Unintentional speed reductions in bottleneck sections significantly contribute to traffic congestion on freeways. To address this issue, the Moving Light Guidance System (MLGS) has been implemented as a traffic management measure designed to counteract speed reductions and facilitate recovery by adjusting its lighting speed to slightly exceed observed vehicle speeds. This paper investigates the MLGS’s impact on lane-changing behavior. Our findings show that, the number of lane changes higher with MLGS than without MLGS. Furthermore, these results suggest that MLGS contributes to inducing lane changes by improving vehicle speed and its homogenization, as well as enhancing the homogenization of headway distances. Additionally, we explore the relationship between traffic states and lane-changing phenomena. The results suggest that MLGS may facilitate lane changes as drivers seek to maintain their desired speed. Furthermore, we analysed the average headway distance between the new leader and new follower during a lane change. It shows that the mean headway distance is smaller, suggesting that MLGS helps create lane-changeable gaps. In summary, the MLGS appears to improve traffic conditions in the passing lane. Under MLGS there are more lane changes likely to be caused by the availability of gaps based on headway distance and the desire to maintain desired speed. This paper shows the mechanisms of MLGS operations and shows that MLGS hence may help reduce traffic disturbances in the other lane, where merging vehicles frequently enter.

Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical processing. However, the validity of this assumption has not been studied yet. In this study, we focused on the shape of the distribution of a stochastic term in the car-following model that predicts an acceleration after a time step. Based on vehicle trajectory data on a freeway in Japan, a car-following model is first developed by using data-driven methodology in which long short-term memory (LSTM) network is applied. In this LSTM network, the acceleration value is discretized and the model parameters are trained with the focal loss function. The relationship between the predicted distributions’ modality, standard deviation (SD), and (Formula presented.) with respect to traffic states is then examined. The findings demonstrate that: 1) the developed model can accurately predict the accelerations; 2) a probabilistic distribution tends to have a large SD and multimodality around a merging point and at the beginning of and along stop-and-go waves; and 3) driving behavior can be classed in one of four clusters based on the variation of the percentile value that a driver takes within the probability distribution. The proposed model and the insights are helpful for improving microscopic simulation models when considering new traffic management measures.