Bikeshare programs are increasingly popular in the United States, and they are an important part of sustainable transportation systems. They offer an important alternative mode choice for many types of last mile trips. Most of the current research on bikeshare focuses on bikeshare benefits (e.g., how to replace auto trips with bike trips and reduce greenhouse-gas emissions) and bikeshare system management (e.g., bike repositioning between stations). Far less attention has been paid to the programmatic potential for providing greater access to jobs and essential services for underserved communities. To date, there is virtually no quantitative research aimed at designing bikeshare systems for underserved communities. The authors develop a new spatial index that identifies bikeshare station locations exhibiting a high potential for providing service for underserved communities. The index can: 1) facilitate the identification of priority areas for bikeshare investment based on current infrastructure and the potential for increased job or essential service access; 2) inform the siting of bikeshare stations and investment in bike infrastructure to better assist underserved populations, and finally 3) provide an estimate of the potential for improved job and social services access via bike‐to‐transit.

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This report examines the ability of existing and proposed affordable housing policies to align with sustainable transportation goals in California. First, the authors compare the ability of Low Income Housing Tax Credit (LIHTC), Redevelopment and inclusionary funded projects to locate in neighborhoods with transit access to employment versus market rate production in the same period. The authors find tax credit funded units outperform market rate production with respect to job accessibility via transit, and the authors attribute this to the scoring criteria of California’s tax credit allocating body, the Tax Credit Allocation Committee (TCAC). However, the authors find this may have come at the cost of concentrating new affordable housing in areas with higher poverty rates. At the federal level, the authors measure how a change in the determination of maximum payouts for Section 8 housing vouchers, known as Fair Market Rents (FMRs), alters the ability of voucher holders to access transit and jobs rich neighborhoods. The results show that changing to “Small Area” FMRs, which are determined at the ZIP code scale, dramatically improves voucher holders’ access to jobs rich neighborhoods. This benefit comes at the cost of nearly eliminating voucher accessibility in neighborhoods that are currently accessible. And finally, at the state level, an analysis is conducted to determine if California’s emphasis on promoting affordable housing in transit and jobs rich neighborhoods is increasing the cost of affordable housing development. The modeling results indicate that affordable housing near transit stops is not significantly more expensive, but that costs increase slightly for projects in jobs rich neighborhoods. Participation in the state’s Transit Oriented Development (TOD) housing program does not significantly impact costs. The results of this research are intended to inform policy makers at every level of government on how best to continue to integrate transportation and housing policies without sacrificing the primary purpose of affordable housing policies: to house people.

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Recent studies on real-world automobile emissions measurements have not adequately addressed the question of whether driving style affects emission levels. In this study, we hypothesized that given the same experimental conditions and a random selection of drivers, the variability associated with individual driving styles (e.g. intensity or duration of acceleration events) would produce statistically significant differences in measured emissions. To test this driver variability hypothesis, we conducted a field study on 24 drivers in a single vehicle on a specified route under low traffic conditions using on-board exhaust emission and engine operating data analyzers and tested for statistically significant differences in CO and NOx emissions between drivers. Our data show significant (95% level) variations in carbon monoxide (CO) and oxides of nitrogen (NOx) emissions among the 24 drivers under driving conditions where we have controlled for driving route, traffic density and vehicle type. Since the ANOVA tests showed significant differences in emissions between drivers but the frequency of driving modes were very similar, this suggests that the intensity of vehicle operation within a give mode, not the modal frequency, explains the emissions variability between drivers.

Markets can play a key role in mitigating the effects of climate change by providing added flexibility, allowing emissions reductions to occur at a lower cost while maintaining a set level of emissions reductions. With careful regulatory design, both industry and consumers can benefit from low costs. We review the state of carbon trading globally and in the United States, the West and California. New policies and regulations related to AB32, which mandates reductions in California’s greenhouse-gas emissions to 1990 levels by 2020, are beginning to take shape. California has a unique opportunity to establish a new ethos for carbon trading by acknowledging unavoidable mitigation costs, and by designing a market-based solution that is fair, equitable and transparent, and protects the most vulnerable members of society.

This paper examines longitudinal mode use trends using four waves of the Puget Sound Transportation Panel. The analysis is conducted using generalized estimating equations for model estimation. In addition to examining mode use frequencies over time, we also consider mode use trends conditioning on household income and lifecycle stage. As expected, results indicate an overall increase in the number of worktrips made between 1989 and 1993 and these trips were marked by increasing use of single occupancy vehicles. The full parameters of the model were also to estimate the rate of increase in terms of percentage increase and their confidence intervals. Results indicate that the mean number of worktrips made by driving alone significantly increased from wave 1 to wave 4; with a 95% C.I. the rate of percent increase was estimated between 8.2 and 24.5%. The ranges for rates of change in high occupancy vehicle modes and non-motorized worktrip frequencies overlap with the range for single occupancy vehicle rate of change, and thus, it cannot be said that rate of change for the high occupancy modes was significantly different from the single occupancy rates of change. The rate of change in the mean frequency for the high occupancy¯transit mode is not only below the range for single occupancy vehicle trips but also suggests, with 95% confidence, the rate of percent decrease was between 2.88 and 44.0%.

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.

With a projected rise in the number of elderly, most of whom have also relied primarily on the private automobile for their mobility, it is likely that future adaptations in vehicle design will be linked in some part to the physical infirmities often faced by the elderly. This paper offers a bridge between medical research on the physical impairments of the elderly and automobile design and driving safety. We describe recent findings on the driving-related physical and cognitive impairments faced by the elderly. We then propose two major types of vehicle design and infrastructure adaptations: (1) modifications for private vehicles, and (2) intelligent technology and support services for private vehicles, which can help to minimize the driving-related effects of these impairments. For example, we present a range of modest vehicle design adaptations for components such as seats and doorways, handles, knobs, and steering wheels, and seat belts. We find that many of these improvements can be made to standard passenger vehicles with little additional design effort, and that the adaptations should also increase overall vehicle marketability. Finally, we argue that while most, if not all, of our proposed adaptations would be made to largely benefit the elderly, they will nevertheless support and improve driving across all age groups.

To more accurately predict hourly running stabilized link volumes for emissions modeling, a new method was recently developed that disaggregates the period-based model link volumes into hourly volumes using observed traffic count data and multivariate multiple regression (MMR). This paper examines the MMR methodology with clustering and classification analyses to account for spatial variability and to accommodate model links that do not have matching observed traffic count data. The methodology was applied to data collected in the South Air Basin. The spatial analysis resulted in identifying five clusters (or 24-h profiles) for San Diego and two clusters for Los Angeles. The MMR models were then estimated with and without clustering. For San Diego, the disaggregated model volumes with clustering were much closer to the observed volumes than those without clustering, with the exception of the a.m. period. For most hours in Los Angeles, the predicted volumes with clustering were only slightly closer to the observed volumes than those predicted without clustering, suggesting that spatial effects are minimal in Los Angeles (i.e., that 24-h volume profiles are fairly similar throughout the region) and clustering is not necessary. Finally, two classification models, one for San Diego and one for Los Angeles were developed and tested for network link data that does not have matching observed count data. The results indicate the procedure is relatively good at predicting cluster assignment for the unmatched location for Los Angeles but less accurate for San Diego.

In general, the limitations of the current California vehicle emissions inventory models can be summarized as:

- There is no direct connection between the regional emissions inventory model and the gridded emissions model. Separate model runs must be processed to develop regional emissions inventories and gridded emissions.

- The emission estimates from the regional emissions inventory model and the gridded emissions model are not in agreement.

- The regional emissions inventory model is not directly connected to the transportation forecasting models.

- The gridded emissions model has an inappropriate interface between the transportation data and the emissions rates.

The purpose of this study is to develop a new transportation and emission interface model. This requires advancing the knowledge with respect to gridded emissions calculations, as well as improving current spatial computations of emissions. Oriented at the grid cell level, the new model will establish a direct connection between regional emissions inventories for conformity and gridded emissions for airshed dispersion modeling.

The new model will be able to use transportation data from the standard four-step travel demand models, and emissions rates from EMFAC2000 or a set of newly developed link-based facility-specific CAMP running exhaust emissions rates. EMFAC2000 represents the latest understanding of mobile emissions development. Its algorithm and methodologies on basic emission rates development, driving cycle adjustment, speed adjustment factors, vehicle fleet age distribution, and traffic activity data significantly improve the emissions inventory estimates. Alternatively, the link-based CAMP emissions rates disaggregate travel activities by facility type, so that emissions rates will more closely represent link-level emissions than previously available. When developing emissions inventories, both transportation data and emissions rates will sit at the link level. This will ensure that transportation data and emissions rates in the new model sit at the same level of interface, and the model will be methodologically sound.

The implementation of the new model will be flexible with the ability to model areas ranging from county level to individual project level. The spatial and temporal emissions evaluation can be conducted at any level of the model's implementation.

The proposal is organized into five chapters. Chapter 2 provides a broad overview of related research in transportation and emissions modeling. A summary of the available transportation, emissions rates, and emissions inventory models is presented, and their strengths and weaknesses are discussed. Model development trends are discussed at the end of the chapter.

Chapter 3 provides an in-depth evaluation of the two emissions inventory models widely used in California (BURDEN7G and DTIM3). The strengths and weaknesses of each model are discussed. The chapter concludes with a summary of the challenges and motivations behind the proposed new interface model.

Chapter 4 presents the design of the new transportation and emission interface model. The model structure, required transportation data, emissions rates, and the interface of transportation data and emissions rates are discussed. Finally, Chapter 5 discusses the strengths and weaknesses of the new model.

Dockless bikeshare systems show potential for replacing traditional dock-based systems, primarily by offering greater flexibility for bike returns. However, many cities in the US currently regulate the maximum number of bikes a dockless system can deploy due to bicycle management issues. Despite inventory management challenges, dockless systems offer two main advantages over dock-based systems: a lower (sometimes zero) membership fee, and being free-range (or, at least free-range within designated service areas). Moreover, these two advantages may help to solve existing access barriers for disadvantaged populations. To date, much of the research on micro-mobility options has focused on addressing equity issues in dock-based systems. There is limited knowledge of whether, and the extent to which dockless systems might help mitigate barriers to bikeshare for disadvantaged populations. Using San Francisco and Los Angeles as case studies, because both cities have both dock-based and dockless systems running concurrently, the research team quantified bikeshare service levels for communities of concern (CoCs) by analyzing the spatial distribution of service areas, available bikes and bike idle times, trip data, and rebalancing among the dock-based and dockless systems. They found that dockless systems can provide greater availability of bikes for CoCs than for other communities, attracting more trip demand in these communities because of a larger service area and frequent bike rebalancing practices. More importantly, they noticed that the existence of electric bikes helps mitigate the bikeshare usage gap between CoCs and other tracts. Besides the data analyses for bikeshare trips, the research team also studied the spatial distribution of online suggested station locations and find that the participants’ desired destinations for work/school purposes have not been covered to the same extent in CoCs as in other communities. The results provide policy insights to local municipalities on how to properly regulate and develop dockless bikeshare systems to improve mobility equity.

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