Current concerns surrounding regional air pollution, climate change, rising gasoline prices, and urban congestion could presage a substantial increase in the 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 with actual data. These shortcomings are addressed 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 underpredicting 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. Future work on data collection and model development is suggested.

a b s t r a c tWith 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.

Diesel–electric locomotives used by U.S. freight railroads are relatively low emitters of criteria air pollutants and greenhouse gases when compared with competing modes. However, the continuous growth in goods movement is cause for concern because locomotive emissions may grow. Railroads account for only 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 the U.S. Environmental Protection Agency and the California Air Resources Board are considered. It is found that each method produces 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 that depend on detailed operational data that are no longer available. Although 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.

Estimates of fuel use and air pollutant emissions from freight rail currently rely highly on aggregate methods and largely obsolete data which offer little insight into contemporary air quality problems. Because the freight industry is for the most part privately held and data are closely guarded for competitive reasons, the challenge is to produce robust estimates using current reporting requirements, while accurately portraying the spatial nature of freight rail impacts. This research presents a new spatially resolved model for estimating air pollutant emissions (hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter less than 10 μm in diameter, sulfur dioxide, and carbon dioxide) from locomotives. Emission estimates are based on track segment level data including track grade, type of train traffic (bulk, intermodal, or manifest) and the local locomotive fleet (EPA tier certification level and fuel efficiency). We model the California Class I freight rail system and compare our results to regional estimates from the California Air Resources Board and to estimates following U.S. Environmental Protection Agency guidance. We find that our results vary considerably from the other methods depending on the region or corridor analyzed. We also find large differences in fuel and emission intensity for individual rail corridors.

No abstract