Towards a transformation in urban commuting analysis with high-precision mobile phone signaling data: Identifying commuting characteristics based on individual scale

Abstract

Due to the widespread use of navigational satellites, the ubiquity of mobile phones, and the rapid advancement of mobile communication technologies, high-precision mobile phone signaling data (HMPSD) holds exceptional promise for discerning fine-grained characteristics of residents’ travel behaviors, owing to its superior spatial and temporal resolution. This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang, China, over the course of a month, using these patterns as the basis for transport mode identification. Leveraging the high-precise geographical coordinates of individuals’ workplaces and homes, along with actual commuting durations derived from the high-frequency positioning of HMPSD, and comparing these with the predicted commuting durations for four transport modes from a navigational map, we have developed a novel approach for identifying individual transport modes, incorporating time matching, frequency ranking, and speed threshold assessments. This approach swiftly and effectively identifies the commuting modes for each resident—namely, driving, public transportation, walking, bicycling, and electric biking—along with their respective commuting distances and durations. Furthermore, to support urban planning and transportation management efforts, we aggregated individual commuting data—including flows, modes, distances, and durations—at a parcel level. This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District, highlights the commuting needs and irrational commuting conditions in peripheral parcels, and informs tailored strategies for adjusting planning layouts and optimizing facility configurations. This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources, providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.

Keywords

Mobile phone signaling data / Navigation map data / Travel mode identification / Urban commuting / Individuals’ stable traffic behavior / Parcel-level commuting analysis

References

[1] Alexander, L. , Jiang, S. , Murga, M. , González, M.C. , 2015. Origin—destination trips by purpose and time of day inferred from mobile phone data. Transport. Res. Part C: Emerging Technologies, Big Data in Transportation and Traffic Engineering 58, 240- 250.

[2] Asgari, F. , 2016. Inferring User Multimodal Trajectories from Cellular Network Metadata in Metropolitan Areas (Phdthesis). Institut National des Télécommunications.

[3] Asgari, F. , Sultan, A. , Xiong, H. , Gauthier, V. , El-Yacoubi, M.A. , 2016. CT-Mapper: mapping sparse multimodal cellular trajectories using a multilayer transportation network. Computer Communications, Mobile Traffic Analytics 95, 69- 81.

[4] Bachir, D. , Khodabandelou, G. , Gauthier, V. , El Yacoubi, M. , Puchinger, J. , 2019. Inferring dynamic origin-destination flows by transport mode using mobile phone data. Transport. Res. C Emerg. Technol. 101, 254- 275.

[5] Bantis, T. , Haworth, J. , 2017. Who you are is how you travel: a framework for transportation mode detection using individual and environmental characteristics. Transport. Res. C Emerg. Technol. 80, 286- 309.

[6] Bloch, A. , Erdin, R. , Meyer, S. , Keller, T. , De Spindler, A. , 2015. Battery-efficient transportation mode detection on mobile devices. In: 2015 16th IEEE International Conference on Mobile Data Management. Presented at the 2015 16th IEEE International Conference on Mobile Data Management, pp. 185-190.

[7] Breyer, N. , Gundlegård, D. , Rydergren, C. , 2021. Travel mode classification of intercity trips using cellular network data. Transportation Research Procedia, 23rd EURO Working Group on Transportation Meeting, EWGT 2020, 16-18 September 2020, Paphos, Cyprus 52, 211- 218.

[8] Bricka, S. , Bhat, C.R. , 2006. Comparative analysis of global positioning system—based and travel survey—based data. Transport. Res. Rec. 1972, 9- 20.

[9] Broach, J. , Dill, J. , McNeil, N.W. , 2019. Travel mode imputation using GPS and accelerometer data from a multi-day travel survey. J. Transport Geogr. 78, 194- 204.

[10] Calabrese, F. , Colonna, M. , Lovisolo, P. , Parata, D. , Ratti, C. , 2011a. Real-time urban monitoring using cell phones: a case study in rome. IEEE Trans. Intell. Transport. Syst. 12, 141- 151.

[11] Calabrese, F. , Di Lorenzo, G. , Liu, L. , Ratti, C. , 2011b. Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Computing 10, 36- 44.

[12] Calabrese, F. , Diao, M. , Di Lorenzo, G. , Ferreira, J. , Ratti, C. , 2013. Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transport. Res. C Emerg. Technol. 26, 301- 313.

[13] Calabrese, F. , Ferrari, L. , Blondel, V.D. , 2014. Urban sensing using mobile phone network data: a survey of research. ACM Comput. Surv. 47 (25), 1- 25, 20.

[14] Chandramouli, D. , Liebhart, R. , Pirskanen, J. , 2019. 5G for the Connected World. John Wiley & Sons.

[15] Chen, C. , Ma, J. , Susilo, Y. , Liu, Y. , Wang, M. , 2016. The promises of big data and small data for travel behavior (aka human mobility) analysis. Transport. Res. C Emerg. Technol. 68, 285- 299.

[16] Chen, X. , Yang, C. , Xu, X. , 2020. Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model. Journal of Transport and Land Use 13, 429- 445.

[17] Chin, K. , Huang, H. , Horn, C. , Kasanicky, I. , Weibel, R. , 2019. Inferring fine-grained transport modes from mobile phone cellular signaling data. Comput. Environ. Urban Syst. 77, 101348.

[18] China Academy of Urban Planning and Design , 2023. 2023 Annual commuting monitoring report of major Chinese cities.

[19] Dabiri, S. , Heaslip, K. , 2018. Inferring transportation modes from GPS trajectories using a convolutional neural network. Transport. Res. C Emerg. Technol. 86, 360- 371.

[20] Danafar, S. , Piorkowski, M. , Krysczcuk, K. , 2017. Bayesian framework for mobility pattern discovery using mobile network events. 2017 25th European Signal Processing Conference(EUSIPCO, pp. 1070-1074.

[21] Doyle, J. , Hung, P. , Kelly, D. , McLoone, S. , Farrell, R. , 2011. Utilising mobile phone billing records for travel mode discovery.

[22] Eftekhari, H.R. , Ghatee, M. , 2016. An inference engine for smart-phones to preprocess data and detect stationary and transportation modes. Transport. Res. C Emerg. Technol. 69, 313- 327.

[23] García, P. , Herranz, R. , Javier, J. , Ramasco , 2016. Big data analytics for a passenger-centric air traffic management system A case study of door-to-door intermodal passenger journey inferred from mobile phone data.

[24] Gu, J. , Wang, D. , 2020. Commuting Models of Shanghai based on big data: methods and application. City Planning Review 44 (11), 69- 77, 2020.

[25] Hadachi, A. , Pourmoradnasseri, M. , Khoshkhah, K. , 2020. Unveiling large-scale commuting patterns based on mobile phone cellular network data. J. Transport Geogr. 89, 102871.

[26] Horn, C. , Gursch, H. , Kern, R. , Cik, M. , 2017. QZTool—automatically generated origin-destination matrices from cell phone trajectories. In: Stanton, N.A., Landry, S., Di Bucchianico, G., Vallicelli, A. (Eds.), Advances in Human Aspects of Transportation, Advances in Intelligent Systems and Computing. Springer International Publishing, Cham, pp. 823-833.

[27] Horn, C. , Kern, R. , 2015. Deriving public transportation timetables with large-scale cell phone data. Procedia Computer Science, The 6th International Conference on Ambient Systems, Networks and Technologies (ANT-2015), the 5th International Conference on Sustainable Energy Information Technology (SEIT-2015)52, 67- 74.

[28] Huang, H. , Cheng, Y. , Weibel, R. , 2019. Transport mode detection based on mobile phone network data: a systematic review. Transport. Res. C Emerg. Technol. 101, 297- 312.

[29] Huang, Z. , Ling, X. , Wang, P. , Zhang, F. , Mao, Y. , Lin, T. , Wang, F.-Y. , 2018. Modeling real-time human mobility based on mobile phone and transportation data fusion. Transport. Res. C Emerg. Technol. 96, 251- 269.

[30] Hui, K.T.Y. , Wang, C. , Kim, A. , Qiu, T.Z. , 2017. Investigating the Use of Anonymous Cellular Phone Data to Determine Intercity Travel Volumes and Modes. Presented at the Transportation Research Board 96th Annual MeetingTransportation Research Board. No. 17-03652. 2017.

[31] Hui, T.Y. , 2017. Investigating the use of anonymous cellular data for intercity travel patterns in alberta.

[32] Jeyakumar, J.V. , Lee, E.S. , Xia, Z. , Sandha, S.S. , Tausik, N. , Srivastava, M. , 2018. Deep convolutional bidirectional LSTM based transportation mode recognition. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, UbiComp ’18. Association for Computing Machinery, New York, NY, USA, pp. 1606-1615.

[33] Jiang, S. , Fiore, G.A. , Yang, Y. , Ferreira, J. , Frazzoli, E. , González, M.C. , 2013. A review of urban computing for mobile phone traces: current methods, challenges and opportunities. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp ’13. Association for Computing Machinery, New York, NY, USA, pp. 1-9.

[34] Jiang, Z. , Huang, A. , Qi, G. , Guan, W. , 2023. A framework of travel mode identification fusing Deep learning and map-matching algorithm. IEEE Trans. Intell. Transport. Syst. 24, 6401- 6415.

[35] Jiaqi, K.C. , 2018. Transport Mode Detection Using Cellular Signaling Data (Case Study of Graz and Vienna, Austria). Master’s thesis. Geographisches Institut der Universität Zürich.

[36] Jenks, George F. , 1967. The data model concept in statistical mapping. Int. Yearb. Cartogr. 7, 186- 190.

[37] Kalatian, A. , Shafahi, Y. , 2016. Travel mode detection exploiting cellular network data. MATEC Web Conf. 81, 03008.

[38] Kyaing , Lwin, K.K. , Sekimoto, Y. , 2020. Identification of various transport modes and rail transit behaviors from mobile CDR data: a case of Yangon City. Asian Transport Studies 6, 100025.

[39] Larijani, A.N. , Olteanu-Raimond, A.-M. , Perret, J. , Brédif, M. , Ziemlicki, C. , 2015. Investigating the mobile phone data to estimate the origin destination flow and analysis; case study: Paris region. Transportation Research Procedia, 4th International Symposium of Transport Simulation (ISTS’14) Selected Proceedings, Ajaccio, France, 1-4 June 2014 6, 64- 78.

[40] Liu, X. , Gao, L. , Ni, A. , Ye, N. , 2020. Understanding better the influential factors of commuters’ multi-day travel behavior:evidence from Shanghai, China. Sustainability 12, 376.

[41] Liu, Y. , Zhao, P. , Lv, D. , 2021. 2021. Big-Data oriented commuting distribution model and application in large cities. Journal of Geo-information Science 23 (7), 1185-1195 (in Chinese).

[42] Lu, J. , Zhou, S. , Kwan, M.-P. , 2023. Does density promote non-motorized travel? The effects of individual space—time activity patterns. Transport. Res. Transport Environ. 116, 103621.

[43] Lu, Z. , Long, Z. , Xia, J. , An, C. , 2019. A random forest model for travel mode identification based on mobile phone signaling data. Sustainability 11, 5950.

[44] Oliver, N. , Matic, A. , Frias-Martinez, E. , 2015. Mobile network data for public health: opportunities and challenges. Front. Public Health 3.

[45] Pan, C. , Lu, J. , Di, S. , Ran, B. , 2006. Cellular-based data-extracting method for trip distribution. Transport. Res. Rec. 1945, 33- 39.

[46] People’s Government of Qiaoxi District, Shijiazhuang City , 2023. Basic Information of Qiaoxi District. http://www.sjzqx.gov.cn/columns/291ad87b-82-4-49d3-84be-204ce863f975/202203/14/d11fd758-a9ce-4-9a-9dab-1958b3d7cea0.html. (Accessed 26 December 2023).

[47] Phithakkitnukoon, S. , Sukhvibul, T. , Demissie, M. , Smoreda, Z. , Natwichai, J. , Bento, C. , 2017. Inferring social influence in transport mode choice using mobile phone data. EPJ Data Sci. 6, 1- 29.

[48] Qu, Y. , Gong, H. , Wang, P. , 2015. Transportation mode split with mobile phone data. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Presented at the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 285-289.

[49] Schlaich, J. , Otterstätter, T. , Friedrich, M. , 2010. Generating trajectories from mobile phone data.

[50] Shijiazhuang Municipal Bureau of Statistics , 2023. Shijiazhuang city 2022 national economic and social development statistical bulletin.

[51] Shijiazhuang Municipal People’s Government , 2021. Advancing Green Development to a New Level: Our City’s Directive to Accelerate the Establishment of a Green, Low-Carbon, Circular Economic System. https://www.sjz.gov.cn/columns/3b0b4d38-3416-4a59-a734-c18f5de83691/202107/27/37388bc0-71ae-44-9-b275-6281712-63b4.html. (Accessed 26 December 2023).

[52] Shijiazhuang Municipal People’s Government , 2019. Office of the People’s Government of Shijiazhuang City: Notice on the Issuance of the "Implementation Plan for Accelerating 5G Development. https://gxj.sjz.gov.cn/ssrh/portal-fe/policy/detail/1232734459-580391424?tabZ1. (Accessed 26 December 2023).

[53] Smoreda, Z. , Olteanu Raimond, A.-M. , Couronné, T. , 2013. Spatiotemporal Data from Mobile Phones for Personal Mobility Assessment, pp. 745-767.

[54] Vij, A. , Shankari, K. , 2015. When is big data big enough? Implications of using GPS-based surveys for travel demand analysis. Transport. Res. C Emerg. Technol. 56, 446- 462.

[55] Wang, H. , Calabrese, F. , Di Lorenzo, G. , Ratti, C. , 2010. Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th International IEEE Conference on Intelligent Transportation Systems. Presented at the 13th International IEEE Conference on Intelligent Transportation Systems, pp. 318-323.

[56] Wang, L. , Yang, F. , Jin, P.J. , Zhou, T. , Guo, Y. , 2022. Detection of traffic pattern based on Fuzzy clustering and wavelet analysis model at different signaling positioning frequencies. Transport. Res. Rec. 2676, 601- 618.

[57] Wang, Y. , Yang, F. , He, L. , Liu, H. , Tan, L. , Wang, C. , 2023. Inferring travel modes from cellular signaling data based on the gated recurrent unit neural network. J. Adv. Transport. 2023, e1987210.

[58] Wang, Y. , Yang, Li R. , Zhou, T. , 2022. Impact evaluation of location frequency on travel mode extraction using cellular phone data. J. Southwest Jiaot. Univ. 1-9 (in Chinese).

[59] Wu, L. , Yang, B. , Jing, P. , 2016. Travel mode detection based on GPS raw data collected by smartphones: a systematic review of the existing methodologies. Information 7, 67.

[60] Xiao, G. , Juan, Z. , Zhang, C. , 2015. Travel mode detection based on GPS track data and Bayesian networks. Comput. Environ. Urban Syst. 54, 14- 22.

[61] Xu, D. , Song, G. , Gao, P. , Cao, R. , Nie, X. , Xie, K. , 2011. Transportation modes identification from mobile phone data using probabilistic Models. In: Tang, J., King, I., Chen, L., Wang, J.(Eds.), Advanced Data Mining and Applications, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp. 359-371.

[62] Yamada, Y. , Uchiyama, A. , Hiromori, A. , Yamaguchi, H. , Higashino, T. , 2016. Travel estimation using Control Signal Records in cellular networks and geographical information. In:2016 9th IFIP Wireless and Mobile Networking Conference(WMNC). Presented at the 2016 9th IFIP Wireless and Mobile Networking Conference (WMNC), pp. 138-144.

[63] Zeng, J. , Yu, Y. , Chen, Y. , Yang, D. , Zhang, L. , Wang, D. , 2023. Trajectory-as-a-Sequence: a novel travel mode identification framework. Transport. Res. C Emerg. Technol. 146, 103957.

[64] Zhang, Y. , Ma, D. , Zhang, F. , Li, Y. , Jin, Y. , Song, L. , Cui, L. , 2022. A research for travel mode identification based on cellular signaling data. In: 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing. Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles, pp. 318-325. SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta).

RIGHTS & PERMISSIONS

2024 The Author(s). Publishing services by Elsevier B.V. on behalf of Higher Education Press and KeAi.

审图号：GS京（2025）0467号