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

TL;DR

The paper tackles the gap in transportation equity by integrating service quality and temporal dynamics into accessibility analysis. It introduces the MDHG equity index, combining a hourly performance metric $z$ with destination access insufficiency $I_{access}$ to form $ZI_{access}$, augmented by a synthetic population via ciDATGAN. Using NYC Citi Bike expansion data, it demonstrates that accessibility alone can be misleading and that accounting for service recovery $SR$ and disparities $D_{sp}$ yields a more nuanced, actionable picture of inequity. The approach provides decision-makers with a high-granularity, population-aware tool for post-project evaluation and targeted interventions in transportation planning.

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.

Figures (20)

• Figure 1: Overview of MDHG equity index
• Figure 2: Overview of the equity index development
• Figure 3: Population synthesis workflow
• Figure 4: Overview of the case study application
• Figure 5: $\zeta$ values for hours 0, 8, 16 before (above dashed line) and after the Citi Bike expansion (below dashed line). Brown represents tracts that do not yet have access to Citi Bike services. Black represents tracts that never received Citi Bike services.