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[1]
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A simple agent-based model for planning for bicycling: Simulation of bicyclists' movements in urban environments
Computers, Environment and Urban Systems,
2024
DOI:10.1016/j.compenvurbsys.2023.102059
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[2]
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Twelve-Year Analysis of Transportation Research Board Annual Meeting’s Official Hashtag
Transportation Research Record: Journal of the Transportation Research Board,
2022
DOI:10.1177/03611981211037232
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Sentiment analysis of popular-music references to automobiles, 1950s to 2010s
Transportation,
2022
DOI:10.1007/s11116-021-10189-1
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[4]
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LDA-BASED COSMETIC SATISFACTION FACTORS MINING
2022 7th International Conference on Information Systems Engineering,
2022
DOI:10.1145/3573926.3573935
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[5]
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Listen to E-scooter riders: Mining rider satisfaction factors from app store reviews
Transportation Research Part D: Transport and Environment,
2021
DOI:10.1016/j.trd.2021.102856
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[6]
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Topic Models from Crash Narrative Reports of Motorcycle Crash Causation Study
Transportation Research Record: Journal of the Transportation Research Board,
2021
DOI:10.1177/03611981211002523
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[7]
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Listen to E-scooter riders: Mining rider satisfaction factors from app store reviews
Transportation Research Part D: Transport and Environment,
2021
DOI:10.1016/j.trd.2021.102856
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[8]
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A comparative study of bike-sharing systems from a user’s perspective: An analysis of online reviews in three U.S. regions between 2010 and 2018
International Journal of Sustainable Transportation,
2021
DOI:10.1080/15568318.2020.1830320
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[9]
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A text mining approach to elicit public perception of bike-sharing systems
Travel Behaviour and Society,
2021
DOI:10.1016/j.tbs.2021.03.002
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[10]
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Crowdsourced data for bicycling research and practice
Transport Reviews,
2020
DOI:10.1080/01441647.2020.1806943
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[11]
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Measuring the Effectiveness of Vehicle Inspection Regulations in Different States of the U.S.
Transportation Research Record: Journal of the Transportation Research Board,
2019
DOI:10.1177/0361198119841563
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[12]
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YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study
Transportation Research Record: Journal of the Transportation Research Board,
2019
DOI:10.1177/0361198119842110
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[13]
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Vehicle Consumer Complaint Reports Involving Severe Incidents: Mining Large Contingency Tables
Transportation Research Record: Journal of the Transportation Research Board,
2018
DOI:10.1177/0361198118788464
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[14]
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Extracting patterns from Twitter to promote biking
IATSS Research,
2018
DOI:10.1016/j.iatssr.2018.09.002
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[15]
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Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling
Transportation Research Part C: Emerging Technologies,
2017
DOI:10.1016/j.trc.2017.01.014
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[16]
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Trying Out Mobility as a Service: Experiences from a Field Trial and Implications for Understanding Demand
Transportation Research Record: Journal of the Transportation Research Board,
2016
DOI:10.3141/2542-07
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