The effects of road geometrics and traffic regulations on driver-preferred speeds in northern Italy. An exploratory analysis
The effects of road geometrics and traffic regulations on driver-preferred speeds in northern Italy. An exploratory analysis
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DOI：
10.1016/j.trf.2014.04.019
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发表时间：
2014-07-01
影响因子：
4.1
通讯作者：
Cirillo, Cinzia
中科院分区：
文献类型：
--
作者：
Bassani, Marco;Dalmazzo, Davide;Cirillo, Cinzia
车速受多个变量的影响，如驾驶员特征、车辆性能、道路几何形状、环境条件和驾驶法规。因此，重要的是研究速度和这些变量之间的关系，以促进现有和规划的道路上有意识的速度管理，并诱导驾驶员选择与张贴的限制一致的速度。这种关系对于那些希望实现道路功能和提高整体道路安全的人来说非常重要。少数研究关注这一目标;然而，其中很少涉及城市道路，并且仅限于特定的道路类型和最近建成的区域。这些研究往往是指第85百分位的速度分布和相关的位置是均匀的几何形状，环境，驾驶法规和车辆type.This本文介绍了从城市干道和收集器进行的研究，其特征在于不同的几何特征，便于列入一个具有充分代表性的变量范围内获得的结果。采用三种不同的策略校准了一个能够预测一般百分位数的运行速度的通用模型：（a）简单的多元回归分析，其中使用贝叶斯信息准则（BIC）选择变量;（B）协方差分析方法，包括对（a）中相同变量集的随机效应;最后，（c）随机效应协方差分析方法和新的变量选择（再次使用BIC）。分析表明，根据所选方法的不同，结果会有很大差异。特别是，当考虑随机效应时，几乎所有的变量都被发现具有统计学意义。(C)2014爱思唯尔有限公司版权所有。
Speeds are affected by several variables such as driver characteristics, vehicle performance, road geometrics, environmental conditions and driving regulations. It is therefore important to study the relationships between speed and such variables in order to facilitate conscious speed management on existing and planned roads, and to induce drivers to select a speed consistent with the posted limit. This relationship is of great interest to those who wish to achieve roadway functionality and improve overall road safety.A small number of studies have focused on this objective; however, few of them concern urban roads and they are limited to specific road types and recently built-up areas. These studies often refer to the 85th percentile of the speed distribution and are relevant to locations which are homogeneous in terms of geometry, environment, driving regulations and vehicle type.This paper presents results obtained from a study carried out on urban arterials and collectors characterized by dissimilar geometric features which facilitated the inclusion of a fully representative range of variables. A general model able to predict operating speed for a generic percentile was calibrated using three different strategies: (a) a simple multiple regression analysis in which the variables were selected using the Bayesian Information Criterion (BIC); (b) the analysis of covariance method including random effects on the same set of variables as in (a); and, finally, (c) the analysis of covariance method with random effects and a new selection of variables (again using BIC). The analysis shows a dramatic variation in results depending on the method selected. In particular, when random effects are considered, almost all the variables are found to be statistically significant. (C) 2014 Elsevier Ltd. All rights reserved.