Multimodal Transportation最新文献

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Autonomous vehicle safety: An advanced bagging classifier technique for crash injury prediction 自动驾驶汽车安全：一种用于碰撞损伤预测的先进袋装分类技术

Multimodal Transportation

Pub Date : 2025-01-07 DOI: 10.1016/j.multra.2025.100189

Sai Sneha Channamallu, Deema Almaskati, Sharareh Kermanshachi, Apurva Pamidimukkala

The increasing utilization of autonomous vehicles (AVs) makes it critical to understand and mitigate their involvement in traffic accidents. This study, therefore, addresses a significant gap in the research on AV safety by focusing on predicting the possibility of injuries in AV-involved crashes. The California Department of Motor Vehicles’ comprehensive dataset of accidents that occurred from 2014 to May 2024 was utilized, and advanced machine learning techniques were applied to develop a model capable of predicting the outcomes of accidents involving AVs. The study found that the bagging classifier model outperforms other models in reliably predicting and identifying severe crashes and minimizing misclassification. Evaluations made through precision-recall, validation, and learning curves confirm the model's robustness, ability to generalize across data subsets, and effectiveness in increasing training data. Key predictors of crash severity include the extent of damage to the AV, vehicle type, manufacturer, and presence of a traffic signal. The study highlights the importance of tailored safety measures, robust safety mechanisms, and advanced traffic management systems to mitigate crash severity. The real-world application of this advanced model promises substantial benefits for vehicle manufacturers, urban planners, policymakers, and end-users, and will contribute to safer roadways.

自动驾驶汽车（AVs）的使用率越来越高，因此了解和减少它们与交通事故的关系至关重要。因此，本研究通过专注于预测自动驾驶事故中受伤的可能性，解决了自动驾驶安全研究中的一个重大空白。利用加州机动车辆管理局2014年至2024年5月发生的事故综合数据集，并应用先进的机器学习技术开发了一个能够预测涉及自动驾驶汽车事故结果的模型。研究发现，套袋分类器模型在可靠地预测和识别严重碰撞和最小化错误分类方面优于其他模型。通过精确召回率、验证和学习曲线进行的评估确认了模型的鲁棒性、跨数据子集的泛化能力以及增加训练数据的有效性。碰撞严重程度的关键预测因素包括自动驾驶汽车的损坏程度、车辆类型、制造商和交通信号的存在。该研究强调了量身定制的安全措施、健全的安全机制和先进的交通管理系统对于减轻碰撞严重程度的重要性。这一先进模型的实际应用为汽车制造商、城市规划者、政策制定者和最终用户带来了巨大的好处，并将有助于提高道路的安全性。

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引用次数: 0

Robust optimization of multi-regional truck scheduling for China-ASEAN cross-border container drayage 面向中国-东盟跨境集装箱运输的多区域卡车调度鲁棒优化

Multimodal Transportation

Pub Date : 2025-01-07 DOI: 10.1016/j.multra.2025.100187

Jie He, Zhiming Fang, Xintong Yan, Yuntao Ye, Hao Zhang, Changjian Zhang

Container drayage plays an important role in realizing cargo pickup and delivery between container yards and customers. To further describe and optimize the cross-border container drayage between China and the Association of Southeast Asian Nations (ASEAN), this paper studies a multi-regional container drayage problem under travel time uncertainty (MR-CDP-TTU), where multi-regional truck participation and travel time uncertainty are simultaneously considered. We first develop a multi-regional truck scheduling robust optimization model with travel time as the uncertain parameter and the objective of minimizing the total cost. Then, a variable neighborhood tabu search algorithm (VNTSA) is designed by introducing a greedy insertion method to generate the initial solution and structurally combining the tabu search algorithm with the variable neighborhood search algorithm. Finally, comparison and sensitivity analysis is conducted on the cross-border container drayage instances from a China's international logistics company. The results demonstrate that the designed algorithm outperforms in best value, average value, and standard deviation, which indicate that the algorithm can generate quality and stable solutions for the MR-CDP-TTU. Furthermore, the method proposed in this study enables China-ASEAN cross-border logistics to rationally schedule the multi-regional trucks according to their risk attitudes and the maximum deviations of travel time.

集装箱拖运在实现集装箱堆场与客户之间的货物装卸中起着重要的作用。为了进一步描述和优化中国与东盟之间的跨境集装箱运输，本文研究了考虑旅行时间不确定性（MR-CDP-TTU）的多区域集装箱运输问题，其中同时考虑了多区域卡车参与和旅行时间不确定性。首先建立了以行程时间为不确定参数，以总成本最小为目标的多区域货车调度鲁棒优化模型。然后，引入贪婪插入法生成初始解，将禁忌搜索算法与可变邻域搜索算法在结构上结合，设计了一种可变邻域禁忌搜索算法（VNTSA）。最后，对中国某国际物流公司的跨境集装箱拖运实例进行对比和敏感性分析。结果表明，所设计的算法在最优值、平均值和标准差方面都优于其他算法，表明该算法能够生成高质量、稳定的MR-CDP-TTU解。此外，本文提出的方法使中国-东盟跨境物流能够根据其风险态度和旅行时间的最大偏差，对多区域卡车进行合理调度。

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引用次数: 0

How realistic a bicycle simulator can be? - A validation study 自行车模拟器能有多逼真？-验证研究

Multimodal Transportation

Pub Date : 2025-01-04 DOI: 10.1016/j.multra.2025.100193

Amira Hammami, Attila Borsos, Ágoston Pál Sándor

The aim of this research is to objectively and subjectively validate the virtual reality Bicycle Simulator (BS) developed using off-the-shelf components at the University of Győr, Hungary.

To this end, this research compares the performance of 32 participants in two real-world environments (Site 1: separated bicycle-pedestrian path and Site 2: advisory bicycle lane) and in their replication in virtual reality (VR). The objective measures collected for the comparison include speed and Cumulative Lateral Position (CLP), whereas subjective measures include the Perceived Level of Realism (PLR) based on participants’ self-reported perceptions in a post-experiment questionnaire. PLR is a new indicator that we propose using subjects' perceptions of speed, BS control, and VR representation. The combination of these subjective and objective measures is proposed as the Degree of Realism (DR) to standardise the classification of the realism level of a bicycle simulator.

Subjectively, the results indicate that the BS provides a high level of safety and comfort for conducting such research. Subject characteristics have no significant influence on VR sickness scores or Perceived Level of Realism. Objectively, for both speed and CLP, we found no significant difference between on-site and the simulation measurements in the case of Site 1, but otherwise for Site 2. However, subjects were not able to accurately perceive either the actual or the relative differences.

In conclusion, our bicycle simulator is a safe and comfortable traffic safety research tool that needs further improvement. The proposed preliminary concept of the degree of realism requires further investigation.

本研究的目的是客观和主观地验证虚拟现实自行车模拟器（BS）使用匈牙利Győr大学的现成组件开发。为此，本研究比较了32名参与者在两个真实世界环境中的表现（站点1：分离的自行车-人行道和站点2：咨询自行车道）以及他们在虚拟现实（VR）中的复制。比较的客观测量包括速度和累积横向位置（CLP），而主观测量包括现实主义感知水平（PLR），基于参与者在实验后问卷中自我报告的感知。PLR是一个新的指标，我们建议使用受试者对速度，BS控制和VR表征的感知。将这些主观和客观的度量结合起来，提出了真实感度（DR）来规范自行车模拟器的真实感等级的分类。主观上，结果表明BS为进行此类研究提供了高水平的安全性和舒适性。受试者特征对虚拟现实疾病得分和现实主义感知水平无显著影响。客观地说，就速度和CLP而言，我们发现在Site 1的情况下，现场和模拟测量之间没有显著差异，但对于Site 2则不然。然而，受试者不能准确地感知实际差异或相对差异。总之，我们的自行车模拟器是一个安全舒适的交通安全研究工具，需要进一步完善。提出的现实程度的初步概念需要进一步调查。

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Investigating the non-linear influence of the built environment on passengers’ travel distance within metro and bus networks using smart card data 利用智能卡数据研究了地铁和公交网络内建筑环境对乘客出行距离的非线性影响

Multimodal Transportation

Pub Date : 2025-01-04 DOI: 10.1016/j.multra.2025.100188

Yang Liu, Donglin He, Jiayou Lei, Mingwei He, Zhuangbin Shi

Understanding the travel behavior of transit passengers and its influencing factors is crucial for promoting transit use and alleviating urban traffic congestion. However, limited studies have examined the determinants of spatial expansion in multimodal public transportation and overlooked the nonlinear influence between variables. To address these gaps, this study employs the travel distance indicator to portray the spatial expansion of transit passengers. Using smart card data collected from Beijing, China, we propose a comprehensive trip chain extraction method within the metro and bus network, considering transfer behaviors. From the extracted trip chain data, we calculate travel distances and observe significant variations across different transit networks: an average travel distance of 8.09 km in the bus network, 14.93 km in the metro network, and 23.10 km in the integrated network. Further, we explore the non-linear relationship between transit travel distance and the built environment by employing a Gradient Boosting Regression Tree (GBRT) model. The finding reveals that the built environment exerts the most significant influence on travel distance (46.80 %), particularly regarding the distance to the nearest metro station and the central business district (CBD). Additionally, all variables exhibit non-linear effects on travel distance, with many exhibiting relevance only within specific ranges. For instance, there is a noticeable decline in travel distance when the bus stop density falls within the range of 15 units/km² and the bus coverage rate within a range of 0.8. Beyond these threshold values, the decline in travel distance becomes gradual. These findings emphasize the significance of considering non-linear relationships and threshold effects in transit and urban planning. Finally, this study provides practicable recommendations regarding non-linearities for the government that could be beneficial in promoting transit usage.

了解公交乘客的出行行为及其影响因素，对于促进公交使用、缓解城市交通拥堵具有重要意义。然而，有限的研究考察了多式联运公共交通空间扩展的决定因素，忽视了变量之间的非线性影响。为了解决这些差距，本研究采用出行距离指标来描绘过境旅客的空间扩张。基于北京的智能卡数据，在考虑换乘行为的基础上，提出了一种综合的地铁和公交网络出行链提取方法。从提取的出行链数据中，我们计算了出行距离，并观察到不同交通网络之间的显著差异：公交网络的平均出行距离为8.09 km，地铁网络的平均出行距离为14.93 km，综合网络的平均出行距离为23.10 km。此外，我们采用梯度增强回归树（GBRT）模型探讨了交通出行距离与建成环境之间的非线性关系。研究结果表明，建筑环境对出行距离的影响最为显著（46.80%），尤其是到最近的地铁站和中央商务区（CBD）的距离。此外，所有变量对旅行距离都表现出非线性影响，其中许多变量仅在特定范围内表现出相关性。例如，当公交站点密度在15个单位/km²范围内，公交覆盖率在0.8范围内时，出行距离明显下降。超过这些阈值，行进距离的下降是逐渐的。这些发现强调了在交通和城市规划中考虑非线性关系和阈值效应的重要性。最后，本研究为政府提供了有关非线性的可行建议，有助于促进公共交通的使用。

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