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Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data
Adriana-Simona Mihăiţă, Clarence Cheung, Artur Grigorev, Tuo Mao, David Lillo-Trynes
TL;DR
Reactive crash-based monitoring leaves proactive identification of risky driving hotspots underexplored. Using connected-vehicle telemetry and LGA-level forecasting, the study finds ARIMA achieves the lowest MAE while remaining comparable to LSTM.
Problem
Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur remains underexplored despite predominantly reactive safety interventions.
Method
The study aggregates g-force-defined risky driving events from connected-vehicle telemetry at LGA level and benchmarks eight models across ensemble, deep-learning, and time-series families.
Results
ARIMA achieved the lowest MAE (162.21), performed comparably to LSTM (163.92), and outperformed all ensemble learning methods.
Takeaways & Limitations
Consistently high-risk CBD, Parramatta, and Bankstown LGAs provide a spatial basis for targeted road safety interventions.
Takeaways & Limitations
Daily aggregated observations were insufficient for data-hungry deep-learning architectures, and evaluation covered only the final 35 days of each LGA series.
Abstract
from arXiv · showhide
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.
1. Introduction
The introduction frames Australian road trauma as an urgent public-health problem and motivates proactive near-miss detection using connected-vehicle telemetry. It positions the study as an LGA-level contribution combining validated g-force proxies, predictive-model benchmarking, and actionable risk mapping.
- Motivation: 39,755 individuals were hospitalised in Australia during 2018–2019, including 10,282 who sustained life-threatening injuries.The introduction presents road trauma as a critical public-health issue and notes that 2025 road deaths reached 1,337, the highest level in 12 years.
- Related work: Event-based in-vehicle data recorders are cost-effective and deployable but may miss incidents not associated with predefined thresholds.Naturalistic driving studies provide continuous, high-fidelity behavioural recording, while connected-vehicle data support spatial clustering of near-crash events.
- Contributions: The paper presents one of the first Australian LGA-level applications of connected-vehicle IoT telemetry for near-miss prediction beyond retrospective crash analysis.Its contributions include a rigorous data-driven definition, systematic benchmarking across eight models and three families, and practical guidance for limited telemetric data.
- Contributions: The study produces place-based risk maps identifying Sydney LGAs prone to dangerous driving to support targeted interventions by transport agencies and local councils.A related Sydney study found statistically significant spatial associations between near-miss clusters and official crash blackspots, validating the g-force proxy.
2. Case study
The case study uses large-scale connected-vehicle telemetry from Australia to characterize near-miss risky driving by event type, road context, speed, timing, and location. Risky-driving incidents concentrate on urban trunk roads, peak travel periods, and inner and western Sydney LGAs, while near-miss hotspots can differ from historical crash hotspots.
- Data and event definition: Over 700,000 vehicles from 64 manufacturers contributed connected-vehicle data collected from April 2020 to December 2021, after removing the top 1% of g-force events.Data were collected through in-vehicle Data Acquisition Systems with cloud-based transmission.
- Incident characteristics: 59.56% of recorded risky-driving actions were braking, 40.13% were steering, and 0.3% were combined events.These classifications were based on manoeuvres recorded in the Compass IoT datasets.
- Incident characteristics: 30.3% of incidents occurred on trunk roads, 25.2% on primary roads, and 16.6% on secondary roads; 51.2% affected one lane.Two-lane and three-lane incidents accounted for 22.8% and 19.4%, respectively.
- Temporal and speed patterns: Most risky-driving events occurred below 20km/h or at medium speeds of 25-40km/h, while events above 100km/h were comparatively infrequent.The study categorized recorded speeds into low-, medium-, and high-impact classes.
- Temporal and spatial patterns: Weekday peaks occurred at 7-9 AM and 3-6 PM, with Wednesdays and Fridays reaching almost 7,000 events; Sydney’s CBD, Parramatta, and Bankstown ranked among the most dangerous LGAs.Most incidents occurred near cities, while 2,681 Sydney grid cells showed Low-Crash, High-Near-Miss patterns; near-miss and crash counts had Moran’s I of 0.1654 (p < 0.001).
3. Methodology
The methodology forecasts each LGA’s next-day incident count from its own historical daily risky-driving series, using multiple machine-learning, deep-learning, and time-series models. Models are evaluated with rolling-origin forecasts over a 35-day held-out window using RMSE, MAE, and MAPE.
- Forecasting framework: Each LGA is modelled independently as a univariate daily incident-count time series to support area-level forecasts for targeted resource deployment and preventive measures.The forecasts use historical risky-driving logs to predict the number of incidents likely to occur in each LGA on the following day.
- Model families: Eight models span ensemble learning, deep learning, and classical time-series forecasting, including RF, XGBoost, LightGBM, ARIMA, ES, LSTM, Prophet, and N-BEATS.The described models include bagged decision trees, gradient boosting, recurrent neural networks, decomposable forecasting, and basis-function forecasting approaches.
- Forecasting framework: All models use only the daily incident-count series per LGA; road geometry, speed limits, weather, infrastructure type, and other external features are excluded from forecasting.For RF, XGBoost, and LightGBM, the univariate series is converted into a supervised learning problem using lagged features.
- Evaluation protocol: The evaluation uses an expanding-window rolling-origin procedure over the final 35 days, producing sequential one-step-ahead forecasts at each daily time step.The preceding observations form the initial training set, and model performance is assessed using RMSE, MAE, and MAPE.
- Classical time-series models: ARIMA uses the default hyperparameters p = 2, d = 1, q = 0 and combines past observations linearly to forecast the next value.ARIMA’s parameters represent autoregression, differencing, and the moving-average component, respectively.
4. Results
Across LGAs, ARIMA and LSTM achieved comparable forecasting accuracy, while ensemble models performed poorly. ARIMA’s strong performance is consistent with the task’s simple temporal dependencies and univariate incident-count inputs, though study limitations may explain why complex models underperformed.
- Evaluation design: All models were trained and evaluated independently for each LGA, with MAE, RMSE, and MAPE averaged across LGAs.The models were grouped into ensemble learning, time-series, and neural network classes.
- Model performance: MAE: 162.21 for ARIMA was the lowest reported error, while LSTM achieved a comparable MAE: 163.92.Without a formal Diebold–Mariano test, the models should not be definitively ranked.
- Model performance: MAE: 227.19 for XGBoost and MAE: 236.84 for Prophet were associated with systematic over- or under-prediction.These models were identified as the poorest performers across the 35-day evaluation window.
- Interpretation: ARIMA suited the task because it contains relatively simple temporal dependencies and linear patterns, while every model used only the univariate incident-count series.Random Forest, XGBoost, and LightGBM received 30-day lagged features from that series.
- Limitations: The unexpected underperformance of complex deep-learning models may relate to limitations of the study, which are discussed in the Conclusions section.The passage characterizes traditional models as performing best for the current research problem.
5. Conclusions
The study presents a connected-vehicle framework for identifying and forecasting near-miss risky driving, finding concentrated risk in several Sydney LGAs and motivating targeted interventions. Its conclusions also stress pandemic-related limitations, limited deep-learning training data, and the need for real-time extensions and post-pandemic validation.
- Conclusions: Over 700,000 vehicles supported LGA-level identification and short-term forecasting of near-miss risky driving events in New South Wales.Risky driving was defined using g-force thresholds and aggregated geographically at the Local Government Area level.
- Conclusions: CBD, Parramatta, and Bankstown were consistently identified as high-risk LGAs, providing a spatial basis for targeted road safety interventions.Recommended actions include prioritising variable message signs and speed enforcement during weekday morning and afternoon peaks.
- Conclusions: Post-pandemic data should re-validate spatial priorities before enforcement or infrastructure resources are operationally deployed.The paper also identifies an unaddressed equity dimension associated with risk concentration in Parramatta and Bankstown.
- Limitations and future studies: April 2020 to December 2021 data were affected by COVID-19 lockdowns, so reduced incident counts may reflect lower traffic volumes rather than safer driving.Future studies should isolate pandemic-affected periods or apply corrections before drawing policy-relevant conclusions.
- Limitations and future studies: Over 700,000 vehicles from 64 manufacturers provided broad geo-spatial coverage, but daily aggregated observations remained insufficient for data-hungry deep-learning architectures.This limitation helps explain why classical time-series models performed competitively.
- Towards proactive road intelligence: Ongoing work is developing real-time near-miss prediction from continuously streaming connected-vehicle telemetry and live situational awareness of dangerous driving conditions.This extends beyond the paper’s daily aggregate predictions.