This research presents a comprehensive approach to predicting the duration of
traffic incidents and classifying them as short-term or long-term across the
Sydney Metropolitan Area. Leveraging a dataset that encompasses detailed
records of traffic incidents, road network characteristics, and socio-economic
indicators, we train and evaluate a variety of advanced machine learning models
including Gradient Boosted Decision Trees (GBDT), Random Forest, LightGBM, and
XGBoost. The models are assessed using Root Mean Square Error (RMSE) for
regression tasks and F1 score for classification tasks.
Our experimental results demonstrate that XGBoost and LightGBM outperform
conventional models with XGBoost achieving the lowest RMSE of 33.7 for
predicting incident duration and highest classification F1 score of 0.62 for a
30-minute duration threshold. For classification, the 30-minute threshold
balances performance with 70.84\% short-term duration classification accuracy
and 62.72\% long-term duration classification accuracy. Feature importance
analysis, employing both tree split counts and SHAP values, identifies the
number of affected lanes, traffic volume, and types of primary and secondary
vehicles as the most influential features.
The proposed methodology not only achieves high predictive accuracy but also
provides stakeholders with vital insights into factors contributing to incident
durations. These insights enable more informed decision-making for traffic
management and response strategies. The code is available by the link:
this https URL

本研究提出了一种全面的方法来预测悉尼大都市区交通事故的持续时间，并将其分类为短期或长期。我们利用包含交通事故详细记录、道路网络特征和社会经济指标的数据集，通过训练和评估包括 Gradient Boosted Decision Trees (GBDT)、Random Forest、LightGBM 和 XGBoost 在内的各种先进机器学习模型。我们的实验结果表明，XGBoost 和 LightGBM 在预测持续时间和分类方面优于传统模型。提出的方法不仅能够实现高度的预测准确性，还能为利益相关者提供有关影响事故持续时间因素的重要见解，从而为交通管理和响应策略提供更明智的决策。

利用机器学习方法预测悉尼大都市区交通事件的持续时间

Predicting the duration of traffic incidents for Sydney greater  metropolitan area using machine learning methods

To translate speech for automatic dubbing, machine translation needs to be
isochronous, i.e. translated speech needs to be aligned with the source in
terms of speech durations. We introduce target factors in a transformer model
to predict durations jointly with target language phoneme sequences. We also
introduce auxiliary counters to help the decoder to keep track of the timing
information while generating target phonemes. We show that our model improves
translation quality and isochrony compared to previous work where the
translation model is instead trained to predict interleaved sequences of
phonemes and durations.

本文介绍了使用 Transformer 模型中的目标因素来预测目标语言音素序列和持续时间的方法，辅助计数器可帮助解码器在生成目标音素时跟踪时序信息，该模型改进了翻译质量和等时性。

利用目标因素和辅助计数器提高等时机器翻译

Improving Isochronous Machine Translation with Target Factors and  Auxiliary Counters

End-to-end models in NLP rarely encode external world knowledge about length
of time. We introduce two effective models for duration prediction, which
incorporate external knowledge by reading temporal-related news sentences
(time-aware pre-training). Specifically, one model predicts the range/unit
where the duration value falls in (R-pred); and the other predicts the exact
duration value E-pred. Our best model -- E-pred, substantially outperforms
previous work, and captures duration information more accurately than R-pred.
We also demonstrate our models are capable of duration prediction in the
unsupervised setting, outperforming the baselines.

本文提出两种利用外部世界知识实现长度预测的有效模型（时间感知预训练），它们分别预测持续时间值落在的区间 / 单位（R-pred）和准确的持续时间值（E-pred）。其中，最好的模型 —E-pred，实现了优于之前工作的显著性能，并且比 R-pred 更精确地捕捉到持续时间信息。我们还证明了我们的模型可以在无监督学习中进行持续时间预测，并且在超过基线性能上更具优越性。