Sustainable road safety engineering research program: community-based, macro-level collision prediction models

道路碰撞造成的伤害所造成的经济和社会负担的严重性被认为是一个具有流行病程度的全球性问题。 可持续发展与道路安全规划联系在一起，因为道路安全问题的根源在于一个已建成的社区形式，这种形式助长了对驾驶的过度依赖。 传统的道路安全工程依赖于一种被动的方法，这种方法的目的是改善道路安全，以应对已确定的危险道路位置。 然而，只有在存在碰撞历史之后，才能将一个地点确定为危险地点，并解决道路安全问题，这通常是在多年的碰撞之后，而且在建成的社区中成本很高。 为了将道路碰撞的发生率和严重程度降低到目前不可接受的高水平以下，研究人员正在采取更积极主动的方法，在社区规划阶段明确考虑道路安全，以免建成的道路出现问题。 然而，目前缺乏可靠的基于经验的科学道路安全规划工具，无法对土地使用和交通规划采取这种积极主动的办法。 宏观碰撞预测模型的开发有可能填补这一知识空白。 然而，它们的开发过程是复杂的，数据密集型的，耗时的，并需要广泛的知识和使用的运输，地理空间，统计和回归建模过程。 研究需要完善，自动化，并促进他们的开发和从业人员的使用，从而提高道路安全规划和改善的积极主动的工程方法的有效性。 可持续道路安全工程研究方案将通过以下目标提供基于科学的研究，以填补这一知识空白：1）开发宏观层面的碰撞预测模型，促进可持续发展和道路安全规划; 2）开发数据提取和开发这些模型的自动化流程，促进其质量和使用的一致性;以及3）促进土地使用和交通规划者和工程师向这些新的经验工具的转让和广泛使用，以消除道路碰撞给社会带来的负担。

The enormity of the economic and social burden of injuries due to road collisions has been cited as a global problem of epidemic proportions. Sustainable development is linked with road safety planning because the root cause of the road safety problem lies in a built community form that nurtures over dependence on driving. Traditional road safety engineering has relied on a reactive approach, which works to improve road safety in reaction to identified hazardous road locations. However, only after a collision history exists can a location be identified as hazardous and the road safety problem fixed, often after years of collisions and at high cost in built communities. To reduce the incidence and severity of road collisions below unacceptably high current levels, researchers are pursuing more proactive methods, to explicitly consider road safety at the community planning stage, before problems occur on built roads. However, there is a lack of reliable empirical science-based road safety planning tools to conduct this proactive approach to land use and transportation planning. The development of macro-level collision prediction models has the potential to address this knowledge gap. However, their development process is complex, data intensive, and time consuming, and requires extensive knowledge and use of transportation, geo-spatial, statistical, and regression modelling processes. Research is needed to refine, automate, and facilitate their development and use by practitioners, and thereby increase the effectiveness of the proactive engineering approach to road safety planning and improvement. The Sustainable Road Safety Engineering Research Program will provide science-based research to address this knowledge gap, via the following objectives: 1) Develop macro-level collision prediction models that facilitate sustainable development and road safety planning; 2) Develop automated processes for data extraction and development of these models that facilitate consistency in their quality and use; and, 3) Promote the transfer to and widespread use of these new empirical tools by land use and transportation planners and engineers, to remove the burden imposed by road collisions on society.