Public transit systems are often accepted as energy and environmental improvements to automobile travel, however, few life cycle assessments exist to understand the effects of implementation of transit policy decisions. To better inform decision-makers, this project evaluates the decision to construct and operate public transportation systems and the expected energy and environmental benefits over continued automobile use. The public transit systems are selected based on screening criteria. Initial screening included advanced implementation (5 to 10 years so change in ridership could be observed), similar geographic regions to ensure consistency of analysis parameters, common transit agencies or authorities to ensure a consistent management culture, and modes reflecting large infrastructure investments to provide an opportunity for robust life cycle assessment of large impact components. An in-depth screening process including consideration of data availability, project age, energy consumption, infrastructure information, access and egress information, and socio-demographic characteristics was used as the second filter. The results of this selection process led to Los Angeles Metro’s Orange and Gold lines.

In this study, the life cycle assessment framework is used to evaluate energy inputs and emissions of greenhouse gases, particulate matter (10 and 2.5 microns), sulfur dioxide, nitrogen oxides, volatile organic compounds, and carbon monoxide. For the Orange line, Gold line, and competing automobile trip, an analysis system boundary that includes vehicle, infrastructure, and energy production components is specified. Life cycle energy use and emissions inventories are developed for each mode considering direct (vehicle operation), ancillary (non-vehicle operation including vehicle maintenance, infrastructure construction, infrastructure operation, etc.), and supply chain processes and services. In addition to greenhouse gas emissions, the inventories are linked to their potential for respiratory impacts and smog formation, and the time it takes to payback in the lifetime of each transit system.

Results show that for energy use and greenhouse gas emissions, the inclusion of life cycle components increases the footprint between 42% and 91% from vehicle propulsion exclusively. Conventional air emissions show much more dramatic increases highlighting the effectiveness of “tailpipe” environmental policy. Within the life cycle, vehicle operation is often small compared to other components. Particulate matter emissions increase between 270% and 5400%. Sulfur dioxide emissions increase by several orders of magnitude for the on road modes due to electricity use throughout the life cycle. NOx emissions increase between 31% and 760% due to supply chain truck and rail transport. VOC emissions increase due to infrastructure material production and placement by 420% and 1500%. CO emissions increase by between 20% and 320%. The dominating contributions from life cycle components show that the decision to build an infrastructure and operate a transportation mode in Los Angeles has impacts far outside of the city and region. Life cycle results are initially compared at each system’s average occupancy and a breakeven analysis is performed to compare the range at which modes are energy and environmentally competitive.

The results show that including a broad suite of energy and environmental indicators produces potential tradeoffs that are critical to decision makers. While the Orange and Gold line require less energy and produce fewer greenhouse gas emissions per passenger mile traveled than the automobile, this ordering is not necessarily the case for the conventional air emissions. It is possible that a policy that focuses on one pollutant may increase another, highlighting the need for a broad set of indicators and life cycle thinking when making transportation infrastructure decisions.

Universities host a large, young and diverse population that commutes to the same location every day, which makes them ideally suited for public transportation ridership. However, at many universities in the US, this potential for high levels of transit ridership is not being maximized. This research aims to identify the areas where Valley Metro’s public transit service to ASU’s Tempe campus is over- and under-performing in comparison with the overall public transportation service to the entire Phoenix metro area. The hypothesis states that proximity to campus and the convenience of using public transportation would be the two main factors in determining the success of an area’s public transportation service. ASU’s Parking & Transit Services provided confidential data with the addresses of all the students and employees who purchased a parking pass, transit pass and bike registration. With these data, the public transportation mode share for commuters to ASU in each census block group was calculated and compared to the mode share for the general public, which was based on US Census data. The difference between the public transit mode shares of ASU pass holders vs. commuting by the general public was then computed and analyzed to identify areas as hot and cold spots. These heat maps are then compared to the hypothesized factors of proximity to campus and the convenience of public transportation in terms of the light rail line, park-and-ride lots, and number of transfers needed to connect to campus. The transfers were estimated using origin and destination survey data provided by Valley Metro. Results show that the convenience of public transportation was a driving factor in explaining where the transit mode share to ASU is higher than that of the general public, whereas the proximity to campus had little impact on the areas with high ASU-specific transit mode shares. There is an absence of hot spots directly around the campus which is explained by the combination of both high transit share for the non-ASU population and the large share of ASU students and employees using active transportation and free circulator buses this close to campus. These findings are significant specifically to ASU because the university can learn where the transit service is performing well and where it is underperforming. Using these findings, ASU PTS can adjust its pricing, policies, services and infrastructure and work with Valley Metro and the City of Tempe to improve the ridership for both students and employees. Future research can compare more factors to further interpret what leads to success for transit service to university campuses.