Lane-changing Time Research Articles

Mechanism of Early-Onset Breakdown at On-Ramp Bottlenecks on Shanghai, China, Expressways

The mechanism of early-onset breakdowns was studied at on-ramp bottlenecks on expressways in Shanghai, China. From four on-ramp breakdown events captured on video, key parameters were extracted: prequeue flow, queue-discharge flow, speed variation per minute, lane change (LC) times in the mainline lanes and the acceleration lane, LC types, and LC locations (longitudinal and lateral). A total of 1,583 LC samples were analyzed. The findings showed a great difference in LC patterns when breakdowns occurred earlier than normal (i.e., before the bottleneck reaches expected capacity). In the case of an early breakdown, most LCs were forced LCs that occurred near the downstream end of the bottleneck, which spread laterally rather quickly. In contrast, in normal breakdowns in the United States, LCs were mostly free LCs that occurred evenly along the bottleneck longitudinally but were concentrated in rightmost lanes laterally.

Capacity Estimation for On-Ramp Merging Section of Urban Expressway Based on Time Headway Loss

This paper proposes a model for estimating capacity of on-ramp merging section of urban expressway based on dynamics and gap acceptance theory, considering lane-changing processes and time headway loss. Survey data were collected from on-ramp merging sections of shanghai urban expressway system and showed that capacity drop of on-ramp merging section is caused by drivers’ lane-changing which may lead to unsteady speed of vehicles and so prolonged time headway compared to the minimum time headway corresponding to the maximum capacity. Three parameters (optimal time headway, time headway loss, and interference quantity of lane-changing) are given and a methodology by accumulating time headway loss due to lane-changing is developed to estimate the capacity drop. Results’ comparisons between real data and microsimulation of on-ramp merging sections and sensitivity analysis show that the proposed model can produce reliable and accurate results. This study also reveals that ramp flow and the difference between the optimal speed and the lane-changing speed of fleet have a great impact on capacity drop. This study is beneficial to evaluate congestion levels, to understand complex traffic phenomena, and so to find efficient solutions.

Lane Change Trajectory Planning and Simulation for Intelligent Vehicle

Aiming at the trajectory planning problem of intelligent vehicle during lane change process, 7 polynomials lane change model was used to control vehicle. Basic model of this model was established at first, and then lane change trajectories were solved by using restriction of movement state. At last, the commonly form of lane change trajectories were obtained. Using real road duration time of lane change, lane change trajectories were simulated with MATLAB. The results shows that this model was suitable for lane change trajectories planning in different speed and it can be used for intelligent vehicle controlling.

Modeling of bicycle flow based on dynamic floor field and cellular automata

In this paper, dynamic floor field is introduced to investigate the characteristics of bicycle flow. To embody the features of bicycles and riders, the repulsive force among the riders and the velocity effect are taken into consideration in this new model. A new concept called lane-changing cost is proposed to study the effect of driving styles, and a new lane-changing rule is proposed based on the traffic environment comparison. The simulation results show that 1) the model can accurately describe the bicycle flow, and the obtained statistics accords with empirical data; 2) the capacity of the road of per unit width deceases as road-width increases; 3) aggressive driving style substantially increases the lane-changing times, which reduces the capacity of the road.

A Study of Driver Behavior Inference Model at Time of Lane Change Using Bayesian Networks

Recent years have brought hope that driving support systems tailored to the characteristics of each driver can be developed. To accomplish this, a driver model must be constructed that considers the driver's psychological function when inferring driver behavior. This paper thus proposes a method to infer driver behavior by capturing time-series steering angle data at the time of lane change. The proposed method uses a static type conditional Gaussian model on Bayesian Networks. By using this method, if the driver behavior of the subject and learned data nearness of features (norms) are below a certain level, it is possible to infer driver behavior with nearly 100% probability. Moreover, compared to the HMM models, this method reduces the rate of incorrect inference inclusion.