Multimodal Transportation最新文献_第3页

Experimental determination of factors causing crashes involving automated vehicles

Multimodal Transportation

Pub Date : 2024-12-30 DOI: 10.1016/j.multra.2024.100186

Teshome Kumsa Kurse , Girma Gebresenbet , Geleta Fikadu Daba , Negasa Tesfaye Tefera

Emergence of technologies to replace human action is occurring in many sectors, with autonomous vehicles being a leading example. Autonomous vehicles do not require human interaction and instead employ various devices to perform essential operations. This paper assesses factors which cause autonomous vehicles to suffer crashes, using field data collected by the Californian Department of Motor Vehicles. Data on these highly automated vehicles (AVs) were clustered based on degree and direction of impact, and analyzed by coding in Excel and RStudio programming. A novel feature of the work is that all clustering, analysis, application of association rules, and determination of degrees of severity of crashes were done by RStudio programming and that the direction of autonomous vehicles impacts was identified based on field data. Our analysis reveals that weather conditions, maneuvering, road conditions, and lighting are major factors in autonomous vehicles crashes. Rear-end crash and minor scratches to autonomous vehicles are the most frequent forms of damage, based on the available data. This study underscores the critical need for enhanced sensor technologies and improved algorithms to better handle adverse weather conditions, complex maneuvers, and varying road and lighting conditions. By identifying the most frequent types of damage, such as rear-end crashes and minor scratches, this research provides valuable insights for manufacturers and policymakers aiming to improve the safety and reliability of autonomous vehicles. The findings can inform future design improvements and regulatory measures, ultimately contributing to the reduction of crash rates and the advancement of autonomous vehicle technology.

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引用次数: 0

Analysis of the driver's stress level while driving in Truck Platooning

Multimodal Transportation

Pub Date : 2024-12-27 DOI: 10.1016/j.multra.2024.100185

Paolo Gandini, Luca Studer, Marta Zecchini, Marco Ponti

The logistic is interested by changes and truck manufacturers are investing in solutions such as truck platooning. This system leads to benefits (fuel consumption, safety, traffic efficiency). The paper presents the analysis of the psychophysical state of drivers during real tests in truck platooning. The peaks in the LF/HF (Low Frequency/High Frequency) parameter are considered, as they are linked to feelings of discomfort. Their occurrence may indicate whether the psychophysical state of the drivers is influenced by the different phases of driving in platoon. A method is defined to monitor and process the HRV (Heart Rate Variability) physiological parameter and the LF/HF ratio, based on the use of commercial smartwatches. An experimental activity, part of the European project C-Roads, allowed the collection of the physiological parameters of drivers and of the data featuring the vehicles in platoon. In general, the correlation between the two data sets revealed that drivers were not negatively affected by driving in platoon. The monitoring of the Follower driver, compared to the Leader, showed a higher level of stress. Peaks in the LF/HF parameter (i.e. high levels of stress) were associated in the 85 % of the cases to punctual situations that were expected to be stressful. Further possible applications of the method are presented, such as the investigation of the C-ITS impacts on the drivers.

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引用次数: 0

The roadmap and strategy for prioritizing the development of public transport in China

Multimodal Transportation

Pub Date : 2024-12-20 DOI: 10.1016/j.multra.2024.100184

Jing Wang , Changjian Liu , Zhouhao Wu , Rufeng Liao , Gengze Li , Huapu Lu

With the acceleration of urbanization and continuous population growth in China, transportation issues in central cities, especially large and mega-cities, have become increasingly prominent. A series of problems such as economic efficiency decline and reduced residents' well-being caused by traffic congestion have become significant factors constraining the sustainable development of cities. As a core component of the urban transportation system, the prioritized development of urban public transportation is crucial for alleviating traffic congestion, improving environmental quality, and enhancing residents' quality of life. However, from the beginning of 2023, the share of public transportation in residents' travel has gradually decreased, with the total passenger volume still lower than the same period in 2019. In response to the challenges faced by the public transportation system under the new circumstances, this paper reviews the necessity of prioritized development of urban public transportation, analyzes the reasons for the decline in the share of public transportation in residents' overall travel modes, and proposes targeted suggestions. On this basis, the paper explores the intrinsic connection between the strategy of prioritizing public transportation development and sustainable urban development.

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引用次数: 0

Challenges in transport modelling and planning

Multimodal Transportation

Pub Date : 2024-12-13 DOI: 10.1016/j.multra.2024.100183

Juan de Dios Ortúzar

引用次数: 0

Indian SUMO traffic scenario-based misbehaviour detection dataset for connected vehicles

Multimodal Transportation

Pub Date : 2024-12-10 DOI: 10.1016/j.multra.2024.100148

Umesh Bodkhe , Sudeep Tanwar

The Internet of Vehicles (IoV) plays a crucial role in intelligent transportation systems (ITS) by enabling communication between interconnected vehicles and supporting infrastructure. Connected vehicles utilize basic safety messages (BSMs) to exchange kinematic data, such as vehicle acceleration, velocity, position, and direction, with neighbouring nodes in the ITS network to enhance road safety. However, these BSMs are susceptible to various security attacks, which disrupt the collaborative functionality of ITS, potentially resulting in accidents or traffic congestion. The scientific community has proposed numerous security mechanisms to protect BSMs. The majority of these assessments have been conducted utilizing either the vehicular reference misbehaviour (VeReMi) dataset or the VeReMi extension dataset. These datasets are specifically designed for the Luxembourg SUMO Traffic (LuST) scenario and are suitable for only evaluating misbehaviour detection methods within a European ITS context. However, there is a notable scarcity of publicly accessible misbehaviour datasets that faithfully depict Indian ITS scenarios. To overcome this limitation, we introduce a new scenario, i.e., the Ahmedabad SUMO Traffic (AhmST) scenario, based on the city of Ahmedabad in Gujarat, India. Moreover, we also introduce the Indian dataset for misbehaviour analysis (AhmST). The proposed dataset includes cases of false data injections affecting the vehicle position, heading, and speed information within BSMs. Finally, we compare the AhmST dataset with recent datasets, assess the proposed dataset using various machine learning techniques and present an optimized model with improved accuracy.

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引用次数: 0

Integration of e-bikes in public transportation based on their impact, importance, and challenges: A systematic review

Multimodal Transportation

Pub Date : 2024-12-10 DOI: 10.1016/j.multra.2024.100182

Isaac Oyeyemi Olayode , Elmira Jamei , Frimpong Justice Alex

In recent years, the integration of e-bikes into public transport systems has received considerable attention, primarily because of their capacity to address environmental issues and enhance urban mobility. Studying the integration of e-bikes into public transit is significant, as they can potentially decrease greenhouse gas emissions, improve air quality, and offer a more cost-effective and accessible means of transportation for both pedestrians and motorists. Previous studies have investigated the advantages and difficulties associated with integrating e-bikes into public transportation systems. However, little attention has been paid to the impact of e-bikes on urban mobility and potential strategies that can be explored or implemented to effectively integrate e-bikes into public transportation. This systematic review filled this research gap. The research methodology used in this systematic review consist of a review of the existing literature from Scopus and Web of Science using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. This systematic review strengthens our understanding of e-bikes and their integration into public transportation. This study will also assist transportation researchers and urban planners in comprehending the fundamental and theoretical frameworks of the challenges and solutions related to e-bikes in public transportation.

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引用次数: 0

Relationship between urban traffic crashes and temporal/meteorological conditions: understanding and predicting the effects 城市交通事故与时间/气象条件之间的关系：了解和预测影响

Multimodal Transportation

Pub Date : 2024-10-19 DOI: 10.1016/j.multra.2024.100175

Xiao Tang, Zihan Liu, Zhenlin Wei

Urban traffic accidents pose significant challenges to public safety and transportation management. Previous studies have revealed that temporal and meteorological factors are the key contributors to accident rate. Besides the inconsistent observations or lack of exploration in some aspects such as snowfall, fog, wind and daily temperatures, it has been shown that these factors are essentially entangled. Furthermore, existing methodologies of analysis or prediction have been limited to relative risk or traditional models. Hence, this study is centered on understanding the detailed correlations between temporal and meteorological factors and accident rate of two types of crashes – moving vehicle and fixed-object crashes using the traffic accident data from Dalian. Further, by incorporating a diverse set of the features, a prediction model leveraging the random forest algorithm is proposed and proved effective in anticipating accident occurrences on the district level. The feature importance analysis has shown that time period and factors such as holiday, temperature and season are most important factors. The rate is higher on working days and in spring, and collisions of both types peak at 6–7 am. When the highest daily temperature is 27 °C and the lowest is 20 °C or -8 °C, the incidence is relatively higher. On the basis, the recommendations are aimed at optimizing transportation systems, mitigating accident risks, and enhancing public safety in urban environments.

城市交通事故给公共安全和交通管理带来了巨大挑战。以往的研究表明，时间和气象因素是造成事故率的关键因素。除了对降雪、大雾、大风和日气温等某些方面的观测不一致或缺乏探索外，研究还表明这些因素在本质上是相互纠缠的。此外，现有的分析或预测方法仅限于相对风险或传统模型。因此，本研究利用大连市的交通事故数据，重点了解时间和气象因素与两类碰撞事故--移动车辆碰撞事故和固定物体碰撞事故--的事故率之间的详细相关性。此外，通过纳入一系列不同的特征，提出了一个利用随机森林算法的预测模型，并证明该模型可有效预测地区一级的事故发生率。特征重要性分析表明，时间段以及节假日、温度和季节等因素是最重要的因素。工作日和春季的碰撞率较高，两种类型的碰撞在上午 6-7 点达到高峰。当日最高气温为 27 °C，最低气温为 20 °C或-8 °C时，发生率相对较高。在此基础上提出的建议旨在优化交通系统，降低事故风险，加强城市环境中的公共安全。

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引用次数: 0

An assignment-based decomposition approach for the vehicle routing problem with backhauls 有回程的车辆路由问题的基于分配的分解方法

Multimodal Transportation

Pub Date : 2024-09-19 DOI: 10.1016/j.multra.2024.100174

Irandokht Parviziomran, Monireh Mahmoudi

In the context of the Vehicle Routing Problem with Backhauls, which involves delivering to linehaul and picking up from backhaul customers, we propose a novel mathematical model that can decompose the main problem into three sub-problems: two Open Vehicle Routing Problems and one Assignment Problem. In our proposed model, Open Vehicle Routing Problems optimize routes for homogeneous vehicles serving linehaul/backhaul customers, while the Assignment Problem matches linehaul and backhaul routes. We utilize Lagrangian decomposition approach and solve subproblems in parallel and sequential layouts. We measure the performance of the foregoing arrangements (in terms of solution quality and computational efficiency) by testing our model on two benchmark datasets, proposed by Goetschalckx and Jacobs-Blecha (1989) and Toth and Vigo (1997) and are known as GJ and TV datasets in the extant literature, respectively. Our model matches best known solutions in 35 % and 33 %, with most within 2 % deviation, for GJ and TV instances, respectively. We also showcase our model on a real-world transportation network containing 100, 250, and 500 customers and geographically located in Lansing, Michigan. To reduce the computational burden of solving the Vehicle Routing Problem with Backhauls on the Lansing dataset, we present a cluster first-route second algorithm and then analyze the impact of vehicle capacity on the solution quality of our proposed algorithm.

有回程的车辆路由问题涉及向线路运输客户送货和从回程客户处取货，在此背景下，我们提出了一个新颖的数学模型，可将主问题分解为三个子问题：两个开放式车辆路由问题和一个分配问题。在我们提出的模型中，"开放式车辆路由问题 "优化为线路/回程客户服务的同质车辆的路线，而 "分配问题 "则匹配线路和回程路线。我们采用拉格朗日分解法，以并行和顺序布局的方式解决子问题。我们通过在 Goetschalckx 和 Jacobs-Blecha（1989 年）以及 Toth 和 Vigo（1997 年）提出的两个基准数据集上测试我们的模型来衡量上述安排的性能（在解决方案质量和计算效率方面），这两个数据集在现有文献中分别称为 GJ 和 TV 数据集。对于 GJ 和 TV 实例，我们的模型与已知最佳解决方案的匹配率分别为 35% 和 33%，偏差大多在 2% 以内。我们还在位于密歇根州兰辛市的一个包含 100、250 和 500 个客户的真实交通网络上展示了我们的模型。为了减轻在兰辛数据集上解决带有回程的车辆路由问题的计算负担，我们提出了一种集群先行-路由后行算法，然后分析了车辆容量对我们提出的算法的求解质量的影响。

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引用次数: 0

An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles 用于规划无载人飞行器异质物流的调整节约算法

Multimodal Transportation

Pub Date : 2024-09-12 DOI: 10.1016/j.multra.2024.100170

Andy Oakey , Antonio Martinez-Sykora , Tom Cherrett

This paper proposes a new extension to the Sustainable Specimen Collection Problem (SSCP), where medical specimens are transported by vans, bikes, and uncrewed aerial vehicles (UAVs, or drones) from local medical practices/offices to a central hospital laboratory for analysis, employing a two-echelon collection approach. Time restrictions from existing operations and literature are also introduced, with the study being formulated as a weighted multi-objective problem seeking to minimise (i) operating costs; (ii) transit times; and (iii) energy/environmental impacts. A new adaptation of the Clarke and Wright Savings Algorithm is subsequently presented to create collection rounds that leverage each mode’s strengths. Subsequently, routes are compiled into workable fixed shifts using a modified bin-packing algorithm in each iteration.

The approach of this study is based on a case study of the UK’s National Health Service (NHS), involving the collection of pathology samples using traditional vans operating within fixed time slots. Using case study data from the Solent region (England), a novel test instance generation methodology was also developed, whereby realistic site positioning and origin-destination travel data are captured to enable effective algorithm experimentation. The findings from applying the proposed algorithm to a set of test instances based on this methodology are subsequently discussed, where it was found that the adapted savings and bin-packing approach produced effective solutions quickly, with 90% of all large instances (200 sites) being solved within 15 min. Further algorithm developments and the application of the devised problem/methodologies are also discussed.

本文对可持续标本采集问题（Sustainable Specimen Collection Problem，SSCP）提出了一个新的扩展，即采用双梯队采集方法，用货车、自行车和无人驾驶飞行器（UAV 或无人机）将医疗标本从当地医疗机构/办公室运送到医院中心实验室进行分析。研究还引入了现有操作和文献中的时间限制，并将其表述为一个加权多目标问题，以寻求最大限度地降低 (i) 运营成本；(ii) 运输时间；(iii) 能源/环境影响。随后，对克拉克和莱特节约算法进行了新的调整，以创建充分利用每种模式优势的收集轮。本研究的方法基于英国国家医疗服务系统（NHS）的一个案例研究，涉及在固定时段内使用传统货车收集病理样本。利用索伦特地区（英格兰）的案例研究数据，还开发了一种新颖的测试实例生成方法，通过该方法可获取真实的站点定位和出发地-目的地旅行数据，从而进行有效的算法实验。随后讨论了基于该方法将所提出的算法应用于一组测试实例的结果，结果发现，经过调整的节省和分仓打包方法能够快速产生有效的解决方案，所有大型实例（200 个站点）中的 90% 都能在 15 分钟内解决。此外，还讨论了算法的进一步发展和所设计问题/方法的应用。

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引用次数: 0

Catastrophic causes of truck drivers’ crashes on Brazilian highways: Mixed method analyses and crash prediction using machine learning 巴西高速公路上卡车司机撞车的灾难性原因：使用机器学习进行混合方法分析和碰撞预测

Multimodal Transportation

Pub Date : 2024-09-07 DOI: 10.1016/j.multra.2024.100173

Rodrigo Duarte Soliani , Ana Rita Tiradentes Terra Argoud , Fábio Santiago , Alisson Vinicius Brito Lopes , Nwabueze Emekwuru

Traffic crashes represent a global challenge, especially in Brazil, where one-third of incidents on federal highways involve trucks, highlighting significant economic and safety risks for truck drivers and the community at large. This study focuses on understanding the specific causes of crashes involving trucks on Brazilian highways, using a decade of data from the Federal Highway Police to develop a predictive model aimed at accident prevention. It analyzes historical crash trends, selects attributes for prediction models, trains classifiers, evaluates predictions through confusion matrices, and enhances reliability via cross-validation techniques, aiming to develop an accident prevention tool. The analysis revealed a temporal pattern, with a slowdown in fatal incidents from 2013 to 2016, followed by an upward trend from 2017. MG-381 emerged as the deadliest highway, and single-lane roads were identified as more accident-prone, emphasizing the need for targeted preventive measures. Additionally, machine learning models achieved an accuracy of over 70 %, with XGBoost and LightGBM leading at 73 %, providing reliable insights for road safety interventions. In transportation engineering and road safety research, these findings highlight the importance of data-driven approaches to understand accident dynamics and design effective interventions to mitigate risks on highways, thereby contributing to increased road safety and social well-being.

交通事故是一项全球性挑战，尤其是在巴西，联邦高速公路上三分之一的事故涉及卡车，这给卡车司机和整个社会带来了巨大的经济和安全风险。本研究的重点是了解巴西高速公路上涉及卡车的交通事故的具体原因，利用联邦公路警察局十年来的数据开发一个旨在预防事故的预测模型。研究分析了历史碰撞趋势，为预测模型选择属性，训练分类器，通过混淆矩阵评估预测结果，并通过交叉验证技术提高可靠性，旨在开发一种事故预防工具。分析发现了一个时间规律，即从 2013 年到 2016 年，致命事故呈放缓趋势，随后从 2017 年开始呈上升趋势。MG-381成为死亡人数最多的高速公路，单车道道路被认为更容易发生事故，这强调了采取有针对性的预防措施的必要性。此外，机器学习模型的准确率超过了 70%，其中 XGBoost 和 LightGBM 以 73% 的准确率遥遥领先，为道路安全干预措施提供了可靠的见解。在交通工程和道路安全研究中，这些发现凸显了数据驱动方法对于了解事故动态和设计有效干预措施以降低高速公路风险的重要性，从而有助于提高道路安全和社会福祉。

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引用次数: 0