Pub Date : 2024-10-01DOI: 10.1038/s44333-024-00009-1
Meead Saberi, Tanapon Lilasathapornkit
This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.
{"title":"Scalability challenges of machine learning models for estimating walking and cycling volumes in large networks","authors":"Meead Saberi, Tanapon Lilasathapornkit","doi":"10.1038/s44333-024-00009-1","DOIUrl":"10.1038/s44333-024-00009-1","url":null,"abstract":"This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00009-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360077","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}
Pub Date : 2024-09-30DOI: 10.1038/s44333-024-00010-8
Bjorn C. P. Sturmberg, Lahiru Hapuarachchi, Laura Jones, Kathryn Lucas-Healey, Justus van Biljon
Vehicle-to-grid technology enables electric vehicles to contribute their large, high-power batteries to power systems reserves. Here we report the first demonstration of a fleet of vehicles discharging to support system security after a frequency contingency in a national grid. Our results highlight the potential of vehicle-to-grid, with vehicles discharging within 6 s of the contingency event, and shortcomings, with vehicles recommencing charging before the power system had fully recovered.
{"title":"Vehicle-to-grid response to a frequency contingency in a national grid","authors":"Bjorn C. P. Sturmberg, Lahiru Hapuarachchi, Laura Jones, Kathryn Lucas-Healey, Justus van Biljon","doi":"10.1038/s44333-024-00010-8","DOIUrl":"10.1038/s44333-024-00010-8","url":null,"abstract":"Vehicle-to-grid technology enables electric vehicles to contribute their large, high-power batteries to power systems reserves. Here we report the first demonstration of a fleet of vehicles discharging to support system security after a frequency contingency in a national grid. Our results highlight the potential of vehicle-to-grid, with vehicles discharging within 6 s of the contingency event, and shortcomings, with vehicles recommencing charging before the power system had fully recovered.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00010-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329431","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}
Pub Date : 2024-09-13DOI: 10.1038/s44333-024-00006-4
Rafał Kucharski, Oded Cats
The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.
{"title":"Hyper pooling private trips into high occupancy transit like attractive shared rides","authors":"Rafał Kucharski, Oded Cats","doi":"10.1038/s44333-024-00006-4","DOIUrl":"10.1038/s44333-024-00006-4","url":null,"abstract":"The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00006-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174391","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}
Pub Date : 2024-07-10DOI: 10.1038/s44333-024-00005-5
Joyeeta Gupta, Yang Chen, Crelis Rammelt
Achieving a socially and environmentally sustainable mobility and transport system necessitates a multifaceted approach that considers just Earth System Boundaries. Just Earth System Boundaries are domain-specific (e.g. climate change, water) thresholds beyond which significant harm is done to people and other species. We have crossed these thresholds in 7/8 domains and not yet met the minimum needs of people worldwide. The challenge is to return to the safe and just corridor while prioritising the access of the poorest people to minimum resources as called for by the principle of leaving no one behind. Within this context, the transport sector, a major contributor to climate change and environmental pollution, requires significant and swift transformations. This comment proposes six key principles for building a sustainable transport system: prioritising equitable access, enhancing public transport and limiting private transport, decarbonising fuel and fleets, decoupling freight transport from fossil fuel trade, repurposing infrastructure, and ensuring just financing. These principles may enable just living within just Earth System Boundaries.
{"title":"Transport within earth system boundaries","authors":"Joyeeta Gupta, Yang Chen, Crelis Rammelt","doi":"10.1038/s44333-024-00005-5","DOIUrl":"10.1038/s44333-024-00005-5","url":null,"abstract":"Achieving a socially and environmentally sustainable mobility and transport system necessitates a multifaceted approach that considers just Earth System Boundaries. Just Earth System Boundaries are domain-specific (e.g. climate change, water) thresholds beyond which significant harm is done to people and other species. We have crossed these thresholds in 7/8 domains and not yet met the minimum needs of people worldwide. The challenge is to return to the safe and just corridor while prioritising the access of the poorest people to minimum resources as called for by the principle of leaving no one behind. Within this context, the transport sector, a major contributor to climate change and environmental pollution, requires significant and swift transformations. This comment proposes six key principles for building a sustainable transport system: prioritising equitable access, enhancing public transport and limiting private transport, decarbonising fuel and fleets, decoupling freight transport from fossil fuel trade, repurposing infrastructure, and ensuring just financing. These principles may enable just living within just Earth System Boundaries.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00005-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597205","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}
Pub Date : 2024-06-27DOI: 10.1038/s44333-024-00004-6
Hamid R. Sayarshad
With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between Origin-Destination (OD) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intra-day electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential.This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.
{"title":"Optimization of electric charging infrastructure: integrated model for routing and charging coordination with power-aware operations","authors":"Hamid R. Sayarshad","doi":"10.1038/s44333-024-00004-6","DOIUrl":"10.1038/s44333-024-00004-6","url":null,"abstract":"With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between Origin-Destination (OD) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intra-day electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential.This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-24"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00004-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489071","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}
Pub Date : 2024-05-22DOI: 10.1038/s44333-024-00002-8
Greg Marsden, Tim Schwanen
The prospect of remaining within 1.5C of planetary warming relies on developed economies tracking increasingly steep and challenging emission reduction pathways. This paper explores how the UK is now proactively planning to miss its targets, using the surface transport sector as a critical case. It discusses how the research–policy interface might both challenge downgraded ambition and provide more actionable routes forward.
{"title":"Planning to fail? How science can respond to reduced climate mitigation ambition","authors":"Greg Marsden, Tim Schwanen","doi":"10.1038/s44333-024-00002-8","DOIUrl":"10.1038/s44333-024-00002-8","url":null,"abstract":"The prospect of remaining within 1.5C of planetary warming relies on developed economies tracking increasingly steep and challenging emission reduction pathways. This paper explores how the UK is now proactively planning to miss its targets, using the surface transport sector as a critical case. It discusses how the research–policy interface might both challenge downgraded ambition and provide more actionable routes forward.","PeriodicalId":501714,"journal":{"name":"npj Sustainable Mobility and Transport","volume":" ","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44333-024-00002-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079012","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}
Pub Date : 2024-05-22DOI: 10.1038/s44333-024-00003-7
Erin E. Bulson, Wissam Kontar, Soyoung Ahn, Andrea Hicks
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.
{"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}
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.
实时碰撞和严重性预测是一项复杂的任务，目前还没有一个框架可以同时预测碰撞可能性和严重性。创建这样一个框架面临着诸多挑战，尤其是非独立且同分布（non-IID）数据、计算成本高的大型模型、缺失数据、灵敏度与误报率（FAR）的权衡以及现实世界的部署策略。本研究引入了一种新型建模技术来应对这些挑战，并开发了一个可部署的真实世界框架。我们利用大量实时交通和天气数据开发了碰撞可能性预测建模原型，充分利用了我们在空间集合建模方面的初步成果。接下来，我们为该空间集合模型配备了局部模型正则化，以校准模型置信度训练。所研究的正则化方法包括权重衰减、标签平滑和知识提炼。此外，我们还对模型输出进行了后校准，以改进严重性评级识别。我们测试了实时预测碰撞和严重程度的框架，将碰撞分为四个等级。我们将结果与基准模型进行了比较，解释了真实世界的部署机制，讨论了交通安全改善潜力和研究的可持续性问题。建模结果显示了卓越的性能，对致命、严重、轻微和 PDO 碰撞严重程度的预测灵敏度分别为 91.7%、83.3%、85.6% 和 87.7%，且 FAR 非常低。同样，我们还对模型预测特定碰撞类型（即所有碰撞类型、追尾碰撞和侧擦/角度碰撞）的不同严重程度的可行性进行了检验，结果表明该模型表现出色。我们的建模技术在缩小模型规模、降低计算成本、提高灵敏度以及最重要的降低故障率方面都表现出了巨大的潜力。最后，讨论了所提出的碰撞和严重程度预测技术的部署策略，该技术在实时预测碰撞和严重程度方面的潜力将为量身定制预防碰撞的具体策略做出重大贡献。
{"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}
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