The Network Design Problem (NDP) is a strategical decision-making problem in planning, designing, and managing road networks with the aim to make efficient use of limited resources for optimizing the road network performance. Sustainability development is a major concern of various social-economic systems throughout the world. As a critical component of sustainable development, transportation systems should be designed to make positive contributions to the economic, environmental, and social sustainability of the served regions and communities. This requirement significantly uplifts the challenges on the modeling and the analysis of NDP. In this paper, we provide a review on the sustainable road NDP. Specifically, an overview on the three dimensions of sustainable development (i.e., economic, environmental, and social) is first provided, focusing on their representative performance measures relevant to road NDP. Then, we review the existing studies with the classification system of economy and environmentoriented sustainable NDP, economy and equity-oriented sustainable NDP, and three-dimensional sustainable NDP. Future research directions are suggested for advancing the methodological advancement and practical applications of sustainable transportation NDP.

Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR) data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI) and Silhouette Coefficient (SC) are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.