arXiv subjects

Fangfang Zheng

Publications and source records attributed to Fangfang Zheng.

4 recordsLinked to original sources

arXiv2026

On the quantification of the spatiotemporal impact of a single discretionary lane change on upstream traffic safety and efficiency

Lane-changing is a critical driving maneuver, and understanding its effects on traffic safety and efficiency is essential for effective traffic management and optimization. However, existing studies provide limited means to account for natural traffic dynamics when identifying disturbances associated with lane changes. Moreover, methods for measuring the spatial extent and duration of the impact of a single discretionary lane change, as well as comprehensive metrics for quantifying its overall spatiotemporal impact, remain underdeveloped. To address these gaps, this study proposes a comprehensive analytical framework to evaluate the spatiotemporal impact of a single discretionary lane change on upstream traffic. The framework identifies affected following vehicles and their impact durations from vehicle trajectories before and after the lane change. A Travel Distance Bias indicator is introduced to measure the motion deviation of following vehicles relative to local reference vehicles, while vehicle-specific pre-event fluctuation envelopes and run-length persistence filters are used to reduce the misattribution of background traffic fluctuations to the lane-change event. Based on the affected vehicles and affected intervals, two aggregate indicators, the Total Efficiency Impact Magnitude (TEIM) and the Total Safety Impact Magnitude (TSIM), are developed to quantify efficiency- and safety-related impact magnitudes. Matched no-lane-change controls and method-comparison experiments provide comparative evidence that the affected-vehicle identification procedure reduces, but does not eliminate, false-positive detections caused by background traffic fluctuations.

math.NA↗

arXiv2023

On the Role of Non-Localities in Fundamental Diagram Estimation

We consider the role of non-localities in speed-density data used to fit fundamental diagrams from vehicle trajectories. We demonstrate that the use of anticipated densities results in a clear classification of speed-density data into stationary and non-stationary points, namely, acceleration and deceleration regimes and their separating boundary. The separating boundary represents a locus of stationary traffic states, i.e., the fundamental diagram. To fit fundamental diagrams, we develop an enhanced cross entropy minimization method that honors equilibrium traffic physics. We illustrate the effectiveness of our proposed approach by comparing it with the traditional approach that uses local speed-density states and least squares estimation. Our experiments show that the separating boundary in our approach is invariant to varying trajectory samples within the same spatio-temporal region, providing further evidence that the separating boundary is indeed a locus of stationary traffic states.

stat.AP↗

arXiv2022

Dynamic Cooperative Vehicle Platoon Control Considering Longitudinal and Lane-changing Dynamics

This paper presents a distributed cascade Proportional Integral Derivate (DCPID) control algorithm for the connected and automated vehicle (CAV) platoon considering the heterogeneity of CAVs in terms of the inertial lag. Furthermore, a real-time dynamic cooperative lane-changing model for CAVs, which can seamlessly combine the DCPID algorithm and the improved sine function is developed. The DCPID algorithm determines the appropriate longitudinal acceleration and speed of the lane-changing vehicle considering the speed fluctuations of the front vehicle on the target lane (TFV). In the meantime, the sine function plans a reference trajectory which is further updated in real time using the model predictive control (MPC) to avoid potential collisions until lane-changing is completed. Both the local and the asymptotic stability conditions of the DCPID algorithm are mathematically derived, and the sensitivity of the DCPID control parameters under different states is analyzed. Simulation experiments are conducted to assess the performance of the proposed model and the results indicate that the DCPID algorithm can provide robust control for tracking and adjusting the desired spacing and velocity for all 400 scenarios, even in the relatively extreme initial state. Besides, the proposed dynamic cooperative lane-changing model can guarantee an effective and safe lane-changing with different speeds and even in emergency situations (such as the sudden deceleration of the TFV).

math.OC↗

arXiv2018

Traffic state estimation using stochastic Lagrangian dynamics

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman-Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.

eess.SY↗