Search Results (324)

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. Full article

Globally, the transport sector accounts for almost a quarter of CO₂ emissions from fuel combustion and generates large amounts of pollutants, placing significant pressure on the environment and human health. By 2050, the European Green Deal requires a 90% reduction in transport-related emissions, making sustainability necessary across all modes of transport. Based on the relevant literature, this study examines the role and potential of railways in decarbonising the EU transport sector. Railway is highly efficient, consuming just 1.9% of transport sector energy while handling 16.9% of freight and 5.1% of passenger transport in the EU, yet is responsible for only 0.4% of total emissions. According to studies, greenhouse gas emissions can be reduced by improving energy efficiency, using low-carbon or renewable energy, and expanding train electrification. The greatest potential for decarbonisation lies in a modal shift to rail. However, this requires significant infrastructure investment: raising line speeds to at least 160 km/h, expanding networks, building terminals, digitalisation, and alignment with TEN-T standards. Although the EU supports the modal shift with funding programmes, the transition is not progressing as expected—the share of road freight transport increased from 74% in 2013 to 78% in 2023. Stronger investment is needed in Member States’ national policies for the development and modernisation of railways. The authors developed a Path Evaluation Matrix (PEM), a quantitative decision framework integrating the fields of energy, transport, politics, and economics. The PEM results indicate that BEMU (battery electric multiple units) is optimal for 68% of secondary lines in south-eastern Europe. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization is computationally efficient, it often experiences premature convergence in complex search spaces. This paper proposes a novel hybrid algorithm, Mushroom–Weed Hybrid Reproduction Optimization (MWHRO), which integrates the colony-based local search of the Mushroom Reproduction algorithm with the fitness-proportional reproduction and competitive elimination mechanisms of Invasive Weed Optimization. Hybridization enhances population diversity and global exploration while preserving fast convergence. The proposed algorithm is evaluated based on a realistic tourist route optimization problem using real-world data from Zagreb, Croatia, across multiple transportation modes and objective-weight scenarios. Performance is compared against Ant Colony Optimization, Invasive Weed Optimization, Particle Swarm Optimization, and standard Mushroom Reproduction Optimization under equal evaluation budgets. Experimental results demonstrate that the proposed MWHRO algorithm consistently achieves high-quality solutions with significantly lower execution times, particularly in constrained and multimodal scenarios. Statistical analysis confirms the robustness and practical suitability of the proposed approach for real-world route optimization. Full article

Informal public transportation remains the backbone of urban mobility in many developing cities. While these systems offer flexible and affordable services, they are often associated with safety issues, unreliability, congestion, and environmental impacts. Consequently, transitioning travelers toward formal public transportation is a key objective for sustainable transport planning. This study investigates travelers’ willingness to shift from their current travel modes to a proposed Metro system in Alexandria, Egypt. The analysis uses stated preference data collected through interviews that presented respondents with multiple service scenarios. A correlated random parameters bivariate probit model with heterogeneity in means is estimated to capture interdependence between responses. The results reveal strong and statistically significant cross-equation error correlations, confirming that decisions are not independent and supporting the use of a joint modeling approach. Empirical results indicate that willingness to shift is influenced by socio-demographic characteristics, trip attributes, and current travel conditions. Female travelers are more sensitive to waiting time, while low-income and older individuals are less likely to shift across scenarios. Physical accessibility, especially walkability to and from stations, emerges as the most influential factor in encouraging adoption. These findings provide policymakers with actionable insights for designing inclusive, accessible, and sustainable public transportation systems. Full article

Pooled rideshare is considered an effective future travel mode for improving vehicle utilization and reducing congestion in urban transportation systems. However, its adoption remains limited due to insufficient passenger acceptance and uncertain economic benefits for transportation network companies (TNCs). The emergence of autonomous vehicles brings new momentum to pooled ridesharing services through centralized fleet management. Nevertheless, most existing studies examine traveler behavior and fleet operations separately, leaving the interaction between passenger preferences and operational strategies insufficiently represented. This study proposed an integrated behavioral–operational framework that jointly considers traveler choice behavior and fleet management decisions. An Integrated Choice and Latent Variable (ICLV) model is estimated using 8296 national survey responses collected in the United States in 2025 to capture post-pandemic traveler attitudes toward pooled rideshare. The behavioral model is embedded into a proactive assignment and repositioning strategy implemented on the POLARIS agent-based simulation platform. Simulation experiments are conducted in two urban networks, Greenville (SC) and Austin (TX), under multiple fleet size scenarios. Results show that the new pooling behavior model significantly increases pooling adoption compared with the previous mixed logit model, indicating that it better captures real-world traveler behavior. And the higher pooling adoption also reshapes the TNC trip structure in Greenville. Compared to the baseline in the POLARIS platform, the integrated framework increases pooling adoption and TNC profitability while reducing VMT, empty seat rates, and overall energy consumption. These findings provide insights for the sustainable deployment of pooled SAV services in heterogeneous urban environments. Full article

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R² = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R² = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors. 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

Background: Immunobiologicals are thermolabile products that require strict storage and transportation conditions to maintain their immunogenic efficacy, particularly in countries where logistical and operational challenges are evident, such as Brazil. Methods: A holistic multiple case study, carried out in five regions of Brazil, in 2022, with 42 workers from different instances of the cold chain was conducted. As a source of evidence, data were collected through interviews and analysis of printed documents and analyzed using Thematic Content Analysis, using the analytical technique of cross-case synthesis. Results: The influence of geoclimatic diversity and transportation modes on immunobiological logistics was highlighted. Challenges and requirements were identified, as well as aspects of monitoring during transportation and distribution. Among the main challenges were long distances, poor road conditions, seasonality and the need to share vehicles due to the unavailability of exclusive transportation. Conversely, positive practices were highlighted, such as the use of air-conditioned vehicles, dataloggers and properly prepared thermal boxes. Conclusions: It is necessary to adopt mitigation strategies that consider regional inequalities and promote equity, through raising awareness among managers, investing in logistical infrastructure and expanding good practices in order to guarantee the universal and qualified distribution of immunobiologicals in the country. Full article

This paper presents a mobile application to encourage sustainable travel in urban areas as a proof-of-concept for user-centred sustainable urban transport. The app provides real-time route evaluation based on the environmental impact of different transport modes and local sensor monitoring feedback. Its core feature is an ecological route assessment using life cycle assessment calculations. Users receive quantitative feedback on their carbon footprint and a mobility score ranging from one (worst, red) to five (best, green). Providing both ecological and time-based navigation assessments, the app generates a comprehensive ecological footprint based on individual behaviour, raising awareness of United Nations climate targets. To increase its appeal, the app integrates a quest model offering vouchers from local partners (e.g., half-priced coffee) and competitions (e.g., complete the most journeys under 5 km by bike or on foot per week). A user-centred development process involving multiple test groups and a physical mock-up has been used to optimize the user interface, concept, and gamification elements. The app will be extended to include location-based quests and interactive chat quizzes. The project addresses key aspects of sustainable individual mobility and could be adapted for other cities, universities, or regions. 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 ridesharing is one of the emerging sustainable transport modes that has been widely adopted by commuters and travelers in cities, it has been extensively studied for over a decade. Although many research issues related to ridesharing have been studied, most studies focus on these issues in the context of single ridesharing service providers. However, the existence of multiple ridesharing service providers poses unaddressed research issues. In economics, collaboration might enable two companies to achieve greater market share and efficiency than they could achieve independently. “One plus one is greater than two” refers to the concept of synergy, where combining two elements creates a result that is more valuable or effective than the sum of their individual parts. An interesting question is whether multiple ridesharing service providers can benefit from collaboration. This study aims to assess and realize the benefits of collaboration among ridesharing service providers using metaheuristic algorithms. In this paper, we will study this research question based on two decision models: (1) Decision Model 1 for multiple independent ridesharing service providers and (2) Decision Model 2 for a Collaborative Ridesharing Service Provider. We formulated the optimization of these two decision models and developed twelve metaheuristic algorithms for the two decision models, and conducted experiments to study their effectiveness in terms of performance and computational efficiency. The results indicate that the benefits that can be realized depend critically on the type of metaheuristic algorithm used. The results of this study show that “one plus one is greater than two” holds for ridesharing if an effective solver is used. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

Accurate multi-modal traffic demand forecasting is key to optimizing intelligent transportation systems (ITSs). To overcome the shortcomings of existing methods in capturing dynamic high-order correlations between heterogeneous spatial units and decoupling intra- and inter-mode dependencies at multiple time scales, this paper proposes a Dynamic Multi-Relation Learning with Multi-Scale Hypergraph Transformer method (MST-Hype Trans). The model integrates three novel modules. Firstly, the Multi-Scale Temporal Hypergraph Convolutional Network (MSTHCN) achieves collaborative decoupling and captures periodic and cross-modal temporal interactions of transportation demand at multiple granularities, such as time, day, and week, by constructing a multi-scale temporal hypergraph. Secondly, the Dynamic Multi-Relationship Spatial Hypergraph Network (DMRSHN) innovatively integrates geographic proximity, passenger flow similarity, and transportation connectivity to construct structural hyperedges and combines KNN and K-means algorithms to generate dynamic hyperedges, thereby accurately modeling the high-order spatial correlations of dynamic evolution between heterogeneous nodes. Finally, the Conditional Meta Attention Gated Fusion Network (CMAGFN), as a lightweight meta network, introduces a gate control mechanism based on multi-head cross-attention. It can dynamically generate node features based on real-time traffic context and adaptively calibrate the fusion weights of multi-source information, achieving optimal prediction decisions for scene perception. Experiments on three real-world datasets (NYC-Taxi, -Bike, and -Subway) demonstrate that MST-Hyper Trans achieves an average reduction of 7.6% in RMSE and 9.2% in MAE across all modes compared to the strongest baseline, while maintaining interpretability of spatiotemporal interactions. This study not only provides good model interpretability but also offers a reliable solution for multi-modal traffic collaborative management. Full article