Multimodal Transportation Planning Data PDF Download

An increasingly multimodal transportation network and the advent of new mobility solutions means that jurisdictions should change their approaches in the planning, design, and performance monitoring of transportation systems. The work is divided into two main parts. The first section is an attempt to improve the understanding of effective multimodal transportation design processes and performance-based decision making through conducting a national-scale literature and document review. The conventional design approach of prioritizing automobiles has led to problems such as the deterioration of the environment and reduction in city quality of life. Therefore, multimodal design is becoming more widely adopted by jurisdictions through designing for multiple travel modes rather than cars only. However, as this is a relatively new field, the information available falls slightly short of the demand and there is not an ultimate source of guidance for effective multimodal design processes. This section aims to fill that gap by synthesizing "the state of the knowledge" via systematically reviewing the available academic literature, as well as "the state of the practice" by reviewing practical documents such as state guidebooks and design manuals. For this study, multimodal will include: active transportation (e.g., pedestrians and bicycles), freight, transit, and single-occupant vehicles. The work compares the current design processes across various jurisdictions and draws conclusions on what are the best practices, data sources, and performance measures for multimodal design. The second section of the thesis focuses on a new data-gathering tool and analysis framework that helps cities more effectively monitor the performance of micromobility services (i.e., shared e-scooters and e-bikes). As many of these services are nowadays dockless, the trips can be ended almost anywhere and not necessarily in a parking hub, which leads to these vehicles often getting mis-parked by the user, blocking sidewalks, or causing issues for people with disabilities. We developed an application for the purpose of crowdsourcing parking data from city residents to ensure that public servants get alarmed as soon as a parking infraction gets reported and to help as a long-term data solution. This tool can also be used for conducting data-driven parking audits of bikes and scooters, on a neighborhood or even a city level. We used the app to conduct two parking audits in the cities of Portland and Seattle. Summary statistics of these case studies are mentioned along with spatial analysis of the data. Several statistical models were also developed to seek for any links between parking violations and elements of the built environment and/or census tract socioeconomic factors and demographics. One of the models for the City of Seattle shows that the number of bike racks negatively correlates with the number of infractions on a census tract level. Even though the results may be biased due to low sample size, the tool itself along with the analysis piece can be used as a framework for other cities to conduct parking audits of micromobility companies.