Transportation Research Procedia最新文献

{"title":"Fusing household travel surveys and external data for impact assessment under complex surrounding conditions: the case of introducing fare free public transport during the pandemic","authors":"Niklas Höing,&nbsp;Conny Louen,&nbsp;Tobias Kuhnimhof","doi":"10.1016/j.trpro.2026.04.206","DOIUrl":"10.1016/j.trpro.2026.04.206","url":null,"abstract":"<div><div>In 2020, the German city of Monheim am Rhein implemented fare-free public transport (FFPT) amid the Covid pandemic, necessitating an impact evaluation despite the strong impact of Covid on mobility behaviour. Evaluating such transport policy measures typically relies on panel or cross-sectional surveys conducted before and after implementation. However, an important requirement of this approach is that other conditions remain largely unchanged. Distinguishing treatment from control groups to assess the influence of further factors is often difficult in comprehensive transport policies. To address the challenge of differentiating between the impact of FFPT and the impact of Covid on mobility behaviour, we utilized additional data sources alongside a representative household survey conducted from 2018 to 2023.</div><div>The study faced significant hurdles due to both the pandemic and ongoing construction affecting public transport during the second year of FFPT. We integrated various external data sources to contextualize the household travel survey results: daily Covid incidence rates in North Rhine-Westphalia, mobile phone travel demand changes compared to 2019, nationwide travel surveys (including the German Mobility Panel), local passenger counts, and quarterly national public transport figures. Our multi-method approach aimed to disentangle the effects of external factors on travel behaviour changes —particularly Covid—from those attributable to FFPT. Descriptively, household survey results were contextualized using external data trends. Quantitative evaluations allowed for a comparison between local ridership data in Monheim am Rhein with national averages post-FFPT introduction. Multivariate analyses indicated that day-specific Covid incidences were not effective proxies for pandemic-related travel demand changes; thus, we focused on mobile phone data for more accurate insights, which also had its limitations. Ultimately, our approach allowed for a robust evaluation of FFPT by effectively combining various data sources with household surveys, mitigating uncertainties related to Covid impacts and offering transferable insights for future mobility survey evaluations.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"97 ","pages":"Pages 680-689"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLO-SAT: An Edge-Efficient UAV-Based Smart Mobility Framework for Real-Time Desert Waste Detection with Data-Centric and Model-Centric Optimization","authors":"Abdulmumin Sa’ad ,&nbsp;Sulaimon Oyeniyi Adebayo","doi":"10.1016/j.trpro.2026.04.010","DOIUrl":"10.1016/j.trpro.2026.04.010","url":null,"abstract":"<div><div>The global waste crisis is escalating, with solid waste generation expected to increase tremendously in the coming years. Traditional waste collection methods, particularly in remote or harsh environments like deserts, are labor-intensive, inefficient, and often hazardous. This motivates smart mobility and logistical strategies that use UAV monitoring to expand coverage and enable targeted clean-up. Recent advances in computer vision and deep learning have opened the door to automated waste detection systems, yet most research focuses on urban environments and recyclable materials, overlooking organic and hazardous waste and underex-plored terrains such as deserts. In this work, we propose YOLO-SAT, an enhanced real-time object detection framework based on a pruned, lightweight version of YOLOv12 integrated with Self-Adversarial Training (SAT) and specialized data augmentation strategies. Using the DroneTrashNet dataset, we demonstrate improvements in precision, recall, and mean average precision (mAP), while achieving low latency and compact model size suitable for deployment on resource-constrained aerial drones. Benchmarking YOLO-SAT against state-of-the-art lightweight YOLO variants further highlights its balance of accuracy and efficiency. Our results validate the effectiveness of combining data-centric and model-centric enhancements for robust, real-time waste detection in desert environments, supporting UAV-assisted monitoring for targeted clean-up planning and operational resource allocation.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"96 ","pages":"Pages 412-419"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of First and Last Mile Mode Choice to Transit Stations","authors":"Muhammad Aamir Basheer","doi":"10.1016/j.trpro.2026.04.018","DOIUrl":"10.1016/j.trpro.2026.04.018","url":null,"abstract":"<div><div>The first and last mile (F&amp;LM) are crucial aspects of transit services shaping the experience of transit users. Understanding the factors influencing travellers’ choice of first and last mile is essential for improving multimodal connectivity and promoting sustainable mobility. This study analyzes F&amp;LM mode choice using survey responses on how travellers’ access/egress transit stations. A multinomial logit (MNL) model was estimated in SPSS using a forward stepwise approach. F&amp;LM mode choice was estimated using socio-demographic factors (age, gender, education, and income) alongside trip purpose and standardized travel time and distance. Distance to the transit station emerged as the most influential determinant of non-walking choices, followed by education level and trip purpose. Shopping and social/recreational trips showed the highest likelihood of using Car/Taxi/Rickshaw, while education-related trips were more likely to involve motorcycles. The findings highlight the critical role of spatial accessibility and socio-economic differences in shaping F&amp;LM travel behaviour and suggest that station-area planning should prioritize walkability, safety, and equitable design for diverse trip purposes and traveller groups.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"96 ","pages":"Pages 476-483"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Workshop Synthesis: Data-Driven Approaches for User-Centric Public Transport Planning and Policy","authors":"Xiaoyu Zhang ,&nbsp;Kai Liu","doi":"10.1016/j.trpro.2026.04.205","DOIUrl":"10.1016/j.trpro.2026.04.205","url":null,"abstract":"<div><div>The workshop “Data-Driven Approaches for User-Centric Public Transport Planning and Policy” sought to explore how diverse data sources and analytical methods can be used to design collective and shared transport systems that better meet passenger needs. The presented works covered three thematic areas: transport modes that come to users, transport modes that users go to, and survey- ing challenges, showcasing ten studies that applied approaches such as stated preference surveys, smart card and GPS data analysis, and data integration techniques. Discussions synthesized these contributions into four cross-cutting themes: strengthening collab- oration between researchers and transit operators, moving beyond straightforward surveys through integrated data approaches, encouraging new types of studies including impact assessments and lessons from failures, and promoting standardization to im- prove research quality and comparability.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"97 ","pages":"Pages 676-679"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Industry 4.0 Technologies in Supply Chain Visibility: A Bibliometric Analysis","authors":"Razia Sultana ,&nbsp;Mohammad Abdul Jabber ,&nbsp;Irfanullah Khan ,&nbsp;Tanjil Hossain ,&nbsp;Muhammad Tayyab","doi":"10.1016/j.trpro.2026.04.041","DOIUrl":"10.1016/j.trpro.2026.04.041","url":null,"abstract":"<div><div>In recent years, research on Supply Chain Visibility (SCV) and Industry 4.0 technologies has increased significantly. This study applied bibliometric analysis to discover the connection between SCV and Industry 4.0, highlighting key research movements, significant studies, and emerging themes in the field. The authors analyzed 426 peer-reviewed academic papers published from 2011 to 2025 that were indexed in Scopus. The study employed the bibliometrix R-package, with the results visualized using VOSviewer. The analysis reveals a significant acceleration of publications post-2018, coinciding with a mean citation per year surge, indicating the field’s growing maturity and relevance. Geographically, China, India, and the USA lead scientific production. Thematic analysis confirms that Blockchain is the most dominant technology and conceptual driver, exhibiting strong linkages with Supply Chain Management, Traceability, and Sustainability. The high concentration of articles clustered around single technologies (e.g., Blockchain or IoT in isolation) underscores the major research gap: there is a lack of integrated, holistic models.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"96 ","pages":"Pages 661-668"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harvesting active travel from smartphone app data: opportunities and challenges","authors":"Chinh Q. Ho ,&nbsp;Chia-Jung Yeh ,&nbsp;Stephen Greaves","doi":"10.1016/j.trpro.2026.04.174","DOIUrl":"10.1016/j.trpro.2026.04.174","url":null,"abstract":"<div><div>Collecting high-quality data on active travelers - pedestrians, cyclists and users of personal mobility devices (PMD) - continues to present challenges for transport analysts. The increasing penetration of smartphones, travel apps, and improved algorithms to infer modal trips, provides potential to collect this information with minimal participant involvement. However, challenges remain around how to expand this app-based data, which is typically limited to a sample of users, to the population at large. The current paper details the design, testing and validation of a new approach for extracting active trips from users of smartphone devices across Australia. An algorithm is developed to extract and route active trips from a digital multimodal journey planner dataset, called TripGo®, which is validated against secondary sources of pedestrian and bicycle counts in an iterative process. Results are encouraging, primarily around the ability to quickly generate reliable estimates of pedestrian activities and hotspots in urban centres from travel app data. Estimates of cycling, PMD users and regional/rural trips are less reliable due to limited coverage in the app data, pointing to the need to incorporate multiple secondary datasets to refine the approach and improve the accuracy and scalability of estimates. By enabling the integration of diverse datasets, the methodology provides a practical framework for generating population-level estimates and actionable insights.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"97 ","pages":"Pages 366-375"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Sociodemographic Groups Based on the Incidence of Long-Distance Travel: Findings from the 2022 US National Household Travel Survey","authors":"Mitchell Fisher (PhD) ,&nbsp;Stacey G. Bricka (PhD, PMP) ,&nbsp;Timothy Reuscher","doi":"10.1016/j.trpro.2026.04.178","DOIUrl":"10.1016/j.trpro.2026.04.178","url":null,"abstract":"<div><div>This paper presents the results of an evaluation of the 2022 United States (US) National Household Travel Survey (NHTS), which included a series of questions related to the respondent’s most recent long-distance trip. Specifically, the US Department of Transportation Federal Highway Administration (FHWA) fielded the 2022 NHTS from January 2022 to January 2023 using an address-based sample (ABS), which is state-of-the-practice in the US. The survey included typical household travel survey questions about household composition, vehicle ownership, demographic and attitudinal details, and daily travel for an assigned 24-hour period. Then, for the first time since 2001, the 2022 NHTS asked each respondent to report details about the most recent long-distance trip, which was defined as one where the farthest destination was at least 50 miles (one-way) from home and not a trip made as a daily commute. For this paper, the results were analyzed based on how long ago the most recent trip was made, recent long-distance frequency, and sociodemographic status. Key findings include: nearly 41 percent of respondents indicated they had completed at least one long-distance trip in the past 30 days; higher income individuals were more likely to have completed a recent long-distance trip, but lower income individuals had a higher average frequency of long-distance trips completed; and workers who worked from home were more likely to have completed a recent long-distance trip, but workers who did not work from home had a higher average frequency of long-distance trips completed.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"97 ","pages":"Pages 404-414"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Feasibility of Adopting the Chinese Electric Mobility Framework in Saudi Arabia","authors":"Muhammad Ahmad ,&nbsp;Sheeraz Iqbal ,&nbsp;Md Shafiullah ,&nbsp;Atif Saeed Alzahrani ,&nbsp;Salman Habib","doi":"10.1016/j.trpro.2026.04.047","DOIUrl":"10.1016/j.trpro.2026.04.047","url":null,"abstract":"<div><div>One of the critical milestones under Saudi Arabia’s Vision 2030 is to achieve net-zero emissions by the end of 2060. Under the prevailing situation, CO2 emissions from combustible fuels totaled 534.68 MtCO2 in 2023, accounting for a 1.5% share in global emissions, from the Kingdom. Additionally, it was noted that 27% of total emissions are contributed by the transportation sector only. To mitigate this situation, from policy to practice, several measures are essential to achieve the ambitious targets of net-zero emissions. This paper takes a significant market player in the e-mobility sector, i.e, China, as a benchmark to evaluate the impact of adopting the e-mobility framework in the Saudi Arabian context. The potential of replicating the Chinese e-mobility model is analyzed using qualitative and quantitative data within the scope of multi-criteria decision analysis (MCDA). Key factors such as policy, governmental support, technology, enforcement, and impact analysis under the Chinese e-mobility model are explored. Subsequently, based on the benchmark analysis under the MCDA approach, the feasibility of replicating the model in the Saudi Arabian context is investigated. The results of the MCDA, based on qualitative and quantitative analyses, indicate that replicating the Chinese e-mobility model in Saudi Arabia will remain a notable challenge, given domestic constraints across technical and non-technical areas.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"96 ","pages":"Pages 708-715"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of transportation on granular level using survey and big data","authors":"Christopher Caratiola ,&nbsp;Florian Ertz ,&nbsp;Florian Groß ,&nbsp;Tim Holthaus ,&nbsp;Bert Leerkamp ,&nbsp;Ralf Münnich","doi":"10.1016/j.trpro.2026.04.191","DOIUrl":"10.1016/j.trpro.2026.04.191","url":null,"abstract":"<div><div>The estimation of transportation demand and road use via survey data often lacks in timeliness and flexibility, in addition to high survey costs. Mobile Network Operator (MNO) data can provide timely mobility pattern estimates with a high spatial resolution. However, due to the different customer bases of the providers, estimates that are only extrapolated from MNO data suffer from different biases.</div><div>The paper provides methods of jointly using survey and MNO data to deliver improved estimates for transportation demand and road use while utilizing the advantages of both data sources. The methods are demonstrated in a microsimulation, as a feasibility study with German data.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"97 ","pages":"Pages 532-541"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148153963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Survey Methodology and Analysis of Large-Scale German Household Travel Surveys: The Impact of Data Harmonization, Fusion, Hierarchical Structures, and Weighting","authors":"Rico Wittwer ,&nbsp;Stefan Hubrich ,&nbsp;Regine Gerike","doi":"10.1016/j.trpro.2026.04.145","DOIUrl":"10.1016/j.trpro.2026.04.145","url":null,"abstract":"<div><div>In recent years, awareness of the methodological implications of hierarchical data structures on statistics has noticeably increased in the field of travel behavior research based on Household Travel Surveys (HTSs). Addressing methodological challenges such as data harmonization, the influence of hierarchically structured data on the analysis, and the effects of survey-specific elements like clustering on accuracy is essential from both methodological and analytical perspectives. This study deals with both aspects using two large-scale repeated cross-sectional HTSs from Germany. The results strongly advocate undertaking the effort of data harmonization and fusion as a highly promising approach to increase the utility of these surveys. Furthermore, it is highly recommended to account for the hierarchical data structure of HTSs using multi-level modeling techniques. We find no evidence that the application of weights in statistical explanatory models for car use significantly affects the model results. In terms of travel behavior, our results largely confirm previous studies: car availability on the reporting day is the strongest factor in increasing car use, while the availability of a public transport season ticket has the greatest effect on reducing it.</div></div>","PeriodicalId":101261,"journal":{"name":"Transportation Research Procedia","volume":"97 ","pages":"Pages 78-88"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148154002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}