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

TL;DR

This paper introduces a multi-dimensional, high-granularity index for transportation equity that considers both accessibility and service quality, providing a nuanced assessment of equitable transportation services using real trip data and synthetic population datasets.

Contribution

The paper proposes the MDHG index, integrating service accessibility, quality, and demographic data, to better evaluate transportation equity beyond traditional accessibility measures.

Findings

The MDHG index effectively identified areas with improved or needing improvement post-expansion.

Using synthetic datasets allows for detailed, user-level analysis of transportation equity.

Case study with NYC Citi Bike expansion demonstrated the index's practical utility.

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.