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The application of renewable energy technologies to rail transit should be evaluated on a comprehensive energy pathway efficiency basis to ensure that the renewable energy tech nology is truly beneficial. One such method is the well-to-wheel analysis method, which combines the energy efficiencies of each component of the energy pathway into a single energy efficiency value. The focus of this paper is on well-to-wheel analysis of electric and hydrogen light rail. The inefficiencies of the hydrogen train’s power plant and hydrogen production process are apparent in the hydrogen train’s well-to-wheel efficiency value of 16.6–19.6%. The electric train, due to improved pathway efficiencies, uses substantially less feedstock energy with a well-to-wheel efficiency value of 25.3%. While this result is specific to Charlotte, North Carolina, the electric train efficiency is influenced by the main source of electricity production—it is 24.6% in Cleveland, Ohio (coal heavy) and 50.3% in Portland, Oregon (hydroelectric heavy).

The objective of this research is to develop and assess bus transit ridership models at a bus-stop level using two spatial modeling methods: spatial proximity method (SPM) and spatial weight method (SWM). Data for the Charlotte (North Carolina) area are used to illustrate 1) the working of the methods and 2) development and assessment of the models. Features available in Geographic Information System (GIS) software were explored to capture spatial attributes such as demographic, socioeconomic, and land use characteristics around each selected bus stop. These, along with on-network characteristics surrounding the bus stop, were used as explanatory variables. Models were then developed, using the generalized estimating equations (GEE) framework, to estimate riders boarding (dependent variable) at the bus stop as a function of selected explanatory variables that are not correlated to each other. Results obtained indicate that Negative Binomial with log-link distribution better fits the data to estimate ridership at the bus-stop level (for both SPM and SWM) than when compared to linear, Poisson with log-link and Gamma with log-link distributions. Although SPM models demonstrated distance decay behavior, statistical parameters indicate that SWM (based on functions 1/D, 1/D2, and 1/D3) does not yield better or more meaningful estimates than when compared to SPM using 0.25mile buffer width data. Journal of Public Transportation, Vol. 15, No. 1, 2012 34 Introduction Transit systems support a broad range of goals that include air quality improvement, energy conservation, congestion reduction, provision of mobility to the disadvantaged, access to employment or attraction centers, the promotion of economic development, sustainability, and enhanced livability. Understanding the factors that influence transit ridership is very important to achieve these goals and increase transit market potential. The Bureau of Transportation Statistics (2010) reports that 6,922,000 people (~5% of the nation’s overall trips) used public transportation as their principal means of transportation to work each day during 2009. Transit system managers and planners often rely on statistical models that are cost effective, developed in a reasonable amount of time using available data inventories, and provide a good understanding of the relationship between the dependent variable and explanatory variables. This research aims to develop statistical models to estimate riders boarding at a bus stop using spatial data and on-network characteristics around/near the bus stop. The data required to develop these models are typically available with most state Departments of Transportation and local agencies as well as in many open source data inventories. The use of bus transit depends on accessibility to a bus stop. Accessibility could be defined in terms of walking time (say, 5, 10, 15, or 20 minutes from an origin to a bus stop) or walking distance (say, 0.25, 0.5, 0.75, or 1 mile from an origin to a bus stop). To better comprehend the substantial effect and area of influence of spatial attributes (includes explanatory variables such as demographic, socio-economic, land use, and on-network characteristics) on ridership (dependent variable), a spatial analysis needs to be conducted at several different buffer widths (say, 0.25, 0.5, 0.75, and 1 mile) to identify the ideal spatial proximity distance to extract data for modeling. The maximum buffer width for consideration depends on the acceptable maximum walking distance to access a bus stop (generally, 1 mile). In general, the number of riders who use bus transit system decreases as the distance from the bus stop increases. Integrating data pertaining to demographic, socio-economic, and land use characteristics from different buffer bandwidths (say, 0–0.25, 0.25–0.5, 0.5–0.75, and 0.75–1 mile) based on this distance decay effect, and using it to develop ridership models may yield better and accurate estimates. The objective of this research is 1) to identify explanatory variables and distribution functions and 2) to develop and assess ridership models at a bus-stop using two spatial modeling methods: spatial proximity method (SPM) and spatial weight

The focus of this manuscript is 1) to compute a transit accessibility index based on demographic/socio-economic characteristics and land use characteristics for each transit stop and route and the entire study area, and 2) identify inaccessible areas to provide an improved public transportation system that maximizes market potential. Transit accessibility indices were computed using spatially-extracted data within a pre-defined walking distance or time (0.25 miles or 5 minutes) for each transit stop (bus-stop) and route, and the entire study area. Results from linear regression analysis showed a statistically significant relationship between boardings and alightings, and the computed transit accessibility index based on demographic/socio-economic characteristics and land use characteristics for transit stops. The spatial distributions of computed transit accessibility indices were used to illustrate identification of spatial gaps, selection of ideal locations for transit stops along a route, extension of an existing route, identification of new transit routes, and expansion of transit area coverage.

Safety and accessibility to bus transit systems play a vital role in increasing transit market potential. Bus passengers often tend to cross the streets from either behind or in front of the bus as crosswalks do not exist near most bus stops, which are typically away from intersections. These unsafe maneuvers frequently result in either auto-pedestrian collisions or conflicts. Identifying hazardous bus stops would serve as a building block to study the causal factors, select mitigation strategies, and allocate safety funds to improve bus passenger safety. The focus of this article is to develop a Geographic Information Systems (GIS) based methodology to assist decisionmakers in identifying and ranking bus stops in high auto-pedestrian collision concentration areas. The working of the GIS-based methodology is illustrated using 2000–2002 auto-pedestrian collision data, traffic volumes, bus stop coverage, transit ridership data, and street centerline coverage for the Las Vegas Metro area. Results obtained are sensitive to buffer radius and ranking methods used to rank hazardous bus stops. Potential strategies and countermeasures to enhance safety at hazardous bus stops are also discussed.

Accessibility to transit service facility (TSF) locations plays a significant role in the success of public transportation systems. The ease with which the end-user can reach a TSF (e.g., bus stops, rail stations, or multimodal centers) plays prominently in the decision making process of the individual. This paper presents a working definition for transit market potential based on accessibility in terms of walking distance and walking time. Further, a measure is constructed to evaluate transit market potential for TSF locations for a transit system. The measure of transit potential is represented by an index value based on demographic criteria such as employment, household size, vehicle ownership, etc. This index can be used to identify locations of TSFs that increase a route's potential for ridership. A methodology is proposed to estimate the Index of Transit Potential (ITP) for TSFs. This methodology involves identifying the accessible network of streets around each TSF that is within an acceptable access threshold for a transit rider, and estimating the transit market potential based on key demographic characteristics. The analytical and visualization capabilities of a Geographic Information Systems (GIS) program are utilizes to help attain the objective. A case study is used to demonstrate the application of the methodology.