School of Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia.
Public Transport Research Group, Monash University, Melbourne, Australia.
Accid Anal Prev. 2019 Nov;132:105270. doi: 10.1016/j.aap.2019.105270. Epub 2019 Aug 21.
Mode shift from private vehicle to public transport is often considered as a potential means of improving road safety, given public transport's lower fatality rates. However, little research has examined how public transport travel contributes to road safety at a macroscopic level. Further, there is a limited understanding of the individual effects of different public transport modes. This paper explores the effects of commuting by public transport on road safety at a macroscopic level, using Melbourne as a case study. A random effect negative binomial (RENB) and a conditional autoregressive (CAR) model are adopted to explore links between total and severe crash data to commuting mode shares and a range of other zonal explanatory factors. Overall, results show the great potential of public transport as a road safety solution. It is evident that mode shift from private vehicle to public transport (i.e. train, tram, and bus), for commuting would reduce not only total crashes, but also severe crashes. Modelling also demonstrated that CAR models outperform RENB models. In addition, results highlight safety issues related to commuting by motorbike and active transport. Effects of sociodemographic, transport network, and land use factors on crashes at the macroscopic level are also discussed.
从私人交通工具向公共交通工具转变通常被认为是提高道路安全的一种潜在手段，因为公共交通工具的死亡率较低。然而，很少有研究探讨公共交通出行如何在宏观层面上有助于道路安全。此外，对于不同公共交通模式的个别影响也缺乏了解。本文以墨尔本为例，探讨了公共交通出行对宏观层面道路安全的影响。采用随机效应负二项（RENB）和条件自回归（CAR）模型，探索了总事故和严重事故数据与通勤模式份额以及一系列其他区域解释因素之间的联系。总的来说，研究结果表明公共交通在道路安全解决方案方面具有巨大的潜力。很明显，从私人交通工具向公共交通工具（即火车、电车和公共汽车）转变，将不仅减少总事故，还减少严重事故。建模还表明，CAR 模型优于 RENB 模型。此外，研究结果还强调了与摩托车和主动交通通勤相关的安全问题。还讨论了宏观层面上与社会人口统计学、交通网络和土地利用因素相关的事故影响。
