Timothy F Welch

A tool for measuring and visualizing connectivity of transit stop, route and transfer center in a multimodal transportation network

Agencies at the federal, state and local level are aiming to enhance the public transportation system (PTS) as one alternative to alleviate congestion and to cater to the needs of captive riders. To effectively act as a viable alternative transportation mode, the system must be highly efficient. One way to measure efficiency of the PTS is connectivity. In a multimodal transportation system, transit is a key component. Transit connectivity is relatively complex to calculate, as one has to consider fares, schedule, capacity, frequency and other features of the system at large. Thus, assessing transit connectivity requires a systematic approach using many diverse parameters involved in real-world service provision. In this paper, we use a graph theoretic approach to evaluate transit connectivity at various levels of service and for various components of transit, such as nodes, lines, and transfer centers in a multimodal transportation system. Further, we provide a platform for computing connectivity over large-scale applications, using visualization to communicate results in the context of their geography and to facilitate public transit decision-making. The proposed framework is then applied to a comprehensive transit network in the Washington-Baltimore region. Underpinning the visualization, we introduce a novel spatial data architecture and Web-based interface designed with free and open source libraries and crowd-sourced contextual data, accessible on various platforms such as mobile phones, tablets and personal computers. The proposed methodology is a useful tool for both riders and decision-makers in assessing transit connectivity in a multimodal transit network in a number of ways such as the identification of under-served transit areas, prioritization and allocation of funds to locations for improving transit service.

Interrelationship Between Airport Enplanements and Accessibility: Case of Three Airports in Metropolitan Washington, D.C., Region

Increasingly, because urbanized areas have access to multiple airports, airlines must compete for passengers. One such location is the Washington, D.C., metropolitan area with three international airports within a 30-mi radius, each governed by a different planning authority. A travelers choice to fly from a particular airport depends on a number of factors, chiefly convenient accessibility to the airport. Transportation planning agencies in the area often plan for network improvements to provide the best accessibility to a single major airport, although such improvements may also provide accessibility benefits to airports outside their jurisdiction. This paper presents an approach to estimate airport accessibility by highway and transit for both peak and off-peak hours. Furthermore, accessibility to these airports for a base year and a 20-year planning horizon are measured. The accessibility measure presented in the paper incorporates congestion travel times as obtained from a travel demand model. The results show that accessibility varies greatly for competing airports and, with that variation, there appears to be a correlation with total airport enplanements. The analysis also reveals the importance of taking a multimodal and multiple time-of-day approach to accessibility analysis.

Rail station access and housing market resilience: Case studies of Atlanta, Baltimore and Portland:

The recent United States housing market crisis resulted in a significant decline in housing market values. Yet, the extent to which urban amenities such as rail stations moderated the market impacts has not been entirely recognised. This study undertakes a repeat sales analysis to understand the impact of station proximity on housing values before, during and after the market crisis. Specifically, a housing price resilience index assesses market changes from 2002 to 2013 for single-family and multifamily homes within a quarter of a mile, half a mile, one mile and greater distances from the nearest rail station. The analysis is replicated in three cities: Atlanta, Georgia; Baltimore, Maryland; and Portland, Oregon. Although the recession had significant negative impacts on properties in each city, our study finds that access played a critical role in helping transit-orientated submarkets retain their value throughout the recession and recover value at a faster rate than homes without convenient fixed transit access.

Shared-use mobility competition: a trip-level analysis of taxi, bikeshare, and transit mode choice in Washington, DC

ABSTRACT The emergence of new shared-use mobility options such as bikeshare and ride-hailing services render the traditional dichotomy between personal vehicles and public transit somewhat irrelevant. Transportation planners and policymakers have yet to conclude whether these mobility technologies are complementing or competing against existing public transit services. The understanding of this relationship is vital given the increasing uncertainty of funding sources for transit services, but limited by the scarcity of meaningful data provided by the private ride-hailing industry. This study applies big data analytic tools on a unique travel data set to uncover the predictors motivating a half-billion transit, taxi, and bikeshare trips in rail station walksheds across Washington, DC. Study findings indicate travel cost and natural environment factors as well as land use diversity and network connectivity metrics significantly impact the likelihood for an individual to travel via taxi or bikeshare rather than rail.

Use of Statewide Models as a Decision Tool for Zero-Emission Vehicles Deployment

Under worldwide environmental stress, zero-emission vehicles (ZEV) are rapidly coming to market. However, it is not clear how such vehicles reduce vehicular emissions at a spatially explicit level, which is crucial for developing specific policies. This study proposed a quantitative approach to estimate the effectiveness of ZEVs in reducing emissions to support investment decisions promoting the use of ZEVs. The approach uses existing statewide travel demand and mobile emission models in an integrated framework. Scenarios are designed to measure the emissions reduction effects of ZEVs at different spatial scales (statewide, county, and roadway) and characteristics (densely and sparsely populated counties) and with various levels of market penetration and driving range limits. Results show significant spatial differentiation of the impact of ZEV deployment from county to roadway levels. Offering greater spatial detail and new insights on decision-making processes, this study described an integrated tool for identifying effective strategies for ZEV implementation.