Search Results (565)

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

The presented research is focused on the development of the bus rapid transit (BRT) system, combining the high capacity of rail transport with the flexibility of bus routes. Classic BRT systems have certain limitations, particularly concerning a single rolling stock capacity. The main motivation of the work is to find efficient and cost-effective solutions to increase passenger traffic in the BRT system while optimizing fuel consumption. The main contribution of this study is the comprehensive analysis and optimization of various configurations of trailer bus trains, which represent a flexible and cost-effective alternative to traditional single or articulated buses. Based on two schemes, four possible options for using trailer bus trains are offered, which differ in the number of sections and working engines. Among the suggested schemes of trailer bus trains, the two-section and three-section schemes with all engines running and the three-section scheme with one engine turned off are appropriate for use due to improved fuel efficiency indicators with better or acceptable traction and speed properties. Calculations carried out on a mathematical model show that, for example, a two-section bus train can provide a reduction of specific fuel consumption per passenger by 6.3% compared to a single bus at full load, while a three-section train can provide even greater savings of up to 8.4%. Selective shutdown of one of the engines in a multi-section train can lead to an additional improvement in fuel efficiency by 5–10%, without leading to a critical reduction in the required traction characteristics. Full article

The design of the suspension system affects handling and stability, vibrations of the steered wheels, vehicle ride comfort, and tyre tread wear. One of the most important vibration parameters is acceleration; high acceleration values can have an adverse effect on both the driver and passengers, as well as on the components of the vehicle’s suspension and handling. This paper presents the results of the effects of acceleration on the components of a front-independent MacPherson suspension system. Data on the accelerations were obtained from theoretical and experimental studies. A simulation study was conducted, taking into account the elastic and damping characteristics of the elastic components. The experimental study was conducted under laboratory conditions by using a suspension tester, BEISSBARTH, and a measuring system developed with LabVIEW 2021 SP1 and MATLAB R2022b software. The experiments were conducted with different tyre pressures and by using bushings made from different materials. The experimental tests were conducted with two rubber bushings within the mounting of the arm, as well as a rubber bushing and a polyurethane bushing. The experimental results were compared and analyzed. Two theoretical models were considered: one is a mathematical model, and the other is a simulation model which uses the finite element method. Numerical dynamic analysis of the suspension was performed using the SolidWorks 2023. Full article

This study presents a comprehensive investigation of acoustic emission (AE) characteristics for condition monitoring of rolling bearings in passenger ropeway systems. Through controlled laboratory experiments and field validation across multiple operational ropeways, we establish an optimized AE-based diagnostic framework. Key findings demonstrate that resonant VS150-RIC sensors outperform broadband sensors in defect detection, showing greater energy response at characteristic frequencies for inner race defects. The RMS parameter emerges as a robust diagnostic indicator, with defective bearings exhibiting periodic peaks and higher mean RMS values. Field tests reveal progressive RMS escalation preceding visible damage, enabling predictive maintenance. Furthermore, we develop a novel Paligemma LLM model for automated wear detection using AE time-domain images. The research validates the AE technology’s superiority over conventional vibration methods for low-speed bearing monitoring, providing a scientifically grounded approach for safety-critical ropeway maintenance. Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

Due to the absence of engine noise in new energy vehicles, road noise and wind noise become particularly noticeable. Therefore, studying the noise transmission through car doors is essential to effectively reduce the impact of these noises on the passenger compartment. To address the optimization of the sound absorption performance of single-layer porous plates combined with lightweight glass wool used in the doors of electric vehicles, this study established a microscopic acoustic performance analysis model based on the transfer matrix method and sound transmission loss theory. The effects of medium type, perforation rate, perforation radius, material thickness, and porosity on the sound absorption coefficient, impedance characteristics, and reflection coefficient were systematically investigated. Results indicate that in the high-frequency range (above 1200 Hz), the sound absorption coefficients of both rigid and flexible media can reach up to 0.9. When the perforation rate increases from 0.01 to 0.2, the peak sound absorption coefficient in the high-frequency band (1400–2000 Hz) rises from 0.45 to 0.85. Increasing the perforation radius to 0.03 m improves acoustic impedance matching. This research provides theoretical support and a parameter optimization basis for the design of acoustic packaging materials for electric vehicles, contributing significantly to enhancing the interior acoustic environment. Full article

This paper presents the results of the mathematical modeling and experimental studies of charging a traction lithium-ion battery of a passenger electric car using an integrated charger based on a traction voltage inverter. An original three-stage charging algorithm (3PT/PN) has been developed and implemented, which provides a sequential decrease in the charging current when the specified voltage and temperature levels of the battery module are reached. As part of this study, a comprehensive mathematical model has been created that takes into account the features of the power circuit, control algorithms, thermal effects and characteristics of the storage battery. The model has been successfully verified based on the experimental data obtained when charging the battery module in real conditions. The maximum error of voltage modeling has been 0.71%; that of current has not exceeded 1%. The experiments show the achievement of a realized capacity of 8.9 Ah and an integral efficiency of 85.5%, while the temperature regime remains within safe limits. The proposed approach provides a high charge rate, stability of the thermal state of the battery and a long service life. The results can be used to optimize the charging infrastructure of electric vehicles and to develop intelligent battery module management systems. Full article

There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article

Traffic congestion constitutes a substantial global issue, adversely impacting economic productivity and quality of life, with associated costs estimated at approximately 2% of GDP in various nations. This systematic review investigates the application of machine learning (ML) in traffic congestion forecasting from 2010 to 2024, adhering to the PRISMA 2020 guidelines. A comprehensive search of three major databases (IEEE Xplore, SpringerLink, and ScienceDirect) yielded 9695 initial records, with 115 studies meeting the inclusion criteria following rigorous screening. Data extraction encompassed methodological approaches, ML techniques, traffic characteristics, and forecasting periods, with quality assessment achieving near-perfect inter-rater reliability (Cohen’s $\kappa$ = 0.89). Deep Neural Networks were the predominant technical approach (47%), with supervised learning being the most prevalent (57%). Classification tasks were the most common (42%), primarily addressing recurrent congestion scenarios (76%) and passenger vehicles (90%). The quality of publications was notably high, with 85% appearing in Q1-ranked journals, demonstrating exponential growth from minimal activity in 2010 to 18 studies in 2022. Significant research gaps persist: reinforcement learning is underutilized (8%), rural road networks are underrepresented (2%), and industry–academia collaboration is limited (3%). Future research should prioritize multimodal transportation systems, real-time adaptation mechanisms, and enhanced practical implementation to advance intelligent transportation systems (ITSs). This review was not registered because it focused on mapping the research landscape rather than intervention effects. Full article

Urban rail transit (URT) systems are critical for sustainable urban mobility but are increasingly vulnerable to disruptions and emergencies. While extensive research has examined the built environment’s influence on transit demand under normal conditions, the nonlinear causal mechanisms shaping URT passenger flow during emergencies remain understudied. This study proposes an artificial intelligence-based causal machine learning framework integrating causal structure learning and causal effect estimation to investigate how the built environment, network structure, and incident characteristics causally affect URT station-level ridership during emergencies. Using empirical data from Shanghai’s URT network, this study uncovers dual pathways through which built environment attributes affect passenger flow: by directly shaping baseline ridership and indirectly influencing intermodal connectivity (e.g., bus connectivity) that mitigates disruptions. The findings demonstrate significant nonlinear and heterogeneous causal effects; notably, stations with high network centrality experience disproportionately severe ridership losses during disruptions, while robust bus connectivity substantially buffers such impacts. Incident type and timing also notably modulate disruption severity, with peak-hour incidents and severe disruptions (e.g., power failures) amplifying passenger flow declines. These insights highlight critical areas for policy intervention, emphasizing the necessity of targeted management strategies, enhanced intermodal integration, and adaptive emergency response protocols to bolster URT resilience under crisis scenarios. Full article

As urbanization has accelerated, rail transit has evolved from being a mere means of transportation to a public area that houses the city’s cultural memory and serves as a crucial portal for the public to understand the culture of the city. As an urban public space with huge passenger flow, the metro (or subway) cultural space has also become a public cultural space, serving communal welfare and representing the image of the city. It is currently attracting more and more attention from the academic community. Wuhan, located in central China, has many subway lines and its engineering construction has set several national firsts, which is a typical sample of urban subway development in China. In this study, we use Python 3.13.0 crawler technology to capture the public’s comments on cultural space of Wuhan metro in social media and adopt SnowNLP sentiment score and LDA thematic clustering analysis to explore the overall quality, distinct characteristics, and deficiencies of Wuhan metro cultural space construction, and propose targeted design optimization strategies based on this study. The main findings are as follows: (1) The metro cultural space is an important window for the public to perceive the city culture, and the public in general shows positive perception of emotions: among the 16,316 data samples, 47.7% are positive comments, 17.8% are neutral comments, and 34.5% are negative comments. (2) Based on the frequency of content in the sample data for metro station exit and entrance space, metro train space, metro concourse and platform space, they are ranked as weak cultural spaces (18%), medium cultural spaces (33%), and strong cultural spaces (49%) in terms of the public’s perception of urban culture. (3) At present, there are certain deficiencies in Wuhan metro cultural space: the circulation paths in concourses and platforms are overly dominant, leaving little space for rest or interaction; the cultural symbols of metro train space are fragmented; the way of articulation between cultural and functional space in the metro station exit and entrance space is weak, and the space is single in form. (4) Wuhan metro cultural space needs to be based on locality landscape expression, functional zoning reorganization, innovative scene creation to optimize the visual symbol system and behavioral symbol system in the space, to establish a good image of the space, and to strengthen the public’s cultural identity and emotional resonance. Full article

Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and nonlinear characteristics. Previous research has proposed the drivable corridor (DC) method, which transforms complex collision avoidance constraints into linear inequalities by constructing time-varying rectangular corridors within the spatiotemporal domains, thereby enhancing optimization efficiency. However, the DC construction process involves repetitive collision detection, leading to an increased computational burden. To address this limitation, this study proposes a novel approach that integrates grid-based obstacle representation with dynamic grid merging to accelerate collision detection and dynamically constructs the DC by adaptively adjusting the expansion strategies according to available spatial dimensions. The feasibility and effectiveness of the proposed method are validated through simulation-based evaluations conducted over 100 representative scenarios characterized by diverse and unstructured environmental configurations. The simulation results indicate that, with appropriately selected grid resolutions, the proposed approach achieves up to a 60% reduction in trajectory planning time compared to conventional DC-based planners while maintaining robust performance in complex environments. Full article

The Controller Area Network (CAN) bus has been implemented in most modern Vehicles. Various attacks can be launched against the CAN bus protocol because it is designed without security mechanisms. It is essential to develop a highly accurate intrusion detection system (IDS) for CAN bus attacks. We design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle’s driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. We first perform data balancing and feature selection. We build and fine-tune random forest, Xtreme gradient boosting, and decision tree supervised learning models. We then combine these models with voting, stacking, and bagging ensemble methods. The results obtained demonstrate the effectiveness of the proposed scheme when trained on real-life CAN traffic datasets to detect and classify these four attacks. The stacking method achieved the highest performance in terms of accuracy, precision, recall, F1-score, and area-under-the-curve receiver operator characteristic (ROC-AUC). The stacking method outperformed other recently proposed methods with an F1-score, precision, recall, and accuracy of 0.993, 0.993, 0.993, and 0.986, respectively. Full article

Regarding the goals for achieving carbon neutrality by 2025, the transportation sector is one of the main causes of various environmental burdens, such as greenhouse gas (GHG) emissions and resource depletion, so reducing the environmental impact of the automobile industry is important. Although many countries are conducting numerous studies on the environmental impact of electric vehicles, they are limited to each country’s vehicles and models, and are limited to the production and process stages. In this study, we compared and analyzed the carbon reductions in electric and internal combustion engine vehicles during the operation stage for the most commonly used mid-sized rental vehicles in South Korea. The research results confirmed a reduction effect of approximately 1 MtCO₂-eq per year based on approximately 570,000 vehicles, and, if applied to all passenger vehicles nationwide, an average annual reduction effect of approximately 36 MtCO₂ can be expected. This figure corresponds to a reduction of approximately 30% in domestic transportation sector carbon emissions in 2024. This study is expected to have potential as a strategic indicator to start with, tailorable to the characteristics of each country’s transportation sector’s decarbonization processes. Full article