Arterial Traffic Flow Estimation Based on Vehicle-to-Cloud Vehicle Trajectory Data Considering Multi-Intersection Interaction and Coordination

Abstract

Conventional detection methods for intersection traffic flow heavily rely on fixed-location inductive loop, video image processing, infared, and microwave radar detectors. The emerging connected vehicles (CV) technologies can potentially reduce such dependencies on conventional vehicle detectors with the vehicle-to-cloud (V2C) CV data. This paper proposes an analytical method for traffic flow estimation in urban arterial corridors based on CV trajectories collected through V2C communication. Different from the existing single-intersection models, the proposed model considers traffic states and the traffic signal coordination among adjacent intersections, therefore, can capture the delay and queuing dynamics in arterial corridors. The queue spillback phenomenon is explicitly considered by applying the shockwave theory. The proposed model is evaluated based on real-world vehicle trajectory data from the DiDi platform collected on an arterial network in Chengdu, China with a penetration rate of less than 10% of the overall traffic. The flow estimation results are compared with traffic counts collected from video detectors. The model parameters are calibrated with more than 300,000 GPS points during a typical workday and tested on a different workday. The evaluation results show a mean absolute percentage error within the range of 4–7% among all intersections, outperforming the results generated by the existing single-intersection model. The results indicate the promising potential of using the proposed methods to evaluate intersection performance without heavy investment in on-site detectors.

Approximating Dynamic Equilibrium Analysis in Multi-Region Network Based on Macroscopic Fundamental Diagram

“…erefore, the value of J is generally not small. For example, the total research time is only 30 minutes, however, J is generally controlled at [25,35] to meet the constraints of DUE. To sum up, f(O(Δ 1 )) ∈ (0, +∞).…”

“…(i) Get the traffic flow of links. An analytical method for traffic flow estimation based on high-resolution vehicle trajectories can be adopted to get the traffic flow of links in the experimental network, seeing literature [35] for details. (ii) Estimate the OD matrix.…”

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

“…Only few works have addressed the problem of estimating traffic volume from mobile data sources. Different techniques have been used, such as statistical models [29], [30], [47], combination of Shockwave theory and Maximum likelihood estimation (MLE) [26], [48]- [50], Kalman filter [27], [28], [51], compressive sensing techniques [52], spatio-temporal correlation data-driven models [23], [47], [53], in addition to Flow Diagram-based models where travel speeds from probe vehicle data for each road are estimated and then converted to traffic volumes by exploiting the relationship between travel speeds and traffic volumes [54], [55]. A more complete review can be found in [31].…”

A Matrix Completion Method for Cycle-based Traffic Volume Estimation Using Sampled Trajectory Data

“…Because of these advantages, trajectory data enjoy broad application prospects in performance monitoring, signal control optimization, and other areas (Ban et al, 2011, Chu and Saitou, 2013, Guo et al, 2019, Xu et al, 2018, Ma et al, 2020. A large number of existing methods have been developed for short-term traffic volume estimation (Nanthawichit et al, 2003, Zhan et al, 2017, Duan et al, 2018, Emami et al, 2019, Luo, et al, 2019, Zhang et al, 2020 or cycle-based traffic volume estimation (Zheng and Liu, 2017, Wang et al, 2020, Yao et al, 2020, Zhang et al, 2021. However, critical challenges resulting from low penetration rates (i.e., less than 10% in most cases) and spatiotemporal stochasticity of samples still remain for the use of sampled vehicle trajectory data.…”