Multimodal Transportation最新文献_第2页

A method for determining pickup and delivery locations of intercity customized bus based on passenger demand and POIs 基于乘客需求和poi的城际定制巴士接送地点确定方法

Multimodal Transportation

Pub Date : 2025-10-24 DOI: 10.1016/j.multra.2025.100270

Yueying Huo , Huijuan Zhou , Feng Hao , Man Zhang , Yachao Liu

Intercity customized bus is a new mode of road passenger transport that relies on the internet platform to obtain passengers' reservation travel demands and provide passengers with “door-to-door” transport service between cities. The determining of Pickup and Delivery locations is essential for its operation, as it provides the possibility of “door-to-door” direct transport service. Existing methods for determining Pickup and Delivery locations mainly focus on clustering passenger demand data, which will lead to the problem of passengers and drivers having difficulty in quickly finding sites in the road network. Therefore, this study aims to propose a new method for determining Pickup and Delivery locations both considering passenger demand data and POIs. Based on the passenger reservation data and AutoNavi Map API, suitable POI categories are selected to derive the actual walking distances and routes between passengers and different POIs. Through two rounds of screening, The POIs with the wider service coverage and the smallest actual walking distance for passengers was selected as the sites. The results show that by utilizing the new method for determining Pickup and Delivery locations, we identified the locations of suitable sites and controlled the actual walking distance of passengers within 500 m in the road network, which will provide convenience to both drivers and passengers. This study will provide a reference basis for optimizing the site setting in intercity customized bus.

城际定制客车是依托互联网平台获取乘客预定出行需求，为乘客提供城际“门到门”运输服务的一种新型道路客运模式。取货地点的确定对其运营至关重要，因为它提供了“门到门”直接运输服务的可能性。现有的取车和送车地点的确定方法主要集中在聚集乘客需求数据上，这将导致乘客和司机难以在路网中快速找到地点的问题。因此，本研究旨在提出一种考虑乘客需求数据和poi的新方法来确定取货和交货地点。根据乘客预订数据和高德地图API，选择合适的POI类别，得出乘客与不同POI之间的实际步行距离和路线。经过两轮筛选，我们选择服务范围较广、乘客实际步行距离最短的机场作为选址。结果表明：利用新的取货地点确定方法，我们确定了合适的地点位置，并将乘客在路网中的实际步行距离控制在500 m以内，这将为司机和乘客提供便利。本研究将为城际定制巴士站点设置的优化提供参考依据。

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引用次数: 0

Dynamic workforce allocation for train-based crowd-shipping: Multi-period optimization incorporating transit discomfort costs 基于列车的人群运输动态劳动力分配：考虑运输不适成本的多周期优化

Multimodal Transportation

Pub Date : 2025-10-16 DOI: 10.1016/j.multra.2025.100269

Qiuhong Huang, Shinya Hanaoka

Last-mile parcel delivery faces challenges such as rising costs, labor shortages, and environmental concerns, especially in urban areas. This paper tackles these issues with a new rolling horizon framework that uses public train networks. We create a dynamic model that assigns delivery tasks to crowd-shippers (commuters) and part-time workers based on real-time data, considering train occupancy and commuter discomfort costs. Our results show that strategic delays in parcel delivery can offer economic benefits, with a delayed end-of-day approach saving 6.3% to 9.1% compared to the faster mixed-workforce approach we studied, which involved both part-timers and crowd-shippers. Additionally, we find that crowdsourced delivery is mainly cost-effective during off-peak transit hours, while part-time staff are more economical during peak times. These findings support adopting a flexible, hybrid workforce strategy that adjusts to changing transit conditions over time.

最后一英里的包裹递送面临着成本上升、劳动力短缺和环境问题等挑战，尤其是在城市地区。本文用一个新的滚动地平线框架解决了这些问题，该框架使用公共列车网络。我们创建了一个动态模型，根据实时数据，考虑到火车占用率和通勤者的不适成本，将送货任务分配给人群运送者（通勤者）和兼职工人。我们的研究结果表明，战略性延迟包裹递送可以提供经济效益，与我们研究的更快的混合劳动力方法相比，延迟的一天结束方法节省了6.3%至9.1%，混合劳动力方法涉及兼职人员和人群快递员。此外，我们发现众包配送主要在非高峰运输时段具有成本效益，而兼职人员在高峰时段更为经济。这些发现支持采用灵活的混合劳动力战略，以适应不断变化的交通条件。

引用次数: 0

DrivNet: Adaptive driving behavior prediction for improving the safety of multimodal transportation in autonomous vehicle systems DrivNet：自适应驾驶行为预测，用于提高自动驾驶车辆系统中多式联运的安全性

Multimodal Transportation

Pub Date : 2025-09-18 DOI: 10.1016/j.multra.2025.100266

J Robert Theivadas, Dr Suresh Ponnan

The safety and efficiency of autonomous vehicles depend on their ability to react swiftly and appropriately to dynamic driving environments. This paper presents DrivNet (Adaptive Driving Network), an adaptive algorithm, DrivNet, aimed at enhancing the reaction time and decision-making capabilities of autonomous vehicles under varying traffic conditions and times of day. DrivNet integrates vehicle parameters and driving behavior with physiological features to improve forward movement within situational awareness environments. The algorithm is validated using a real-time driving dataset collected from expert drivers, capturing both vehicle and driving behavior data under heavy and normal traffic conditions, as well as day and night scenarios. Distinct driving behavior patterns are generated for three key situational awareness conditions: accelerating on clear roads, navigating through critical situations, and maintaining fuel efficiency. These patterns serve as the basis for adapting vehicle control decisions. To validate the effectiveness of these driving behavior patterns, a Recurrent Neural Network (RNN) architecture is employed, enabling the detection and classification of psychological features such as mental workload, stress, and fatigue. The proposed DrivNet algorithm offers valuable insights into distinguishing safe from unsafe driving modes, thereby supporting an intelligent control mechanism that enhances the overall safety of autonomous transportation systems. The results demonstrate the potential of DrivNet to improve autonomous vehicle performance, contributing to the future of safe and efficient self-driving technologies.

自动驾驶汽车的安全性和效率取决于它们对动态驾驶环境做出快速、适当反应的能力。本文提出了自适应驾驶网络（Adaptive Driving Network），一种旨在提高自动驾驶汽车在不同交通条件和时间下的反应时间和决策能力的自适应算法。DrivNet将车辆参数和驾驶行为与生理特征相结合，以改善在态势感知环境下的前进运动。该算法使用从专业驾驶员那里收集的实时驾驶数据集进行验证，该数据集捕获了在繁忙和正常交通条件下以及白天和夜间场景下的车辆和驾驶行为数据。在三种关键的态势感知条件下，会产生不同的驾驶行为模式：在空旷的道路上加速，在紧急情况下导航，以及保持燃油效率。这些模式作为调整车辆控制决策的基础。为了验证这些驾驶行为模式的有效性，采用了循环神经网络（RNN）架构，可以检测和分类心理特征，如心理工作量、压力和疲劳。提出的DrivNet算法为区分安全与不安全驾驶模式提供了有价值的见解，从而支持智能控制机制，提高自动运输系统的整体安全性。测试结果证明了drivenet在提高自动驾驶汽车性能方面的潜力，为未来安全高效的自动驾驶技术做出了贡献。

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引用次数: 0

Defense against multi-path attacks on EV charging networks: A dependency chain analysis and DQN-BASED SOLUTION 电动汽车充电网络多路径攻击防御：依赖链分析及基于dqn的解决方案

Multimodal Transportation

Pub Date : 2025-09-03 DOI: 10.1016/j.multra.2025.100254

Xin Chen , Jiaxin Peng , Luanjuan Jiang , Jixiang Cheng , Shouyu Wu

As electric vehicle (EV) charging infrastructures evolve to become more intelligent, integrated, and interconnected, they encounter escalating cybersecurity threats. Existing studies largely emphasize single-component vulnerabilities while overlooking systemic risks arising from multi-path dependency chains. This paper conceptualizes electric vehicle charging networks as a cyber-physical system (CPS) integrated with power, communication, and control layers. To tackle the issue of multi-path attack propagation within the EV charging network, we present a risk assessment method combined with graph-based system modeling with Deep Q-Networks (DQN). The attacker’s behavior is modeled as a Markov decision process, utilizing DQN to learn optimal attack paths based on cumulative rewards. This method identifies the most vulnerable components and critical propagation pathways, facilitating the development of optimized defense strategies for the deployment of constrained security resources. Comparative experiments indicate that the proposed DQN-based defense strategy outperforms random and traditional dependency-based allocations, leading to diminished cumulative attacker rewards and enhanced network resilience through more efficient resource utilization. These findings can offer practical insights for strengthening the robustness of smart grid ecosystems against multi-stage cyberattacks.

随着电动汽车（EV）充电基础设施向智能化、集成化和互联化发展，它们面临的网络安全威胁也在不断升级。现有的研究大多强调单一组件的漏洞，而忽略了多路径依赖链带来的系统性风险。本文将电动汽车充电网络概念化为一个集成了电源、通信和控制层的网络物理系统（CPS）。为了解决电动汽车充电网络中的多路径攻击传播问题，提出了一种基于深度q网络（Deep Q-Networks， DQN）的基于图的系统建模风险评估方法。将攻击者的行为建模为马尔可夫决策过程，利用DQN学习基于累积奖励的最优攻击路径。该方法识别出最易受攻击的组件和关键传播路径，促进了约束安全资源部署的优化防御策略的发展。对比实验表明，提出的基于dqn的防御策略优于随机分配和传统的基于依赖分配，通过更有效的资源利用，减少了累积攻击者奖励，增强了网络弹性。这些发现可以为加强智能电网生态系统抵御多阶段网络攻击的稳健性提供实用的见解。

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引用次数: 0

Simulation-based optimization for transportation system analysis: State-of-the-art research and future endeavors 基于仿真的交通系统分析优化：最新研究和未来努力

Multimodal Transportation

Pub Date : 2025-07-18 DOI: 10.1016/j.multra.2025.100253

Ziyuan Gu , Bowei Ru , Yifan Li , Wei Ma , Hai L. Vu , Qixiu Cheng , Yuan Kuang

引用次数: 0

Autonomous vehicle adoption behavior and safety concern: A study of public perception 自动驾驶汽车使用行为与安全关注：公众认知研究

Multimodal Transportation

Pub Date : 2025-07-05 DOI: 10.1016/j.multra.2025.100252

Fatemeh Nazari , Mohamadhossein Noruzoliaee , Abolfazl (Kouros) Mohammadian

Realizing the economic and societal benefits of autonomous vehicles (AVs) hinges on widespread public acceptance. However, existing research offers limited insights into two key behavioral factors shaping AV acceptance, namely, perceived AV safety concern and travel behavior, the latter reflecting how heterogenous mobility patterns influence the AV acceptance. These factors are often treated as exogenous, limiting insight into their true behavioral interdependencies with AV acceptance and their distinct behavioral roots. This study addresses these gaps by introducing a recursive trivariate econometric model that jointly estimates AV acceptance, perceived safety concern, and current travel behavior (proxied by annual vehicle-miles traveled or VMT). The recursive structure accounts for structural endogeneity, enabling the unbiased estimation of how safety concern and travel behavior influence AV acceptance, while treating both as endogenous constructs shaped by their own determinants. To further enhance behavioral realism, the model incorporates latent psychological constructs using structural equation modeling. Empirical results from a California stated preference dataset highlight that safety concern and latent vehicle cost consciousness are the two dominant deterrents to AV acceptance, suggesting that policies such as trust-building campaigns and financial incentives can stimulate AV acceptance. Despite showing less safety concern, high-VMT individuals exhibit lower AV acceptance, suggesting potential habitual inertia in ceding driving control and challenging conjectures that users embrace in-vehicle saving and that AVs promote urban sprawl. Shared mobility enthusiasm and latent vehicle performance preference alleviate AV safety concern. Gender and racial gaps persist, with women expressing greater safety concerns and Asians exhibiting higher AV acceptance.

实现自动驾驶汽车的经济和社会效益取决于公众的广泛接受。然而，现有研究对影响自动驾驶汽车接受度的两个关键行为因素，即感知自动驾驶汽车安全关注和出行行为的见解有限，后者反映了异质性出行模式如何影响自动驾驶汽车接受度。这些因素通常被视为外源性因素，限制了对其与AV接受的真正行为相互依赖性及其独特行为根源的了解。本研究通过引入递归的三变量计量经济模型来解决这些差距，该模型联合估计自动驾驶汽车的接受程度、感知到的安全问题和当前的出行行为（以年车辆行驶里程或VMT为代表）。递归结构解释了结构内生性，使安全关注和出行行为如何影响自动驾驶接受的无偏估计成为可能，同时将两者视为由其自身决定因素形成的内生结构。为了进一步增强行为现实性，该模型采用结构方程模型将潜在心理构念纳入其中。来自加州声明偏好数据集的实证结果强调，安全问题和潜在的车辆成本意识是自动驾驶汽车接受度的两个主要障碍，这表明建立信任活动和财政激励等政策可以刺激自动驾驶汽车的接受度。尽管对安全问题的关注程度较低，但行驶里程高的人对自动驾驶汽车的接受程度也较低，这表明，人们可能会习惯性地放弃驾驶控制权，这也挑战了人们的猜测，即用户喜欢车内节能，以及自动驾驶汽车促进了城市扩张。共享出行热情和潜在的车辆性能偏好缓解了自动驾驶汽车的安全担忧。性别和种族差异仍然存在，女性表达了更多的安全担忧，而亚洲人则表现出更高的AV接受度。

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引用次数: 0

Assessing the spatial heterogeneous impacts of urban heat island effects on active travel by leveraging social media data 利用社会媒体数据评估城市热岛效应对主动出行的空间异质性影响

Multimodal Transportation

Pub Date : 2025-05-23 DOI: 10.1016/j.multra.2025.100243

Teng Li , Zhuo Chen , Shuli Luo , Alexa Delbosc

This study investigates the impacts of urban heat island (UHI) effects on active travel by leveraging social media data. A multiscale geographically weighted regression (MGWR) model is utilized to investigate the spatial heterogeneity of integrated influences of UHI effects, built environment, and sociodemographic factors on travel frequency for both peri-summer and all-year trips. The investigation is showcased in Greater Melbourne, Australia, where Twitter posts related to active travel were collected and analyzed to identify active travelers’ travel frequency in different suburbs. The results reveal that UHI effects had a significant negative impact on all suburbs, with greater intensity during peri-summer trips. Moreover, the results proved the spatial heterogeneity of the influence of UHI effects on active trips, with a more intensive influence in residential regions with high urban heat index values. Additionally, the density of tram stops, parkland areas, population density, and young adults had significant positive effects, while the unemployment rate and dwellings with one motor vehicle had negative impacts. This study contributes to the field of travel behavior analysis by completing location-contained social media data. Moreover, it identifies areas heavily impacted by UHI effects, enabling targeted measures such as expanding green spaces, using cooling materials, and enhancing energy practices to reduce UHI effects and promote a sustainable urban environment.

本研究利用社交媒体数据调查了城市热岛效应对主动出行的影响。利用多尺度地理加权回归（MGWR）模型，研究了城市热岛效应、建筑环境和社会人口因素对夏季和全年出行频率综合影响的空间异质性。该调查在澳大利亚的大墨尔本进行展示，收集并分析了与活跃旅行相关的Twitter帖子，以确定活跃旅行者在不同郊区的旅行频率。结果表明，城市热岛效应对所有郊区都有显著的负向影响，且在夏季出行时影响更大。此外，研究结果还表明，城市热岛效应对活跃出行的影响具有空间异质性，在城市热指数值较高的居住区影响更为强烈。此外，有轨电车车站密度、公园面积、人口密度和年轻人对城市发展有显著的正向影响，而失业率和拥有一辆机动车的住房对城市发展有负向影响。本研究通过完善包含位置的社交媒体数据，为旅游行为分析领域做出了贡献。此外，它还确定了受热岛效应严重影响的地区，从而能够采取有针对性的措施，如扩大绿地、使用冷却材料和加强能源实践，以减少热岛效应并促进可持续的城市环境。

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引用次数: 0

Artificial intelligence, machine learning and deep learning in advanced transportation systems, a review 人工智能、机器学习和深度学习在先进交通系统中的应用综述

Multimodal Transportation

Pub Date : 2025-05-21 DOI: 10.1016/j.multra.2025.100242

Siavash Saki , Mohsen Soori

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing transportation systems, addressing critical challenges such as congestion, inefficiency, safety, and sustainability. This paper provides a comprehensive review of these transformative technologies, exploring their applications across various domains, including traffic management, autonomous vehicles, smart parking systems, public transit optimization, freight and logistics, sustainability initiatives, safety enhancements, and infrastructure monitoring. Real-world implementations are examined, highlighting their successes and limitations in practical contexts. While AI-driven solutions have demonstrated significant potential, they face persistent challenges, including data scarcity, limited model generalization, and high computational demands that hinder scalability and reliability. Ethical and regulatory issues, including bias, accountability, and privacy concerns, further complicate adoption. This paper identifies these challenges and discusses emerging research opportunities, such as federated learning, multimodal transportation optimization, and energy-efficient AI systems, to address gaps and advance the field. By synthesizing current advancements, identifying limitations, and proposing future directions, this paper emphasizes the critical role of AI, ML, and DL in shaping smarter, safer, and more sustainable transportation systems.

人工智能（AI）、机器学习（ML）和深度学习（DL）正在彻底改变交通系统，解决拥堵、低效率、安全性和可持续性等关键挑战。本文全面回顾了这些变革性技术，探讨了它们在各个领域的应用，包括交通管理、自动驾驶汽车、智能停车系统、公共交通优化、货运和物流、可持续性举措、安全增强和基础设施监控。研究了现实世界的实现，突出了它们在实际环境中的成功和局限性。虽然人工智能驱动的解决方案已经显示出巨大的潜力，但它们面临着持续的挑战，包括数据稀缺、有限的模型泛化以及阻碍可扩展性和可靠性的高计算需求。伦理和监管问题，包括偏见、问责制和隐私问题，使采用进一步复杂化。本文确定了这些挑战，并讨论了新兴的研究机会，如联邦学习、多式联运优化和节能人工智能系统，以解决差距并推动该领域的发展。通过综合当前的进展，识别局限性，并提出未来的方向，本文强调了人工智能、机器学习和深度学习在塑造更智能、更安全、更可持续的交通系统方面的关键作用。

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引用次数: 0

A duopoly competition problem of shared autonomous vehicles in a multimodal transportation system with government regulation 政府监管下多式联运系统中共享自动驾驶汽车的双寡头竞争问题

Multimodal Transportation

Pub Date : 2025-04-30 DOI: 10.1016/j.multra.2025.100241

Qing Li , Zihao Yan , Ke Lu , Feixiong Liao

Shared autonomous vehicles (SAVs) are expected to revolutionize urban mobility. To explore the complex dynamics of competition and cooperation between operators and other traditional transportation modes, this study proposes a tri-level programming model with equilibrium constraints in a multimodal transportation system. At the upper level, the government regulates the fleet size constraints and hub locations for SAVs. The middle level captures the effect of duopoly competition of SAV operators on fleet size and pricing considering the regulation constraints, which is represented as a 2-player noncooperative game with each player maximizing its profit. At the lower level, travelers’ responses to operational strategies are captured by the dynamic activity-travel assignment model in a multimodal transportation system. A hybrid genetic algorithm, involving a hub-based SAV relocation assignment and a route-swapping algorithm for travelers’ path choice at the lower level, is designed to solve the multi-objective programming problem at the middle level with certain government decisions. A numerical example with two SAV operators shows that the operator with higher-quality vehicles charges more but deploys a smaller fleet compared to the competitor deploying lower-cost vehicles. Government regulations can boost fleet utilization but are less effective when not strict.

共享自动驾驶汽车（sav）有望彻底改变城市交通。为了探索运营商与其他传统运输方式之间竞争与合作的复杂动态，本研究提出了多式联运系统中具有平衡约束的三层规划模型。在上层，政府规定了sav的机队规模限制和枢纽位置。中间层描述了考虑监管约束的航空运输船运营商双寡头竞争对机队规模和价格的影响，表示为每个参与者都最大化其利润的2人非合作博弈。在较低的层次上，多式联运系统中的动态活动-出行分配模型捕捉了乘客对运营策略的反应。设计了一种混合遗传算法，其中包括基于枢纽的交通工具重新分配和下层行人路径选择的路径交换算法，以解决具有特定政府决策的中层多目标规划问题。一个有两个SAV运营商的数值例子表明，与使用低成本车辆的竞争对手相比，拥有高质量车辆的运营商收取更高的费用，但部署的车队规模更小。政府法规可以提高机队利用率，但如果不严格，效果就会降低。

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引用次数: 0

Uncovering individual-level determinants of shared e-scooting travel frequency 揭示共享电动滑板车出行频率的个人层面决定因素

Multimodal Transportation

Pub Date : 2025-04-26 DOI: 10.1016/j.multra.2025.100228

Sajad Askari , Mahsa Merikhipour , Ehsan Rahimi , Farideddin Peiravian , Abolfazl (Kouros) Mohammadian

Shared electric scooter services (SESS) can potentially contribute to sustainable urban transport. However, utilization rates remain low and cast doubt on their cost-effectiveness, energy and resource efficiency, and environmental benefits. While aggregate-level studies have examined shared e-scooter usage, individual-level factors remain underexplored. This study addresses that gap using a behavioral survey of 1,425 responses. We employed a random parameter ordered probit model to quantify the factors that influence the frequency of SESS usage. Study findings reveal a gender and generational gap, with women and older adults less likely to utilize e-scooter sharing compared to men, Millennials, and Gen Z. Additionally, low-income individuals and those without vehicles have a higher probability of being regular users. Multimodal transit users and individuals who receive reduced-fare transit are more likely to use SESS frequently. Individuals who shop online regularly, often a tech‑savvy group, are also more inclined to be frequent users. Furthermore, our findings indicate that built environment attributes are important. Specifically, the results show that living in areas with higher employment entropy, denser road networks, greater accessibility by transit, and highly walkable environments increases the likelihood of frequent SESS use.

共享电动滑板车服务（SESS）可能有助于可持续的城市交通。然而，利用率仍然很低，使人怀疑它们的成本效益、能源和资源效率以及环境效益。虽然总体水平的研究调查了共享电动滑板车的使用情况，但个人水平的因素仍未得到充分探索。这项研究通过对1425人的行为调查来解决这一差距。我们采用随机参数有序概率模型来量化影响SESS使用频率的因素。研究结果揭示了性别和代际差距，与男性、千禧一代和z世代相比，女性和老年人更不可能使用电动滑板车共享。此外，低收入人群和没有车的人更有可能成为定期用户。多式联运用户和获得低价运输的个人更有可能频繁使用SESS。经常在网上购物的人，通常是一个精通技术的群体，也更倾向于成为频繁的用户。此外，我们的研究结果表明，建筑环境属性是重要的。具体而言，研究结果表明，生活在就业熵更高、道路网络更密集、交通可达性更高、步行环境高度适宜的地区，会增加频繁使用SESS的可能性。

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引用次数: 0