Multimodal Transportation最新文献_第7页

Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout 基于车辆安全距离的防御性驾驶行为评估：城市环岛无人机视频计算机视觉案例研究

Multimodal Transportation

Pub Date : 2025-04-22 DOI: 10.1016/j.multra.2025.100227

Yagnik M. Bhavsar , Mazad S. Zaveri , Mehul S. Raval , Shaheriar B. Zaveri

While driving, maintaining a sufficient distance helps reduce collision risk. A time gap of two or three seconds on urban roads from a vehicle ahead is advised in defensive driving. The scenario becomes even more challenging in densely populated and developing countries because of limited road infrastructure, lane indiscipline, and heterogeneous traffic. The safe distance between vehicles and the driver’s reaction can be used as surrogate safety measures (SSMs) to evaluate defensive driving behaviour. This paper presents a case study evaluating defensive driving behaviour using the vision-based methodology and UAV video. This paper proposes two novel SSMs based on distance and acceleration and studies defensive driving behaviour, such as “for how long did a vehicle keep driving under another vehicle’s blind spots?” and “how is a vehicle driving (an interaction pattern) when another vehicle ahead is in its stopping distance range?.” Finally, each driver’s star rating depends on their interactions with other vehicles. We observed that around 48 % of the vehicles did not follow defensive driving practices. In our vehicle inter-class interaction analyses, we also found 16.6 % Rear-End, 6.3 % Side-Swipe, and 1.5 % Angled collision risks occurred between car-car, car-car, and 2Wheeler(2W)-car, respectively. Our methodology could help traffic law enforcement agencies and policy-makers elevate road traffic safety by taking counter-measures against the low-star vehicle categories in developing countries. Example videos of star rating are available on https://www.youtube.com/@YagnikBhavsar.

在驾驶时，保持足够的距离有助于减少碰撞风险。在城市道路上，防守型行驶时，建议与前方车辆保持2 ~ 3秒的时间差。在人口稠密的发展中国家，由于道路基础设施有限、车道不规范和交通混杂，这种情况更具挑战性。车辆之间的安全距离和驾驶员的反应可以作为替代安全措施（SSMs）来评估防御性驾驶行为。本文介绍了一个使用基于视觉的方法和无人机视频评估防御性驾驶行为的案例研究。本文提出了两种基于距离和加速度的新型ssm，并研究了防御性驾驶行为，如“一辆车在另一辆车的盲点下持续行驶多久？”以及“当前面的另一辆车在其停车距离范围内时，车辆如何驾驶（交互模式）？”最后，每位司机的星级评级取决于他们与其他车辆的互动。我们观察到，大约48%的车辆没有遵循防御性驾驶做法。在我们的车辆间相互作用分析中，我们还发现汽车-汽车、汽车-汽车和2W -汽车之间分别发生16.6%的追尾、6.3%的侧滑和1.5%的角度碰撞风险。我们的方法可以帮助交通执法机构和政策制定者通过对发展中国家的低星级车辆类别采取对策来提高道路交通安全。星级评定的示例视频可在https://www.youtube.com/@YagnikBhavsar上找到。

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引用次数: 0

Exploring walkability patterns in diverse urban landscapes: A comprehensive spatial geographic analysis of a megacity in Thailand 探索不同城市景观中的步行模式：泰国一个特大城市的综合空间地理分析

Multimodal Transportation

Pub Date : 2025-03-26 DOI: 10.1016/j.multra.2025.100226

Pawinee Iamtrakul , Sararad Chayphong , Varameth Vichiensan , Paramet Luathep , Nuwong Chollacoop , Atsushi Fukuda , Tuenjai Fukuda

Urbanization, urban sprawl, and the increasing population contribute to challenges in density and diversity within each metropolitan area, creating the need to find sustainable solutions for urban transport. This study investigates the correlation between the urban context and walkability, specifically in Bangkok, Thailand. A spatial geographic approach was employed for urban typology analysis, utilizing multivariate clustering, including walkability assessment through a geographic information system and urban and mobility indices. The findings of the urban context analysis revealed five distinct forms: rural, rural town, suburban, urban, and urban core, each characterized by varying levels of walking abilities. The findings contribute to understanding the nature of activities characteristic of both urban environments and mobility in terms of spatial environmental conditions present in each urban context. Urban and transport planners should explore strategies that align with the specific environmental context of each area to promote walking effectively.

城市化、城市扩张和不断增长的人口给每个大都市地区的密度和多样性带来了挑战，因此需要找到可持续的城市交通解决方案。本研究调查了城市环境与步行性之间的关系，特别是在泰国曼谷。城市类型分析采用空间地理方法，利用多变量聚类，包括地理信息系统的步行性评价和城市与交通指数。城市文脉分析的结果揭示了五种不同的形式：农村、农村城镇、郊区、城市和城市核心，每一种形式都以不同的步行能力为特征。研究结果有助于理解城市环境和流动性在每个城市环境中存在的空间环境条件下的活动特征的性质。城市和交通规划者应该探索与每个地区的具体环境背景相一致的策略，以有效地促进步行。

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引用次数: 0

A coordinated passenger flow control model for urban rail transit considering willingness to board 考虑上车意愿的城市轨道交通协调客流控制模型

Multimodal Transportation

Pub Date : 2025-03-22 DOI: 10.1016/j.multra.2025.100225

Guanghui Su , Pei Li , Deheng Lian , Pengli Mo

Effective control of passenger flow is essential for optimizing train capacity utilization in urban rail transit systems, enhancing operational efficiency and service quality. Passenger travel demand patterns and platform preferences significantly influence train capacity utilization, as passengers often exhibit preferences for specific trains, reflecting their willingness to board. This paper proposes a coordinated passenger flow control model that incorporates passengers’ willingness to board, further improving travel efficiency and fairness. The model applies the First-Come-First-In (FCFI) rule to represent passenger demand within flow control strategies and introduces a mechanism that permits the limited left-behind passenger volume to prevent trip abandonment caused by restrictive controls.

有效的客流控制是优化城市轨道交通列车运力利用率、提高运营效率和服务质量的关键。乘客出行需求模式和站台偏好显著影响列车运力利用率，因为乘客通常表现出对特定列车的偏好，反映了他们的上车意愿。本文提出了一种考虑乘客登机意愿的协调客流控制模型，进一步提高了出行效率和公平性。该模型采用先到先入（FCFI）规则来表示流量控制策略中的乘客需求，并引入了一种允许有限剩余乘客数量的机制，以防止限制性控制导致的放弃行程。

引用次数: 0

The comparison of main features and minimum service standards of air-rail integrated services (ARIS) implementation in Indonesia 印尼实施空铁综合服务（ARIS）的主要特点和最低服务标准的比较

Multimodal Transportation

Pub Date : 2025-03-22 DOI: 10.1016/j.multra.2025.100224

Danny Setiawan , Sigit Priyanto , Mukhammad Rizka Fahmi Amrozi , Imam Muthohar

Air-rail integrated services (ARIS) is a recent innovation in multimodal transport integration that combines air and rail transportation. This study investigates fundamental questions related to the development of ARIS in Indonesia: (1) does the existence of ARIS positively impact airplane passenger satisfaction? (2) how can airplane passenger satisfaction with ARIS be maintained and optimized? This study explores ARIS passenger satisfaction based on occupancy rate and social media big data. Optimization of ARIS in Indonesia is based on implementing main features and aligning them with minimum service standards (MSS) regulations by applying comparative methods and factor analysis. Passenger complaints are related to error systems while attempting to book and pay for tickets online using the airport train mobile application. In addition, there is no infant fare policy, travel schedules that cause long waiting times, and improvement in the quality of customer services. Standardization of the main features of ARIS in Indonesia is urgently needed and will provide time and cost-efficiency benefits for airplane passengers.

空铁综合服务（ARIS）是一种结合航空和铁路运输的多式联运一体化的最新创新。本研究探讨了与印尼ARIS发展相关的基本问题：(1)ARIS的存在是否会对飞机乘客满意度产生积极影响？(2)如何保持和优化飞机乘客对ARIS的满意度？本研究基于入住率和社交媒体大数据来探讨ARIS乘客满意度。印度尼西亚ARIS的优化基于实施主要特征，并通过应用比较方法和因素分析使其与最低服务标准（MSS）法规保持一致。乘客投诉与使用机场列车移动应用程序在线预订和支付机票时的错误系统有关。此外，没有婴儿票价政策，没有导致长时间等待的旅行时间表，也没有提高客户服务质量。印度尼西亚迫切需要对ARIS的主要功能进行标准化，这将为飞机乘客提供时间和成本效益方面的好处。

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引用次数: 0

Evaluating the usefulness of VGI for citizen co-producing city services from citizen perspective: A case study of crowdsourcing pedestrian navigation 从市民视角评估VGI对市民共同提供城市服务的有用性：以众包行人导航为例

Multimodal Transportation

Pub Date : 2025-03-19 DOI: 10.1016/j.multra.2025.100223

Shanqi Zhang , Maju Sadagopan , Xiao Qin

With over 50 % of the world's population now living in the cities and the number continuing to grow, cities are increasingly responsible for delivering services to people and businesses. Recent developments in volunteered geographic information (VGI) have provided new opportunities for improving city services by enabling citizens instantly and collectively share and report issues. However, the usefulness of VGI for such use has not been evaluated from a citizen perspective. This paper aims to bridge this research gap through a case study that innovatively uses geosocial media, as an example of VGI, for reporting accessibility issues to local governments and for providing customized navigation services to the general public. Particularly, a study website was developed that allows citizen participants to evaluate the usefulness of geosocial media for issue reporting and for pedestrian navigation. The results suggest that citizens consider geosocial media useful for helping them maneuver dynamic urban environments and for providing a convenient tool for issue reporting. These results suggest that citizens evaluate the usefulness of VGI differently from government officials and that VGI can facilitate government-citizen communication as well as the provision of customized public services, both of which are important to the development of smart cities.

目前，世界上超过50%的人口居住在城市，而且这一数字还在不断增长，城市在为人们和企业提供服务方面承担着越来越大的责任。志愿地理信息（VGI）的最新发展为改善城市服务提供了新的机会，使市民能够即时和集体地分享和报告问题。然而，VGI在这种用途上的有用性尚未从公民的角度进行评估。本文旨在通过一个案例研究来弥合这一研究差距，该案例研究创新地使用地理社交媒体，以VGI为例，向地方政府报告无障碍问题，并向公众提供定制的导航服务。特别是，开发了一个研究网站，允许公民参与者评估地理社交媒体在问题报告和行人导航方面的有用性。结果表明，市民认为地理社交媒体有助于他们驾驭动态的城市环境，并为问题报告提供了方便的工具。这些结果表明，公民对VGI有用性的评估不同于政府官员，VGI可以促进政府与公民的沟通，并提供定制的公共服务，这两者对智慧城市的发展都很重要。

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引用次数: 0

Sustainable urban transportation planning: Integrating an electrified metro system into Kampala metropolis 可持续城市交通规划：将电气化地铁系统整合到坎帕拉大都市

Multimodal Transportation

Pub Date : 2025-03-18 DOI: 10.1016/j.multra.2025.100220

Ismail Kimuli , John Baptist Kirabira , Ismael Nkambwe , Saadat L.K. Nakyejwe , Michael Lubwama , Kasimu Sendawula , Nashua K Nabaggala

With the United Nations (UN) predicting that 60% of the global population will reside in cities by 2050, sustainable transportation planning is a prominent global trend. This study examines Kampala's transportation sustainability and addresses existing knowledge gaps. It leverages the TIMES-VEDA model, an acronym for “The Integrated MARKAL-EFOM System - Versatile Data Analyst.” TIMES-VEDA is an engineering optimizer used to explore four scenarios: Business-as-Usual (BAU), Reduction in Electricity Consumption (REC), Renewable Electricity Portfolio (REP), and Carbon Reduction Target (CRT). These scenarios analyze the inherent aspects of the Kampala metropolis energy system, providing a foundation for evidence-based decision-making. The approach aligns with the United Nations’ Sustainable Development Goals (UN's SDGs 7, 11, & 13), Uganda's Vision 2040, and the third National Development Plan (NDPIII). The analysis demonstrates that sustainability is within range and highlights the imperative of a holistic approach, the potential of mass rapid transit, anchored by an electrified metro system, to advance green mobility. It sheds light on sustainable practices and trade-offs among distinctive pathways, suggesting a mix of policy measures to combat climate change. The KAMPALA-TIMES model, a bottom-up framework, reveals that a region-specific policy package, particularly the CRT scenario, achieves significant decarbonization, promoting eco-friendly multimodal transportation and paving the way for a more sustainable future for Kampala until 2060. The findings inform policy on urban planning and sustainable transportation that is adaptable elsewhere, ensuring long-term environmental and economic resilience.

联合国（UN）预测，到2050年，全球60%的人口将居住在城市，可持续交通规划是一个突出的全球趋势。本研究考察了坎帕拉的交通可持续性，并解决了现有的知识差距。它利用了TIMES-VEDA模型，这是“综合MARKAL-EFOM系统-多功能数据分析师”的首字母缩写。TIMES-VEDA是一个工程优化器，用于探索四种场景：照常经营（BAU）、减少电力消耗（REC）、可再生电力组合（REP）和碳减排目标（CRT）。这些情景分析了坎帕拉大都市能源系统的内在方面，为循证决策提供了基础。该方法与联合国可持续发展目标(UN’s SDGs 7、11、&；13)、乌干达2040年愿景和第三个国家发展计划（NDPIII）。分析表明，可持续发展是在范围内的，并强调了整体方法的必要性，即以电气化地铁系统为基础的大规模快速交通的潜力，以促进绿色交通。它揭示了可持续实践和不同途径之间的权衡，提出了应对气候变化的政策措施组合。Kampala - times模型是一个自下而上的框架，它揭示了一个针对特定区域的一揽子政策，特别是CRT方案，实现了显著的脱碳，促进了生态友好的多式联运，并为坎帕拉到2060年的更可持续的未来铺平了道路。研究结果为城市规划和可持续交通政策提供了参考，这些政策可在其他地方适用，确保长期的环境和经济韧性。

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引用次数: 0

STG-LSTM: Spatial-temporal graph-based long short-term memory for vehicle trajectory prediction STG-LSTM：基于时空图的车辆轨迹预测长短期记忆

Multimodal Transportation

Pub Date : 2025-03-14 DOI: 10.1016/j.multra.2025.100222

Daniela Daniel Ndunguru , Fan Xing , Chrispus Zacharia Oroni , Arsenyan Ani , Chao Li

Vehicle trajectory prediction plays a crucial role in enhancing the safety, efficiency, and effectiveness of intelligent transportation systems. Accurate predictions of future vehicle movements are essential for applications such as autonomous driving, traffic management, and collision avoidance systems. However, many existing methods either focus solely on spatial or temporal dimensions, neglecting the dynamic interactions between vehicles, which reduces prediction accuracy, especially in complex traffic scenarios. To address these limitations, the study proposes a Spatial-Temporal Graph-Based Long Short-Term Memory model, which integrates graph convolutional networks with long short-term memory networks to effectively capture both spatial relationships and temporal dependencies in vehicle trajectories. The proposed model employs a proximity-based method to construct dynamic adjacency matrices that represent real-time vehicle interactions. To capture spatial dependencies between vehicles, the study uses graph convolutional networks to model the relationships between neighboring vehicles. The long short-term memory network is then applied to capture temporal dynamics by learning the sequential dependencies in vehicle movement patterns. The output from the long short-term memory network is passed through a fully connected layer, which generates trajectory predictions for each vehicle. The study experimental results demonstrate that the proposed model outperforms existing state-of-the-art models across various prediction metrics. Specifically, at 3s and 4s prediction horizons, the model reduces the root mean square error by 22.4 % and 25.5 %, respectively, compared to the best performing interaction-aware long short-term memory model. At the 5s prediction horizon, the model achieves a significant root mean square error reduction of 26.6 %. These findings highlight the model's potential to improve safety and decision-making in autonomous driving systems and traffic management applications.

车辆轨迹预测在提高智能交通系统的安全性、效率和有效性方面起着至关重要的作用。准确预测未来车辆的运动对于自动驾驶、交通管理和防撞系统等应用至关重要。然而，现有的许多方法只关注空间或时间维度，忽略了车辆之间的动态相互作用，从而降低了预测的准确性，特别是在复杂的交通场景中。为了解决这些限制，该研究提出了一个基于时空图的长短期记忆模型，该模型将图卷积网络与长短期记忆网络集成在一起，以有效地捕捉车辆轨迹中的空间关系和时间依赖性。该模型采用基于接近度的方法构建动态邻接矩阵，表示实时车辆交互。为了捕捉车辆之间的空间依赖关系，该研究使用图形卷积网络对相邻车辆之间的关系进行建模。然后应用长短期记忆网络通过学习车辆运动模式中的顺序依赖关系来捕捉时间动态。长短期记忆网络的输出通过一个完全连接的层，该层为每辆车生成轨迹预测。研究实验结果表明，所提出的模型在各种预测指标上优于现有的最先进模型。具体来说，与表现最好的交互感知长短期记忆模型相比，在第3和第4个预测阶段，该模型分别将均方根误差降低了22.4%和25.5%。在5s的预测范围内，该模型实现了显著的均方根误差减小26.6%。这些发现突出了该模型在提高自动驾驶系统和交通管理应用的安全性和决策方面的潜力。

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引用次数: 0

A learning-to-rank method to identify crash hotspots based on large-scale ride-hailing crash data 基于大规模网约车事故数据识别事故热点的学习排序方法

Multimodal Transportation

Pub Date : 2025-03-10 DOI: 10.1016/j.multra.2025.100219

Xiang Wen , Pengfei Cui , Yuanwei Luo , Runbo Hu , Yanyong Guo

Machine learning have been widely used in crash hotspot identification due to its superior prediction accuracy. Existing studies mainly treat hotspot identification as a classification or regression problem. This paper proposed a learning-to-rank(LTR) method to identify hotspots on a single trip and deviced a risk warning system based on the method to verify its effectiveness in crash mitigation. Ride-hailing crashes for a year in China were used as training and testing data. Three kinds of features were extracted to describe the safety level of each road segments, namely, road design features, time-related features, and traffic features. LambdaMART, a pairwise LTR algorism was applied to rank the road segments based on the extracted features. The experiment results suggested that the proposed LTR model outperforms three traditional machine learning models in terms of NDCG@10. The proposed LTR risk warning system integrated with Didi's ride-hailing service outperforms traditional zone-based warning system and bring a significant drop in Average Death Rate per Billion Kilometers.

机器学习以其优越的预测精度在碰撞热点识别中得到了广泛的应用。现有研究主要将热点识别作为分类或回归问题。本文提出了一种LTR （learning-to-rank）方法来识别单次行程的热点，并基于该方法设计了一个风险预警系统，验证了该方法在碰撞缓解中的有效性。中国一年的网约车事故被用作训练和测试数据。提取三种特征来描述每个路段的安全等级，即道路设计特征、时间相关特征和交通特征。基于提取的特征，采用双LTR算法LambdaMART对路段进行排序。实验结果表明，提出的LTR模型在NDCG@10方面优于三种传统的机器学习模型。与滴滴网约车服务相结合的LTR风险预警系统优于传统的基于区域的预警系统，并显著降低了每十亿公里的平均死亡率。

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引用次数: 0

Quantifying the life-saving impact of seatbelt usage: A random forest analysis of unobserved heterogeneity and latent risk factors in vehicular fatalities 量化安全带使用对生命的影响：车辆死亡中未观察到的异质性和潜在风险因素的随机森林分析

Multimodal Transportation

Pub Date : 2025-03-07 DOI: 10.1016/j.multra.2025.100221

Ittirit Mohamad

Seatbelt use significantly reduces the severity of injuries and fatalities in vehicular accidents. This study leverages the Random Forest algorithm to evaluate the impact of seatbelt usage on fatality probabilities in Thailand, with a novel focus on drivers who caused the accidents. The model demonstrated high accuracy, correctly identifying 95.10 % of non-fatal cases and 91.60 % of fatal cases, though some misclassifications were observed. A key contribution of this research is the identification of hidden risk factors influencing fatality rates, including temporal patterns that revealed a surge in fatalities after 17:00, with increased risks associated with non-seatbelt use during late evening and early morning hours. Younger drivers, particularly active at night, were found to exhibit higher rates of non-seatbelt usage and were more likely to be involved in severe accidents. These findings highlight the critical need for targeted seatbelt enforcement and safety interventions during high-risk periods, especially among younger drivers who are at fault in accidents.

安全带的使用大大降低了车辆事故中受伤和死亡的严重程度。本研究利用随机森林算法来评估安全带使用对泰国死亡概率的影响，重点关注造成事故的司机。该模型显示出较高的准确率，正确识别了95.10%的非致命病例和91.60%的致命病例，尽管存在一些错误分类。这项研究的一个关键贡献是确定了影响死亡率的潜在风险因素，包括时间模式，揭示了17:00之后死亡人数激增，深夜和清晨不使用安全带的风险增加。研究发现，年轻司机，尤其是夜间活跃的司机，使用非安全带的比例更高，更有可能发生严重事故。这些发现强调了在高风险时期，特别是在事故中有过错的年轻司机中，有针对性地实施安全带和安全干预的迫切需要。

引用次数: 0

Autonomous fleet management system in smart ports: Practical design and analytical considerations 智能港口的自主船队管理系统：实际设计和分析考虑

Multimodal Transportation

Pub Date : 2025-03-05 DOI: 10.1016/j.multra.2025.100211

Rui Chen , Jing Zhang , Hua Wang

Automated horizontal transportation in container terminals represents a significant advancement in the field of autonomous commercial vehicles. Traditionally, these systems rely on the individual intelligence of each vehicle, similar to autonomous passenger vehicles. However, recent uses of automated technology in select container terminals have demonstrated the benefits of integrating vehicles with a centralized Autonomous Fleet Management System (AFMS). This collaboration not only mitigates information silos but also enhances operational efficiency, safety, and fosters fleet cluster intelligence. This study examines current applications in automated container terminals, analyses practical scenarios, and identifies the essential characteristics of an effective AFMS to support horizontal transportation management. The insights from this comprehensive analysis assist port operators in designing and operating their systems and help scholars better understand and define research questions in this field.

集装箱码头的自动水平运输代表了自动商用车领域的重大进步。传统上，这些系统依赖于每辆车的个人智能，类似于自动驾驶乘用车。然而，最近在特定集装箱码头使用的自动化技术已经证明了将车辆与集中式自动车队管理系统（AFMS）集成的好处。这种合作不仅减轻了信息孤岛，还提高了运营效率、安全性，并促进了舰队集群智能。本研究考察了自动化集装箱码头的当前应用，分析了实际场景，并确定了有效的AFMS的基本特征，以支持横向运输管理。这一综合分析的见解有助于港口运营商设计和操作他们的系统，并帮助学者更好地理解和定义该领域的研究问题。

引用次数: 0