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Trip Purpose Inference Based on the Relationship between Route Search Records and Regional Characteristics

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Abstract

This study inferred the route-search system user travel profiles and purpose based on the relationship between the travel details in the route-search history data and the regional characteristics around bus stops along the routes of interest. Specifically, the search-history data of “Bus-Net,” a route-search system in Japan were used to define the travel demand. The route’s regional characteristics were expressed using the population and facility-location data. Subsequently, it was possible to construct a regression model with an adaptive Lasso penalty and demonstrate that residents and tourists use route-search systems for “non-routine” travel purposes, such as shopping and tourism.

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References

  1. Kusakabe, T., Asakura, Y.: Bahavioural data mining of transit smart card data: a data fusion approach. Transp. Res. Part C: Emerg. Technol. 46, 179–191 (2014)

    Article  Google Scholar 

  2. Alsger, A., Tavassoli, A., Mesbah, M., Ferreira, L., Hickman, M.: Public transport trip purpose inference using smart card fare data. Transp. Res. Part C: Emerg. Technol. 87, 123–137 (2018)

    Article  Google Scholar 

  3. Meng, C., Cui, Y., He, Q., Su, L., Gao, J., “Travel Purpose Inference with GPS Trajectories, POIs and Geo-Tagged Social Media Data”, in Proceedings of 2017 IEEE International Conference on Big Data, 1319–1324 (Boston), (2017)

  4. Ota, K., Nozu, N.: “Traffic and tourism behavior analysis based on route search request logs: things you can tell by the big data of traffic demand”, papers of research meeting on civil engineering. Planning. 52, 9 (2015) (in Japanese)

    Google Scholar 

  5. Kenjo, G., Ito, M., Kawamura, T., Sugahara, K., “User Behavior Analysis of the Web Service for Public Transit Assistant”, Papers and Proceedings of the Geographic Information Systems Association, 4 Pages (2012) (in Japanese)

  6. Kagawa, T., Kuwano, M., Fukuyama, K., Tanimoto, K., Kawamura, T., Sugahara, K.: An analysis of movement desire characteristics using bus route search record. JSTE J. Traff. Eng. (Spec. Ed. A). 2(2), A_115-A_124 (2016) (in Japanese)

    Google Scholar 

  7. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B. 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  8. Zou, H.: The adaptive lasso and its oracle properties. J. Am. Stat. Assoc. 101, 1418–1429 (2006)

    Article  MathSciNet  Google Scholar 

  9. Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 12(1), 55–67 (1970)

    Article  Google Scholar 

  10. Basu, T., Einbeck, J., Troffaes, M.C.M., “A Sensitivity Analysis of the Adaptive Lasso”, 27 Pages, (2019), https://www.researchgate.net/profile/Tathagata_Basu/publication/335570398_A_sensitivity_analysis_of_the_adaptive_lasso/links/5d6e1d8d4585150886098446/A-sensitivity-analysis-of-the-adaptive-lasso.pdf

  11. Chen, K., Chan, K.S.: Subset ARMA selection via the adaptive lasso. Stat. Interf. 4, 197–205 (2011)

    Article  MathSciNet  Google Scholar 

  12. Melkumova, L.E., Shatskikh, S.Y.: Comparing ridge and lasso estimators for data analysis. Proc. Eng. 201, 746–755 (2017)

    Article  Google Scholar 

  13. Guenther, J., Sawodny, O.: Feature selection for thermal comfort modeling based on constrained lasso regression. IFAC-PapersOnLine. 52, 400–405 (2019)

    Article  Google Scholar 

  14. Spencer, B., Alfandi, O., Al-Obeidat, F.: A refinement of lasso regression applied to temperature forecasting. Proc. Comput. Sci. 130, 728–735 (2018)

    Article  Google Scholar 

  15. Min, X., Li, M., Dong, D., Feng, Z., Zhang, P., Ke, Z., You, H., Han, F., Ma, H., Tian, J., Wang, L.: Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method. Eur. J. Radiol. 115, 16–21 (2019)

    Article  Google Scholar 

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Acknowledgments

This study was supported by JSPS Grants-in-Aid for Scientific Research (KAKENHI) Grant Number 20H02277. The authors thank Mr. Kentaro Nakai, the first year graduate student at the Tottori University, for the data aggregation.

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Authors and Affiliations

  1. Department of Management of Social Systems and Civil Engineering, Tottori University, 4-101 Koyama-Minami, Tottori, Japan

    Mio Hosoe, Masashi Kuwano & Taku Moriyama

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  1. Mio Hosoe
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  2. Masashi Kuwano
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  3. Taku Moriyama
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Correspondence to Mio Hosoe.

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Hosoe, M., Kuwano, M. & Moriyama, T. Trip Purpose Inference Based on the Relationship between Route Search Records and Regional Characteristics. Int. J. ITS Res. 20, 299–308 (2022). https://doi.org/10.1007/s13177-022-00295-4

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