Urban mobility is on the cusp of transformation with the emergence of shared,
connected, and cooperative automated vehicles. Yet, for them to be accepted by
customers, trust in their punctuality is vital. Many pilot initiatives operate
without a fixed schedule, thus enhancing the importance of reliable arrival
time (AT) predictions. This study presents an AT prediction system for
autonomous shuttles, utilizing separate models for dwell and running time
predictions, validated on real-world data from five cities. Alongside
established methods such as XGBoost, we explore the benefits of integrating
spatial data using graph neural networks (GNN). To accurately handle the case
of a shuttle bypassing a stop, we propose a hierarchical model combining a
random forest classifier and a GNN. The results for the final AT prediction are
promising, showing low errors even when predicting several stops ahead. Yet, no
single model emerges as universally superior, and we provide insights into the
characteristics of pilot sites that influence the model selection process.
Finally, we identify dwell time prediction as the key determinant in overall AT
prediction accuracy when autonomous shuttles are deployed in low-traffic areas
or under regulatory speed limits. This research provides insights into the
current state of autonomous public transport prediction models and paves the
way for more data-informed decision-making as the field advances.

城市交通正在发生转变，通过共享、连接和协作的自动驾驶车辆的出现。然而，为了被顾客接受，对它们的准时性的信任至关重要。本研究提出了一种用于自主穿梭车的到达时间预测系统，利用分别针对停留时间和行驶时间的模型，并通过现实世界五个城市的数据进行验证。除了传统方法如 XGBoost 外，我们还探索了使用图神经网络（GNN）集成空间数据的好处。为了准确处理穿梭车跳过站点的情况，我们提出了一个分层模型，组合了随机森林分类器和 GNN。最终的到达时间预测结果是令人满意的，在预测多站点时也显示出较低的错误率。然而，并无一种单一模型在各方面都表现出优势，我们提供了影响模型选择过程的试点站点特征的见解。最后，我们确定停留时间预测是在低交通地区或受到监管限速时自动穿梭车整体到达时间预测准确性的关键决定因素。本研究为自主公共交通预测模型的现状提供了见解，并为领域的进一步发展铺平了道路。

自主班车服务实际世界中的到达时间预测：来自五个城市的证据

Arrival Time Prediction for Autonomous Shuttle Services in the Real  World: Evidence from Five Cities

Accurate trajectory prediction (TP) for climbing aircraft is hampered by the
presence of epistemic uncertainties concerning aircraft operation, which can
lead to significant misspecification between predicted and observed
trajectories. This paper proposes a generative model for climbing aircraft in
which the standard Base of Aircraft Data (BADA) model is enriched by a
functional correction to the thrust that is learned from data. The method
offers three features: predictions of the arrival time with 66.3% less error
when compared to BADA; generated trajectories that are realistic when compared
to test data; and a means of computing confidence bounds for minimal
computational cost.

针对攀爬飞机的精确轨迹预测，由于飞机操作的认知不确定性存在，会导致预测轨迹与实际观测轨迹之间的显著错误。本文提出了一种生成模型用于攀爬飞机，该模型通过从数据中学习推力的功能修正来丰富标准的飞机基础数据模型（BADA 模型）。该方法具备三个特点：与 BADA 模型相比，到达时间预测误差减少了 66.3%；生成的轨迹与测试数据相比更加真实；并且以最小的计算成本计算置信区间的方法。

从雷达数据学习攀爬飞机的生成模型

Learning Generative Models for Climbing Aircraft from Radar Data

The mortality rate for pedestrians using wheelchairs was 36% higher than the
overall population pedestrian mortality rate. However, there is no data to
clarify the pedestrians' categories in both fatal and nonfatal accidents, since
police reports often do not keep a record of whether a victim was using a
wheelchair or has a disability. Currently, real-time detection of vulnerable
road users using advanced traffic sensors installed at the infrastructure side
has a great potential to significantly improve traffic safety at the
intersection. In this research, we develop a systematic framework with a
combination of machine learning and deep learning models to distinguish
disabled people from normal walk pedestrians and predict the time needed to
reach the next side of the intersection. The proposed framework shows high
performance both at vulnerable user classification and arrival time prediction
accuracy.

研究表明，使用轮椅的行人死亡率比整体人口的行人死亡率高 36％。使用机器学习和深度学习模型，结合先进的交通传感器，设计了一个系统架构，可以区分残障人士和正常行人，并预测到达下一个路口的时间。该方案表现出了较高的脆弱用户分类和到达时间预测准确性。