A Hierarchical and Logistic Regression-Based Evaluation Framework for Smartness of Expressway Service Areas: A Case Study in Wuhan, China

Smartness upgrade in expressway service areas (ESAs) is increasing with the development of intelligent transportation systems. In planning and design practice, many smart functions can be considered, but it is not easy to decide which ones should be built first. One reason is that smartness involves numerous indicators across operation management, public services, and cost, while weighting these indicators is often difficult and may cause bias. To solve this issue, this study proposes a TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) evaluation framework with a hierarchical and logistic regression-based weighting approach. Specifically, indicators evaluating the smartness level of ESAs are first divided into three levels and indicator weights within each level are computed based on the logistic regression technique. Next, the final weights of indicators are obtained based on the hierarchical structure, and TOPSIS is adopted to rank different design schemes of ESAs. A case study was conducted for the under-construction project of Caijiazha service area in Wuhan, China, with four design schemes (i.e., smart-monitoring, high-safety, passenger-convenience, and typical schemes). The results confirm the stability of the logistic regression-based weighting method and show clear differences in the non-hierarchical weighting approach, such as under-weighing the economic indicators, compared to the hierarchical weighting approach. The proposed method can support plan comparison and step-by-step implementation for the construction of ESAs.