Transportation Research Part D-transport and Environment最新文献

This study investigates the influence of fellow passenger interactions on travel satisfaction and wellbeing among commuters with disabilities (CWD) in a developing country. Employing qualitative methods such as ethnography and interviews, it explores the challenges CWD faces in public transport. In addition to building on previous studies regarding interaction on public transport, the study introduces the concept of Commuter-to-Commuter interaction. This study unveils the impact of fellow passengers on travel experiences and categorises interactions as positive, neutral, or negative, elucidating key features of this dynamic. Understanding these interactions is pivotal for developing interventions aimed at improving transportation accessibility. Unlike previous studies focusing on religion, race, gender, and age, this research addresses a gap by focusing on disabilities. Enhancing comprehension of inclusion dynamics informs policies for more equitable transportation systems, ultimately contributing to a supportive and accommodating environments for all commuters, including those with disabilities.

The real-driving emission (RDE) test was employed involving a conventional compressed natural gas (CNG) bus and its hybrid counterpart powered by the same engine type along the same route. By calculating the energy balance of engine and vehicle, hybrid bus saves energy in three ways: engine efficiency increase (30 %), regenerative braking (21 %) and without idling (7 %). But the higher curb weight offset 32 % of the benefits, resulting in 24 % improvement in comprehensive efficiency. The NOx and particulate number (PN) emissions of the hybrid bus could be reduced by 46 % and 39 %, respectively. The emission causes of all pollutants were further classified into: cold-start, restart and hot running. Restart accounts for 52 % of CO emission and hot running for 55 % of NOx emission. Frequent engine restart and short engine running durations (<50 s) impair efficiency and emissions. The findings can help policymakers assess the environmental impact of transport electrification.

To realize the resourceful utilization of roadway waste for recycling and low-carbon construction, two environmental-friendly pavement technologies pertaining to cold recycled foamed asphalt mixture and crumb rubber asphalt (CRA) were combined to prepare foamed rubberized asphalt cold recycled mixture (FRA-CRM). The results indicate that pre-swelled activated crumb rubber (CR) by waste oil with foaming agent can enhance the foaming effect of CRA and reduce the manufacturing temperature by 15 °C, and it presents better fatigue resistance and low-temperature performance. Moreover, the moisture susceptibility, high-temperature rutting resistance, and low-temperature cracking resistance of FRA-CRM have been substantially ameliorated in comparison with traditional foamed matrix asphalt cold recycled mixture. Life cycle assessment analysis shows that compared with conventional hot mix CRA mixture, the production demand of FRA-CRM per 1 t contributes to approximate 45.13 % reduction in greenhouse gas emissions and 51.38 % of energy saving.

Generally, when generating noise maps, the Weiler-Atherton algorithm is employed for polygon intersection calculations to estimate building obstruction attenuation. However, for larger areas, the computational efficiency of this method heavily depends on computational power and expensive hardware. To address these challenges, our method employs binary images to streamline the calculation of intersection area between the building polygon and the noise propagation path, thereby improving computational efficiency. The proposed method consists of two features: converting polygon shapefiles into binary images and subsequently applying the Bresenham Algorithm to calculate building occlusion attenuation. Our study demonstrated that this approach reduced the calculation time to 39.1 % of the original duration, with a mean absolute discrepancy between the two methods of 0.77 dB(A). By utilizing the proposed method to generate noise maps with comparable accuracy, our approach offers an attractive alternative that reduces hardware requirements.

Massive amounts of highly time-resolved and freely available flight tracking data are fed into a modelling tool previously devised by the authors, which was improved to perform optimal reconstruction of low-altitude aircraft operations and more accurate prediction of airport noise. The benefits of the high-resolution data, key novelty of this work, include easier flight operation identification, higher-quality ground track reconstruction, and an upgraded aircraft performance estimation. This is conducted with a new version of the authors’ mixed analysis-synthesis approach, where more degrees of freedom are added to the prescribed flight procedures and the aircraft take-off weight is estimated from the tracking data. The results obtained for Zurich Airport and 2022 traffic show the ability of the proposed approach to capture the actual flight procedures during departure and arrival operations, ultimately leading to a slight underestimation (1.7 dB(A) on average) of the exposure-based cumulative noise level in the airport area.

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.