Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data

澳大利亚主动道路安全干预：利用联网车辆数据预测危险驾驶热点

Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a critical but underexplored challenge.

摘要： 道路安全监测在历史上一直是被动式的，主要依赖于在伤亡事故发生后的碰撞记录分析。在事故发生前主动识别高风险地点和危险驾驶行为，是一项至关重要但尚未得到充分探索的挑战。

This paper addresses this gap using connected vehicle telemetry data from Greater Sydney, Australia, to detect and forecast near-miss risky driving events at the Local Government Area (LGA) level. Risky driving is quantified through g-force thresholds (hard braking >0.6g, harsh cornering >0.47g, harsh acceleration >0.5g), and spatio-temporal heatmaps are constructed to identify high-risk zones.

本文利用澳大利亚大悉尼地区的联网车辆遥测数据，在地方政府区域（LGA）层面检测并预测了“险些发生”的危险驾驶事件，从而填补了这一研究空白。研究通过重力加速度（g-force）阈值（急刹车 >0.6g，急转弯 >0.47g，急加速 >0.5g）对危险驾驶行为进行量化，并构建了时空热力图以识别高风险区域。

Eight predictive models are benchmarked across three families: ensemble learning (Random Forests, XGBoost, LightGBM), deep learning (LSTM, N-BEATS), and classical time-series methods (ARIMA, Exponential Smoothing, Prophet). ARIMA achieves the lowest mean absolute error (MAE: 162.21), performing comparably to LSTM (MAE: 163.92) and outperforming all ensemble methods, with N-BEATS reaching an MAE of 180.75.

研究对三大类共八种预测模型进行了基准测试：集成学习（随机森林、XGBoost、LightGBM）、深度学习（LSTM、N-BEATS）以及经典时间序列方法（ARIMA、指数平滑法、Prophet）。结果显示，ARIMA 实现了最低的平均绝对误差（MAE: 162.21），其表现与 LSTM（MAE: 163.92）相当，且优于所有集成学习方法，而 N-BEATS 的 MAE 为 180.75。

These results demonstrate that parsimonious time-series models are competitive with deep learning approaches when training data volume is limited. The study highlights the potential of IoT-based connected vehicle data to support proactive road safety interventions, with Sydney’s inner and western LGAs (CBD, Parramatta, Bankstown) identified as persistent high-risk zones warranting targeted policy action.

这些结果表明，在训练数据量有限的情况下，简洁的时间序列模型在竞争力上并不逊色于深度学习方法。该研究强调了基于物联网的联网车辆数据在支持主动道路安全干预方面的潜力，并指出悉尼内城区和西部的多个地方政府区域（如 CBD、帕拉马塔、班克斯敦）是持续的高风险区域，值得采取针对性的政策行动。