Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework : an application to understanding shared mobility service usage levels

Activity-travel choices of decision makers are influenced by spatial dependency effects. As decision makers interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among decision makers is no longer solely dependent on spatial proximity. Rather, the strength of associations among decision makers may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this thesis, the frequency of usage of car-sharing and ride-sourcing services, collectively termed as shared mobility services, is modeled using a sequential generalized heterogeneous data model – spatial ordered response probit (GHDM - SORP) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014-2015 Puget Sound Regional Travel Study survey sample, with inter-dependence in attitudinal space defined using latent psychometric constructs reflecting inherent attitudes, lifestyle preferences and habits. These latent constructs are based on variables in the data set that represent observed travel and locational choice behavior, as well as responses to attitudinal questions. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage, over and above what is explained by spatial dependency. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.Civil, Architectural, and Environmental Engineerin

Using Smart Farecard Data to Support Transit Network Restructuring: Findings from Los Angeles

Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors

0-6877 (Phase 2): Communications and Radar-Supported Transportation Operations and Planning (CAR-STOP) [Project Summary]

A recent report from the National Highway Traffic Safety Administration indicates that more than 80% of all annual car crashes could be prevented by vehicular communications. To that end, the focus of this project was to develop a framework (conceptualizations, processes, procedures, and algorithms) to harness and mature sensing and communication technology to improve transportation safety, primarily focused on the development of an advanced driver assistance system (ADAS)