The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for…
Abstract
Purpose
The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.
Design/methodology/approach
The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.
Findings
The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.
Originality/value
This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.
Details
Keywords
Liang Xu, Sheng Jin, Bolin Li and Jiaming Wu
This study aims to make full use of the advantages of connected and autonomous vehicles (CAVs) and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping.
Abstract
Purpose
This study aims to make full use of the advantages of connected and autonomous vehicles (CAVs) and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping.
Design/methodology/approach
The authors developed a signal coordination model for arteries with dedicated CAV lanes by using mixed integer linear programming. CAV non-stop constraints are proposed to adapt to the characteristics of CAVs. As it is a continuous problem, various situations that CAVs arrive at intersections are analyzed. The rules are discovered to simplify the problem by discretization method.
Findings
A case study is conducted via SUMO traffic simulation program. The results show that the efficiency of CAVs can be improved significantly both in high-volume scenario and medium-volume scenario with the plan optimized by the model proposed in this paper. At the same time, the progression efficiency of regular vehicles is not affected significantly. It is indicated that full-scale benefits of dedicated CAV lanes can only be achieved with signal coordination plans considering CAV characteristics.
Originality/value
To the best of the authors’ knowledge, this is the first research that develops a signal coordination model for arteries with dedicated CAV lanes.