This study developed a method to extract valid temporal and spatial characteristics of residents’ trips based on cellular signaling data to support urban transportation planning activity. The study first identified data triggered by active modes as redundant data by analyzing the characteristics of the trigger modes of cellular signaling data, which were then labeled and excluded from further analysis. Thus, only the data triggered by the passive mode were used in the study. Then, the temporal and spatial characteristics of residents’ trips were extracted by mapping the cellular signaling data onto study regions, dividing them into traffic analysis zones (TAZs), and extracting the origin–destination (OD) matrix. Finally, real data from Beijing, China were used in a case study to verify the feasibility of the proposed method. The extracted temporal and spatial characteristics of residents’ trips were compared with those from the 4th Comprehensive Transport Survey. It was observed that the results from the two data sources had a high correlation with correlation coefficients higher than 0.9.
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References
1.
PhithakkitnukoonS.HoranontT.LorenzoG. D.ShibasakiR.RattiC.Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data. Proc., 1st International Conference on Human Behavior Understanding, Istanbul, Turkey, 2011, pp. 14–25.
2.
HenpraserttaeA.ThiemjarusS.MarukatatS.Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location. Proc., 2011 International Conference on Body Sensor Networks,Dallas, TX, IEEE, 2011, pp. 41–46.
3.
BrezmesT.GorrichoJ.CotrinaJ.Activity Recognition from Accelerometer Data on a Mobile Phone. Proc., International Work-Conference on Artificial Neural Networks, ICANN, Salamanca, Spain, Springer, Berlin, Heidelberg, 2009, pp. 796–799.
4.
CaceresN.WidebergJ. P.BenitezF. G.Deriving Origin Destination Data from a Mobile Phone Network. IET Intelligent Transport Systems, Vol. 1, No. 1, 2007, pp. 15–26.
5.
WhiteJ.WellsI.Extracting Origin Destination Information from Mobile Phone Data. Proc., 11th International Conference on Road Transport Information and Control, London, England, UK, 2002.
6.
CaceresN.RomeroL. M.BenitezF. G.Inferring Origin–Destination Trip Matrices from Aggregate Volumes on Groups of Links: A Case Study Using Volumes Inferred from Mobile Phone Data. Journal of Advanced Transportation, Vol. 47, No. 7, 2013, pp. 650–666.
7.
IqbalM. S.ChoudhuryF.WangP.GonzálezM.Development of Origin–Destination Matrices Using Mobile Phone Call Data. Transportation Research Part C: Emerging Technologies, Vol. 40, No, 1, 2014, pp. 63–74.
8.
CalabreseF.LorenzoG. D.LiuL.RattiC.Estimating Origin-Destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area. IEEE Pervasive Computing, Vol. 10, No, 4, 2011, pp. 36–44.
9.
YooB. S.KangS. P.ChonK.KimS.Origin-Destination Estimation Using Cellular Phone BS Information. Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, 2005, pp. 2574–2588.
10.
SohnK.KimD.Dynamic Origin–Destination Flow Estimation Using Cellular Communication System. IEEE Transactions on Vehicular Technology, Vol. 57, No. 5, 2008, pp. 2703–2713.
11.
AlexanderL.JiangS.MurgaM.GonzálezetM.Origin–Destination Trips by Purpose and Time of Day Inferred from Mobile Phone Data. Transportation Research Part C: Emerging Technologies, Vol. 58, 2015, pp. 240–250.
12.
KungK. S.GrecoK.SobolevskyS.RattiC.Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data. PLoS One, Vol. 9, No. 6, 2014, pp. 1–15.
13.
SchlaichJ.OtterstätterT.FriedrichM.Generating Trajectories from Mobile Phone Data. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010, pp. 2–17.
14.
HoteitS.SecciS.SobolevskyS.RattiC.PujolleG.Estimating Human Trajectories and Hotspots through Mobile Phone Data. Computer Networks, Vol. 64, No. 4, 2014, pp. 296–307.
15.
WangH.CalabreseF.LorenzoG. D.RattiC.Transportation Mode Inference from Anonymized and Aggregated Mobile Phone Call Detail Records. Proc., 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, IEEE, 2010, pp. 3–8.
16.
ReddyS.MunM.BurkeJ.EstrinD.HansenM.SrivastavaM.Using Mobile Phones to Determine Transportation Modes. ACM Transactions on Sensor Networks (TOSN), Vol. 6, No. 2, 2010, pp. 1–27.
17.
WidhalmP.NitscheP.BrändieN.Transport Mode Detection with Realistic Smartphone Sensor Data. Proc., 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, IEEE, 2012, pp. 573–576.
18.
ManzoniV.ManiloD.KloecklK.RattiC.Transportation Mode Identification and Real-Time CO2 Emission Estimation Using Smartphones. Technical Report. SENSEable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 2010.
19.
ByonY.AbdulhaiB.ShalabyA.Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices. Journal of Intelligent Transportation Systems, Vol. 13, No. 4, 2009, pp. 161–170.
20.
WitayangkurnA.HoranontT.SekimotoY.ShibasakiR.Trip Reconstruction and Transportation Mode Extraction on Low Data Rate GPS Data from Mobile Phone. Proc., International Conference on Computers in Urban Planning & Urban Management, CUPUM, Utrecht, Netherlands, 2013, pp. 1–19.
21.
Beijing Transportation Research Center. Beijing the 4th Comprehensive Transport Survey Summary Report. Technical Report. Beijing Municipal Commission of Transport, Beijing, China, 2012.
22.
SunB.PanX.NingY.Analysis on Influence of Job-Housing Balance on Commute Travel in Shanghai. Urban Planning Forum, Vol. 1, 2008, pp. 77–82.
23.
LiC.GuoJ.AnZ.ZhaoH.Comments on Urban Comprehensive Transportation Survey: Learn Lessons from the 5th Beijing Comprehensive Transportation Survey. Urban Traffic, No. 2, 2016, pp. 29–34.
