Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
142 Ahas, R., Aasa, A., Mark, Ã., Pae, T., and Kull, A. (2007). Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management, 28(3), 898â910. https://doi.org/10.1016/j.tourman.2006.05.010. Axhausen, K. W., Löchl, M., Schlich, R., Buhl, T., and Widmer, P. (2007). Fatigue in long-duration travel diaries. Transportation, 34(2), 143â160. https://doi.org/10.1007/s11116-006-9106-4. Bagrow, J. P., Wang, D., and Barabási, A.-L. (2011). Collective response of human populations to large-scale emergencies. PLOS ONE, 6(3), e17680. https://doi.org/10.1371/journal.pone.0017680. Becker, R., Cáceres, R., Hanson, K., Isaacman, S., Loh, J. M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A., and Volinsky, C. (2013). Human mobility characterization from cellular network data. Communications of the ACM, 56(1), 74â82. https://doi.org/10.1145/2398356.2398375. Bekhor S., Cohen, Y., and Solomon, C. (2013). Evaluating long-distance travel patterns in Israel by tracking cellular phone positions. Journal of Advanced Transportation, 47(4), 435â446. (First published online Feb. 2011.) https://doi.org/10.1002/atr.170. Bekhor S., Hirsh, M., Nimre, S., and Feldman, I. (2015). Identifying Spatial and Temporal Congestion Character- istics Using Passive Mobile Phone Data. Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C. Bengtsson, L., Lu, X., Thorson, A., Garfield, R., and von Schreeb, J. V. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geo spatial study in Haiti. PLOS Medicine, 8(8), 1â9. https://doi.org/10.1371/journal.pmed.1001083. Carrion C., Pereira, F. C., Ball, R., Zhao, F., Kim, Y., Zheng, N., Zegras, P. C., and Ben-Akiva, M. E. (2014). Evaluat- ing FMS: A preliminary comparison with a traditional travel survey. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C. Chen, C., Gong, H., Lawson, C., and Bialostozky, E. (2010). Evaluating the feasibility of a passive travel survey col- lection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A: Policy and Practice, 44(10), 830â840. https://doi.org/10.1016/j.tra.2010.08.004. Federal Highway Administration and Federal Transit Administration. (2013). Status of the nationâs highways, bridges, and transit: Conditions & performance. U.S. Department of Transportation. https://www.fhwa.dot.gov/ policy/2013cpr/pdfs/cp2013.pdf. Girardin, F., Vaccari, A., Gerber, A., Biderman, A., and Ratti, C. (2009). Towards estimating the presence of visitors from the aggregate mobile phone network activity they generate. https://pdfs.semanticscholar.org/ 0801/bc25c0fc5a86ae40c3aecbc5314e3ca3cc66.pdf. Gong, H., Chen, C., Bialostozky, E., and Lawson, C. T. (2012). A GPS/GIS method for travel mode detection in New York City. Computers, Environment and Urban Systems, 36(2), 131â139. https://doi.org/10.1016/ j.compenvurbsys.2011.05.003. Gur, Y. J., Bekhor, S., Solomon, C., and Kheifits, L. (2009). Intercity person trip tables for nationwide trans- portation planning in Israel obtained from massive cell phone data. Transportation Research Record, 2121, 145â151. https://doi.org/10.3141/2121-16. Hartgen, D. T., and San Jose, E. (2009). Costs and trip rates of recent household travel surveys. http://www. hartgengroup.net/Projects/National/USA/household_travel_summary/2009-11-11_Final_Report_Revised.pdf. Hu, P. S., and Reuscher, T. R. (2004). Summary of travel trends: 2001 National Household Travel Survey. FHWA, U.S. Department of Transportation. http://nhts.ornl.gov/2001/pub/stt.pdf. Isaacman, S., Becker, R. A., Cáceres, R., Kobourov, S. G., Martonosi, M., Rowland, J., and Varshavsky, A. (2011). Ranges of human mobility in Los Angeles and New York. In K. Lyons, J. Hightower, and E. M. Huang (Eds.), Pervasive computing. Pervasive 2011. Lecture Notes in Computer Science, 6696. Berlin, Heidelberg: Springer. https://www.researchgate.net/publication/221036879_Ranges_of_Human_Mobility_in_Los_Angeles_ and_New_York. Additional Resources
Additional Resources 143 Isaacman, S., Becker, R. A., Cáceres, R., Kobourov, S. G., Martonosi, M., Rowland, J., and Varshavsky, A. (2011). Identifying important places in peopleâs lives from cellular network data. In K. Lyons, J. Hightower, and E. M. Huang (Eds.), Pervasive computing. Pervasive 2011. Lecture Notes in Computer Science, 6696. Berlin, Heidelberg: Springer. https://link.springer.com/chapter/10.1007%2F978-3-642-21726-5_9. Jiang, S., Ferreira, J., Jr., and González, M. C. (2017). Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Transactions on Big Data, 3(2), 208â219. Milone, R. (2014). Initial analysis of AirSage O-D cellular data for the TPB modeled area. Presentation to the Travel Forecasting Subcommittee, National Capitol Region Transportation Planning Board and Metropolitan Washington Council of Governments, July 18. http://www1.mwcog.org/uploads/committee-documents/ ZV1YW1Zc20140718142637.pdf. Rwanda hits 55pc mobile phone penetration rate. (2012). Africa Review. http://www.africareview.com/ BusinessâFinance/Rwanda-mobile-phone-penetration-rate/-/979184/1713912/-/format/xhtml/-/ dr1a8kz/-/index.html. Sagl, G., Loidl, M., and Beinat, E. (2012). A visual analytics approach for extracting spatio-temporal urban mobil- ity information from mobile network traffic. ISPRS International Journal of Geo-Information, 1(3), 256â271. https://doi.org/10.3390/ijgi1030256. Sevtsuk, A., and Ratti, C. (2010). Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. Journal of Urban Technology, 17(1), 41â60. https://doi.org/10.1080/10630731003597322. Sun, J. B., Yuan, J., Wang, Y., Si, H. B., and Shan, X. M. (2011). Exploring spaceâtime structure of human mobility in urban space. Physica A, 390(5), 929â942. https://doi.org/10.1016/j.physa.2010.10.033. Tarasov, A., Kling, F., and Pozdnoukhov, A. (2013). Prediction of user location using the radiation model and check-ins. Proceedings of the 2nd ACM SIGKD International Workshop on Urban Computing, Article No. 8, New York. https://doi.org/10.1145/2505821.2505833. Traag, V.A., Browet, A., Calabrese, F., and Morlot, F. (2011). Social event detection in massive mobile phone data using probabilistic location inference. IEEE Third International Conference on Privacy, Security, Risk, and Trust, and IEEE Third International Conference on Social Computing (pp. 625â628). http://ieeexplore.ieee.org/ document/6113183/. Wang, M., Chen, C., and Ma, J. (2015). On making more efficient location prediction. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C. Wang, M., Chen, C., and Ma, J. (2015). Time-of-day dependence of location variability: An application of passively- generated mobile phone dataset. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C. Wang, P., González, M. C., Hidalgo, C. A., and Barabási, A.-L. (2009). Understanding the spreading patterns of mobile phone viruses. Science, 324(5930), 1071â1076. https://doi.org/10.1126/science.1167053.