Vehicle Population Research Articles

Understanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data

Understanding vehicle travel behavior patterns is crucial for effectively managing urban traffic congestion and mitigating the associated risks and excessive emissions. Existing research predominantly focuses on commuting patterns, with limited attention given to the spatiotemporal characteristics of other travel behaviors, and sparse investigation into the congestion risks and emissions associated with these patterns. To address this gap, the present study examines various travel behavior patterns and their associated congestion risks and emissions, using one week of License Plate Recognition (LPR) data from the megacity expressway network. First, we classify vehicles into different travel modes based on spatiotemporal features extracted from the LPR data and propose a scalable mode recognition method suitable for large-scale applications. We then assess the congestion risks associated with each mode and estimate the excessive emissions resulting from congestion. The findings reveal notable differences in congestion risks among travel modes, with a bimodal distribution influenced by the temporal rhythm of traffic flow. Furthermore, although commercial vehicles constitute only one-third of the total vehicle population, the excessive emissions attributed to congestion from commercial vehicles are comparable to those from privately owned vehicles. This suggests that focusing exclusively on commuting patterns may underestimate both the congestion risks and excessive emissions. The results of this study not only deepen our understanding of the relationship between individual travel behavior and traffic congestion but also support the optimization of personal travel time and health management, providing a foundation for the development of personalized and proactive traffic demand management strategies.

Expression of Concern: A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

Expression of Concern: A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The Impact of Fiscal and Monetary Policies on The Growth of The Electric Vehicle Population in Indonesia

The Impact of Fiscal and Monetary Policies on The Growth of The Electric Vehicle Population in Indonesia

Heavy metal emissions from on-road vehicles in Xia-Zhang-Quan metropolitan area in southeastern China from 2015 to 2060: impact of vehicle electrification.

Vehicle electrification is an important means of reducing urban air pollution. However, vehicle electrification does not necessarily reduce particulate matter (PM2.5 and PM10) and heavy metals (HM) due to the increase in non-exhaust emissions. In this study, we established the emission inventories of PM2.5, PM10, and their associated heavy metals (PM2.5-HM and PM10-HM) from the on-road vehicles in the Xiamen-Zhangzhou-Quanzhou metropolitan area in southeastern China between 2015 and 2060. In the base year 2021, brake wear emissions account for 66.6% of PM2.5-HM and 76.9% of PM10-HM, much higher than the contributions of exhaust emissions to PM2.5-HM (12.4%) and PM10-HM (6.2%). Light-duty passenger vehicles, heavy-duty trucks, and light-duty trucks are the three main contributors to PM and HM. HM emissions have a high emission density in urban areas. In the business-as-usual (BAU) scenario, HM emissions continue to increase from 2021 to 2060 due to the combined effects of the stricter emission standards and the growth of vehicle population, while the health risk of HM shows an initial decrease and then an increasing trend. Compared with BAU, moderate and aggressive electrification scenarios show a significant reduction in PM2.5-HM emissions between 2030 and 2060, but not in PM10-HM emissions. Further increases in vehicle electrification will bring forward the peak of PM2.5-HM emissions, with the potential to reduce adverse health effects. In the process of vehicle electrification, the reduction of heavy metal emissions from the braking system should be prioritized in order to effectively reduce traffic pollution.

The Correlation of the Smart City Concept with the Costs of Toxic Exhaust Gas Emissions Based on the Analysis of a Selected Population of Motor Vehicles in Urban Traffic

The intensive development of road transport has resulted in a significant increase in air pollution. This phenomenon is particularly noticeable in urban areas. This creates the need for analyses and forecasts of the scale and extent of future emissions of harmful substances into the environment. The aim of this study was to estimate the costs of the emission of toxic components of exhaust gases generated by all users of conventionally propelled vehicles travelling on a section of urban road in the next 25 years. The traffic study was carried out on an urban traffic route, playing a key role for road transport in the dimension of a given urban agglomeration. The traffic forecast for the analysed road section was based on the results of our own measurements carried out in April 2023 and external data from the General Directorate for Roads and Motorways. The results of the observations concerned six categories of vehicles for the morning and afternoon rush hours. Based on the data obtained, the generic structure of the vehicle population on the analysed section and the average daily traffic were determined. Using the methodology contained in the Blue Book of Road Infrastructure, parameters were calculated in the form of annual indicators of traffic growth on the analysed section, travel speed, and annual air pollution costs for selected vehicle categories, remembering at the same time that the Blue Book-based methodology does not distinguish between unit costs in relation to the type of emissions. The results of the study confirmed that there was an increase in the cost of toxic emissions for each vehicle category over the projected 25-year period. The largest increases were seen for trucks with trailers and passenger cars. In total, for all vehicle categories, emission costs nearly doubled from 2024 to 2046, from EUR 3,745,229 to EUR 7,443,384, due to the doubling of the number of vehicles resulting from the traffic forecast. The analyses presented here provide an answer to the question of what pollution costs may be faced by cities in which road transport will continue to be based on conventional types of propulsion. In addition, the research presented can be used to develop urban mobility transformation plans for the coming years, within the scope of the widely promoted smart city concept and the idea of electromobility, by pointing out to local authorities the direct economic benefits of these changes.

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

Black-Spot Identification from Ramol Cross Road to Kathwada Cross Road

Road accidents are a vital issue that is occurring all over the world. Accident severity is increasing with the exponential increase in vehicle population. Accident leads to disability, pain, suffering, grievous injuries, hospitalization expenditure, damage to health and property, social suffering, general degradation of the environment, and, in extreme cases, death. This study aims to identify black spots in the selected stretch involving four major intersections from Ramol crossroad to Kathwada crossroad of Ahmedabad. Data collection consists of collecting past accident data from traffic police station headquarters. The collected data are categorized into fatal accidents, grievous significant accidents, and minor accidents. The data is analyzed using the accident data collected, the Weighted Severity Index (WSI) method, and the PTV Visum software. WSI is estimated for the accident data for the year 2019-2023. By accident analysis, seven black spots were identified in this study. Inspecting the road stretch noted several issues and probable causes of accidents were identified. Poor road conditions, including cracks and potholes, affect road safety. Improper road markings and signs are one of the causes of accidents. The audited road stretch includes recommendations to prevent the number and severity of accidents.

Research on the group effect of new energy vehicles from different policy perspectives

Abstract Policies play a pivotal role in fostering the growth of new energy vehicles. This paper delves into the benefits and risks associated with the development of new energy vehicles under varying policy landscapes. Firstly, the intricate relationship between the competition and growth of new energy vehicles and internal combustion engine vehicles is captured through the application of the extended Lotka-Volterra model. Secondly, a simulation flow diagram is constructed, taking into account the actual mediums of policy impact and drawing insights from diverse research methodologies. This flow diagram enables a comprehensive evaluation of environmental benefits, health risks, and traffic pressure. Finally, a critical evaluation of the actual impact of policies on new energy vehicles is conducted, along with an exploration of the root causes of potential policy paradoxes. The findings reveal that a standalone infrastructure support policy, research and development support policy, or carbon tax policy can, in certain periods, lead to an increase in carbon emissions and traffic pressure. Although the driving restriction policy does not exert negative impacts on the environment, health, and traffic at the national level, it can result in a surge in the total vehicle population. Therefore, it is imperative to adopt a combination of policies to mitigate the potential risks associated with any single policy.

An experimental investigation of the combustion process and the injection strategy of a heavy-duty diesel engine equipped with a common rail fuel injection system

Heavy-duty (HD) diesel vehicles exert significant environmental impact, especially in the case of nitrogen oxides (NOx) and soot, despite representing a relatively small portion of the overall vehicle population. Modern HD diesel engines employ multi-pulse fuel injection strategies to optimize the combustion process to achieve a better compromise between fuel economy and tail-pipe emissions, especially NOx. As such, improving our understanding of the combustion process and the characteristics of modern HD diesel engines operating under various fuel injection strategies is imperative. This research investigates the fuel injection strategy, combustion process and exhaust emissions of a Navistar 2021 MY HD E39 diesel engine. The impact of engine speed, load and injection strategy on engine performance, the combustion process, engine-out NOx and SCR efficiency is experimentally investigated. The relationships between these parameters provide a better understanding of the influence of the injection strategies on the combustion process, fuel economy and exhaust emissions. Fuel was injected into the cylinder with up to four injection pulses, including up to two pilot injections followed by one main injection pulse and one post injection pulse. The heat release process is featured with three peaks. The first and second peaks are associated with the combustion of fuel injected during the pilot and main fuel injection pulses, respectively. However, the third peak observed at medium to high load is associated with the combustion of fuel injected during post injection process. In comparison to its predecessors, the third peak observed at low load was associated with the diffusion combustion of fuel injected during the main injection pulse. The combustion process, which occurred at medium to high loads, was optimized to achieve high engine thermal efficiency, with the peak cylinder pressure held within the allowed limit while the exhaust temperature was high to achieve acceptable SCR efficiency. In comparison, the combustion phasing at low load was dramatically retarded to increase the exhaust temperature with the aim of maintaining SCR efficiency. The strategies for achieving the ideal compromise between fuel economy and tailpipe emissions are discussed and presented herein.

Using the load distribution between girders to monitor the condition of bridges

This study looks at the load distribution among beams in a 3-dimensional bridge Finite Element Model under normal traffic loading and the resulting effect of damage, modelled as a localised loss of elements in a beam. The load distribution is captured, both transversely across the girders and also longitudinally along the girders, and used to identify damage that is remote from the strain sensor location. When there is a loss of elements in a section of the bridge (e.g. due to a strike from a vehicle passing underneath), a portion of the load will redistribute along alternative load paths in the structure. This is most pronounced when the position of the loading and the location of the damage align but also happens when they do not. The study examines the effect of vehicles of different weight and axle configuration on the load distribution, and how grouping results by axle configuration can overcome this. A further novelty of this approach is that the strain response can be transformed into an influence line, which then allows data from large populations of general traffic to be used irrespective of axle configuration. The effect of various transverse positions of vehicles within the driving lane is overcome by combining data from large populations of vehicles. An advantage of this method is that the load distribution from a vehicle crossing is calculated as a ratio i.e. when the bridge is experiencing the same general environmental conditions, and thus reducing the difficulty in controlling the effects of varying environmental conditions when comparing results to a healthy baseline.

Emission Control in Expressway Systems: Vehicle Emission Inventory and Policy Scenario Analysis

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles.

Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data

Objectives With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment. The process of retrospectively evaluating ADS safety impact (as seen with seatbelts, airbags, electronic stability control, etc.) can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a “benchmark” crash rate. Most benchmarks generated to-date have focused on the current human-driven fleet, which enable researchers to understand the impact of the introduced ADS technology on the current crash record status quo. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. Methods: All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Results Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identified several important crash rate dependencies (geographic region, road type, and vehicle type), and show how failing to account for these features in ADS comparisons can bias results. Conclusions Working with police-reported crash data to create crash rate benchmarks is fraught with challenges. Researchers should be cautious in their selection of crash rate benchmarks. We present these challenges, discuss their consequences, and provide analytical guidance for addressing them. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.

Emission characteristics and health effects of PM2.5 from vehicles in typical areas.

Vehicle emissions have become an important source of urban air pollution, and the assessment of air pollution emission characteristics and health effects caused by specific pollution sources can provide scientific basis for air quality management. In this paper, vehicle PM2.5 pollution in typical urban agglomerations of China (the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), the triangle of the Central China urban agglomeration (TCCUA) and the Chengdu-Chongqing urban agglomeration (CCUA)) were used as research samples to evaluate the emission characteristics, health effects and economic losses of vehicle PM2.5 pollution based on the emission inventory, air quality model and exposure-response function from 2010 to 2020. The results indicated that PM2.5 emissions from vehicles in the three urban agglomerations during 2010-2020 first showed an upward yearly trend and then showed a slow decrease in recent years. Heavy-duty trucks and buses are the main contribution vehicles of PM2.5, and the contribution rates of light-duty vehicles to PM2.5 is increasing year by year. The contribution rate of PM2.5 in Beijing decreased significantly. In addition to capital cities and municipalities directly under the central Government, the emission of pollutants in other cities cannot be ignored. The evaluation results of the impact of PM2.5 pollution from vehicles on population health show that: the number of each health endpoint caused by PM2.5 pollution from vehicles in the BTHUA and CCUA showed an overall upward trend, while the TCCUA showed a downward trend in recent years. Among them, PM2.5 pollution from vehicles in the three major urban agglomerations cause about 78,200 (95% CI: 20,500-138,800) premature deaths, 122,800 (95% CI: 25,600-220,500) inpatients, and 628,400 (95% CI: 307,400-930,400) outpatients and 1,332,400 (95% CI: 482,700-2,075,600) illness in 2020. The total health economic losses caused by PM2.5 pollution from vehicles in the three major urban agglomerations in 2010, 2015 and 2020 were 68.25 billion yuan (95% CI: 21.65-109.16), 206.33 billion yuan (95% CI: 66.20-326.20) and 300.73 billion yuan (95% CI: 96.79-473.16), accounting for 0.67% (95% CI: 0.21-1.07%), 1.19% (95% CI: 0.38%-1.88%) and 1.21% (95% CI: 0.39%-1.90%) of the total GDP of these cities. Due to the differences in vehicle population, PM2.5 concentration, population number and economic value of health terminal units, there are differences in health effects and economic losses among different cities in different regions. Among them, the problems of health risks and economic losses were relatively prominent in Beijing, Chengdu, Chongqing, Tianjin and Wuhan.

Can Sri Lanka be a net-zero nation by 2050?—Current renewable energy profile, opportunities, challenges, and recommendations

Sri Lanka as a country has tremendous potential for harnessing energy from renewable sources such as solar, wind, and hydro. However, as of 2018, only 39 % of Sri Lanka's energy generation capacity was harnessed through renewable energy sources. The continuous increase in electrical energy demand and the drastic increase in vehicle population over the past few years have resulted in much of its annual income being spent on purchasing fossil fuels from foreign countries. This has placed the country's future at risk due to the predicted shortage of fossil fuel reserves and in release of an unexpected level of harmful emissions to the environment. In the meantime, Sri Lanka also has an ambitious plan of achieving Net Zero by 2050. The study conducted a systematic review followed by a time series analysis to first identify the present state of the renewable energy progress of the country and through the time series analysis recognize any discrepancies in these efforts. The initial findings revealed the lack of coordination amongst relevant institutions and contrasting government policies such as the increase in investment for non-renewable energy resources as well as backing away from providing initial investment needed to boost the usage of renewable sources for businesses and smaller entities. The study further identified sectors such as transportation and non-renewable power generation activities as the two main barriers deterring the country from having a feasible plan for its efforts for net zero by 2050. From a non-governmental perspective, the study also recognized the knowledge gap and lack of awareness in the wider population of the long-term benefits of switching to renewable sources.

Ex vivo assessment of cellular mass response for personalized therapy selection of hyperthermic intraperitoneal chemotherapy (HIPEC) agents.

3132 Background: Despite progress in the development of HIPEC protocols for the treatment of peritoneal carcinomatosis, agent selection remains largely empiric with few biomarkers for guiding treatment. Ex vivo assessment of drug response using single-cell mass measurements offers a promising option for therapy selection. Previously, we have shown that mass response testing (MRT) is a malignancy- and drug-agnostic means of assessing drug response (1) and can predict a patient’s clinical response to therapy in a range of solid and hematological malignancies (2). Methods: Tissue specimens were collected from patients with peritoneal carcinomatosis as part of an ancillary study for a HIPEC clinical trial (NCT04847063) to explore the feasibility of performing MRT with these specimen types. Briefly, after overnight shipment of viable tumor tissue, tumor cells were isolated and treated with various therapies ex vivo. Single-cell mass distributions of vehicle and drug-treated populations were compared to determine the functional activity of each agent. Results: MRT was attempted for 60 patients with metastatic disease, 10 of whom had peritoneal carcinomatosis and ultimately received HIPEC. The HIPEC cases included histologies such as peritoneal mesothelioma (n=4), colorectal cancer (n=3), ovarian cancer (n=2), and small bowel adenocarcinoma (n=1). In total, 8 out of 10 peritoneal specimens had a sufficiently high tumor cell count, viability, and purity to perform MRT. These specimens displayed clear and statistically distinguishable mass response patterns of response and non-response to several different agents commonly used for HIPEC treatment such as cisplatin, oxaliplatin, doxorubicin, and mitomycin C. Study design:These feasibility results serve as the foundational data for launching a single-site phase II interventional study at the NCI’s Center for Cancer Research. The study will evaluate the potential benefit of personalized treatment regimens determined by MRT for patients with peritoneal carcinomatosis, particularly mesothelioma, a context where therapeutic options have relative equipoise in clinical efficacy. Tumor biopsies will be obtained via laparoscopy prior to HIPEC and submitted to a CLIA-certified lab for MRT to assess the responses of various clinically acceptable agents. The HIPEC regimen will be selected based on these MRT results returned within two days of sample collection. Enrolled patients will then undergo cytoreduction and HIPEC, and progression-free and overall survival will be monitored. As a single-arm, non-randomized trial, patient responses will be compared with a matched synthetic control to assess the impact of MRT on efficacy of HIPEC regimens. Conclusions: MRT is feasible for peritoneal carcinomatosis tissues, with future studies focusing on its role in guiding HIPEC treatment. 1. Nat Comm Bio,2022. 2. JCO PO, 2024. Clinical trial information: NCT04847063 .

Analisis Model Pengambilan Keputusan Dalam Implementasi Kebijakan Ganjil Genap Di Jakarta Guna Mengurangi Kemacetan Dilihat Dari Teori Rasionalitas

DKI Jakarta faces significant traffic congestion due to the increasing population and number of motor vehicles. This research uses a qualitative approach through literature review to identify the factors causing congestion and to formulate effective solutions.The findings reveal that congestion is caused by high private vehicle usage, low use of public transportation, and limited road space. The proposed solutions in this study include improving public transportation infrastructure, implementing an odd-even traffic policy, and enforcing a 3 in 1 rule for cars. The analysis results show that implementing the odd-even policy is the most effective solution to reduce congestion. This policy has proven to reduce the volume of vehicles on the road and encourages the public to switch to public transport, significantly reducing exhaust emissions, improving air quality, increases the efficiency of road space usage, and lowers the traffic accident rate. The success of this solution requires active support and participation from the public. Therefore, the government needs to continuously involve the public in the decision-making process and provide adequate education about the impact and urgency of congestion issues. Collaboration between the government and the public is crucial to creating effective solutions to address congestion and enhance sustainable mobility in Jakarta.

Mean Field Game-Based Algorithms for Charging in Solar-Powered Parking Lots and Discharging into Homes a Large Population of Heterogeneous Electric Vehicles

An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy available in a parking lot between vehicles where the statistics of their arrival states of charge are dictated by an aggregator. Then, we develop a vehicle-to-grid strategy so that vehicle owners with a satisfactory level of energy in their batteries could help to decongest the grid when they return by providing backup power to their homes at an aggregate level per vehicle based on a duration proposed by an aggregator. Both strategies, with concepts from Mean Field Games, would be implemented to reduce the standard deviation in the states of charge of batteries at the end of charging/discharging vehicles while maintaining some fairness and decentralization criteria. Realistic numerical results, based on deterministic data while considering the physical constraints of vehicle batteries, show, first, in the case of charging in a parking lot, a strong to slight decrease in the standard deviation in the states of charge at the end, respectively, for the sunniest day, an average day, and the cloudiest day; then, in the case of discharging into the grid, over three days, we observe at the end the same strong decrease in the standard deviation in the states of charge.

Environmental impacts of COVID–19 responses on passenger vehicle transport scenarios: A life cycle approach

COVID–19 has disrupted human life considerably since the beginning of 2020. The transport sector is one of the most affected sector due to restrictions imposed to curb COVID–19 pandemic. This paper evaluates the environmental impacts of COVID–19 response scenarios using life cycle assessment approach. Six regions selected for the study are Australia, China, European Union (EU), India, United Kingdom (UK), and United States of America (USA). Data for vehicle population, vehicle kilometers travelled, fleet type, and fuel efficiency were used for 2015–2020. The environmental impacts were calculated for five scenarios; Baseline–2019, Business As Usual (BAU) and COVID–19 Response Scenario (CRS) for 2020 and 2030 namely, 2020–BAU, 2020–CRS, 2030–BAU, and 2030–CRS. The results indicate that COVID–19 occurrence and related response measures did not contribute to significant environment benefits in terms of Global Warming Potential (GWP) and other pollutants in short term and long term. This implies that although immediate reductions in emissions were observed in many studies, the long term effects were minimal due to irregular policies and unmanaged demand. Further, to achieve environment benefits sustained measures should be implemented in long term with proper planning and supporting infrastructure. Although electric vehicle strategies will provide benefits; emphasis on vehicle production and fuel production technologies is also needed to achieve the emission reduction targets. The findings are relevant for policy makers while devising policies to achieve the targets.

Photocatalytic Hydrogen-Future Energy

The rapid increase in energy consumption, driven by the rising vehicle population and travel demand, has led to a surge in greenhouse gas (GHG) emissions. This necessitates the development of sustainable and eco-friendly technologies and fuels to mitigate global and local environmental impacts. Four primary justifications support transitioning to alternative fuels and technologies: dwindling oil reserves worldwide, adverse environmental effects of vehicle pollution, escalating oil production costs, and the complex challenges of maintaining energy security in international markets. To address these concerns, there is a growing global demand for hydrogen as a sustainable energy source. Utilizing photocatalytic and photoelectrochemical (PEC) water-splitting reactions, particularly with graphene quantum dots-based composites, offers promising avenues for efficient and eco-friendly hydrogen generation. The selection of these systems is based on their unique physical and chemical properties, with graphene providing strong support and efficient electron transport, while components like cadmium sulfide (CdS) and zinc oxide (ZnO) enhance light absorption and charge separation. This integrated approach shows potential for efficient and sustainable hydrogen production, further bolstered by advances in metal-containing photocatalysts and collaborative efforts across energy markets.

Enhancing Electric Vehicle Range Prediction through Deep Learning: An Autoencoder and Neural Network Approach

The burgeoning adoption of electric vehicles (EVs) signifies a pivotal shift towards sustainable transportation, necessitated by the global imperative to mitigate climate change impacts. Central to this transition is the resolution of range anxiety, a significant barrier impeding wider EV acceptance. This research introduces a novel deep learning framework combining autoencoders and deep neural networks (DNNs) to predict EV range more accurately and reliably. Leveraging a comprehensive dataset from the "Electric Vehicle Population Data," we embarked on a meticulous process of data cleaning, feature engineering, and preprocessing to prepare the dataset for analysis. The study innovatively applies an autoencoder for unsupervised feature learning, effectively reducing dimensionality and extracting salient features from high-dimensional EV data. Subsequently, a DNN model utilizes these features to predict the EV range, offering insights into the vehicle's performance across various conditions. Employing a 10-fold cross-validation approach, the model's efficacy is rigorously evaluated, ensuring robustness and generalizability of the predictions. Our methodology demonstrates a significant enhancement in prediction accuracy compared to conventional machine learning models, as evidenced by the Mean Squared Error (MSE) metric. This research not only contributes to the academic discourse on sustainable transportation and deep learning applications but also provides practical insights for manufacturers, policymakers, and consumers aiming to navigate the complexities of EV adoption and infrastructure development. By addressing the critical challenge of range prediction, this study paves the way for advancing EV analytics, ultimately supporting the transition to a more sustainable and efficient transportation ecosystem.