TRACLUS algorithm based on partition-and-group framework could not be distinguished the optimal partitioning accurately when the migration of trajectory points on both sides of corridor middle line was greatly offset, and the algorithm was sensitive to the input parameters. According to above deficiency, an improved high-density sub-trajectory clustering algorithm (HTRACLUS_DL) is proposed under the practical application background of a traffic corridor identification. Initially, sub trajectories are divided based on the spatio-temporal characteristic similarity of trajectories. Furthermore, a sub-trajectory parallel boundary method is constructed, which has higher precision than the partitioning algorithm used in TRACLUS. Additionally, sub-trajectory clustering center neighborhoods possess local high density and surrounded by lower density sub trajectories. However, the different sub-trajectory clustering centers are heterogeneity. Finally, a new sub-trajectory clustering algorithm is robust to input parameters based on subtrajectory entropy. Experimental results based on trajectory data of mobile phone user in two cities show that HTRACLUS_DL could be solved the deficiency of TRACLUS. At the same time, the method obtains better clustering result based on spatio-temporal characteristics of sub trajectory and does not depend on parameter selection. HTRACLUS_DL could be identified traffic corridor of urban group effectively.

Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model.

The identification of critical road links is greatly important to the management and control of the transportation system. Existing works fail to fully consider the influence of the distribution of traffic flow and its dynamic characteristics on critical road link identification. In this paper, we propose a criticality calculation method for urban road networks considering the effect of cascading failures which models the distribution change of traffic flow after a specific road link failed. Firstly, a sequence diagram calculation method is proposed to model how the traffic failure on one road link propagates to related links. Secondly, the diagram of the cascade failure sequence is divided into different stages according to the consistency of the objective function. The influence value of each stage is computed for the target road link. Finally, the failure probability model and the importance indicator are proposed to calculate the criticality for each road link. We evaluate our critical road link identification method on both simulated and read scenes. In our simulation, we achieve 90.6% and 91.7% for the accuracy on two key metrics, respectively, i.e., the length of failure road link and the total parking delay, which proves the feasibility of our method. Our method also achieves reasonable conclusions on real data and helps to find the critical road links.

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