectively), and even more so when output was measured as vehicle revenue miles rather than as passenger miles traveled; ( b) the transit sector was highly labor-intensive, because it represented on average 64% of total costs (operating and capital) for bus and 40% for rail; ( c) compensation per employee rose at a faster pace than inflation in 85% of the agencies analyzed; and ( d) compensation per employee rose at a faster pace than the average local wage rate in 65% of the agencies analyzed. These findings support the hypothesis that not only does the transit sector suffer from Baumol’s cost disease but also that additional factors contribute to spiraling labor costs. Although no antidote to the disease is clear, policy makers should recognize that, as the economy becomes more productive overall, it can continue to support growing levels of transit service in recognition of the growing external benefits, despite the sector’s inherently stagnant productivity growth.
A growing number of researchers and transit agencies are using fare card and vehicle location data to infer passengers’ origins, destinations, and transfers. A number of researchers have suggested that these new data sets provide valuable information for transit network design, but few concrete applications have been developed to address bus network design and service planning problems. This paper proposes new service planning procedures to aggregate these automated data to examine travel patterns to specific locations of interest to propose needed improvements. The data from existing passengers’ trips are then analyzed to assess the benefits of the proposed service changes. In particular, the number of existing passengers who would likely experience shorter travel times with the service changes is calculated according to the geometry of how a proposed new or extended route intersects with the existing transit network. The results of this analysis provide planners with better information than is currently available to support decisions on how to allocate the scarce resources typically available for service changes. Several case studies from the Massachusetts Bay Transportation Authority are presented to illustrate these analytical techniques.
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