A multi-dimension and high-granularity equity measurement for transportation services through accessibility and reliability

• URL: http://arxiv.org/abs/2501.14761v1
• Date: Tue, 24 Dec 2024 01:49:11 GMT
• Title: A multi-dimension and high-granularity equity measurement for transportation services through accessibility and reliability
• Authors: Mengke, Ma, Zilin Bian, Jingqin Gao, Hai Yang, Joseph Chow, Kaan Ozbay,
• Abstract summary: We propose a transportation equity index called the multi-dimensional, high-granularity index (MDHG) index.<n>MDHG index considers service accessibility and quality alongside population demographics.<n>To test out the MDHG Index, we coupled a highly granular synthetic population dataset with data from NYC Citi Bike expansion.
• Score: 3.767956927182431
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Transportation equity research has traditionally emphasized service accessibility and destination reachability while often overlooking the critical aspects of service quality, such as infrequent schedules or overcrowded vehicles. This oversight can lead to a skewed understanding of equity, as high accessibility does not guarantee high-quality service. Addressing this gap, we propose a transportation equity index called the multi-dimensional, high-granularity (MDHG) index. Such an index considers service accessibility and quality alongside population demographics. This approach ensures that areas with high accessibility but low service quality are recognized as inequitable. The MDHG Index addresses service performance by incorporating performance data with temporal variations based on actual trip data, thus offering a more nuanced view of transportation equity that reflects the real-world experiences of service users. Furthermore, to effectively identify and address the needs at the user level, we need to use a highly granular population dataset. Due to the low granularity of census and other open-source datasets, we opted to use a highly granular synthetic dataset. To test out the MDHG Index, we coupled a highly granular synthetic population dataset with data from NYC Citi Bike expansion to use as a case study to assess changes in accessibility and service quality before and after the expansion. The MDHG approach effectively identified areas that improved post-expansion and highlighted those requiring further enhancement, thus showing the effectiveness of the index in targeted improvements for transportation equity.

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