Search Results (50)

Advanced driver assistance systems (ADAS) require a comprehensive understanding of multiple tasks related to the physical and mental states of drivers and traffic situations. Existing ADAS studies perform driver emotion recognition (DER), driver behavior recognition (DBR), traffic context recognition (TCR), and vehicle behavior recognition (VBR) using models designed based on single-task learning, thereby failing to reflect the interactions among tasks in real driving environments. This paper proposes perception and recognition with inter-modal selective multi-task learning (PRISM-MTL), an integrated multimodal and multi-task learning framework that jointly recognizes DER, DBR, TCR, and VBR. The proposed PRISM-MTL consists of a hierarchical stage-wise attention network (HSA-Net)-based multimodal encoder that extracts spatial features from heterogeneous multimodal inputs and task-specific modality fusion (TSMF), which selectively learns effective modality information for each task. This design addresses negative transfer, a key challenge in multi-task learning. In the multimodal encoder, HSA-Net extracts visual modality tokens that emphasize global structural patterns and key spatial regions from multi-view images, while Token-SE generates joint modality tokens that reflect the spatial configuration of joint data. TSMF generates task-specific fusion features that selectively emphasize the modality cues for each task. The generated task-specific fusion features are summarized through temporal mean pooling, and final predictions of driver states and traffic situations are produced by each task head. Experimental results show that the proposed PRISM-MTL achieves state-of-the-art performance on the public AIDE database, with an mAcc of 86.25% ± 0.35 for multi-task recognition of driver states and traffic situations. Full article

Within the subway systems of megacities, individual passenger transfer experiences have long been marginalized due to an overemphasis on macro-level, systemic, and functional performance, positioning low transfer efficiency as a pervasive bottleneck in enhancing the overall network efficacy. Adopting an individual passenger perspective, this study takes the Changping Line of the Beijing Subway as an empirical case. By using walking speed to evaluate transfer efficiency and through field survey, behavioral experiment, and quantitative model analysis, this paper reveals the key built environment factors influencing transfer efficiency and their underlying impact mechanisms and also provides empirical evidence for the synergistic optimization of transfer efficiency and the built environment in megacity subway systems. The findings indicate that the built environment impacts transfer efficiency in macro-non-linear and micro-linear ways, specifically manifesting across six specific mechanisms: the geographic location mechanism, the pressure mechanism of high-density development, the spatial exclusivity mechanism of regional transport hubs, the topological penalty mechanism of transfer paths, the bottleneck constraint mechanism of node facilities, and the compensatory mechanism of information guidance. Furthermore, as a medium affecting transfer efficiency, the shaping of the built environment is essentially determined by the city’s subway planning and construction institutions, the external technical conditions of the particular stations, and localized tactical governance to manage the dynamic daily traffic mobility. Based on these findings, this study suggests that improving the transfer efficiency of megacity metro systems like the Changping Line should implement systemic strategies from four aspects: tailored TOD at the macro-spatial planning phase, the micro-spatial integration of indoor and outdoor built environments during the station design phase, differentiated collaborative governance to alleviate station-external intermodal transfer competitions during the operation phase, and digitally empowered transfer guidance to proactively manage transfer demand across three scenarios. Full article

This study examines the potential effect of hypothetical Urban Air Mobility (UAM) demand redistribution on congestion and signalized intersection performance in the urban environment of Topoľčany, Slovakia. Based on a calibrated microsimulation model, scenarios involving the redistribution of a portion of ground traffic demand away from the road network were analyzed at 30% and 50% during the morning peak period. The evaluation focused primarily on travel times and intersection load. The results indicate that a moderate reduction in ground traffic demand leads to a significant reduction in travel times and traffic intensity. The most substantial improvement was observed in the 30% redistribution scenario. In comparison, a further increase to 50% did not yield proportional benefits, suggesting a nonlinear threshold effect in the transport system’s performance. It should be emphasized that the UAM scenarios in this study do not represent a full operational simulation of Urban Air Mobility, including aerial corridors, vertiports, waiting times, intermodal transfers, or airspace capacity. Instead, they represent demand redistribution scenarios used to evaluate the response of the existing signalized road network to reduced ground traffic demand. The study identifies limitations arising from simplified model assumptions and the absence of broader environmental, operational, and social considerations. Nevertheless, the findings show that even a moderate reduction in road traffic demand, potentially associated with future multimodal mobility concepts, can contribute to improved traffic efficiency in congested urban networks. Full article

Understanding the advance ticket purchase behavior of air–rail intermodal passengers is essential for travel demand forecasting, schedule coordination optimization, and revenue management. Using actual booking data, this study investigates passengers’ advance purchase time (APT) decisions. A Bayesian network (BN) model integrating expert knowledge and data-driven learning is established, with socioeconomic attributes and ticket characteristics as input variables and APT as the output variable. Based on this BN, group analysis is conducted across four routes of varying distances: Tianjin–Shanghai, Tianjin–Changsha, Tianjin–Guangzhou, and Tianjin–Sanya. The results indicate that socioeconomic and ticket attributes influence advance purchase behavior both directly and indirectly through interactive effects. Inferential analysis reveals that elderly passengers, male travelers, morning departures, and longer-distance trips are associated with earlier ticket purchases. Sensitivity analysis shows that fare, transfer time, and age exert heterogeneous effects across routes. Compared with short- and medium-haul journeys, airfare, rail travel time, and transfer time impose stronger impacts on long-haul intermodal trips. Finally, targeted revenue management strategies are proposed to improve air–rail ticket sales. Full article

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article

To investigate the impact of transfer node service priority on multimodal transport path selection under carbon trading price uncertainty, this study models carbon price fluctuations using a “carbon K-line” distribution and quantifies service priority via cargo time value, optimising node service processes for multi-task handling. An interval robust optimisation model is formulated to minimise total transport costs (including transport, time, cargo time value, and carbon emission costs), subject to constraints such as service priority, transfer capacity limits, and mixed time windows. The model is solved using a catastrophe-adaptive genetic algorithm with Monte Carlo sampling. Case studies of three transport tasks reveal that (1) incorporating service priority alters transport paths, reducing total cargo time value loss by 12.64% and decreasing comprehensive costs by 2.26%; (2) carbon price uncertainty increases rail transport distance share by 10.86% on average and raises carbon emission cost proportions by 0.23%, ultimately increasing comprehensive costs by 3.48%. These findings assist multimodal operators in holistically evaluating cargo types, shipper requirements, and carbon markets. By forecasting carbon prices and implementing service priority, stakeholders can select low-carbon intermodal paths that balance cost efficiency, service priority, and emission reduction, thereby supporting sustainable freight transport decision-making. Full article

China’s low-altitude economy (LAE) is moving from policy experimentation to coordinated industrial deployment, yet existing assessments often treat the LAE as a homogeneous sector or equate aircraft capability with deployment readiness. This study develops a scenario-gated sustainability readiness framework for six representative LAE and urban air mobility (UAM) scenarios in China: emergency medical logistics and disaster response, infrastructure inspection and public-service monitoring, urban instant logistics, airport shuttle and intermodal passenger transfer, urban air taxi, and low-altitude tourism. The proposed framework consists of a scenario layer, an eight-dimensional readiness layer, and a decision layer integrating 0–4 ordinal scoring, evidence-confidence tagging, non-compensatory gate conditions, and readiness classification. The eight dimensions cover mission and demand fit; airspace and traffic controllability; infrastructure and site readiness; digital communication, navigation, surveillance, and data security; vehicle, energy, and environmental performance; weather and route-environment robustness; workforce and organizational readiness; and social acceptance and legal legitimacy. The illustrative application indicates that infrastructure inspection is the only routine scaling candidate; emergency medical logistics and urban instant logistics are suitable for bounded routine operation; airport shuttle and tourism should remain controlled pilot candidates; and open-network urban air taxi is still at the pre-pilot stage. The study contributes a scenario-based deployment logic for sustainable aviation and UAM governance. Full article

Transit connectivity is a critical indicator for evaluating the transfer efficiency and network performance of hub airports within integrated transport systems. However, conventional connectivity models primarily rely on flight frequency and schedule coordination, while passenger path choice behavior and multimodal competition effects are often overlooked. To address this limitation, this study develops an enhanced transit connectivity evaluation framework that incorporates passenger path choice preferences and air–rail intermodal effects. A novel air–rail intermodal gain coefficient is introduced to capture the context-dependent interplay between aviation and high-speed rail, quantifying synergistic effects when HSR complements air transfer and substitution effects when it competes with it. The proposed model integrates direct transfer connectivity ($C^{\mathrm{d}}$) and indirect transfer connectivity ($C^{\mathrm{i}\mathrm{n}\mathrm{d}}$) within a unified quantitative framework, embedding transfer time compliance and detour factor constraints to improve behavioral realism and operational applicability. A case study of Xi’an Xianyang International Airport demonstrates that the introduction of the intermodal gain mechanism increases overall transit connectivity from 3606.3 to 3664.1, with the gain concentrated in the 500 to 800 km distance band where HSR journey times are most competitive with door-to-door air travel. The results reveal strong polarization in direct transfer connectivity and the limited effectiveness of indirect transfer routes due to transfer time constraints. The proposed framework offers a replicable assessment tool for hub airport network connectivity and multimodal transport planning, with potential for broader application across hub airports operating within integrated air–rail networks. Full article

Logistics centers play a significant role in regional economic growth and development by optimizing logistics chains, minimizing transportation and transfer costs, shortening transit times, and enabling centralized management through support services. Intermodal transportation is an important function that enables goods to be transported efficiently using multiple modes of transport at logistics centers. This study examines 12 operational logistics centers in Türkiye, evaluating five types of transportation: unimodal (highway, railway) and intermodal (highway/railway, highway/airway, and highway/marine). The assessment considers four key criteria (transportation cost, carbon emissions, transportation risk, and transportation time) under various transportation distance and volume scenarios. The Fuzzy AHP method is employed to weight these criteria, and a goal programming model is developed to optimize transport mode selection. Among the evaluated transport modes, air transportation was not selected in any scenario due to its high cost and carbon emissions, aligning with the study’s focus on cost-efficiency and sustainability. The findings provide scenario-based recommendations for the most suitable transportation modes at each logistics center, contributing to more efficient and sustainable logistics operations. Full article

Place recognition is a fundamental challenge for robotics and autonomous vehicles. While visual place recognition has achieved high precision, cross-modal place recognition—specifically, visual localization within large-scale point cloud maps—remains a formidable problem. Existing methods often struggle with the significant domain gap between modalities and can be computationally prohibitive, especially those processing raw 3D point clouds. Furthermore, they frequently fail to learn features invariant to viewpoint and scale variations, limiting generalization to unseen environments. In this paper, we formulate cross-modal recognition as a problem of learning a scale-invariant, unified embedding space. Our framework employs a hierarchical Swin Transformer to extract multi-scale features from unified 2D representations of both modalities. The central principle of our method is a multi-scale self-distillation paradigm, which recasts feature learning as an intra-modal knowledge transfer task. Specifically, the coarse-scale “teacher” features provide supervision for the fine-scale “student” features. The final inter-modal alignment is then achieved via a global contrastive loss, exclusively leveraging the semantically rich “teacher” embeddings to ensure a reliable and discriminative matching. Extensive experiments on the KITTI and KITTI-360 datasets demonstrate that our method achieves state-of-the-art performance. Notably, using only the KITTI-trained model without fine-tuning, Recall@1 exceeds 60% on all evaluable sequences of KITTI-360 at a 10 m threshold. Code and pre-trained models will be made publicly available upon acceptance. Full article

In order to maximize passenger travel satisfaction and enhance the sustainability of the intercity multimodal transportation system, this paper proposes a two-stage model for intercity multimodal timetable coordination optimization under uncertainty. In the first stage, a robust spatio-temporal graph is built to allocate intermodal passenger flows in order to determine passengers’ route selection results to minimize the total travel cost. At the same time, explicit capacity constraints and transfer behaviors are considered in order to be more realistic. In addition, passengers can take multiple transportation modes (High-speed Rail, Ordinary Rail, EMU, and Coach) in a single trip. The outputs of the first stage are subsequently integrated into the second-stage interval multi-objective timetable optimization model to determine departure times and stopping patterns under uncertain dwell and travel times. It is able to achieve the maximum reduction of passenger travelling time and waiting time within the minimum timetable adjustment, which further improves the integration level of transportation services. To ensure the diversity and convergence of model solving on the basis of retaining uncertain information, we propose an integrated algorithm PSO-IMOEA-MC involving Particle Swarm Optimization algorithm (PSO) and Interval Many-objective Evolutionary Algorithm combined with Monte Carlo (IMOEA-MC). Finally, the effectiveness of the proposed two-stage model and algorithm is validated using three intercity networks: Beijing–Zhangjiakou, Chengdu–Chongqing, and Guangzhou–Qingyuan. The results demonstrate the performance of the method in finding high-level solutions that retain more uncertainty. The findings of this study provide technical support for timetable adjustments under diverse operational scenarios. Full article

As large-scale remote sensing data continue to proliferate, research on remote sensing image–text retrieval (RSITR) has become progressively more prominent. Nevertheless, RSITR still faces two primary challenges. First, remote sensing data exhibit substantially higher intra-modal similarity than typical natural image–text corpora, complicating the discrimination of positive and negative pairs. Second, vision–language models pretrained on natural images (VLP), such as CLIP, are not readily adaptable to remote sensing scenarios without undergoing large-scale remote sensing pretraining that entails substantial cost. To tackle these challenges, we introduce DCCA, a novel framework designed for discriminative and consistent cross-modal alignment. We develop a global contrastive learning strategy with negative pair expansion mechanism to boost representation discrimination when intra-modal similarity is pronounced. Additionally, we introduce a bidirectional distribution matching constraint that jointly aligns intra- and inter-modal distributions, promoting consistent cross-modal alignment beyond the instance level. To further enhance domain adaptation, we propose a remote sensing information injection module that transfers knowledge from a pretrained remote sensing image recognition model into VLP, thereby improving its visual discriminability in remote sensing scenarios. Evaluations conducted on publicly available RSITR benchmarks indicate that DCCA consistently surpasses baseline methods, while attaining performance on par with models trained using large-scale remote sensing datasets under markedly reduced data requirements. These findings verify that the framework is both effective and well-suited for practical deployment. Full article

The dynamic response of a supercritical composite shaft is inherently nonlinear and constitutes a critical aspect of its structural and operational design. In this work, a flexible composite shaft operating in an ultra-supercritical turbine regime is idealized as a cantilever beam. A combined experimental, numerical, and analytical framework is employed to characterize the nonlinear flexural response of the CFRP cantilever subjected to high-frequency external base excitation. The governing equations of motion are formulated by incorporating inertia-related nonlinear effects. Despite excitation in the vicinity of the third flexural mode, the system response is predominantly governed by the first bending mode, indicating strong nonresonant modal coupling. As the excitation amplitude is increased from 0.8 g to 2.8 g, the modulation sidebands around the third-mode frequency space out from 1.8 Hz to 4.1 Hz, while the amplitude of the induced nonresonant response associated with the first mode decreases monotonically from 2.2 g to 0.02 g. This intermodal energy transfer between widely separated modes is attributed to the presence of cubic nonlinearities inherent to the laminated composite material. Full article

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. Full article

This study develops an integrated bi-level operations–assignment model to optimise express service on the Gyeongin Line, a core corridor connecting Seoul and Incheon. The upper level jointly selects express stops and time-of-day headways under coverage constraints—a minimum share of key stations and a maximum inter-stop spacing—while the lower level assigns passengers under user equilibrium using a generalised time function that incorporates in-vehicle time, 0.5× headway wait, walking and transfers, and crowding-sensitive dwell times. Undergrounding and alignment straightening are incorporated into segment run-time functions, enabling the co-design of infrastructure and operations. Using automatic-fare-collection-calibrated origin–destination matrices, seat-occupancy records, and station-area population grids, we evaluate five rail scenarios and one intermodal extension. The results indicate substantial system-wide gains: peak average door-to-door times fall by approximately 44–46% in the AM (07:00–09:00) and 30–38% in the PM (17:30–19:30) for rail-only options, and by up to 55% with the intermodal extension. Kernel density estimation (KDE) and cumulative distribution function (CDF) analyses show a leftward shift and tail compression (median −8.7 min; 90th percentile (P90) −11.2 min; ≤45 min share: 0.0% → 47.2%; ≤60 min: 59.7% → 87.9%). The 45-min isochrone expands by ≈12% (an additional 0.21 million residents), while the 60-min reach newly covers Incheon Jung-gu and Songdo. Backcasting against observed express/local ratios yields deviations near the ±10% band (PM one comparator within and one slightly above), and the Kolmogorov–Smirnov (KS) statistic and Mann–Whitney (MW) test results confirm significant post-implementation shifts. The most cost-effective near-term package combines mixed stopping with modest alignment and capacity upgrades and time-differentiated headways; the intermodal express–transfer scheme offers a feasible long-term upper bound. The methodology is fully transparent through provision of pseudocode, explicit convergence criteria, and all hyperparameter settings. We also report SDG-aligned indicators—traction energy and CO₂-equivalent (CO₂-eq) per passenger-kilometre, and jobs reachable within 45- and 60-min isochrones—providing indicative yet robust evidence consistent with SDG 9, 11, and 13. Full article