Bus-Car Mode Identification: Traffic Condition–Based Random-Forests Method
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 10
Abstract
Travel mode identification is one of the key issues in travel behavior analysis. A number of algorithms have been proposed to detect travel modes particularly by using global positioning system (GPS) data, whereas most algorithms rarely consider traffic conditions. To fill the gap, this paper distinguishes two representative travel modes, i.e., bus and car, by using the random-forests method, of which the corresponding feature variables are examined under various traffic conditions. Local congestion variables are defined to reduce uncertainties between bus and car. The results indicate that the overall detection accuracy of the not-in-congestion trips is as high as 94.0%, and that of in-congestion trips is 91.1%, demonstrating that distinguishing traffic conditions using random forests can reliably improve travel modes detection accuracy. It is found that distinguishing local traffic conditions can further improve accuracy. The paper contributes to travel behavior analysis and modeling, and the proposed method is ready for a wide range of transportation practices, including traffic planning and management.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The research is supported by the National Key Research and Development Program of China (Grant No. 2018YFB1601300), the National Natural Science Foundation of China (Grant Nos. 61873109 and 71871010), and the Humanity and Social Science Foundation of the Ministry of Education (Grant No. 18YJA630157).
References
Assemi, B., H. Safi, M. Mesbah, and L. Ferreira. 2016. “Developing and validating a statistical model for travel mode identification on smart-phones.” IEEE Trans. Intell. Transp. Syst. 17 (7): 1920–1931. https://doi.org/10.1109/TITS.2016.2516252.
Böcker, L., M. Dijst, and J. Prillwitz. 2013. “Impact of everyday weather on individual daily travel behaviours in perspective: A literature review.” Transp. Rev. 33 (1): 71–91. https://doi.org/10.1080/01441647.2012.747114.
Bohte, W., and K. Maat. 2009. “Deriving and validating trip purposes and travel modes for multi-day GP-based travel surveys: A large-scale application in the Netherlands.” Transp. Res. Part C: Emerging Technol. 17 (3): 285–297. https://doi.org/10.1016/j.trc.2008.11.004.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Chen, C., H. Gong, C. Lawson, and E. Bialostozky. 2010. “Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study.” Transp. Res. Part A: Policy Pract. 44 (10): 830–840. https://doi.org/10.1016/j.tra.2010.08.004.
Cools, M., E. Moons, L. Creemers, and G. Wets. 2010. “Changes in travel behavior in response to weather conditions: Do type of weather and trip purpose matter?” Transp. Res. Rec. 2157 (1): 22–28. https://doi.org/10.3141/2157-03.
Cui, L., J. Hu, B. B. Park, and P. Bujanovic. 2018. “Development of a simulation platform for safety impact analysis considering vehicle dynamics, sensor errors, and communication latencies: Assessing cooperative adaptive cruise control under cyber attack.” Transp. Res. Part C: Emerging Technol. 97 (Dec): 1–22. https://doi.org/10.1016/j.trc.2018.10.005.
Evaluation Index System of Urban Road Traffic Management. 2016. Beijing municipal administration of quality and technology supervision. [In Chinese.]. Beijing: Urban Road Traffic Performance Index.
Feng, T., and H. J. P. Timmermans. 2013. “Transportation mode recognition using GPS and accelerometer data.” Transp. Res. Part C: Emerging Technol. 37 (Dec): 118–130. https://doi.org/10.1016/j.trc.2013.09.014.
Feng, T., and H. J. P. Timmermans. 2016. “Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data.” Transp. Plann. Technol. 39 (2): 180–194. https://doi.org/10.1080/03081060.2015.1127540.
Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random forests for land cover classification.” Pattern Recognit. Lett. 27 (4): 294–300. https://doi.org/10.1016/j.patrec.2005.08.011.
Gong, H., and C. Chen. 2012. “A GPS/GIS method for travel mode detection in New York City.” Comput. Environ. Urban Syst. 36 (2): 131–139. https://doi.org/10.1016/j.compenvurbsys.2011.05.003.
Gong, L., R. Kanamori, and T. Yamamoto. 2018. “Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons.” Travel Behav. Soc. 11 (Apr): 131–140. https://doi.org/10.1016/j.tbs.2017.03.004.
Gong, L., T. Yamamoto, and T. Morikawa. 2016. “Comparison of activity type identification from mobile phone GPS data using various machine learning methods.” Asian Transp. Stud. 4 (1): 114–128. https://doi.org/10.11175/eastsats.4.114.
Guan, W., and S. Y. He. 2008. “Statistical feature of traffic flow on urban freeways.” Physica A 387 (4): 944–954. https://doi.org/10.1016/j.physa.2007.09.036.
He, Z., and Z. Liang. 2017. “Visualizing traffic dynamics based on floating car data.” J. Transp. Eng. 143 (5): 04017005. https://doi.org/10.1061/JTEPBS.0000024.
He, Z., Y. Lv, L. Lu, and W. Guan. 2019a. “Constructing spatiotemporal speed contour diagrams: Using rectangular or non-rectangular parallelogram cells?” Transportmetrica B: Transp. Dyn. 7 (1): 44–60. https://doi.org/10.1080/21680566.2017.1320774.
He, Z., G. Qi, L. Lu, and Y. Chen. 2019b. “Network-wide identification of turn-level intersection congestion solely using sparse probe vehicle data.” Transp. Res. Part C: Emerging Technol. 108 (Oct): 320–339. https://doi.org/10.1016/j.trc.2019.10.001.
Hemminki, S., P. Nurmi, and S. Tarkoma. 2013. “Accelerometer-based transportation mode detection on smartphones.” In Proc., 11th ACM Conf. on Embedded Networked Sensor Systems. Roma, Italy: SenSys.
Jiang, H. F., J. Hu, S. An, M. Wang, and B. B. Park. 2017. “Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment.” Transp. Res. Part C: Emerging Technol. 79 (Jun): 290–307. https://doi.org/10.1016/j.trc.2017.04.001.
Liang, Y., Z. Wu, and J. Hu. 2019. “Road side unit location optimization for optimum link flow determination.” Comput. Aided Civ. Infrastruct. Eng. 35 (1): 61–79. https://doi.org/10.1111/mice.12490.
Liu, C., Y. O. Susilo, and A. Karlström. 2014. “Examining the impact of weather variability on non-commuters’ daily activity-travel patterns in different regions of Sweden.” J. Transp. Geogr. 39 (Jul): 36–48. https://doi.org/10.1016/j.jtrangeo.2014.06.019.
Liu, C., Y. O. Susilo, and A. Karlström. 2015. “The influence of weather characteristics variability on individual’s travel mode choice in different seasons and regions in Sweden.” Transp. Policy 41 (Jul): 147–158. https://doi.org/10.1016/j.tranpol.2015.01.001.
Liu, C., Y. O. Susilo, and A. Karlström. 2016. “Measuring the impacts of weather variability on home-based trip chaining behaviour: A focus on spatial heterogeneity.” Transportation 43 (5): 843–867. https://doi.org/10.1007/s11116-015-9623-0.
Ma, J., J. Hu, E. Leslie, and F. Zhou. 2019. “An eco-drive experiment on rolling terrains for fuel consumption optimization with connected automated vehicles.” Transp. Res. 100 (Mar): 125–141. https://doi.org/10.1016/j.trc.2019.01.010.
MOHURD (Ministry of Housing and Urban-Rural Development of the People Republic of China). 2018. Code for design of urban road public transportation stop, terminus and depot engineering.. Beijing: China Architecture Publishing and Media.
Rasmussen, T. K., J. B. Ingvardson, and O. A. Nielsen. 2015. “Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area.” Comput. Environ. Urban Syst. 54 (Nov): 301–313. https://doi.org/10.1016/j.compenvurbsys.2015.04.001.
Rose, G., F. Ahmed, M. Figliozzi, and C. Jakob. 2011. “Quantifying and comparing effects of weather on bicycle demand in Melbourne, Australia, and Portland, Oregon.” In Proc., 90th Annual Meeting of Transportation Research Board. Washington, DC: Transportation Research Board.
Schuessler, N., and K. Axhausen. 2009. “Processing raw data from global positioning systems without additional information.” Transp. Res. Rec. 2105 (1): 28–36. https://doi.org/10.3141/2105-04.
Shafique, M. A., and E. Hato. 2015. “Use of acceleration data for transportation mode prediction.” Transportation 42 (1): 163–188. https://doi.org/10.1007/s11116-014-9541-6.
Stopher, P., Q. J. Jian, and C. FitzGerald. 2005. “Processing GPS data from travel surveys.” In Proc., 28th Australasian Transport Research Forum, ATRF 05. Sydney, Australia: Transport Engineering in Australia.
Sun, C., S. G. Ritchie, and K. Tsai. 1998. “Algorithm development for derivation of section-related measures of traffic system performance using inductive loop detectors.” Transp. Res. Rec. 1643 (1): 171–180. https://doi.org/10.3141/1643-21.
Transportation Research Board. 2010. Highway capacity manual. Washington, DC: Transportation Research Board.
Tsui, S. Y. A., and A. S. Shalaby. 2006. “Enhanced system for link and mode identification for personal travel surveys based on global positioning systems.” Transp. Res. Rec. 1972 (1): 38–45. https://doi.org/10.1177/0361198106197200105.
Xiao, G. N., Z. C. Juan, and C. Q. Zhang. 2015. “Travel mode detection based on GPS track data and Bayesian networks.” Comput. Environ. Urban Syst. 54 (Nov): 14–22. https://doi.org/10.1016/j.compenvurbsys.2015.05.005.
Zhang, L., and D. Levinson. 2010. “Ramp metering and freeway bottleneck capacity.” Transp. Res. Part A: Policy Pract. 44 (4): 218–235. https://doi.org/10.1016/j.tra.2010.01.004.
Zhu, L., J. Gonder, and L. Lin. 2017. “Prediction of individual social-demographic role based on travel behavior variability using long-term GPS data.” J. Adv. Transp. 2017: 1–13. https://doi.org./10.1155/2017/7290248.
Zhu, Q. H., M. Zhu, M. Li, and M. Z. Li. 2016. “Identifying transportation modes from raw GPS data.” In Proc., Int. Conf. of Young Computer Scientists. Singapore: Springer.
Zong, F., Y. D. Tian, and Y. N. He. 2019. “Trip destination prediction based on multi-day GPS data.” Physica A 515 (1): 258–269. https://doi.org/10.1016/j.physa.2018.09.090.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 10 • October 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jan 1, 2020
Accepted: Jun 4, 2020
Published online: Jul 24, 2020
Published in print: Oct 1, 2020
Discussion open until: Dec 24, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.