Raccagni Stefano, Ventura Roberto, Barabino Benedetto
Department of Civil, Environmental, Architectural Engineering and Mathematics (DICATAM), University of Brescia, Brescia, Italy.
Heliyon. 2024 Oct 17;10(20):e39459. doi: 10.1016/j.heliyon.2024.e39459. eCollection 2024 Oct 30.
In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V). While V can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented. This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained. The results revealed that V increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model. These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.
在管理道路基础设施时，一个关键基准是车速（V）的第85百分位数。虽然V可以从点速度样本中得出，但每个城市道路往往都缺乏这些样本。因此，预测模型成为研究V与道路特征之间关系的宝贵工具。尽管存在各种针对农村道路的模型，但路边特征和标记对城市道路网络中V的影响仅得到了部分研究，而交通稳静化措施的效果仍然是零散的。本研究旨在通过应用一种方法来填补这些空白，该方法能够揭示一些影响城市道路上V的变量的作用。具体而言，该方法在意大利布雷西亚市的城市道路网络中选择并划分道路，并收集道路特征数据以及48000多个点速度信息。在数据清理之后，根据三种不同的多元回归模型对这些数据进行处理，以分析各种预测变量对V的影响。一旦估计出最佳模型，就对其性能进行评估，并获得显著预测变量的最终列表。结果表明，V随着同质路段变长、到连续交叉路口的距离增加、车道更多的沥青混凝土道路以及树木、可见道路标记和张贴速度限制的存在而增加。相反，当存在路边停车和其他障碍物（如墙壁和路桩）时，当道路交叉路口和人行横道的密度增加时，当左横杆宽度增加时，以及当所穿过的土地用途为商业或办公、住宅或工业时，V会降低。然而，模型中包含的几种交通稳静化措施并未发现有显著影响。这些发现可以帮助道路管理部门核查道路运行状况，并规划基础设施干预措施以降低车速，从而为所有使用者创造一个更安全的城市环境。
