Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management

[1]  S. Rasouli,et al.  The implications of Mobility as a Service for urban emissions , 2022, Transportation Research Part D: Transport and Environment.

[2]  T. Wallington,et al.  A data-driven method of traffic emissions mapping with land use random forest models , 2022, Applied Energy.

[3]  Ziyou Gao,et al.  Identifying intercity freight trip ends of heavy trucks from GPS data , 2021, Transportation Research Part E: Logistics and Transportation Review.

[4]  Christopher G. Hoehne,et al.  Exploring the future energy-mobility nexus: The transportation energy & mobility pathway options (TEMPO) model , 2021 .

[5]  C. Ratti,et al.  The universal visitation law of human mobility , 2021, Nature.

[6]  N. Zhang,et al.  A Novel Development Scheme of Mobility as a Service: Can It Provide a Sustainable Environment for China? , 2021, Sustainability.

[7]  Dongchu Sun,et al.  The improved AdaBoost algorithms for imbalanced data classification , 2021, Inf. Sci..

[8]  James J. Q. Yu,et al.  Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning , 2021, IEEE Transactions on Intelligent Transportation Systems.

[9]  Lixia Lou,et al.  Socio-economic disparity in visual impairment from cataract. , 2021, International journal of ophthalmology.

[10]  Can Wang,et al.  Achieving net-zero emissions in China’s passenger transport sector through regionally tailored mitigation strategies , 2020 .

[11]  Wenjing Yi,et al.  Energy consumption and emission influences from shared mobility in China: A national level annual data analysis , 2020 .

[12]  Zhenbo Lu,et al.  Investigating impact of the heterogeneity of trajectory data distribution on origin‐destination estimation: a spatial statistics approach , 2020, IET Intelligent Transport Systems.

[13]  Ryosuke Shibasaki,et al.  Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential , 2020 .

[14]  Chandan K. Reddy,et al.  Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data , 2020, IEEE Transactions on Knowledge and Data Engineering.

[15]  JunSeong Kim,et al.  Feature-First Add-On for Trajectory Simplification in Lifelog Applications , 2020, Sensors.

[16]  Maozu Guo,et al.  Transportation Mode Recognition With Deep Forest Based on GPS Data , 2020, IEEE Access.

[17]  Helena Beatriz Bettella Cybis,et al.  The evolution of city-scale GHG emissions inventory methods: A systematic review , 2020 .

[18]  Yang Zhou,et al.  Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai , 2019, Transportation Planning and Technology.

[19]  E. Heinen,et al.  Multimodality and CO2 emissions: A relationship moderated by distance , 2019, Transportation Research Part D: Transport and Environment.

[20]  Jinzhou Cao,et al.  Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example. , 2019, Transportation research. Part C, Emerging technologies.

[21]  Hongjun Mao,et al.  Past and future trends of vehicle emissions in Tianjin, China, from 2000 to 2030 , 2019, Atmospheric Environment.

[22]  A. Bigazzi Comparison of marginal and average emission factors for passenger transportation modes , 2019, Applied Energy.

[23]  Ghim Ping Ong,et al.  Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data , 2018, Transportation Research Part C: Emerging Technologies.

[24]  M. Kamargianni,et al.  The potential of mobility as a service bundles as a mobility management tool , 2019 .

[25]  Ian D. Williams,et al.  A practical model for predicting road traffic carbon dioxide emissions using Inductive Loop Detector data , 2018, Transportation Research Part D: Transport and Environment.

[26]  Ning Jia,et al.  Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice – A cross-city study in China , 2018 .

[27]  Yonghong Liu,et al.  A high temporal-spatial vehicle emission inventory based on detailed hourly traffic data in a medium-sized city of China. , 2018, Environmental pollution.

[28]  Hai Jin,et al.  Predicting Transportation Carbon Emission with Urban Big Data , 2017, IEEE Transactions on Sustainable Computing.

[29]  Andrei Lobov,et al.  Travel mode estimation for multi-modal journey planner , 2017 .

[30]  Weining Jiang,et al.  Vehicle emission trends and spatial distribution in Shandong province, China, from 2000 to 2014 , 2016 .

[31]  Daisuke Fukuda,et al.  Updating origin–destination matrices with aggregated data of GPS traces , 2016 .

[32]  Weibo Li,et al.  A Critical Review of New Mobility Services for Urban Transport , 2016 .

[33]  Zijia Wang,et al.  Carbon emission from urban passenger transportation in Beijing , 2015 .

[34]  Zhaohua Wang,et al.  Determinants of CO2 emissions from household daily travel in Beijing, China: Individual travel characteristic perspectives , 2015 .

[35]  Satish V. Ukkusuri,et al.  Understanding short-term travel behavior under personal mobility credit allowance scheme using experimental economics , 2015 .

[36]  A. Bertello,et al.  Compilation of a road transport emission inventory for the Province of Turin: Advantages and key factors of a bottom-up approach , 2014 .

[37]  Peter R. Stopher,et al.  Review of GPS Travel Survey and GPS Data-Processing Methods , 2014 .

[38]  Thomas Adler,et al.  Generating Route-Specific Origin–Destination Tables Using Bluetooth Technology , 2012 .

[39]  Philip S. Yu,et al.  Transportation mode detection using mobile phones and GIS information , 2011, GIS.

[40]  L. Ntziachristos,et al.  Validation of road vehicle and traffic emission models – A review and meta-analysis , 2010 .

[41]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[42]  Johan Wideberg,et al.  Deriving origin destination data from a mobile phone network , 2007 .

[43]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[44]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.