Date of Award

5-2024

Document Type

Project

Degree Name

Master of Science in Computer Science

Department

School of Computer Science and Engineering

First Reader/Committee Chair

Hou, Yunfei

Abstract

This research offers an in-depth analysis of vehicular traffic within San Bernardino County, California, aiming to spotlight congestion areas and suggest improvements for more efficient and sustainable transportation. Leveraging 2021 data from StreetLight Data, traffic patterns in 15 key cities were examined based on their population sizes, covering various vehicle types to dissect dynamics and flow. The methodology focused on analyzing trip purposes and metrics to calculate Vehicle Miles Traveled (VMT) and its influence on congestion and environmental factors.

Findings indicate considerable disparities in traffic volume, purposes, and timings across different urban areas, with population density and intercity connections significantly affecting traffic trends. For instance, Rancho Cucamonga and Fontana saw traffic exceed 2.9 million trips, illustrating the impact of dense population. Additionally, 40% of the traffic was attributed to non-work-related activities, shedding light on the varied nature of travel demands. A critical observation was that 60% of departing traffic was directed towards Los Angeles and Riverside, highlighting the necessity for an integrated regional approach to transportation planning.

By utilizing data-driven insights, this study promotes a shift towards more sustainable transit options and public transport improvements to address San Bernardino County's infrastructural challenges. It provides a methodology and insights that can guide policymakers and transit agencies in developing strategies that aim for enhanced community well-being and environmental preservation. The meticulous examination of VMT and other traffic indicators reveals the profound implications of urban density and regional connectivity on mobility, offering a richer comprehension of the county's traffic phenomena.

Furthermore, the findings of this research, serving as applications of the traffic data analysis, have been presented at ACM KDD 2023 [Appendix A] and IEEE FISTS 2024 & CRB SEED Symposium [Appendix B], highlighting their practical relevance and potential impact on transportation planning and policymaking.

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