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Enhancing Trip Distribution Prediction with Twitter Data: Comparison of Neural Network and Gravity Models

Published: 06 November 2018 Publication History

Abstract

Predicting human mobility within cities is an important task in urban and transportation planning. With the vast amount of digital traces available through social media platforms, we investigate the potential application of such data in predicting commuter trip distribution at small spatial scale. We develop back propagation (BP) neural network and gravity models using both traditional and Twitter data in New York City to explore their performance and compare the results. Our results suggest the potential of using social media data in transportation modeling to improve the prediction accuracy. Adding Twitter data to both models improved the performance with a slight decrease in root mean square error (RMSE) and an increase in R-squared (R2) value. The findings indicate that the traditional gravity models outperform neural networks in terms of having lower RMSE. However, the R2 results show higher values for neural networks suggesting a better fit between the real and predicted outputs. Given the complex nature of transportation networks and different reasons for limited performance of neural networks with the data, we conclude that more research is needed to explore the performance of such models with additional inputs.

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    cover image ACM Conferences
    GeoAI '18: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
    November 2018
    68 pages
    ISBN:9781450360364
    DOI:10.1145/3281548
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 06 November 2018

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    Author Tags

    1. Machine learning
    2. Twitter
    3. mobility
    4. neural networks
    5. social media
    6. transport modeling

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    • (2024)Towards the Evaluation of Carbon Footprint for University Communities' Mobility: Challenges, Opportunities, and Reduction Strategies2024 Third International Conference on Sustainable Mobility Applications, Renewables and Technology (SMART)10.1109/SMART63170.2024.10815400(1-6)Online publication date: 22-Nov-2024
    • (2024)Unsupervised origin-destination flow estimation for analyzing COVID-19 impact on public transport mobilityCities10.1016/j.cities.2024.105086151(105086)Online publication date: Aug-2024
    • (2023)An Empirical Spatial Network Model Based on Human Mobility for Epidemiological Research: A Case StudyAnnals of the American Association of Geographers10.1080/24694452.2023.2187339113:6(1461-1482)Online publication date: 21-Apr-2023
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    • (2021)Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES DataISPRS International Journal of Geo-Information10.3390/ijgi1101001511:1(15)Online publication date: 30-Dec-2021
    • (2021)The Analysis of Opinions About Teaching Profession on Twitter Through Text MiningResearch on Education and Media10.2478/rem-2020-000212:1(3-12)Online publication date: 28-May-2021
    • (2021)Inferring Origin-Destination Flows from Population DistributionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3075928(1-1)Online publication date: 2021
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