Search Results (281)

This study explores the impact of driving behaviors, specifically cautious and aggressive, on the performance of human-driven vehicles (HDVs) and autonomous vehicles (AVs) in traffic flow dynamics. It focuses on various metrics, including level of service (LOS), average speed, traffic volume, queue delays, carbon emissions, and fuel consumption, to assess their effects on overall performance. The findings reveal significant differences between cautious and aggressive AVs, particularly at varying market penetration rates (MPRs). Aggressive autonomous vehicles demonstrate greater traffic efficiency compared to their cautious counterparts. They achieve higher levels of service, improving from poor performance at low MPRs to significantly better performance at higher MPRs and in fully autonomous scenarios. In contrast, cautious AVs often experience poor service ratings at low MPRs, with an improvement in performance only at higher MPRs. Regarding environmental performance, aggressive AVs outperform cautious ones in terms of reduced emissions and fuel consumption. The emissions produced by aggressive AVs are significantly lower than those from cautious AVs, and they further decrease as the MPRs increases. Additionally, aggressive AVs show a considerable reduction in fuel usage compared to cautious AVs. While cautious AVs improve slightly at higher MPRs, they continue to generate higher emissions and consume more fuel than their aggressive counterparts. In conclusion, aggressive AVs offer better traffic efficiency and environmental performance than both cautious AVs. Their ability to improve road efficiency and reduce congestion positions them as a valuable asset for sustainable transportation. Strategically incorporating aggressive AVs into transportation systems could lead to significant advancements in traffic management and environmental sustainability. Full article

Road transportation is a major contributor to greenhouse gas (GHG) emissions in Thailand. This study assesses the potential for GHG mitigation in the road transport sector from 2018 to 2030. Emission factors for various vehicle types and technologies were derived using the International Vehicle Emissions (IVE) model. Emissions were then estimated based on country-specific vehicle data. In the baseline year 2018, total emissions were estimated at 23,914.02 GgCO₂eq, primarily from pickups (24.38%), trucks (20.96%), passenger cars (19.48%), and buses (16.95%). Multiple mitigation scenarios were evaluated, including the adoption of electric vehicles (EVs), improvements in fuel efficiency, and a shift to renewable energy. Results indicate that transitioning all newly registered passenger cars (PCs) to EVs while phasing out older models could lead to a 16.42% reduction in total GHG emissions by 2030. The most effective integrated scenario, combining the expansion of electric vehicles with improvements in internal combustion engine efficiency, could achieve a 41.96% reduction, equivalent to 18,378.04 GgCO₂eq. These findings highlight the importance of clean technology deployment and fuel transition policies in meeting Thailand’s climate goals, while providing a valuable database to support strategic planning and implementation. Full article

This study investigates the factors influencing the energy consumption and reliability of haul trucks in open-pit mines and quarries, where fuel costs and the environmental impact are significant. Traditional analysis of haulage systems often overlooks crucial aspects such as energy efficiency in the specific mining environment and the effect of road configurations on truck performance. As sustainability becomes increasingly important, reducing fuel consumption not only reduces costs but also reduces greenhouse gas emissions. A key focus of the study is the link between haul truck reliability and overall efficiency. Frequent breakdowns increase maintenance costs, lead to unplanned downtime, and increase fuel consumption, all of which have an impact on the environment. Reliable transport systems, on the other hand, improve efficiency, reduce costs, and support sustainability goals. The authors analyze the energy consumption of trucks in relation to vehicle performance parameters and transport route characteristics. Discrete modeling of the transport system showed the impact of the operating environment on the variability of energy consumption and vehicle reliability. The study highlights the importance of understanding specific energy consumption in order to optimize the choice of transport system, as transport costs are a major cost of resource extraction. By analyzing the effect of road quality on vehicle performance, the authors suggest that improvements to the road surface can more easily improve vehicle reliability and energy intensity than changes to other road design elements. The study presents a quantitative analysis of the impact of haul road conditions on the operational efficiency of haul trucks in mining environments. Through discrete simulation models, two scenarios were analyzed. Total operational time decreased by 11.2% when road quality improved, demonstrating the critical role of surface maintenance. Additionally, breakdown times were reduced by 44%, maintenance by 15%, and empty travel by 9% in the optimized scenario. These findings underscore the necessity of maintaining optimal road conditions to prevent substantial efficiency losses and increased maintenance costs. Full article

This study assesses the energy, economic, and environmental implications of switching Tanzania’s road transport sector to natural gas, which is slowly transitioning. In energy, the main goal is to identify the energy demand for petroleum fuel (diesel and petrol) and natural gas during the transition, while in the economy, the government revenue in the form of taxes for shifted and unshifted vehicles, as well as the loss in government revenue from petroleum fuel revenue post-transition, is assessed. In the environment, carbon emission in terms of carbon dioxide equivalent (CO_2e), carbon tax revenues, and carbon credit revenues post-transition is estimated. The shift involved 10, 20, and 30% of the road vehicle population. The 10, 20, and 30% shift targeted about 142,247, 183,893, and 225,540 vehicles, which in turn dropped diesel and petrol demand by 7 and 3.68%, 7 and 3.8%, and 15 and 7.5%, respectively. In natural gas, the demand started at 0.0916 billion kg and grew exponentially by 200% and later by 300%. The transition has consequences in government revenue, which takes the form of taxes on petroleum products. The shift from 10 to 30% could lead to foregone taxes amounting to Tanzania shilling TZS 0.09, 0.31, and 0.54 trillion (US$ 33,358,680, US$ 11,490,212, and US$ 20,015,208), indicating a tax loss of about 3, 9, and 15%. Contrary, the government may benefit from these losses by lowering the amount of foreign currency necessary for oil importation. In environmental benefits, the 10, 20, and 30% shift could offset approximately 8,959,198.92119, 8,438,863.65528, and 7,918,528.38937 tCO_2e, equivalent to 5.4, 10.97, and 16.47% of the road emissions. The post-transition road emissions might result in a carbon tax revenue of about US$ 71,673,591.37, 67,510,909.24, and 63,348,227.11 per year. The post-transition carbon credit revenue of about US$ 20,813,410.64, 41,626,821.27, and 62,440,231.91 is expected annually. The findings are critical for policy design and promoting a transition in the road transport sector. Full article

Road transport is a major contributor to air pollution, necessitating sustainable solutions for urban logistics. This study presents a time-dependent vehicle routing problem (VRP) model aimed at minimizing fuel consumption and greenhouse gas emissions while addressing stochastic customer demands. By incorporating key environmental factors such as road gradients, vehicle load, temperature, wind direction, and asphalt type, the proposed model provides a comprehensive approach to reducing transportation-related pollutants. To solve the computationally complex problem, a hybrid algorithm combining the gray wolf optimizer (GWO) and the multilayer perceptron (MLP) neural network is introduced. The algorithm demonstrates superior performance, achieving an error rate of less than 2% for medium-scale problems and significantly reducing fuel and driver costs. Sensitivity analyses reveal the profound impact of environmental parameters, with wind speed and direction altering optimal routing in over 80% of cases for large-scale instances. This research advances green logistics by integrating dynamic environmental considerations into routing decisions, balancing economic objectives with sustainability. The proposed model and algorithm offer a scalable solution to real-world challenges, enabling policymakers and logistics planners to improve environmental outcomes while maintaining operational efficiency. Full article

This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce traffic oscillations, smooth speed and acceleration fluctuations, and enhance overall system performance. Stability analysis shows that TD3-BCM effectively suppresses traffic fluctuations, with system stability improving from 1.132 to 1.182 as AV penetration increases. At an AV penetration rate of 40%, TD3-BCM surpasses both Cooperative Adaptive Cruise Control (CACC) and traditional Bilateral Control Model (BCM) approaches in terms of traffic efficiency, safety, and energy use—raising trailing vehicle speed by 12.6%, shortening average headway by 19.0%, increasing Time-to-Collision (TTC) by 87.3%, and lowering fuel consumption by 14.8%. When AV penetration reaches 70%, fuel savings rise to 19.7%, accompanied by further improvements in both traffic stability and safety. TD3-BCM provides a scalable and sustainable solution for intelligent transportation systems, particularly in high-penetration AV environments, by significantly enhancing stability, operational efficiency, and road safety. Full article

Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of risk, road dynamics, and emissions constraints. In contrast, the current paper presents a mixed-integer linear programming (MILP) model for scheduling fleets, selecting transportation modes in multiple modes of transportation, and meeting emissions regulation requirements according to dynamic transportation requirements. Risk-aware routing and taking the factor of congestion and CO₂ emission limits proposed by the government into consideration, this model can offer a more efficient and flexible optimization strategy. From the case study, we observe the significant result that the proposed model achieves, a 23% reduction in transport costs, a 25% improvement in fleet use, a 33.3% decrease in the delivery delay, and a 24.6% decrease in CO₂ emissions. The model dynamically delivers shipments utilizing both road and rail transportation and improves mode choice by minimizing idle vehicle time. This is confirmed through sensitivity analysis which addresses factors such as traffic congestion, changing fuel prices, and changing environmental standards. Full article

Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers and planners to reduce the number of vehicles on the road. Various strategies have been proposed, such as incentives for public transport, parking restrictions, parking pricing and car sharing. It is very important that these strategies are implemented by the institutions in order to reduce traffic during the commuting hours, which coincide with the rush hour. Especially in areas such as shipyards and industrial zones, which are far from the city center and relatively difficult to reach but which provide employment opportunities for thousands of people, a shuttle service is one of the most preferred strategies to discourage employees from using private cars. However, in companies with thousands of employees, this situation generates costs that cannot be ignored. The examined case study similarly needs to optimize and reduce operational costs related to fuel consumption, maintenance and tax expenses by optimizing the number of two different types of service vehicles required for employee transportation at the Yalova Shipyard. For this aim, a dynamic linear programming (DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service. According to the analysis results, it was concluded that the annual fuel cost of the vehicles will be reduced by 33.9%, the maintenance cost by 35.2% and the annual tax cost by 49.3% by disposing of the unneeded vehicles (27%) in the studied Yalova Shipyard. Taking all these positive improvements into account, it is clear that the optimization study significantly reduces the costs incurred by the service. Full article

This study examines the policy and technological dynamics shaping China’s road transport sector’s transition to low-carbon sustainability, focusing on battery electric vehicles (BEVs) and hydrogen fuel cell electric vehicles (HFCEVs). As the world’s second-largest carbon emitter, China faces significant challenges in reducing its fossil fuel dependency in road transport, which accounts for diverse emissions and energy security risks. The present work, using a dual tech multi-level perspective (DTMLP) framework integrating multi-level perspective (MLP) and an advocacy coalition framework (ACF), analyzes the interplay of landscape pressures (global carbon constraints), regime dynamics (policy–market interactions), and niche innovations (BEV/FCEV competition). The results reveal BEVs’ dominance in light-duty markets, achieving remarkable operational emission reductions but facing lifecycle carbon lock-ins from battery production and coal-dependent power grids. HFCEVs demonstrate potential for heavy-duty decarbonization but struggle with gray hydrogen reliance and infrastructure gaps. Policy evolution highlights shifting governance from subsidies to market-driven mechanisms, alongside regional disparities in implementation. This study proposes a three-phase roadmap: structural optimization (2025–2030), technological adaptation (2030–2045), and hydrogen–electric system integration (post-2045), emphasizing material innovation, renewable energy alignment, and multi-level governance. Our findings underscore the necessity of coordinated policy–technology synergies, grid decarbonization, and circular economy strategies, to overcome institutional inertia and achieve China’s ‘Dual Carbon’ targets. This work provides actionable insights for global sustainable transport transitions amid competing technological pathways and geopolitical resource constraints. Full article

As global initiatives to reduce greenhouse gas emissions and combat climate change expand, electric vehicles (EVs) powered by fuel cells and lithium-ion batteries are gaining global recognition as solutions for sustainable transportation due to their high energy conversion efficiency. Considering the driving range limitations of battery electric vehicles (BEVs) and the low efficiency of internal combustion engines (ICEs), fuel cell hybrid vehicles offer a compelling alternative for long-distance, low-emission driving with less refuelling time. To facilitate their wider scale adoption, it is essential to understand their energy performance through models that consider external weather effects, driving styles, road gradients, and their simultaneous interaction. This paper presents a microlevel, multicriteria assessment framework to investigate the performance of BEVs, fuel cell electric vehicles (FCEVs), and hybrid electric vehicles (HEVs), with a focus on energy consumption, drive systems, and emissions. Simulation models were developed using MATLAB 2021a Simulink environment, thus enabling the integration of standardized driving cycles with real-world wind and terrain variations. The results are presented for various trip scenarios, employing quantitative and qualitative analysis methods to identify the most efficient vehicle configuration, also validated through the simulation of three commercial EVs. Predictive modelling approaches are utilized to estimate a vehicle’s performance under unexplored conditions. Results indicate that trip conditions have a significant impact on the performance of all three vehicles, with HEVs emerging as the most efficient and balanced option, followed by FCEVs, making them strong candidates compared with BEVs for broader adoption in the transition toward sustainable transportation. Full article

With the rising demand for renewable hydrogen as an alternative sustainable fuel, efficient transport strategies have become essential, particularly for regional and small-scale applications. While most previous studies focus on the long-distance transport of hydrogen, little attention has been given to the application in regions that are remote from major transmission infrastructure. This study evaluates the techno-economic performance of hydrogen road transport using multiple-element hydrogen gas containers and compares it with multimodal transport using rail. The comparison is performed for the southeastern region of the Czech Republic. The comprehensive techno-economic assessment incorporates detailed technical evaluations, precise fuel and energy consumption calculations, and real-world infrastructure planning to enhance accuracy. Results showed that multimodal transport of hydrogen can significantly reduce the cost for distances exceeding 90 km. The cost is calculated based on annual vehicle utilization, assuming the remaining utilization will be allocated to other tasks throughout the year. However, the cost-effectiveness of rail transportation is influenced by track capacity limits and possible delays. Additionally, this study highlights the crucial role of regional logistics hubs in optimizing transport modes, further reducing costs and improving efficiency. Full article

This paper introduces a novel methodology for vehicle routing services called Route Optimization with Multiple Delivery Points (ROMP), which works by modeling urban street networks as analog electrical circuits. This methodology translates road networks into a linear electrical circuit where the resistances of circuit branches represent parameters like vehicular flow and street length, derived from geographic positions between intersections. By applying Modified Nodal Analysis (MNA) to this circuit, ROMP identifies high-current paths that closely approximate minimal travel distances. The practical performance of ROMP is demonstrated through three case studies, showing its potential to yield shorter routes and faster route-finding compared to OpenRouteService (ORS). The resultant improvements can lead to fuel savings, reduced labor costs, and enhanced logistics operations, particularly in applications involving a single origin and multiple delivery points, such as goods delivery and patient transport. In addition, this proposal supports sustainability by optimizing routes, which helps reduce the environmental impact of transportation and lower greenhouse gas emissions. Furthermore, shorter travel distances and improved efficiency promote better energy use, enhancing air quality and urban sustainability. Future work aims to integrate new street models and real-time traffic data to expand ROMP’s applicability in vehicle routing research. Full article

Although truck platooning enhances transportation efficiency, reduces fuel consumption, and lowers freight transport costs, it can also create limited overtaking opportunities, potentially leading to risky overtaking maneuvers. The present study examines the impact of platooned heavy military vehicles on the quantification of Passing Sight Distance (PSD). Two distinct cases are examined: single and platooned military vehicles passing, the latter formed by five trucks. The authors, by realistically modeling the passing task, examined the interaction between vehicle dynamic parameters and roadway grade utilizing an existing vehicle dynamics model. The analysis of various speed values revealed significant PSD variations depending on the examined impeding (overtaken) vehicle’s platooning configuration and utilized grade. The present assessment accurately quantifies the grade impact on the required PSDs for such special vehicle arrangements and can be applied to any vehicle platooning configuration. Moreover, a preliminary tool is introduced to assist road designers in accurately assessing the impact of roadway grade on the passing process. This tool, when combined with a more in-depth analysis of additional factors, can help justify the need for an extra lane in road sections where platooning regularly occurs. Full article

The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combination of trucks and cars. However, these studies have not fully addressed the heterogeneous mixed traffic flow consisting of CAVs and HDVs, including trucks and cars, influenced by varying human driving styles. Therefore, this study investigates the influences of the market penetration rate (MPR) of CAVs, truck proportion, and driving style on operational characteristics in heterogeneous mixed traffic flow. A total of 1105 events were extracted from highD dataset to analyze four car-following types: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). Principal Component Analysis (PCA) and clustering techniques were employed to categorize distinct driving styles, while the Intelligent Driver Model (IDM) was calibrated to represent the various car-following behaviors. Subsequently, microscopic simulations were conducted using the Simulation of Urban Mobility (SUMO) platform to evaluate the impact of CAVs on sustainable traffic operations, including road capacity, stability, safety, traffic oscillations, fuel consumption, and emissions under various traffic conditions. The results demonstrate that CAVs can significantly enhance road capacity, improve emissions, and stabilize traffic flow at high MPRs. For instance, when the MPR increases from 40% to 80%, the road capacity improves by approximately 25%, while stability enhances by approximately 33%. In contrast, higher truck proportions lead to reduced capacity, increased emissions, and decreased traffic flow stability. In addition, an increased proportion of mild drivers reduces capacity, raises emissions per kilometer, and improves stability and safety. However, a high proportion of mild human drivers (e.g., 100% mild drivers) may negatively impact traffic safety when CAVs are present. This study provides valuable insights into evaluating heterogeneous traffic flows and supports the development of future traffic management strategies for more sustainable transportation systems. Full article

The promotion of carbon reduction in the private car sector is crucial for advancing sustainable transportation development and addressing global climate change. This study utilizes vehicle trajectory big data from Guangdong Province, China, and employs machine learning, an LDA topic model, a gradient descent-based fuzzy cognitive map model, and grey correlation analysis to investigate the influencing factors and emission reduction pathways of carbon emissions from private cars. The findings indicate that (1) population density exhibits the strongest correlation with private car carbon emissions, with a coefficient of 0.85, rendering it a key factor influencing emissions, (2) the development of public transportation emerges as the primary pathway for carbon reduction in the private car sector under a single-factor scenario, and (3) coordinating public transport with road network density and fuel prices with traffic congestion are both viable pathways as well for reducing carbon emissions in the private car sector. This study attempts to integrate multiple factors and private car carbon emissions within a unified research framework, exploring and elucidating carbon reduction pathways for private cars with the objective of providing valuable insights into the green and low-carbon transition of the transportation sector. Full article