发表机构
Data Science Institute, University of Technology Sydney (UTS); COMPASS IOT PTY LTD（悉尼科技大学数据科学研究所; COMPASS IOT私人有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用澳大利亚大悉尼网联车辆数据，量化危险驾驶并构建时空热图，对比多种模型预测险些发生的危险驾驶事件，发现ARIMA表现最优，识别出悉尼内城和西部高风险区域，为主动道路安全干预提供支持。
AI 中文摘要
道路安全监测历来是被动式的，依赖于死亡和伤害发生后的事故记录分析。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关键但未被充分探索的挑战。本文利用澳大利亚大悉尼地区的网联车辆遥测数据，在地方政府区域（LGA）层面检测和预测险些发生的危险驾驶事件。危险驾驶通过g力阈值量化：急刹车>0.6g、急转弯>0.47g、急加速>0.5g，并构建时空热图以识别高风险区域。对三类共八种预测模型进行基准测试：集成学习（随机森林、XGBoost、LightGBM）、深度学习（LSTM、N-BEATS）和经典时间序列方法（ARIMA、指数平滑法、Prophet）。ARIMA的平均绝对误差（MAE）最低，为162.21，表现与LSTM（MAE：163.92）相当，且优于所有集成方法，N-BEATS的MAE为180.75。这些结果表明，当训练数据量有限时，简约的时间序列模型可与深度学习方法相媲美。该研究凸显了基于物联网的网联车辆数据支持主动道路安全干预的潜力，悉尼的内城区和西部LGA（中央商务区、帕拉马塔、班克斯敦）被确定为需要针对性政策行动的持续高风险区域。
英文摘要
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. 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. 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. 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.
Comments15 pages, 11 figures, 2 tables, Submitted to the ATRF 2026 Conference to take place in November 2026 Sydney, Australia
Journal ref47th Australasian Transport Research Forum 24 to 26 November 2026, Sydney, Australia
