Mohanty Malaya, Panda Rachita, Gandupalli Srinivasa Rao, Arya Ritik Raj, Lenka Sarthak Kumar
School of Civil Engineering, KIIT Deemed to be University Bhubaneswar, India.
Department of Civil Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India.
Heliyon. 2022 Nov 10;8(11):e11531. doi: 10.1016/j.heliyon.2022.e11531. eCollection 2022 Nov.
One of the major concerns in developing countries like India is to maintain traffic safety under mixed and heterogenous scenario. Although zero accidents is the need of the hour, the first step to attain it is ensuring zero deaths and no serious long-term disabling injuries in road crashes. To reduce the road crash fatalities, explicit and detailed studies have been conducted by utilising historical road crash data of two emerging smart cities of India - Bhubaneswar and Visakhapatnam. Traffic flow data and characteristics of road infrastructure has also been collected by performing field studies at accident prone locations. Various factors including vehicular characteristics, road user characteristics, and road infrastructure have been analyzed using various non-parametric tests to identify the contributing factors resulting in fatalities. It is observed that out of 14 variables used for study, 8 factors were significantly related to fatal crashes. These included categories of victim and accused, 85th percentile speed, presence of road markings, availability of sight distance, etc. The significant factors were subjected to binary logistic regression to determine the odd's ratio of significant factors. The logistic regression predicted 79% of deaths correctly. Crash fatality prediction models are developed using both Classification and Regression Tree (CART) classification tree with 83% accuracy. Although CART classification led to higher accuracy, binary logistic regression is more robust as it considered more significant factors as compared to CART. Subsequently, a severity index has been proposed based on proportions of actual fatal crashes and usage of K-means clustering technique. The proposed indices shall be really helpful in traffic safety management, specifically in reduction of fatalities during road crashes.
在印度这样的发展中国家，一个主要问题是在混合和异构场景下维持交通安全。虽然零事故是当务之急，但实现这一目标的第一步是确保道路交通事故中零死亡和无严重的长期致残伤害。为了减少道路交通事故死亡人数，利用印度两个新兴智慧城市——布巴内斯瓦尔和维沙卡帕特南的历史道路交通事故数据进行了明确而详细的研究。还通过在事故多发地点进行实地研究收集了交通流量数据和道路基础设施特征。使用各种非参数检验分析了包括车辆特征、道路使用者特征和道路基础设施在内的各种因素，以确定导致死亡的促成因素。据观察，在用于研究的14个变量中，有8个因素与致命事故显著相关。这些因素包括受害者和被告类别、第85百分位速度、道路标线的存在、视距可用性等。对这些显著因素进行二元逻辑回归，以确定显著因素的优势比。逻辑回归正确预测了79%的死亡人数。使用分类与回归树（CART）分类树开发了事故死亡预测模型，准确率为83%。虽然CART分类导致了更高的准确率，但二元逻辑回归更稳健，因为与CART相比，它考虑了更多显著因素。随后，基于实际致命事故的比例和K均值聚类技术的使用提出了一个严重程度指数。所提出的指数在交通安全管理中，特别是在减少道路交通事故死亡人数方面将非常有帮助。
