A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne
研究提出地理支持向量回归框架，利用墨尔本活动模型数据，分析交通、基础设施等因素对总碰撞和严重伤亡碰撞的影响，发现步行和骑行用户安全风险更高，并提出了可适应多种机器学习技术的特征选择方法。
• Introduced the Geographical Support Vector Regression (GSVR) framework for detailed spatial analysis of crash data at the zonal level. • Utilized comprehensive Melbourne Activity-Based Model (MABM) data to assess varied factors affecting road safety in Greater Melbourne. • Highlighted significant road safety discrepancies among pedestrians and cyclists compared to other road users on shared networks. • Revealed the diverse impact of infrastructure near tram stops and train stations on total and serious injury crashes. • Proposed a feature selection process adaptable across various machine learning techniques to enhance model performance. The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne’s zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.