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Towards Understanding the Benefits and Challenges of Demand Responsive Public Transit- A Case Study in the City of Charlotte, NC

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arxiv 2304.06467 v2 pith:HUAARSXL submitted 2023-04-09 cs.CY

classification cs.CY
keywords transitcharlottepublicsystemchallengescurrentaccessibilityessential
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Access to reliable public transportation is essential for addressing socio-economic disparities, particularly in low-income communities that rely heavily on transit for accessing jobs, healthcare, and essential services. This study investigates the challenges faced by transit-dependent populations in Charlotte, NC, focusing on the spatial and service-related inequities within the current public bus system. Our research initially evaluates critical issues such as extended wait times, unreliable schedules, and limited accessibility, which significantly impact the daily lives of low-income residents. In response to these challenges, we gathered data to assess the potential for a connected, demand-responsive bus system designed to minimize transit gaps and enhance service efficiency in the future. This evaluation included an analysis of the existing Charlotte Area Transit System (CATS) mobile applications and the exploration of user acceptance for a proposed smart, on-demand transit technology. Through surveys conducted across key bus lines-including the Sprinter line and Bus Lines 7, 9, and 97-99-we identified significant shortcomings in the current system. However, our findings also indicate a strong willingness among participants to adopt new transit solutions, provided that they effectively address current issues and alleviate concerns related to smartphone accessibility, privacy, and trust. This research contributes valuable insights into the modernization of public transit systems in Charlotte, highlighting the importance of user-centric approaches in developing innovative, equitable, and efficient transportation solutions.

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  1. Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit

    cs.LG 2025-01 conditional novelty 4.0 of 10

    A three-layer fully connected neural network predicts next-stop departure time deviations on 151 Boston MBTA routes with a root-mean-square error of 77.8 seconds.

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