State-of-the-art methods for the safe and efficient design of bicycle facilities are based on difficult to collect data and potentially dubious assumptions regarding cyclist behavior. Simulation models could offer a way forward, but existing bicycling models in the academic literature have not been validated using actual data. This paper attempts to address both of these shortcomings simultaneously by conducting a field study to obtain real-world bicycle data and implementing a simulation using a multilane and inhomogeneous cellular automata model to reproduce the observations. The resulting model is found to emulate field conditions while possibly under-predicting bike path capacity. The analysis indicates that the model's potential as for planning could be high given additional work on the underlying model specification and the collection of additional data.

Given current concerns surrounding regional air pollution, climate change and urban congestion, this research is timely. If we begin to see more substantial mode shifting to non-motorized modes, this and similar models could become standard tools in the city or regional planner's toolkit.

After a discussion of the context in which this research is being conducted, we review the relevant literature on bicycle facility design and bicycle traffic operation, before summarizing the real-world data collection methods and results. The simulation model is then presented along with results and discussion comparing the modeled to observed data. We conclude with suggestions for future work in data collection and model development.

Current concerns surrounding regional air pollution, climate change, rising gasoline prices and urban congestion could presage a substantial increase in bicycle mode share. However, state-of-the-art methods for the safe and efficient design of bicycle facilities are based on difficult to collect data and potentially dubious assumptions regarding cyclist behavior. Simulation models offer a way forward, but existing bicycling models in the academic literature have not been validated using actual data. This paper addresses these shortcomings by obtaining real-world bicycle data and implementing a multilane, inhomogeneous cellular automaton simulation model that can reproduce observations. The existing literature is reviewed to inform the data collection and model development. It is found that the model emulates field conditions while possibly under-predicting bike path capacity. Since the simulation model can “observe” individual cyclists, it is ideally suited to determine level of service based on difficult to observe cycling events such as passing. The conclusion suggests future work on data collection and model development.

Diesel-electric locomotives used by U.S. freight railroads are relatively low emitters of criteria air pollutants and greenhouse gases when compared to competing modes; however, the continuous growth in goods movement is cause for concern as locomotive emissions may grow. Railroads only account for a small fraction of all mobile source emissions, but the concentration of emissions along rail facilities raises questions about equity, in particular environmental justice, and the relative benefits of competing modes of goods movement. This paper provides a synthesis and review of current data and methods used to account for regional locomotive activity. Understanding data limitations and methodological issues at the regional scale provides a starting point for development of more spatially detailed locomotive emission models. Methods developed by EPA and the California Air Resources Board are considered. It is found that each method produces very different results and is inadequate for use at the regional (or smaller) spatial scale. Problems arise from activity measures that ignore differences in geography and freight rail services between regions or depend on detailed operational data that are no longer available. While detailed activity data do exist, they are not always available because they are owned by private railroads. New methods should minimize the use of detailed or confidential railroad data yet still be sensitive to local factors. Fuel based methods provide the most hope, but greater cooperation between regulatory agencies and railroads is required.

Combined, aviation and marine transportation are responsible for approximately 5 percent of total greenhouse (GHG) emissions in the United States and 3 percent globally and are among the fastest growing modes in the transportation sector. Controlling the growth in these emissions will be an important part of reducing emissions from the transportation sector. A range of near-, medium- and long-term mitigation options are available to slow the growth of energy consumption and GHG emissions from aviation and marine shipping. Implementation of these options could result in reductions of more than 50 percent below BAU levels by 2050 from global aviation and more than 60 percent for global marine shipping. For these reductions to be realized, however, international and domestic policy intervention is required. Developing an effective path forward that facilitates the adoption of meaningful policies remains both a challenge and an opportunity.

“Aviation and Marine Transportation: GHG Mitigation Potential and Challenges” presents an introduction to aviation and marine transportation and a discussion of the determinants of GHG emissions from transportation; gives overview of current emissions and trends and growth projections; explains the technological mitigation options and potential GHG emission reductions; and discusses policy options at both the domestic and international level to achieve deep and durable reductions in emissions.

With the passage of the Global Warming Solutions Act of 2006 (AB32), California has begun an ambitious journey to reduce in-state GHG emissions to 1990 levels by 2020. Under the direction of executive order S-20-06, a mandated Market Advisory Committee (MAC) charged with studying market-based mechanisms to reduce GHG emissions, including cap and trade systems, has recommended taking an “upstream” approach to GHG emissions regulation, arguing that upstream regulation will reduce administrative costs because there are fewer agents. In this paper, we argue that, the total costs to society of a GHG cap and trade scheme can be minimized though downstream regulation, rather than the widely proposed upstream approach. We propose a household carbon trading system with four major components: a state allocation to households, household-to-household trading, households to utility company credit transfers, and utility companies to government credit transfers. The proposed system can also be considered more equitable than carbon taxes and upstream cap and trade systems to control GHG emissions from residential energy use and is consistent with AB32.