Transportation Research Part D-transport and Environment最新文献

As the ownership of hybrid vehicles soars, accurately predicting and assessing CO₂ emissions become crucial. This study utilized the AVL portable emission measurement system (PEMS) to reveal the actual CO₂ emission of three types of hybrid vehicles. Firstly, engine speed is a key factor influencing CO₂ emissions of range-extended electric vehicle (REEV) and plug-in hybrid electric vehicle (PHEV), while vehicle specific power (VSP) affects HEV. Secondly, in terms of fuel consumption, when the battery levels of REEV and PHEV are low, their fuel consumption tends to be higher than that of HEV. Specifically, the CO₂ emission factor (the amount of CO₂ emitted by a vehicle per unit distance during operation) ratios of REEV to PHEV range from 1.19 to 1.89, while the ratios of REEV to HEV are between 1.41 and 2.57. Thirdly, in terms of NO_X control, HEV performed significantly worse.

Aiming to address the lack of available and accurate runoff coefficients of various roads in urban flooding simulations and effectiveness assessments of permeable pavement on runoff reduction, the rainfall-runoff response characteristics of typical urban road pavements were investigated by laboratory-scaled tests. The results showed that average runoff coefficients and initial runoff times of pervious road pavements were almost 0.1 ∼ 0.2 and 7 ∼ 20 times those of impervious pavements, respectively. Moreover, permeable brick (PB) pavement presented better capacity for runoff mitigation than permeable asphalt concrete (PAC) pavement when the average rainfall intensity was 1.11 or 1.80 mm/min. The average runoff coefficient of cement concrete (CC) pavement ranged from 0.939 to 0.985 under all rainfall intensity and longitudinal slope combinations, while that of asphalt concrete (AC) was between 0.907 and 0.961. These results may be beneficial to improving the precision of runoff computation generated from roads or other site areas in urban flooding simulations.

Vehicle navigation and path optimization require a more meticulous approach when it deals with EVs (electric vehicles) and SDVs (software-defined vehicles), due to lengthy charging times and the lack of charging infrastructure. Long-distance freight EV trucking needs path guidance with accurate energy consumption estimates to prevent charging-related failures. We developed a novel energy consumption estimation approach that only uses battery log data to extract major vehicle parameters to increase EV navigation accuracy without additional sensors. This is enabled by extracting multiple drive modes from the log data for analysis. The system provides 1) routes, 2) charge locations, 3) charging times, and 4) optimal vehicle speeds that guarantee the shortest travel time. We successfully validated the system using log data collected from an EV and Tesla’s Supercharging map in the US and compared it with the commercially available navigation system, Tesla’s trip planner, whose capabilities solely include charging time and routing.

When examining pathways to decarbonise transport, one must examine users’ mobility and examine ways to enable them to adapt. Electric vehicle (EV) adoption is incentivised to help reach emission reduction targets; however, existing research predominantly focuses on the private sector. This study analyses data on EV grants for commercial operators and the spatial distribution of commercial industries in Ireland. Results reveal a disparity in EV adoption between the commercial and private sectors. Retail, Professional Activities, Education, and Construction sub-sectors show the highest likelihood of embracing EVs. Spatial heatmaps identify high-density commercial clusters that could be useful for allocating public EV charging stations. The findings underscore the significance of the commercial sector’s transition to EVs towards achieving net-zero targets. Importantly, this study highlights that policies aimed at promoting EV uptake in the commercial sector need to be refined as its requirements are distinct from the private sector.

Minimizing the detrimental effects of road transport greenhouse gas (GHG) emissions on climate change and global warming requires accurate emission forecasting. To forecast greenhouse gas emissions from industrial and civilian transportation on roads in China, we present new approaches that use data extraction and managed machine learning methods for regression and identification. Four methods are examined: decision tree, multinomial logistic regression, multivariate linear regression, and artificial neural network (ANN) multiple-layer perceptron. The findings suggest that the multiple-layer perceptron approach of ANN has superior prediction accuracy compared to other models. Ensemble modelling techniques, such as Bagging and Boosting, significantly improved the predictive performance of the developed multilayer perceptron system. The paper’s conclusions are significant for transport policymakers, regulators, and international organizations in mitigating GHG emissions.

In this study, a bi-level model is developed to quantify the value of orderly electric vehicle (EV) charging in carbon reduction. Specifically, the upper-level model optimizes each EV driver’s charging schedule to diminish the total carbon emissions without impacting their travel plans, and the lower-level problem aims to fulfill electricity demands with minimal electricity dispatch cost. Based on real-world operation data obtained from 3,777 battery EVs (BEVs) in Shanghai over 11 months and local power plant data, the total carbon emissions generated by BEVs in Shanghai is calculated as 1,176,637 tons over this period, averaging 73 gCO${}_2$/km per BEV. By administering charging control to all BEVs in Shanghai, the above emission could be curtailed by 39%. Sensitivity analyses uncover that augmenting battery capacity and integrating wind power can significantly enhance emission reductions, while increasing the flexibility of the power plant might diminish the effectiveness of orderly EV charging.

Container-terminal equipment is the main source of emissions at ports, and the environmental and economic impacts of alternative fuels on them have not been sufficiently investigated. In this study, a novel framework for quantitative evaluation of environmental and economic performances is constructed by considering four dimensions: various fuel pathways, full fuel lifecycles, fuel preparation sources, and economic policies. A case study is conducted through empirical data from Qingdao Container Terminal, and the combined impacts of the five sensitive factors at different periods are studied in depth. The result shows that liquefied natural gas, electricity, and diesel-electric hybrid offer substantial overall benefits. Owing to energy transformation, technological progress, and cost reduction, hydrogen and electricity may emerge as the most advantageous energy sources. Policies are crucial in reducing emissions by port enterprises, and the government should improve emission regulations, stabilize incentive policies, and promote the use of new energy.

About 6.6 million people die every year from air pollution diseases globally. Transportation industry is considered one of the leading contributors in air pollution. This research utilizes deep learning and machine learning techniques to predict China’s transport-related CO₂ emissions and energy needs by utilizing variables like population, car kilometers, year and GDP per capita. The outcomes have been analyzed using six analytical measures: determination coefficient, RMSE, relative RMSE, mean absolute percentage error, mean bias error and mean absolute bias error. Findings indicate that yearly increase in transport-related CO₂ emissions in China will be 3.66%, and transport energy consumption will increase by 3.8%. Energy consumption and transport CO₂ emissions are projected to rise by roughly 3.5 times by 2050 as compared to current levels. Therefore, government should re-evaluate its energy investment plans for the future and institute new rules, and standards regarding transport-related energy consumption and pollution reduction.

Wildlife-vehicle collisions (WVCs) raise concerns for both human safety and animal welfare. As the literature has reported increased animal-related crash frequency on full moon nights in several regions, we investigated if a similar pattern is observed in Texas. We counted WVC and non-WVC frequencies on full moon nights and new moon nights in Texas between January 2011 and January 2020. Analysis revealed a 45.80% (95% confidence interval (CI): 29.94–61.29%) increase in WVCs on full moon nights compared to new moon nights, with no statistically significant difference for non-WVCs (95% CI: -2.58–1.45%). The association was pronounced in rural areas than in urban areas. It is likely that brighter moonlight is strongly associated with higher WVC rates. The results illuminate the importance of heightened caution for drivers even on bright nights, particularly when driving through areas with high wildlife density.

The resilience of bus and metro systems to extreme weather events is a critical concern in urban planning, given their growing complexity and interconnectivity. Traditional studies often simplify or overlook the interdependency between different transportation modes, focusing on recovery strategies for a single mode to enhance system resilience. This study proposes an integrated resilience assessment framework for bus and metro systems, conceptualized as a Bus-Metro Double-Layer Network (B-M DLN). The framework considers both network structure and system function to accurately evaluate the B-M DLN resilience. A resilience optimization model for B-M DLN based on Genetic Algorithm (GA) is established to suggest the optimal recovery sequence of damaged stations, emphasizing the importance of station repair time, node strength, and node degree in recovery prioritization. Through a case analysis of Xi’an City, China, the B-M DLN shows significantly enhanced resilience when applying the optimal recovery strategy, especially in large-scale failure scenarios.