npj Sustainable Mobility and Transport最新文献

{"title":"Reduced travel emissions through a carbon calculator with accessible environmental data: a case study in Madison, Wisconsin","authors":"Erin E. Bulson, Wissam Kontar, Soyoung Ahn, Andrea Hicks","doi":"10.1038/s44333-024-00003-7","DOIUrl":"10.1038/s44333-024-00003-7","url":null,"abstract":"The echoing environmental toll of the transportation system calls for a drastic need to move beyond carbon-intensive modes of transportation into more sustainable ones. With the rise of emerging modes of transportation, this transition is more promising than ever. In this work, we take a travel-centric approach to promoting and accelerating the transition away from carbon-intensive modes of transportation by informing travelers about their emissions. A carbon calculator—as a function of trip distance and Well-to-Wheel (WTW) Life Cycle Assessment (LCA)—was developed and embedded on a website platform. Users would input their trip distance, and the calculator outputs the carbon footprint (CO2e) of the trip if it was to be done through seven different modes: car (gasoline), car (hybrid), car (electric), bus, electric bike, bike, and walking. In addition, the calculator outputs the equivalent of CO2e as cheeseburgers for a more intuitive display. The overall goal of this work is to understand how travelers respond to being exposed to carbon footprint information. This serves as a step forward in realizing a sustainable transportation system. We make available the calculator online through this link . Study results indicated that trip distance, environmental awareness, age, income, and mode of transportation used were the most influential features in predicting modal shifts. Importantly, the majority of modal shifts resulted in reduced CO2e emissions.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00003-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibrated confidence learning for large-scale real-time crash and severity prediction","authors":"Md Rakibul Islam, Dongdong Wang, Mohamed Abdel-Aty","doi":"10.1038/s44333-024-00001-9","DOIUrl":"10.1038/s44333-024-00001-9","url":null,"abstract":"Real-time crash and severity prediction is a complex task, and there is no existing framework to predict crash likelihood and severity together. Creating such a framework poses numerous challenges, particularly not independent and identically distributed (non-IID) data, large model sizes with high computational costs, missing data, sensitivity vs. false alarm rate (FAR) trade-offs, and real-world deployment strategies. This study introduces a novel modeling technique to address these challenges and develops a deployable real-world framework. We used extensive real-time traffic and weather data to develop a crash likelihood prediction modeling prototype, leveraging our preliminary work of spatial ensemble modeling. Next, we equipped this spatial ensemble model with local model regularization to calibrate model confidence training. The investigated regularizations include weight decay, label smoothing and knowledge distillation. Furthermore, post-calibration on model outputs was conducted to improve severity rating identification. We tested the framework to predict crashes and severity in real-time, categorizing crashes into four levels. Results were compared with benchmark models, real-world deployment mechanisms were explained, traffic safety improvement potential and sustainability aspects of the study were discussed. Modeling results demonstrated excellent performance, and fatal, severe, minor and PDO crash severities were predicted with 91.7%, 83.3%, 85.6%, and 87.7% sensitivity, respectively, and with very low FAR. Similarly, the viability of our model to predict different severity levels for specific crash types, i.e., all-crash types, rear-end crashes, and sideswipe/angle crashes, were examined, and it showed excellent performance. Our modeling technique showed great potential for reducing model size, lowering computational costs, improving sensitivity, and, most importantly, reducing FAR. Finally, the deployment strategy for the proposed crash and severity prediction technique is discussed, and its potential to predict crashes with severity levels in real-time will make a substantial contribution to tailoring specific strategies to prevent crashes.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00001-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}