Making sense of electrical vehicle discussions using sentiment analysis on closely related news and user comments

Everts, Josh; Xuan, Jianhua

doi:10.48550/arxiv.2112.12327

Cited by 2 publications

(13 citation statements)

References 2 publications

(6 reference statements)

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“…Then the percentage of full and empty stations at a given instant is included in their simulation to get the peak value and low value of vehicle using. Balać M, et al [3] use a figure to show the location of all the pickup and dropoff stations. Lu M, et al [5] include cost of connect mode to get a more accurate travel cost.…”

Section: B) Variables Related To Vehiclesmentioning

confidence: 99%

“…e) Utility: Utility functions are used to evaluate the performance of operation, while parameters and factors concerned in current studies are different. Standard MATSim utility function [3](also named MATSIM scoring function) can be used to determine whether the equilibrium is reached. Agents will gain utility when they are performing activities and disutility when they are traveling.…”

Section: B) Variables Related To Vehiclesmentioning

confidence: 99%

“…Alberto Fernández, et al establish a microscopic agent-based model, where different types of users can be defined and adjust according to private objectives, to test different station capacities, station distributions, and balancing strategies. [3] After the wide spread of micromobility in San Francisco Bay area, the focus sfhift to mesoscopic simulation to find out passengers' choice on a larger scale. Focusing on the impacts of different policies of incentives, Alberto L opez Santiago, et al build and validate the model.…”

Section: Introductionmentioning

confidence: 99%

“…(2) We provide a comprehensive review on micromobility simulation, how it is defined, and how it is related to or different from other similar simulation paradigms, such as transit simulation, ride and hail simulation, car simulation and pedestrian simulation. (3) We have compiled a list of challenges and future directions for research in micromobility simulation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

All one needs to know about shared micromobility simulation: a complete survey

Liu¹,

Tang²,

Liu³

2022

Preprint

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As the shared micromobility becomes a part of our daily life and environment, we expect the number of lowspeed modes for first-and-last mile trips to grow rapidly. The shared micomobility is expected to serve billions of humans, bringing us considerable advantages. With this growth, shared micromobility simulation such as docked stations based shared bikes, dockless shared bikes and e-scooters, are regarded as promising solutions to deal with a large number of first-andlast mile trips. In this paper, we first provide a comprehensive overview of shared micromobility simulation and its related validation metrics. Next, we classify the research topics of shared micromobility simulation, summarize, and classify the existing works. Finally, challenges and future directions are provided for further research.

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Section: B) Variables Related To Vehiclesmentioning

confidence: 99%

Section: B) Variables Related To Vehiclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

All one needs to know about shared micromobility simulation: a complete survey

Liu¹,

Tang²,

Liu³

2022

Preprint

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“…This research addresses the need for efficient and safe air transportation solutions in urban environments. Additionally, Jiang and Everts (2021) employ sentiment analysis on closely related news and user comments to gain insights into electric vehicle discussions. By understanding public perceptions and sentiment, this study contributes to the understanding of consumer attitudes towards electric vehicles and can inform marketing strategies and policy decisions.…”

Section: Introductionmentioning

confidence: 99%

Advancing Transportation and Urban Mobility Optimization: Insights from a Comprehensive Review and Analysis

Lin¹,

Sinclair²,

Mitchell³

2023

Preprint

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This paper presents a comprehensive study on transportation and urban mobility optimization by synthesizing and analyzing a collection of relevant research papers. The selected papers cover a wide range of topics, including air corridor evaluation, sentiment analysis in electric vehicle discussions, fluid simulation parallelization, integration of urban air mobility, ridership and operations visualization, economies of scale in mobility sharing markets, prediction of last-mile delivery routes, and machine learning in choice analysis and travel behavior prediction.The research findings highlight the significance of purpose-specific metrics in evaluating air corridors for urban air mobility operations, providing insights for safe and efficient integration. Additionally, sentiment analysis reveals positive attitudes towards electric vehicles, supporting strategies for promoting their adoption. The parallelization of fluid simulation offers improved computational modeling capabilities, benefiting industries such as aerospace and manufacturing.Integration of urban air mobility into existing transportation systems requires coordinated planning, addressing infrastructure, airspace management, and regulatory challenges. The introduction of visualization tools aids in understanding transit performance and optimizing passenger journeys. Analysis of mobility sharing markets highlights the importance of balanced growth and mitigating operational complexities and regulatory compliance.Prediction of last-mile delivery routes using advanced neural networks enables efficient logistics operations. The comparative analysis demonstrates the potential of machine learning classifiers in accurately predicting travel behavior. The results contribute to decision-making processes for policymakers, urban planners, and industry stakeholders in enhancing transportation systems' efficiency, sustainability, and user satisfaction.This study consolidates research findings, identifies research gaps, and provides valuable insights for future exploration in the field of transportation and urban mobility optimization. The comprehensive analysis serves as a resource for researchers, practitioners, and policymakers seeking to improve transportation systems and address emerging challenges in the dynamic urban environment.

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Making sense of electrical vehicle discussions using sentiment analysis on closely related news and user comments

Cited by 2 publications

References 2 publications

All one needs to know about shared micromobility simulation: a complete survey

All one needs to know about shared micromobility simulation: a complete survey

Advancing Transportation and Urban Mobility Optimization: Insights from a Comprehensive Review and Analysis

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