Jing Fan

The smart road stud (SRS) system can improve the driver’s overtaking behavior through light guidance, which shows great potential in raising the traffic efficiency of two-way two-lane roads (TWTL). In this paper, we propose a light guidance system based on SRS and a combination of driving simulator and microscopic traffic simulation methodologies for evaluating the effect of smart road studs on a TWTL traffic flow. The driving simulation results reveal that SRSs do not only drastically alter microscopic driving characteristics but also it significantly influences drivers’ decision-making process for overtaking. The frequency of overtaking with SRS escalated by 114.58% compared to that without, with the key differential in overtaking decision patterns manifesting predominantly in the selection of distance between oncoming vehicles traveling in the opposite direction. Microsimulation results demonstrate that the implementation of a smart stud system can enhance both the safety and traffic efficiency on the TWTL roadway with limited sight.

In normal car-following (CF) states, the minimum safe braking distance (MSBD) is virtually an unmeasurable variable, mainly due to the diversity of drivers’ reaction times and vehicles’ braking performance. The average MSBD regarding the reaction time and decelerations as constant values is sometimes greater than the distance used for safe braking of the following vehicle when the leading vehicle applies an emergency brake, which is named the short-distance CF behavior. The short-distance CF conveys that drivers adopt strategies of lower acceptable safety levels, which can be applied to intelligent connected technology (ICT). The objective of this paper was to extend the CF model to accommodate manual driving behavior on the state of different safety levels, and to analyze road traffic flow in the environment from manual driving to high-level intelligent driving with different delays. First, the cognitive bias variable was defined as the ratio of the actual braking distance available to the average MSBD to indirectly analyze different safety levels. Second, the Gipps model was extended, depending on the cognitive bias variable threshold and the duration length of the short-distance CF state, to reproduce driving behaviors with different acceptable safety levels more accurately by numerical simulation. Finally, using models to numerically simulate the impact of vehicles on road traffic flow was carried out. CF behaviors with lower acceptable safety levels under manual driving conditions increase traffic efficiency, and road capacity and safety are significantly improved due to ICT enabling a shortened reaction time. The short-distance driving applied to ICT is expected to be a strategy for traffic congestion mitigation.