Cities become more productive and enjoyable places to live with improved transportation management. However, most cities in developing countries still lack it. In this context, the present study examines the relationship between the different types of vehicles (measured by passenger and freight vehicles) and urbanization (measured by city population size and density) in India. We also assess whether the relationship is linear or nonlinear. We consider data from 45 cities in India with populations of 1 million or more from 2011 to 2020. The study employs Fixed Effects and Feasible Generalized Least Squares panel data model. The results show that urban population and density are positively associated with private passenger vehicles, particularly two-wheelers, light passenger motor vehicles, and cars. Public and shared transport modes such as buses and omnibuses show negative associations with urban population concentration. Urban population has a negative relationship with freight-related vehicles, especially light motor vehicles for commodities and multi-axled trucks. The results also reveal that, while larger cities have greater transportation infrastructure, the expansion of private vehicles remains more closely associated with within-city population growth over time. Finally, the U-tests show no statistically significant inverse U-shaped relationship between urbanization and vehicle numbers. This indicates that congestion does not decrease or is not mitigated with the availability of inadequate infrastructure. Finally, we suggest several policy options to make Indian cities more compact, inclusive and sustainable through an efficient transport system.

The increasing charging demands of electric and hydrogen fuel cell vehicles necessitate the co-planning of power systems and electric-hydrogen Hybrid Charging Stations (HCSs). However, existing planning models are static and lack progressive transformation consideration in vehicle market share evolution, thereby leading to impractical planning decisions that are incompatible with the long-term dynamic transformation. To this end, this paper proposes a multistage decision-dependent distributionally robust co-planning framework for the low-carbon orderly transformation of power and transportation systems. This multistage framework comprehensively encompasses the progressive replacement of traditional gas stations by HCSs, the expansion of corresponding facilities and a detailed operational model to derive a robust, flexible, and economic planning strategy. To describe the phenomena of competition and substitution between different types of vehicles, we develop a novel dynamic vehicle market share model leveraging a three-dimensional competitive Lotka-Volterra equation. Based upon this model, two decision-dependent ambiguity sets are constructed to explicitly capture the endogenous uncertainty in the diffusion of alternative fuel vehicles and reduction in internal combustion vehicles. Subsequently, a nested multistage decision-dependent distributionally robust formulation is established for the coplanning problem of power systems and HCSs. To effectively address the computational challenges for solving the proposed problem, we develop a customized solution algorithm based on the strengthened Benders cut and stochastic dual dynamic integer programming algorithm. Case studies on IEEE 33-bus and IEEE 123-bus systems validate the effectiveness and superiority of the proposed planning framework.

While the classification of electromagnetic (EM) leakage signals from various electronic devices has been explored in recent research, the use of EM leakage signals from vehicles for classification tasks remains relatively underexplored. In this study, we simulate EM leakage signals from the engines of three different types of vehicles to efficiently classify them using deep learning and machine learning models. For this evaluation, we relied on two convolutional neural network (CNN)-based deep learning models—a vanilla CNN and a residual CNN—along with two machine learning models—random forest and light gradient boosting model. In the experiments, we used features such as the Mel-frequency cepstral coefficient, Mel-spectrogram (MEL), short-time Fourier transform (STFT), and fast Fourier transform, along with the features obtained through feature fusion. Among the single features, we observed that models using STFT tended to exhibit better performance. Moreover, the deep learning models showed improved performance upon the implementation of the SpecAugment technique for data augmentation. The highest classification accuracy of 93.89% was achieved by the residual CNN model using the feature fusion of MEL and STFT in combination with SpecAugment. These findings confirm the feasibility of classifying vehicles using EM leakage signals.

This article presents part of a model study on the abrasion resistance of an anti-icing agent impregnation composition, related to the calculation and modeling of stresses transmitted by a model wheel to a concrete road, taking into account actual indicators and scaling. The aim of the research was to assess the normal stresses arising from the impact of motor vehicles, taking into account the variability of the contact patch resulting from a decrease or increase in tire pressure. Calculations were performed for different types of vehicles, from bicycles to trucks, which were conditionally divided into categories A1-A5 (in ascending order of vehicle weight). The calculation results are presented as ranges of normal stresses on the road surface caused by the operation of motor vehicles, depending on their type and standard load capacity. The calculations were performed using the linear graph method of universal tire characteristics, taking into account normal deflection and variable potential pressure in the tire. The obtained calculation results will allow for more accurate development of calculation schemes in numerical or large-scale modeling of road situations.

Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles (IoV)

ABSTRACT This paper describes a magnetic field generator that can reconstruct magnetic signatures of individual vehicles and a realistic traffic flow. It is composed of the microcontroller, the solenoid, and capacitors. The microcontroller generates fixed‐frequency and variable duty cycle (DTC) pulses to drive the solenoid, which produces the magnetic field. The capacitors eliminate sudden current changes through the solenoid. It is an essential tool for testing vehicle detectors that perform vehicle detection by measuring the distortion in the Earth's magnetic field. The magnetic field generator receives instructions from a personal computer or laptop. This allows the user to choose the simulation parameter, such as the type and velocity of an individual vehicle or random traffic flow. To our knowledge, there are no studies that describe the simulation of magnetic signatures of vehicles. The communication interface, simple hardware design, and ability to create an arbitrary waveform of the magnetic field are novel compared with existing topologies of magnetic field generators. The magnetic signatures of four different types of vehicles, moving at 10 different velocities, were generated using the proposed generator and compared with signatures recorded by the magnetic field sensor using Pearson's correlation coefficient. Results show that the waveforms of the generated signatures are above 97% identical to those measured at velocities up to 25 km/h, above 93% at velocities up to 72 km/h, and above 85% at velocities up to 120 km/h. The RMSE, MAE, and PSNR values indicate that the reconstructed magnetic signatures are more accurate at lower velocities, but the accuracy is at a satisfactory level at higher observed velocities as well.

With the continuous expansion in China’s vehicle fleet, emissions of CO2 and air pollutants from the on-road transportation sector are widely projected to be rising, posing a challenge to realizing China’s targets of carbon peaking in 2030 and carbon neutrality in 2060, as well as the national target for air quality improvement. Therefore, vehicle electrification in the on-road transportation sector is urgently needed to reduce emissions of CO2 and air pollutants, as it serves as a key pathway to align transportation development with sustainability goals. While vehicle electrification is supposed to be the primary solution, there is a research gap in quantifying the provincial, environmental, and economic impacts of implementing such a policy in China. To bridge this gap, we projected the provincial-level ownership of different types of vehicles based on historical trends, assessed the emission reduction potential for CO2 and air pollutants using the LEAP model from 2021 to 2060, and predicted the provincial marginal abatement costs at different mitigation stages under various scenarios with different strategies of vehicle electrification and development patterns of electricity structure. Our results show that the implementation of vehicle electrification lowers the national carbon peak by 0.2–0.6 Gt yr−1 and advances its achievement by 1–3 years ahead of 2030. The marginal abatement cost ranges from $532 to $3466 per ton CO2 (tCO2−1) in 2025 and from −$180 to −$113 tCO2−1 in 2060 across scenarios. The provincial marginal abatement cost curves further indicate that China’s vehicle electrification should be prioritized in cost-effective regions (e.g., Shanghai and Guangdong), while concurrently advancing nationwide grid decarbonization to guarantee the supply of low-carbon electricity across the country. This optimized pathway ensures that transportation decarbonization aligns with both environmental and economic requirements, providing actionable support for China’s sustainable development strategy.

Road accidents are among the leading causes of fatalities worldwide, often exacerbated by delayed emergency response and a lack of real-time information. This paper presents the design and implementation of an embedded accident detection and alerting system designed to reduce response times and enhance road safety. The proposed system integrates multiple sensors, an accelerometer (ADXL335) for detecting sudden motion changes, a vibration sensor (SW420) for sensing impact, and an ultrasonic sensor (HC-SR04) for collision confirmation. These sensors are interfaced with an Arduino Uno microcontroller, which processes the sensor data to accurately identify accidents while minimizing false alarms. Upon accident detection, the system utilizes a GSM module (SIM800L) to transmit an alert message containing the geographical coordinates obtained from a GPS module (NEO-6M) to predefined emergency contacts, including police stations and medical facilities. Both simulation and hardware implementation validate the system’s effectiveness in real-time accident detection, location tracking, and rapid alert dissemination. Experimental results demonstrate improved accuracy and reduced response time, thereby enhancing the likelihood of timely medical intervention. The proposed system is cost-effective, portable, and energy-efficient, making it adaptable across different types of vehicles. Potential future enhancements include integration of wireless imaging for accident scene capture and cloud-based storage for data analysis and intelligent transportation systems

The traffic rules governing the passage of different types of vehicles at signal-free intersections are distinct, especially for the emergency vehicles. Although these traffic rules can be described through deliberate mathematical expressions, but showing limitations in terms of the user-friendliness of rule description. This paper proposes an improved STL-based trajectory planning method for CAVs at signal-free intersections, describing traffic rules with STL formulas, which bridges the gap between human-understandable and machine-understandable traffic rules. The MPC framework is utilised to guarantee real-time trajectory planning by reducing travel time of vehicles passing through the intersection. Simulation results show improved traffic efficiency compared with other methods while ensuring adherence to yielding rules for emergency CAVs.

The article deals with estimating errors in the measurement systems during experimental tests of unmanned aerial vehicles in the wind tunnel. The instrumentation and technique used for calibrating pressure parameters are described. Features of the measurement in the wind tunnel are characterized. The main sources of pressure measurement are listed. The features of measuring pressure in the wind tunnel are discussed. The structural diagram of the calibrating channel of pressure measurement. The transformation function of the measuring channel was proposed. The approach for estimating relative errors is represented. The estimation of high-velocity head measurement errors has been carried out. Relative and absolute errors of velocity head were estimated. Errors in measuring airflow speed have been estimated. It is shown that the velocity of the airflow is the result of an indirect nonlinear measurement. The obtained results can be useful for testing unmanned aerial vehicles of different types. They can also be applied to the measurement of the pressure in various experimental equipment.

Identification of risky driving behaviours throughout a driver's journey and timely implementation of warning and corrective measures are crucial for proactive prevention of traffic accident risk. By identifying and evaluating risky driving behaviours during the journey, driving trajectory data serves as a foundation for proactive traffic accident prevention. This study involves the collection of driving trajectory data from drivers of different types of vehicles via onboard On-Board Diagnostics (OBD) devices. Based on journey segmentation, driving behaviour indicators were calculated. Then the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-Principal Component Analysis (PCA) method was utilised to form the basis for driving risk assessment, and machine learning techniques were employed to predict driving risks, while the accumulated local effects (ALE) methodology was implemented to examine the interactions among the influence factors. The results indicate that the interplay between jerk, acceleration/deceleration, and other driving behaviours adheres to a complex nonlinear pattern, exerting multifaceted influences on driving risk. This study advances the understanding of driving behaviours and their implications for traffic safety, providing a foundation for the development of more holistic and impactful traffic safety strategies.

Traffic flow in most urban areas is augmenting due to the growth in transport and continual demand for it. It is multimodal and includes use of different types of vehicles, motorcycles and even walking. The assessment of uninterrupted traffic flow is traditionally based on empirical methods. This study was based on the macroscopic model which is a mathematical model that formulates the relationships among traffic flow characteristics like density, flow, mean and speed of a traffic stream. The study considered traffic models first developed by Lighthill and Whitham [14] and later Richards [20] shortly called LWR traffic flow model. Simulation by use of this method enables control strategies of congestion dissipation and has suggested some recommended measures to rationalize the design of roads and implementation of regulations of road users considering some regulations and infrastructural gaps in Kisii town. This paper focuses on two finite difference schemes, that is, first order Explicit Upwind Difference Scheme-EUDS (forward time, backward space) and second order Lax-Wendroff Difference Scheme-LWDS (forward time, centred space) for solving first order PDE as well as the traffic density $\rho(t,x)$ was computed by solving LWR macroscopic conservation form of traffic flow model using both schemes. The conditions of stability were numerically verified and it is shown that LWDS is superior to EUDS in terms of time step selection. The results obtained were compared with average key data, which provided the initial and boundary conditions used for numerical simulation.

Repair of double-strand DNA breaks generated by site-directed endonucleases, like Cas9, is the hallmark of gene editing based on homology-directed repair (HDR). HDR uses an exogenous DNA template to restore the cleaved DNA sequence and can facilitate specific gene corrections as well as insertion of genes or partial complementary DNA (cDNA) sequences. For CRISPR/Cas-directed gene editing, co-administration of the Cas9/single guide RNA (sgRNA) ribonucleoprotein (RNP) complex and a DNA template typically involves two different delivery strategies or different types of vehicles. This requires exquisite timing of delivery and may potentially challenge safety and therapeutic applicability. There is a need therefore for technologies that can ferry complete editing tool kits into cells. Here, we demonstrate the use of lentivirus-derived nanoparticles (LVNPs) to transport both RNP complexes and vector RNA, which upon reverse transcription serves as a repair template for HDR-directed gene editing. Such 'all-in-one' LVNPs support targeted gene insertion with reduced off-target effects relative to nucleofection procedures. We show potent editing in the HBB gene in human erythroid progenitor cells as well as HDR-directed editing in hematopoietic stem and progenitor cells. Our findings mark a first step toward using a single virus-derived vehicle for delivering a full HDR gene editing kit.

Nowadays, many various types of drive and transmission mechanisms characterized by various parameters and characteristics for different types of vehicles have been designed, developed and optimized with regard to the featured applications. Our research is focused on the creation of a complex simulation model of the drive and transmission mechanism of the vehicle Iveco LMV 4 × 4 M65. The correctness of the simulation model was verified using an analytical approach. The created numerical simulation model will serve as a basis for the further optimization of dynamic, operational and economical parameters of the vehicle. As the modification or replacement of the particular components of the drive and transmission mechanism is very complicated regarding the vehicles used in military operations, our research is focused on the enhancement of the control processes. More specifically, the main goal of the presented research activities is the modification of the gearshift logic and the adjustment of the gearshift map in order to improve the dynamic properties of the vehicle and, at the same time, reduce the fuel consumption. In spite of its complexity, the proposed simulation model can serve as a basis for the optimization of not only the gearshift control under specific input or output quantities and operational or environmental conditions but also for the simulation of the system behavior with modified or replaced components of the drive and transmission mechanism of this type of truck vehicle.

This paper illustrates a dual battery electric vehicle (EV) development with integrated solar charging designed to increase the stability, range, and overall performance of the vehicle. The system is equipped with two large-capacity lithium-ion batteries; one serves as the power source, and the other powers the electronic device. Both are controlled by the battery management system (BMS), power distribution unit (PDU), and vehicle Control Unit (VCU). These components work together to provide efficient power distribution, seamless battery transfer, and optimum performance under various driving conditions. The integration of solar panels and smart devices reduces dependency on external energy sources by using renewable energy. This innovation helps increase the number of vehicles, reduce operating costs, and lower carbon emissions. The system also features advanced thermal management and instant fault detection to ensure vehicle reliability, safety, and longevity. To further enhance usability, the system features a user-friendly interface that enables monitoring of battery life, solar energy collection, and overall system health. The system is designed with scalability in mind to accommodate vehicles of different types and sizes, making it a versatile solution for a variety of applications. Through extensive and practical testing, the paper aims to create a future-proof, efficient, and effective solution to electric vehicle challenges that will set new standards in electricity usage.

Abstract Thorax injury is one of the main injuries during pedestrian impacts, and vehicle collision plays an important role in increasing the risk of pedestrian thorax injury. It is necessary to conduct in-depth research on the influence of vehicle front structural parameters on thorax injury in pedestrian accidents. Accident statistical analysis is conducted based on 452 cases from the Future Mobile Traffic Accident Scenario Study/FASS database. There are differences in the severity of thorax injuries caused by different types of vehicles. Then, a typical pedestrian thorax injury accident in the FASS database is simulated, and the effectiveness of the model is verified. Finally, 149 simulations are conducted based on the model, and the relationship between the front structural parameters and pedestrian kinematic response and thorax injury values is analyzed using multiple linear regression methods. The significant probability p of the influence of the bonnet angle/BA, bonnet length/BL, bonnet leading edge height/BLEH, and bumper bottom height/BBH on pedestrian thorax injuries are as follows: 0.000, 0.953, 0.000, 0.797, indicating that BA and BLEH have significant influences on thorax injuries, while BBH and BL have little influence. And when the BLEH and BA values increase, the thorax acceleration value tends to increase.

• Charging/discharging of EV parking lots in a large-scale renewable-based microgrid. • Considering six EV types for each distributed parking lot in load points. • Calculating the expected number of vehicles of different types in each parking lot. • Risk-averse MIQP-based stochastic programming to meet various uncertainties. • Outlining the effects of EVs on the energy losses of the system. This paper investigates the bidirectional charging management of distributed parking lots accommodating multiple types of electric vehicles (EVs). The distribution of each EV type accross parking lots is determined based on various factors such as the proportion of EV types at specific load points, the EV penetration rate, peak demand at related load points, the residential load share, and the average household consumption. An 18-bus microgrid containing 8376 EVs of six types is analyzed using a stochastic programming model that incorporates uncertainties in load demand, electricity price, renewable sources, and EV energy usage. The proposed model accounts for power losses in the grid-connected microgrid via an approximate power flow model. Risk management significantly reduces the economic risk of the worst-case scenario by around 20%, while flexible loads reduce carbon emissions by about 10.5% and the expected cost by roughly 4%. Additionally, increasing the willingness rate of EV owners to discharge their vehicles contributes to an additional 2.3% reduction in expected costs. Findings highlight the model’s capability to effectively manage bidirectional charging scheduling for diverse EV types, offering a practical solution for real-world systems facing complex technical and economic challenges.

Abstract This study focuses on the energy intensity of rail and road freight transport. The research compares different types of vehicles in both modes of transport with the aim of quantifying the amount of energy required to transport a specific volume of freight over a given distance. Several generations of electric and diesel locomotives were selected for measurement purposes, along with comparable road freight vehicles that are commonly used in operation. Real-world energy consumption measurements were carried out on a section of route where the rail and road routes run parallel, with the aim of determining which mode of transport is more energy-efficient in the context of freight transport, using real operational data.

Buffer stops have always been installed on blind tracks to mitigate the hazards associated with overruns due to insufficient or wrong braking. Conventional buffer stops fixed to the rails may absorb only limited energy while Energy-Absorbing Buffers Stops (EABS) dissipate higher energy hydraulically and/or by friction from sliding blocks clamped to the rail head. The assessment of EABS performances in terms of maximum stopping distance and maximum allowed deceleration is usually performed by using the common kinematic rules of motion and considering the overrunning train as a single mass hitting the buffer stop. This paper studies the dynamic characteristics of the collision of entire trains with a friction EABS applying a Longitudinal Train Dynamics (LTD) approach. Several realistic scenarios using the UIC approved TrainDy software were simulated considering various train compositions, with different types of vehicles (locomotives, freight wagons and passenger coaches) and different kinds of buffers. The results show that high dynamic loads are exerted on the vehicles within the train, while the average deceleration and the stopping distance are not greatly influenced when compared with a simpler Finite Element Method (FEM) approach that does not consider the train composition. The progressive application of the EABS braking force increases the stopping distance but can reduce the peak deceleration of about 50%. The results may be used to tune the design parameters of friction EABS according to the currently available specifications and standards for rolling stock structural assessment considering that no international standards for EABS exist currently.

The electrification of public transport is important for a sustainable future and directly serves the 11th goal of the SDGs, which is Sustainable Cities and Communities. Nowadays, transportation emissions are increasing day by day. Within the scope of this study, the energy consumption and global warming potential (GWP) analyses of three different types of vehicles (12-m electric, 12-m FCEV fuel cell, and 18-m electric) during their production and usage phases were compared, aiming to guide a sustainable future. For the usage phase of the product, life tests and literature studies were used, regression modelling was utilized for the manufacturing stages, and the SimaPro Ecoinvent module was used for GWP. Within the scope of the study, three different EV and HFC commercial vehicles coming out of the same production line were compared in terms of energy consumption, and while the 12-m electric vehicle had the lowest energy consumption (1.625 kWh/km), the 18-m electric vehicle had the highest energy consumption (2.250 kWh/km). Comparing the GWP potentials of the vehicles, 12-m EV (5.97*10-4 GWP), 12-m FCEV (1.31*10-3 GWP), and 18-m EV (2.57*10-3 GWP) were calculated for the manufacturing stage. In the use phase, the GWPs of the three vehicles were calculated to be approximately the same. Future studies can contribute to the carbon-neutral roadmaps of countries by calculating the carbon footprint and environmental impact analysis across all processes from cradle to grave.