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.

The metro system plays a very important role in the urban multimodal transportation system, yet it is susceptible to accidents. A well-designed metro system needs to provide alternative routes to travellers both in the disruptive events and the normal operating conditions for providing rerouting opportunities and balancing crowded lines. This paper provides a new dimension of assessing metro network performance—travellers’ route redundancy (or route diversity), which is defined as the number of behaviourally effective routes between each origin-destination (O-D) pair in the network. The route redundancy of metro network is evaluated by statistical indicators of the distribution of the O-D-level number of effective routes. Compared with the existing connectivity and accessibility measures of topology network performance, route redundancy is also based on the topology network, but it takes the travellers’ route choice into consideration. Specifically, the effective routes between each O-D pair would provide disaggregated information from the travellers’ perspective. Case studies in four metropolises in the world, i.e., Shanghai, Beijing, London, and Tokyo, are conducted to examine the predisaster preparedness of the four metro networks explicitly from the perspective of route redundancy. The results indicate that the London metro network has the best route redundancy performance in terms of the statistical indicators of the distribution of the O-D level number of effective routes. Furthermore, the results of route redundancy are compared with typical measures of topology network performance in terms of measuring connectivity and accessibility of metro networks. Their differences are attributed to the fact that the route redundancy measure considers the travellers’ O-D-level route choice beyond the pure network topology and the shortest path considerations of the existing measures. The route redundancy proposed in this paper could assist in evaluating the predisaster preparedness of current or planning metro networks from O-D level to network level.

Trip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.