Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States.
Beaman Distinguished Professor & Transportation Program Coordinator, Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, AL 37996, United States.
J Safety Res. 2020 Jun;73:25-35. doi: 10.1016/j.jsr.2020.02.006. Epub 2020 Feb 28.
Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes.
The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina.
This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume).
Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).
自行车使用者属于道路弱势使用者，其安全是一个关键问题。本研究通过应用空间建模方法，揭示交通事故中自行车使用者受伤严重程度的非平稳相关因素，从而获得有关他们安全的新知识。
该方法是地理加权有序逻辑回归（GWOLR），通过纳入交通事故的空间视角，从常规有序逻辑回归（OLR）扩展而来。GWOLR 建模方法允许受伤严重程度与其影响因素之间的关系在空间域中变化，以考虑空间异质性。该方法利用地理参考数据。本研究探索了北卡罗来纳州超过 7000 个地理参考的自行车-机动车碰撞。
本研究进行了一系列非平稳性测试，以确定空间域内变化较大的局部关系。这些局部关系与自行车使用者（自行车使用者年龄、自行车使用者行为、自行车使用者醉酒、自行车行驶方向、自行车位置）、机动车驾驶员（驾驶员年龄、驾驶员醉酒、驾驶员行为、车辆速度、车辆类型）和交通（交通量）有关。
OLR 的结果通常与先前的研究结果一致。例如，自行车使用者受伤严重程度的增加与自行车使用者年龄较大、醉酒和机动车速度较高有关。GWOLR 的结果显示，影响因素与自行车使用者受伤严重程度之间存在局部（而非全局）关系。
研究人员和从业人员可以使用 GWOLR 为特定地区的自行车安全对策确定优先级。例如，本研究中的 GWOLR 建模估计突出了北卡罗来纳州的西部（从夏洛特到阿什维尔），由于包括自行车使用者和驾驶员在内的道路使用者醉酒，自行车使用者受伤严重程度增加。因此，如果对策涉及道路使用者醉酒，那么夏洛特到阿什维尔地区（相对于北卡罗来纳州的其他地区）可能是一个优先事项。
