分析街景和土地利用对城市事故的影响,并通过使用机器学习算法预测未来的事故 (案例研究:马什哈德)
Seyed Amir Mohammad Bagheri1, Barat Mojaradi1, Neda Kamboozia1
1School of Civil Engineering, Iran University of Science and Technology, Iran.
Heliyon
|July 19, 2024
概括
街道景观和土地利用对城市事故产生重大影响. 商业区和开放的街道景观经历了更多的事故,突出了对更安全的城市规划的需求.
科学领域:
- 城市规划和交通安全.
- 地理信息系统 (GIS) 和运输科学.
- 数据科学和机器学习在城市研究中的应用.
背景情况:
- 街景设计和土地利用是影响城市交通安全的关键因素,但往往被忽视.
- 了解城市形状和事故发生之间的关系对于制定有效的安全干预措施至关重要.
- 之前的研究还没有完全量化特定的街景特征和土地使用类型对事故发生率的影响.
研究的目的:
- 调查街景特征和土地使用对2017年至2021年马什哈德城市交通事故的影响.
- 分析不同城市区和土地使用类型 (住宅,商业,混合) 的事故数据.
- 评估基于街景和土地使用数据的机器学习模型在预测城市事故方面的预测准确度.
主要方法:
- 通过在马什哈德三个不同的城市区域进行各种调查收集数据.
- 分析与相邻土地使用 (住宅,商业,混合) 和街道景观类型 (开放式与封闭式) 相关联的事故统计数据.
- 实现和比较机器学习算法,包括随机森林回归 (RFR),多层神经网络感知子回归 (MLP) 和极端提升梯度回归 (XGBoost) 用于事故预测.
主要成果:
- 商业用地地区的事故数量大约是住宅区的三倍.
- 75%的事故发生在开放的街景环境中,而封闭的街景占开放区域事故的三分之一.
- 机器学习模型显示出高预测精度:RFR (85%),XGBoost (81%) 和MLP (75%).
结论:
- 街景设计和土地利用是城市事故发生频率的重要决定因素.
- 商业区和开放的街道景观代表了需要有针对性的安全措施的高风险环境.
- 机器学习模型为预测城市事故提供了可靠的方法,有助于积极的安全规划和干预.
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