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Utilization of Smartphone Data for Driving Cycle Synthesis Based on Electric Two-Wheelers in Shanghai
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Multirate Signal Processing for Software Radio Architectures
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Electrical consumption of two-, three- and four-wheel light-duty electric vehicles in India
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Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing
inferredcites →https://doi.org/10.1016/j.apenergy.2016.05.094openalex-snapshot
Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas
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Lifetime optimized charging strategy of Li-ion cells based on daily driving cycle of electric two-wheelers
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Driving cycles construction for electric vehicles considering road environment: A case study in Beijing
inferredcites →https://doi.org/10.1016/j.atmosenv.2005.07.040openalex-snapshot
Development of a local real world driving cycle for motorcycles for emission factor measurements
inferredcites →https://doi.org/10.1016/j.atmosenv.2011.06.023openalex-snapshot
Development of driving cycles for motorcycles and light-duty vehicles in Vietnam
inferredcites →https://doi.org/10.1016/j.egypro.2017.03.967openalex-snapshot
A Study of Driving Cycle for Electric Special-purpose Vehicle in Beijing
inferredcites →https://doi.org/10.1016/j.energy.2017.11.134openalex-snapshot
Method for evaluating the real-world driving energy consumptions of electric vehicles
inferredcites →https://doi.org/10.1016/j.enpol.2008.03.008openalex-snapshot
The future of electric two-wheelers and electric vehicles in China
inferredcites →https://doi.org/10.1016/j.jpowsour.2007.06.010openalex-snapshot
From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation
inferredcites →https://doi.org/10.1016/j.procs.2018.04.176openalex-snapshot
Investigating Real-World Energy Consumption of Electric Vehicles: A Case Study of Shanghai
inferredcites →https://doi.org/10.1016/j.sbspro.2013.08.182openalex-snapshot
Comparison Study on Travel Characteristics between Two Kinds of Electric Bike
inferredcites →https://doi.org/10.1016/j.sbspro.2013.08.208openalex-snapshot
Travel Characteristics of E-bike Users: Survey and Analysis in Shanghai
inferredcites →https://doi.org/10.1016/j.scs.2019.101594openalex-snapshot
Where we ride faster? Examining cycling speed using smartphone GPS data
inferredcites →https://doi.org/10.1016/j.trc.2004.07.010openalex-snapshot
Tracking survey for individual travel behaviour using mobile communication instruments
inferredcites →https://doi.org/10.1016/j.trc.2016.02.016openalex-snapshot
Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users
inferredcites →https://doi.org/10.1016/j.trd.2008.03.003openalex-snapshot
Characterization of vehicle driving patterns and development of driving cycles in Chinese cities
inferredcites →https://doi.org/10.1016/j.trd.2014.10.007openalex-snapshot
Electric vehicles’ energy consumption measurement and estimation
inferredcites →https://doi.org/10.1016/j.trd.2015.10.010openalex-snapshot
Electric vehicles’ energy consumption estimation with real driving condition data
inferredcites →https://doi.org/10.1016/j.trd.2017.09.001openalex-snapshot
Speed, travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data
inferredcites →https://doi.org/10.1016/s0165-1684(00)00104-3openalex-snapshot
Multirate, multiresolution, recursive Kalman filter
inferredcites →https://doi.org/10.1061/(asce)te.1943-5436.0000547openalex-snapshot
Development of City-Specific Driving Cycles for Transit Buses Based on VSP Distributions: Case of Beijing
inferredcites →https://doi.org/10.1109/5.554205openalex-snapshot
An introduction to multisensor data fusion
inferredcites →https://doi.org/10.1109/itsc.2012.6338758openalex-snapshot
Vehicle's energy estimation using low frequency speed signal
inferredcites →https://doi.org/10.1109/itsc.2018.8569547openalex-snapshot
Derivation of a real-life driving cycle from fleet testing data with the Markov-Chain-Monte-Carlo Method
inferredcites →https://doi.org/10.1109/maes.2010.5546308openalex-snapshot
Particle filter theory and practice with positioning applications
inferredcites →https://doi.org/10.1109/mcs.2017.2674458openalex-snapshot
Nonlinear Observers for Integrated INS\/GNSS Navigation: Implementation Aspects
inferredcites →https://doi.org/10.1109/tits.2016.2530999openalex-snapshot
Online Travel Mode Identification Using Smartphones With Battery Saving Considerations
inferredcites →https://doi.org/10.1109/tits.2016.2630733openalex-snapshot
A Data-Driven Method for Trip Ends Identification Using Large-Scale Smartphone-Based GPS Tracking Data
inferredcites →https://doi.org/10.1109/tits.2017.2723523openalex-snapshot
Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier
inferredcites →https://doi.org/10.1109/tvt.2012.2219889openalex-snapshot
Drive Cycle Generation for Design Optimization of Electric Vehicles
inferredcites →https://doi.org/10.1109/tvt.2013.2259645openalex-snapshot
Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles
inferredcites →https://doi.org/10.1109/tvt.2016.2546338openalex-snapshot
Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information
inferredcites →https://doi.org/10.1109/vppc.2015.7352876openalex-snapshot
A Methodology to Obtain a Synthetic Driving Cycle through GPS Data for Energy Analysis
inferredcites →https://doi.org/10.4271/2012-01-0743openalex-snapshot
GPS Data Filtration Method for Drive Cycle Analysis Applications
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Methodology for Determining Motorcycle Driving Cycles Applying Computational Intelligence Tools: Case Study of Medellín, Colombia
inferred← citeshttps://doi.org/10.1007/s11116-025-10631-8openalex-snapshot
A context-aware approach to construct driving cycles in a volatile urban environment
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Energy consumption characteristics based driving conditions construction and prediction for hybrid electric buses energy management
inferred← citeshttps://doi.org/10.1016/j.latran.2026.100051openalex-snapshot
Developing of motorcycle driving cycles with road gradient using machine learning techniques: A case study in Medellín, Colombia
inferred← citeshttps://doi.org/10.1016/j.seta.2024.103826openalex-snapshot
Establishment of electric two-wheeler driving cycle for energy economy and life cycle assessment under Indian Tier-2 city driving environments
inferred← citeshttps://doi.org/10.1016/j.trip.2025.101625openalex-snapshot
Development of a realistic Euro 7 Real Driving Cycle for chassis dyno testing
inferred← citeshttps://doi.org/10.1057/s41599-025-05220-0openalex-snapshot
Enhanced socially oriented mission-based driving cycles generation and simulation framework for light electric vehicles
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Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques
inferred← citeshttps://doi.org/10.1109/access.2024.3508754openalex-snapshot
Expediency Analysis of Clustering Algorithms for Electric Two-Wheeler Driving Cycle Development Under Indian Smart City Driving Conditions
inferred← citeshttps://doi.org/10.1109/agec57922.2023.00037openalex-snapshot
Analysis of Critical Issues in Retrofitting of IC Vehicles to Electric Vehicle: A Technical Review
inferred← citeshttps://doi.org/10.1109/appeec50844.2021.9687799openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icc45041.2023.10279482openalex-snapshot
Collaborative Navigation in Urban Environments via GNSS and 5G Signals
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A Robust Evolutionary Particle Filter Technique for Integrated Navigation in Urban Environments via GNSS and 5G Signals
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inferred← citeshttps://doi.org/10.1109/tim.2023.3301891openalex-snapshot
An Efficient Implementation Approach to FFT Processor for Spectral Analysis
inferred← citeshttps://doi.org/10.1109/tits.2021.3139738openalex-snapshot
2021 Index IEEE Transactions on Intelligent Transportation Systems Vol. 22
inferred← citeshttps://doi.org/10.1109/tits.2022.3160275openalex-snapshot
A Clustering-Based Optimization Method for the Driving Cycle Construction: A Case Study in Fuzhou and Putian, China
inferred← citeshttps://doi.org/10.1109/tits.2022.3166864openalex-snapshot
Deep Active Learning Intrusion Detection and Load Balancing in Software-Defined Vehicular Networks
inferred← citeshttps://doi.org/10.1177/0361198121991850openalex-snapshot
Simple Methodology for the Development and Analysis of Local Driving Cycles Applied in the Study of Cars and Motorcycles in Recife, Brazil
inferred← citeshttps://doi.org/10.1177/03611981231186977openalex-snapshot
Evaluation of the Minimum Number of Local Driving Cycles Required to Represent the Traffic of Distinct Cities: A Case Study of Two Brazilian Metropolises
inferred← citeshttps://doi.org/10.3390/en13205434openalex-snapshot
Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics
inferred← citeshttps://doi.org/10.3390/s23208571openalex-snapshot
Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information
inferred← citeshttps://doi.org/10.3390/vehicles4010018openalex-snapshot
Estimation of Parallel Hybrid Scooter’s Energy Consumption through Real Urban Drive Cycle Using IMU
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