Purpose This study aims to explore the relationship between speed behavior of participants and driving styles on interchange ramps. A spiral interchange in Chongqing was selected as an experimental road to carry out field driving experiment. Design/methodology/approach The continuous operating speed during experiment was selected by Mobile Eye, and the driving style was selected via two inventories. Findings Different driving behaviors showed great differences in age, driving mileage and driving experience. During driving process, male pursued driving stimulation more, whereas female pursued driving steadiness more. Therefore, driving characteristics of male were more disadvantageous to driving safety than that of female. Except for the large speed difference at the entrance and exit of the ramps, the differences at other positions were small. And the operating speed of male was slightly higher than that of female. The difference between different genders at the ascending end position achieved 4–5 kph, and the difference at other feature points were mostly 1–2 kph. During driving process, risky participants were more likely to pursue driving stimulation, and the poor speed control behavior was reflected in wide range of desired operating speed. Based on the results of analyzing at feature points, melancholy and sanguine participants more tended to take a high operating speed, and the poor speed control behavior was reflected in the most widely desired speed range. The speed control behavior of mixed participants was more cautious. Originality/value Advanced driving assistance system combined with two inventories was used to explore difference of speed behavior.

The lateral oscillations of vehicle trajectories are a significant cause of collisions. There is a dearth of research, however, on the oscillatory behaviors of vehicles driving on straight sections of freeways. This study aimed to investigate the effects of vehicle type, lane position, and speed on oscillation behavior and to propose quantitative indicators to explain lateral oscillation characteristics. Based on these characteristics, a more appropriate lane width can be determined. First, the k-means algorithm was performed to cluster the vehicles into three categories: passenger cars, medium-large cars, and extra-large trucks. Then, statistical methods such as analysis of variance (ANOVA) and regression analysis were employed to elaborate on the speed distribution, lateral amplitude (LA), and distance traveled within the oscillation cycle (DTOC) for various vehicle types. The results show that different types of vehicles have different lateral oscillation tendencies. The LA and DTOC for passenger cars are generally more extensive than for medium-large cars and extra-large trucks, and their oscillation patterns are the most complicated. The vehicle trajectory oscillation pattern varies significantly for different lane positions and speeds, but speed is the dominant influencing factor. The naturalistic driving dataset from German freeways served as the foundation for this study. These results can assist road engineers in better understanding the behavioral characteristics of vehicle trajectory oscillations and designing safer freeways.

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Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen’s d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen’s d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.