A Calibrated Force-Based Model for Mixed Traffic Simulation

Investigating and modeling crash risk for interactions between motorized and non-motorized in intersection center areas

OBJECTIVE

Motorized vehicles (MV) and non-motorized vehicles (NMV) are mixed in the intersection center area (ICA). This mixing leads to complicated interactions between vehicles, which seriously affects traffic safety, especially at mixed intersections of high density. To deep understanding of the interaction course between motorized and non-motorized vehicles in ICAs.

METHODS

Two intersections with a high density of interaction behavior between motorized and non-motorized vehicles were investigated through high-resolution traffic video. Firstly, to extract high-precision trajectories from roadside video, we proposed a new trajectory extraction framework that integrates Yolov7, Deepsort, and the trajectory reconstruction algorithm, which integrated the social force model and particle filtering (SFPF) proposed in our previous research. Second, 183 complete interaction events between motorized and non-motorized vehicles were extracted based on the surrogate safety indicator TTC, and latent variables affecting the course of interaction behavior between motorized and non-motorized vehicles were defined based on turning direction, kinetic state, surrounding environment, signal light, vehicle action behavior, and types of NMV. Third, an ordered logit model was built to study the interactions.

RESULTS

Analyzing the significance of the model showed that the following variables have a significant effect on the severity of the conflict (p < 0.05 or lower): the turning directions of the two vehicles, their speeds, steering behaviors, the distance between the conflict point and the vehicle, and the surrounding environment. The vehicles entering the ICA 10 s before the end of the signal phase have a higher probability of having a serious crash event while making the interaction.

CONCLUSIONS

The study contributes to developing active safety control and driver assistance strategies.

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

Adaptive physics-informed trajectory reconstruction exploiting driver behavior and car dynamics

As more and more trajectory data become available, their analysis creates unprecedented opportunities for traffic flow investigations. However, observed physical quantities like speed or acceleration are often measured having unrealistic values. Furthermore, observation devices have different hardware and software specifications leading to heterogeneity in noise levels and limiting the efficiency of trajectory reconstruction methods. Typical strategies prune, smooth, or locally modify vehicle trajectories to infer physically plausible quantities. The filtering strength is usually heuristic. Once the physical quantities reach plausible values, additional improvement is impossible without ground truth data. This paper proposes an adaptive physics-informed trajectory reconstruction framework that iteratively detects the optimal filtering magnitude, minimizing local acceleration variance under stable conditions and ensuring compatibility with feasible vehicle acceleration dynamics and common driver behavior characteristics. Assessment is performed using both synthetic and real-world data. Results show a significant reduction in the speed error and invariability of the framework to different data acquisition devices. The last contribution enables the objective comparison between drivers with different sensing equipment.