Search Results (1,388)

Transit-Oriented Development (TOD), formalized by Calthorpe and Poticha in 1993, emerged to counter urban sprawl, reduce car dependency, and revitalize historical community centers. Rooted in “new urbanism”, TOD emphasizes integrated regional land-use planning and high-capacity public transportation. In the Middle East, TOD implementation remains understudied, particularly regarding heritage integration and social equity in arid climates. Doha’s rapid social and economic transformation presents both opportunities and risks: growth offers urban revitalization yet threatens to displace communities and dilute cultural identity. Shifts in urban planning have aimed to address sustainability, connectivity, and heritage preservation. This study examines Msheireb Downtown Doha (MDD) to assess how TOD can restore historic districts while managing gentrification, enhancing accessibility and promoting inclusiveness. A mixed-methods approach was applied, including 12 semi-structured interviews with stakeholders (Qatar Rail, Msheireb Properties, Ministry of Municipality and Environment), purposive surveys of 80 urban users, site observations, and spatial mapping. Using the Node-Place-People (NPP) model, the study evaluates TOD effectiveness across transportation connectivity (node), built environment quality (place), and equity metrics (people). The findings show that MDD successfully implements fundamental TOD principles through its design, which enhances connectivity, walkability, social inclusiveness, and heritage preservation. However, multiple obstacles remain: the “peripheral island effect” limits benefits to the core, pedestrian–vehicular balance is unresolved, and commercial gentrification is on the rise. This research provides evidence-based knowledge for GCC cities pursuing sustainable urban regeneration by demonstrating both the advantages of TOD and the necessity for critical, context-sensitive implementation that focuses on social equity together with physical transformation. Full article

This study develops a compact elasticity-based framework for assessing how autonomous truck adoption influences corridor-level performance, freight demand, modal competition, and CO₂ emissions in multimodal freight Intelligent Transportation Systems. The model specifies the constant elastic (log-linear) responses of traffic performance and generalized costs to adoption, enabling the closed-form characterization of system-level rebound and road–rail reallocation effects. The analytical results show that an internal adoption threshold $P^{*}$ emerges, defined by $d\widehat{E}/dP=0$, which separates a beneficial regime (efficiency gains dominate) from an adverse regime (rebound and modal shift dominate). Comparative statics indicate that $P^{*}$ decreases with stronger ITS capability A and increases with rebound intensity R and the road–rail carbon intensity contrast K. Numerical experiments across representative corridor contexts illustrate induced demand effects exceeding 25% under high-rebound conditions and threshold ranges around $P^{*}\approx 0.3$–0.4 for plausible parameters. The results provide interpretable guidance for coordinating autonomous truck deployment with intermodal logistics design and decarbonization strategies. Full article

To assess the market competitiveness of high-speed rail (HSR) express and forecast its freight volume, this paper develops an integrated framework combining strategic analysis, market forecasting, and competition assessment. A hybrid SWOT-AHP model identifies and quantifies key strategic factors, clarifying HSR express positioning. Considering macroeconomic and consumption factors, a GM(1,N) model forecasts intercity express volume. Based on a generalized cost function covering timeliness, economy, safety, and stability, an improved Logit model calculates HSR’s mode share against road and air express, deriving future HSR freight volume. Using China as a case study, results show: (1) A proactive strategy leveraging intrinsic strengths is recommended, supported by rapid intercity express growth; (2) HSR can capture over 20% mode share initially, showing strong competitiveness in medium-long distance transport; (3) Transport cost is the most sensitive factor, a 20% reduction raises mode share by 10%, while rising timeliness demands enhance long distance advantages. This study offers a quantitative basis for HSR express strategic planning. Full article

Congested platforms in public transportation systems can jeopardize the safety and comfort of passengers. Real-time crowd size estimation using Device-Free Wireless Sensing (DFWS) can offer a privacy-preserving solution for monitoring and preventing overcrowding. However, no public dataset exists on DFWS in public transportation environments. In this work, we introduce a new dataset comprising two different public transportation environments, which contains data on the presence of rail vehicles at the platform, as well as manual people counts at regular intervals. By providing this dataset, we aim to offer a foundation for other DFWS researchers to explore novel algorithms and methods in public transportation environments. Full article

To identify the emission reduction potential and policy synergies of Tokyo’s road passenger and urban road freight transport under the “carbon neutrality target,” this paper constructs an assessment framework for megacities. First, based on macroeconomic socioeconomic variables (population, GDP, road length, and employment), regression equations are used to predict traffic turnover for different modes of transport from 2021 to 2050. Then, the prediction results are imported into the LEAP (Long-range Energy Alternatives Planning) model. By adjusting three policy levers—vehicle technology substitution (ZEV: EV/FCEV), energy intensity improvement, and upstream electricity and hydrogen supply decarbonization—a “single-factor vs. multi-factor (policy synergy)” scenario matrix is designed for comparison. The results show that the emission reduction potential of a single measure is limited; upstream decarbonization yields the greatest independent emission reduction effect, while the emission reduction effect of deploying zero-emission vehicles and improving energy efficiency alone is small. In the most ambitious composite scenario, emissions will decrease by approximately 83% by 2050 compared to the baseline scenario, with cumulative emissions decreasing by over 35%. Emissions from rail and taxis will approach zero, while buses and freight will remain the primary residual sources. This indicates that achieving net zero emissions in the transportation sector requires not only accelerated ZEV penetration but also the simultaneous decarbonization of electricity and hydrogen, as well as policy timing design oriented towards fleet replacement cycles. The integrated modeling and scenario analysis presented in this paper provide quantifiable evidence for the formulation of a medium- to long-term emissions reduction roadmap and the optimization of policy mix in Tokyo’s transportation sector. Full article

A removable module design is proposed to enable the use of open wagons for container transport. It acts as an intermediate adapter between an open wagon body and a tank container, ensuring the possibility of their reliable interaction. The dynamic loading of its structure was determined to substantiate the proposed scheme of tank container transport on an open wagon. A mathematical model characterizing the longitudinal load of a tank container placed on an open wagon was developed for this purpose. The determined accelerations acting on the tank container were considered when studying its strength. It was established that, considering the proposed fastening scheme, the maximum stresses occurring in the tank container structure are lower by 21.8% than the permissible ones. Therefore, the strength of the tank container is ensured. Accelerations acting on an open wagon body and a tank container were determined and considered when studying their strength. It was found that the maximum stresses occurring in the open wagon body and the tank container, considering the proposed fastening scheme, are lower than the permissible ones. Therefore, the strength of their structures is ensured. The research conducted will contribute to increasing the efficiency of tank container transport by rail and, therefore, container transportation in general. Full article

Low adhesion conditions can lead to significant wheel slip during braking for high-speed trains, resulting in severe wheel–rail rolling contact fatigue issues. The objective of this paper is to reproduce the dynamic wheel–rail adhesion characteristics of high-speed train braking with large creepage using the transient non-Hertzian ECF model under wet conditions and to clarify the underlying mechanisms. The Kik–Piotrowski (KP) model is used to solve the wheel–rail normal contact problem, and the corresponding non-elliptical adaptive method is adopted to modify the ECF model considering time-dependent effects for solving the tangential contact problem. The dynamic large creepage adhesion characteristics of high-speed trains under wet conditions during braking are analyzed. Furthermore, the effect of braking initial speeds and longitudinal creepage variation curves on dynamic adhesion characteristics is discussed. The results indicate that the large creepage adhesion characteristic curve of high-speed trains during braking exhibits a loading stable phase and an unloading stable phase, both of which effectively enhance the utilization of wheel–rail adhesion. Full article

This study employed machine learning to investigate the mechanical behavior of one-part geopolymer (OPG)-stabilized soil subjected to acid erosion. Based on the unconfined compressive strength (UCS) data of acid-eroded OPG-stabilized soil, eight machine learning models, namely, Adaptive Boosting (AdaBoost), Decision Tree (DT), Extra Trees (ET), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), along with hyper-parameter optimization by Genetic Algorithm (GA), were used to predict the degradation of the UCS of OPG-stabilized soils under different durations of acid erosion. The results showed that GA-SVM (R² = 0.9960, MAE = 0.0289) and GA-XGBoost (R² = 0.9961, MAE = 0.0282) achieved the highest prediction accuracy. SHAP analysis further revealed that solution pH was the dominant factor influencing UCS, followed by the FA/GGBFS ratio, acid-erosion duration, and finally, acid type. The 2D PDP combined with SEM images showed that the microstructure of samples eroded by HNO₃ was marginally denser than that of samples eroded by H₂SO₄, yielding a slightly higher UCS. At an FA/GGBFS ratio of 0.25, abundant silica and hydration products formed a dense matrix and markedly improved acid resistance. Further increases in FA content reduced hydration products and caused a sharp drop in UCS. Extending the erosion period from 0 to 120 days and decreasing the pH from 4 to 2 enlarged the pore network and diminished hydration products, resulting in the greatest UCS reduction. The results of the study provide a new idea for applying the ML model in geoengineering to predict the UCS performance of geopolymer-stabilized soils under acidic erosion. Full article

Drill-and-blast tunnel construction continuously releases high-intensity dust during drilling, blasting, and shotcreting, while conventional forced ventilation is often insufficient to control dust migration and worker exposure. This study develops three-dimensional Euler–Lagrange gas–solid two-phase models for these three typical processes to clarify the spatiotemporal dispersion of polydisperse dust and to explore effective control strategies. The simulations show that all processes generate a persistent high-concentration dust belt near the tunnel face, and a low-velocity recirculation zone at the crown acts as a structural hotspot of dust accumulation that is difficult to purge by longitudinal ventilation. Particle size strongly affects dispersion behaviour: coarse particles rapidly settle near the source under gravity, whereas fine and medium-sized particles remain suspended for long periods and can be transported over long distances, particularly after blasting. Based on these findings, a rail-mounted purification system with a dynamically adjustable position along the tunnel is proposed, and its preferred deployment zones are determined to work synergistically with the main airflow. The system is designed to perform near-source and crown-targeted removal, providing an engineering-oriented “dynamic local purification plus overall ventilation dilution” pathway for improving air quality in drill-and-blast tunnel construction. Full article

This paper is dedicated to analysing sustainability and digitalisation in the transport systems of the European Union (EU) and Ukraine, with a particular focus on three representative subsectors: freight rail, urban public transport and last-mile postal logistics. It explores how technological innovation, operational efficiency and environmental responsibility interact within these sectors under distinct institutional and economic conditions: mature, market-based systems in the EU and resilience-driven systems in wartime Ukraine. This study applies a comparative, descriptive–analytical methodology using secondary data drawn from corporate sustainability reports, official statistics and sectoral databases for 2022. Quantitative KPls were complemented with a qualitative assessment of digitalisation maturity to ensure cross-country comparability. Through a comparative analysis of KPIs, such as freight volumes, emissions intensity, revenue efficiency and digital maturity, this study identifies structural and policy gaps that hinder progress toward sustainable mobility. This study develops a multi-dimensional framework combining operational, financial, environmental and digital indicators. In this paper, digital integration refers to the degree to which transport operators embed digital tools such as tracking, data management and automation into their core processes, while environmental efficiency denotes the ability to deliver transport services with minimal resource consumption and carbon emissions per operational unit. Institutional resilience is understood here as the capacity of transport organisations and governing institutions to maintain functionality, adapt and recover under crisis or systemic stress, which is particularly relevant for Ukraine’s wartime context. The findings demonstrate that while EU operators lead in transparency, digital integration and environmental performance, Ukrainian actors exhibit rapid adaptive innovation and significant potential for technological leapfrogging during reconstruction. This paper concludes that the EU must overcome regulatory inertia and infrastructure fatigue, while Ukraine should institutionalise resilience and transparency. Full article

The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, and susceptibility to human error. To address these limitations, this paper presents a rail transit equipment inspection system based on Edge Intelligence (EI) and 5G technology. The proposed system adopts a cloud–edge–end collaborative architecture that integrates Computer Vision (CV) techniques to automate inspection tasks; specifically, a fine-tuned YOLOv8 model is employed for object detection of personnel and equipment, while a ResNet-18 network is utilized for equipment status classification. By implementing an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin), the system enhances data processing efficiency and network performance, while further strengthening security through the use of a 5G private network that isolates critical infrastructure data from the public internet, and improving robustness via distributed edge nodes that eliminate single points of failure. The proposed solution has been deployed and evaluated in real-world scenarios on Beijing Metro Line 6. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@0.5) of 92.7% ± 0.4% for equipment detection, and the ResNet-18 classifier attains 95.8% ± 0.3% accuracy in distinguishing normal and abnormal statuses. Compared with a cloud-centric architecture, the EI-based system reduces the average end-to-end latency for anomaly detection tasks by 45% (28.5 ms vs. 52.1 ms) and significantly lowers daily bandwidth consumption by approximately 98.1% (from 40.0 GB to 0.76 GB) through an event-triggered evidence upload strategy involving images and short video clips, highlighting its superior real-time performance, security, robustness, and bandwidth efficiency. Full article

Autonomous-rail Rapid Transit (ART) systems operate on standard roadways while maintaining dedicated right-of-way privileges. Owing to their sustainability, punctual operation, and cost efficiency, ART systems have emerged as a promising solution for medium-capacity urban transit. However, the exclusive lane usage for ART systems frequently leads to inefficient lane utilization, thereby intensifying congestion for non-ART vehicles. This study proposes a moving-block-based lane-sharing strategy for ART with a leading eco-driving approach. First, dynamic lane-access rules are introduced, allowing non-ART vehicles to temporarily use the ART lane without forced clearance or signal coordination. Second, a modified eco-driving trajectory optimization algorithm is constructed on a discrete time–space–state network, allowing the ART trajectory to be obtained through an efficient graph-search procedure while simultaneously guiding following vehicles toward energy-efficient driving patterns. Finally, simulation experiments are conducted to evaluate the impacts of traffic demand, arrival interval, and non-ART vehicles’ compliance rate on system performance. The results demonstrate that the proposed strategy significantly reduces delay and energy consumption for non-ART vehicles by 72.6% and 24.6%, respectively, without compromising ART operations efficiency. This work provides both technical insights and theoretical support for the efficient management of ART systems and the sustainable development of urban transportation. Full article

Brake particulate matter (PM) represents a significant portion of the non-exhaust related soot emissions from all forms of transport, posing significant environmental and health concerns. Euro 7 standards only regulate road automotive emissions, while no regulation covers train transportation. This study compares two brake PM samples from rail and automotive applications. Rail brake PM was generated from composite brake pads subjected to real-world urban rapid transit braking conditions, while automotive brake PM was generated using ECE brake pads and discs under World Harmonized Light-Duty Test Cycle (WLTC) conditions. Transmission electron microscopy (TEM) and energy-dispersive X-ray (EDX) analyses were performed to assess PM morphology and composition. Both samples showed PM in coarse (10–2.5 µm), fine (2.5–0.1 µm), and ultrafine (<0.1 µm) size ranges, with angular flakes in automotive PM and rounded particles in rail PM. The rail PM exhibited a uniform size distribution, with a mean Feret diameter of 1 µm. In contrast, the automotive PM shifted toward larger particles, with ultrafine PM representing only 4% of the population. Excluding carbon and oxygen, automotive PM was dominated by iron (6 at.%) and magnesium (1 at.%). Rail PM showed lower iron (0.6 at.%) and higher aluminium (0.7 at.%) and calcium (0.8 at.%), with a broader non-C/O composition. This study tackles source-specific PM features, thereby supporting safer and more efficient non-exhaust emissions regulations. Full article

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable. Full article

Object detection in Unmanned Aerial Vehicle (UAV) imagery has gained significant traction in applications such as railway inspection and waste management. While emerging end-to-end detectors like DEIM show promise, they often struggle with weak feature responses and spatial misalignment in aerial scenarios. To address these issues, this paper proposes SCA-DEIM, a context-aware real-time detection framework. Specifically, we introduce the Adaptive Spatial and Channel Synergistic Attention (ASCSA) module, which refines existing attention paradigms by transitioning from a static gating mechanism to an active signal amplifier. Unlike traditional designs that impose rigid bounds on feature responses, this improved architecture enhances feature extraction by dynamically boosting the saliency of faint small-target signals amidst complex backgrounds. Furthermore, drawing inspiration from infrared small object detection, we propose the Cross-Stage Partial Shifted Pinwheel Mixed Convolution (CSP-SPMConv). By synergizing asymmetric padding with a spatial shift mechanism, this module effectively aligns receptive fields and enforces cross-channel interaction, thereby resolving feature misalignment and scale fusion issues. Comprehensive experiments on the VisDrone2019 dataset demonstrate that, compared with the baseline model, SCA-DEIM achieves improvements of 1.8% in Average Precision (AP), 2.3% in AP for small objects ($A{P}_s$), and 2.0% in AP for large objects ($A{P}_l$), while maintaining a competitive inference speed. Notably, visualization results under different illumination conditions demonstrate the strong robustness of the model. In addition, further validation on both the UAVVaste and UAVDT datasets confirms that the proposed method effectively enhances the detection performance for small objects. Full article