Quantum mechanics

Computer hardware

Algorithm

Engineering

Transport engineering

Aerospace engineering

Traffic flow (computer networking)

Computer security

Real-time computing

Computer science

Mathematical optimization

Computer network

Physics

Trajectory

Mathematics

Astronomy

FIFO (computing and electronics)

Intersection (aeronautics)

Volume (thermodynamics)

Queueing theory

Transportation Research Part C Emerging Technologies

Cycle-based traffic volume estimation is important for the dynamic evaluation and optimization of signal control schemes at intersections. With the development of intelligent mobility and connected vehicle technologies, massive high-resolution vehicle trajectory data have become available, which can provide a rich source of information for estimating traffic flow parameters at signalized intersections. Existing methods for traffic volume estimation using sampled vehicle trajectories are commonly driven by shockwave or probabilistic models, in which the traffic volume estimation is usually modelled as a parameter estimation problem. These methods commonly require certain assumptions for the vehicle arrival distribution and a First-In-First-Out (FIFO) queuing rule. However, these model-driven methods have limitations when the vehicle arrival distribution is unknown or when there is more than one lane for the same movement at an intersection approach, as the FIFO rule is no longer true. In addition, the accuracy and stability of these models remain challenging under low penetration rates. In this paper, we propose a tensor decomposition method to estimate cycle-based traffic volume at signalized intersections using sampled vehicle trajectories. Unlike the existing model-driven methods, the proposed method is purely data-driven and does not rely on any prerequisite assumptions for the arrival distributions and queuing rules. In the proposed method, the traffic volume of each cycle is first divided into a known part and an unknown part, based on the queuing position of the last queued sample vehicle. Then, these two parts of the volume as a whole, along with two relevant traffic observations (i.e., the number of sampled vehicles and the observation duration), are then integrated into a three-dimensional tensor. This tensor can effectively preserve the temporal correlations between adjacent cycles and the interrelationships among the traffic observations. Finally, the problem of cycle-based traffic estimation is transformed into a tensor completion problem, and the Tucker decomposition method is adopted to solve the completion problem. The proposed method is evaluated using both empirical and simulation data. The results indicate that the proposed method is capable of producing accurate and reliable estimates for cycle-based traffic volumes even under low penetration rates, i.e., less than 5%.

A tensor decomposition method for cycle-based traffic volume estimation using sampled vehicle trajectories