Alice Mirailler , Ana-Maria Trunfio-Sfarghiu , Valentin Massardier , Adina-Nicoleta Lazar , Nathalie Bernoud-Hubac , Mickaël Catinon , André Pierre Schroder , Salah Khardi
{"title":"Physicochemical characterization of brake abrasion particles from trucks under laboratory conditions","authors":"Alice Mirailler , Ana-Maria Trunfio-Sfarghiu , Valentin Massardier , Adina-Nicoleta Lazar , Nathalie Bernoud-Hubac , Mickaël Catinon , André Pierre Schroder , Salah Khardi","doi":"10.1016/j.trd.2025.104864","DOIUrl":"10.1016/j.trd.2025.104864","url":null,"abstract":"<div><div>The study of non-exhaust particle emissions is crucial in the context of the global vehicle fleet’s electrification. These particles, often overlooked, have harmful impacts on the environment and human health. This paper presents an exhaustive characterization of particles emitted from heavy-duty truck brake wear under controlled conditions. Braking tests were analyzed by correlating braking conditions with particle concentration collected by an Electrical Low Pressure Impactor (ELPI + ). The chemical composition was determined using Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy (SEM-EDX). Results show that ultrafine particles (<100 nm) dominate emissions, with brake wear tracers such as Fe, Cu, Mo identified. The study also reveals that brake disc temperature significantly influences emissions. This work aims to enhance understanding of non-exhaust particulate emissions from heavy-duty vehicles, contributing to the development of effective particle collection systems and raising awareness of their environmental and health effects.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104864"},"PeriodicalIF":7.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xingang Zhou , Chenying Yang , Linchuan Yang , Yongping Zhang
{"title":"Nonlinear effects of the built environment on metro-integrated bikesharing and ridesourcing usage","authors":"Xingang Zhou , Chenying Yang , Linchuan Yang , Yongping Zhang","doi":"10.1016/j.trd.2025.104898","DOIUrl":"10.1016/j.trd.2025.104898","url":null,"abstract":"<div><div>Limited attention has been paid to revealing nonlinear relationships between the built environment and the integration of metro and shared mobility. This study applies the random forest model to examine and compare the nonlinear effects of the built environment on metro-integrated bikesharing and ridesourcing usage. It takes Shanghai as the study area, supported by massive user-generated bikesharing and ridesourcing trips. The results show that the distance to the city center and the number of entrances are the two most significant impact factors, while other built environment factors show varying degrees of influence. Interestingly, secondary and primary road densities play a significant role in predicting the usage of bikesharing and ridesourcing services, respectively. Moreover, nonlinear effects of the built environment have been investigated. For example, when secondary road density is below 5.2 km/km<sup>2</sup>, it has a positive effect on bike-sharing usage. However, this relationship does not hold when the secondary road density exceeds this threshold. This study offer valuable insights for transportation planning and policy-making.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104898"},"PeriodicalIF":7.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pengfei Fan , Guohua Song , Zhiqiang Zhai , Kanok Boriboonsomsin
{"title":"Road grade and truck weight matter: Investigating link-level energy consumption uncertainty","authors":"Pengfei Fan , Guohua Song , Zhiqiang Zhai , Kanok Boriboonsomsin","doi":"10.1016/j.trd.2025.104900","DOIUrl":"10.1016/j.trd.2025.104900","url":null,"abstract":"<div><div>Modeling heavy-duty truck energy consumption and emissions under real-world conditions is challenged by uncertainties from dynamic road grade and truck weight. This study integrates second-by-second operational, fuel consumption, and NOx emissions data with accurate road grade and weight information across over 12,000 100-meter road links. The data were collected from 48 in-use trucks operating along a 60-kilometer highway with varying topography. This study quantifies link-level modeling uncertainty and analyzes the influence of road grade and truck weight. Multiple regression and machine learning models are applied to evaluate energy consumption prediction performance under different feature combinations. Results show that using only average speed for 5-km segments results in a 25% error, which decreases to 11% when road grade, truck weight, and acceleration are incorporated. These findings underscore the need to integrate road grade and truck weight into predictive models to improve energy and emissions analysis.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104900"},"PeriodicalIF":7.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Examining the links between public transport provision, suppressed travel and (in)sufficient accessibility","authors":"Jean Ryan , Chiara Vitrano , Karel Martens","doi":"10.1016/j.trd.2025.104883","DOIUrl":"10.1016/j.trd.2025.104883","url":null,"abstract":"<div><div>This study engages a sufficientarian perspective to examine the role current basic levels of public transport provision play in delivering sufficient accessibility for inhabitants of sparsely populated areas. A survey was conducted among people living in areas which meet the criteria set out for basic levels of public transport provision for the Västra Götaland Region, Sweden. Mixed methods were employed in the collection and analysis of the survey data. The links between socio-economic, socio-demographic, time constraints, and accessibility and transport-related characteristics on the one hand and suppressed travel combined with activity participation levels on the other were examined. Our findings indicate that the current provision levels play a limited role in delivering (sufficient) accessibility, with many reports of personal adaptive strategies and forgone trips. We found that young people were significantly more likely to report suppressed travel. We propose recommendations for the improvement of public transport-based accessibility in such contexts.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104883"},"PeriodicalIF":7.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Consumer preferences for hydrogen fuel cell vehicles adoption: A discrete choice survey","authors":"Yuyao Liu , Ying Li , Kunhui Ye , Xingjun Huang","doi":"10.1016/j.trd.2025.104892","DOIUrl":"10.1016/j.trd.2025.104892","url":null,"abstract":"<div><div>The hydrogen economy, driven by the promotion of hydrogen fuel cell vehicles (HFCVs), is emerging as the key to the electrification of transportation. Despite this, consumers remain hesitant, facing a dilemma where HFCVs have not yet decisively influenced their purchasing preferences. The complex market landscape and policy nuances also contribute to consumer decision-making uncertainty. Using a Discrete Choice Model combining Multinomial Logit and Latent Class Cluster Analysis, we analyzed 1077 survey responses from Chongqing, China, identifying four consumer groups based on perceived value and risk (“LH,” “HH,” “LL,” “HL”). Key findings show that drivable range, refueling accessibility, and personal carbon credits (PCC) enhance HFCV competitiveness. Hydrogen station accessibility boosts preferences for “LL” group, while PCC mainly influences “HH” consumers. Toll exemptions attract high-risk-perception groups, and mileage subsidies benefit vulnerable groups like “LL.” Targeted policies are essential to address diverse consumer needs and accelerate HFCV adoption.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104892"},"PeriodicalIF":7.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cohered model of adopting electric vehicle fleets in the East of England","authors":"Frank Nyame-Asiamah , Brendon Shaw , Tom Stacey","doi":"10.1016/j.trd.2025.104897","DOIUrl":"10.1016/j.trd.2025.104897","url":null,"abstract":"<div><div>There is limited research on effective electric vehicle (EV) fleet adoption for businesses to achieve decarbonisation of transport. This study applies cohered emergent theory to propose a model for adopting electric commercial vehicles to decarbonise vehicle fleet operations. We employed action research and used a mixed-methods approach to gather data from six different commercial vehicle fleet operating companies in the East of England. We modelled the quantitative data to forecast fuel cost-savings for the companies to switch their internal combustion engine vehicles to EVs. We complemented the quantitative insights with the thematic analysis of in-depth interviews with the fleet operators and feedback from the consuming public actors to explain how fleet operating companies can adopt EVs to generate cost-savings, reduce carbon footprint and potentially improve their operations. The study’s theoretical cohered EV adoption model offers useful insights for researchers and managers to consider decarbonising business transportation. Policy implications are discussed.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104897"},"PeriodicalIF":7.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenyu Duan , Xiao Fu , Daimin Tang , Teng Zhong , Lingshu Zhong
{"title":"Equitable carbon budget allocation: Integrating travel mode preference and multi-activity accessibility","authors":"Zhenyu Duan , Xiao Fu , Daimin Tang , Teng Zhong , Lingshu Zhong","doi":"10.1016/j.trd.2025.104894","DOIUrl":"10.1016/j.trd.2025.104894","url":null,"abstract":"<div><div>Carbon trading policies (e.g., carbon pricing, carbon credit systems) are crucial for reducing travel-related emissions. These policies necessitate an equitable initial allocation of carbon budget. While travel choice behavior and the built environment influence this allocation, few studies integrate both factors. This study addresses this gap by incorporating travel mode preference and multi-activity accessibility (i.e., the ability to access various activity opportunities) into a carbon budget allocation model at group and individual levels, thereby addressing systemic fairness and individual mobility needs. The model integrates travel behavior, multimodal transport networks, and carbon emissions for equitable allocation and personalized emission reduction strategies. To support a just transition towards net-zero emissions in the transportation sector, decent living standards serve as the baseline for allocation. Analysis of mobility data from China Telecom in Suzhou City (October 11–15, 2021) demonstrates this model ensures equitable emission reductions with most residents meeting the baseline, aiding policymakers’ decision-making.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104894"},"PeriodicalIF":7.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiahao Zhao , Ke Liang , Wei Guan , Hailang Sang , Shengkai Zhou , Lei Deng , Mingzhang Pan
{"title":"Energy management strategy for diesel vehicles based on fuel consumption prediction","authors":"Jiahao Zhao , Ke Liang , Wei Guan , Hailang Sang , Shengkai Zhou , Lei Deng , Mingzhang Pan","doi":"10.1016/j.trd.2025.104896","DOIUrl":"10.1016/j.trd.2025.104896","url":null,"abstract":"<div><div>Although hybrid electric and pure electric vehicles are developing rapidly, diesel vehicles also remain their dominant position in some fields such as transportation. For this reason, it is indispensable for diesel vehicle to reduce energy waste by an effective energy management strategy (EMS), with accurate fuel consumption prediction as its foundation. In this study, a hybrid long short-term memory model optimized by grey wolf optimization (GWO-LSTM) and an EMS of diesel vehicles based on model predictive control combined with GWO-LSTM (GL-MPC) are proposed for predicting and controlling fuel consumption in real-world diesel vehicles. The results of contrast experiment indicate mean squared error (MSE) of the proposed GWO-LSTM model can achieve 0.0141. What’s more, the results of tracking effectiveness analysis indicate the proposed GL-MPC model can achieve stable tracking of the reference trajectory after 0.3 s, which prove it can control fuel consumption in a predetermined value.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104896"},"PeriodicalIF":7.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Robust electric bus charging in photovoltaic-energy storage systems with dual uncertainties","authors":"Hong Gao , Kai Liu , Jian Zhao , Liyuan Zhao","doi":"10.1016/j.trd.2025.104888","DOIUrl":"10.1016/j.trd.2025.104888","url":null,"abstract":"<div><div>This study optimizes the charging schedule of electric buses (EBs) within a photovoltaic-energy storage system (PESS) to address dual uncertainties in energy consumption and photovoltaic generation. Both deterministic and robust models are developed to minimize costs related to grid electricity, energy storage, and carbon emissions. Uncertainty sets with budget parameters are employed to control solution conservativeness. Case studies using real data from Zhengzhou, China reveal that the deterministic model with PESS reduces daily total costs by 25.04% and carbon emissions by 94.35% compared to traditional charging systems (TCS). The two uncertainties demonstrate significant cumulative effects, with photovoltaic uncertainty showing greater impact than consumption uncertainty. Sensitivity analyses provide operational insights, including maintaining additional 200 kWh storage capacity during photovoltaic uncertainty periods, setting off-peak electricity prices above storage costs to promote photovoltaic self-consumption, and prioritizing PESS implementation at stations with space-constrained charging facilities where vehicle-to-charging pile ratios exceed 9.5:1.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104888"},"PeriodicalIF":7.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keya Roy, Lok Sang Chan, Xiaocai Zhang, Neema Nassir
{"title":"Multi-task deep learning for joint prediction of traffic emissions and travel delay","authors":"Keya Roy, Lok Sang Chan, Xiaocai Zhang, Neema Nassir","doi":"10.1016/j.trd.2025.104846","DOIUrl":"10.1016/j.trd.2025.104846","url":null,"abstract":"<div><div>Signalised intersections play a crucial role in urban traffic management, ensuring the smooth movement of vehicles across road networks. However, urban intersections are often hotspots for congestion, increasing emissions, extending travel delay, and posing challenges for sustainable operations of traffic. The existing traffic management methods typically focus on either travel delay or emissions in isolation, neglecting their inherent interdependence; congestion simultaneously increases emissions and travel delay. This study introduces a novel deep learning framework termed multi-task temporal convolutional network (MT2CN) that jointly predicts traffic emissions and travel delay at signalised intersections. It is evident from our findings that the proposed MT2CN approach outperforms the conventional single-task models, indicating a significant finding for predictive modelling. By utilising advanced deep learning techniques and explainable artificial intelligence techniques, such as Shapley additive explanations (SHAP), our framework provides more accurate predictions and explainable insights to facilitate sustainable and intelligent traffic management.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104846"},"PeriodicalIF":7.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}