Henry Martin

Henry Martin

Doctoral student in Geoinformation Engineering at ETH Zurich

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

  1. Martin, H., Wiedemann, N., Reck, D. J., & Raubal, M. (2023). Graph-based mobility profiling. Computers, Environment and Urban Systems, 100, 101910.
  2. Martin, H., Wiedemann, N., Reck, D. J., & Raubal, M. (2023). Graph-based mobility profiling. Computers, Environment and Urban Systems, 100, 101910.
  3. Marti*, H., Hong, Y., Wiedemann, N., Bucher, D., & Raubal, M. (2023). Trackintel: An open-source Python library for human mobility analysis. Computers, Environment and Urban Systems, 101, 101938.
  4. Kim, J., Kim, J. H., & Lee, G. (2022). GPS data-based mobility mode inference model using long-term recurrent convolutional networks. Transportation Research Part C: Emerging Technologies, 135, 103523. https://doi.org/10.1016/j.trc.2021.103523
  5. Yu, Q., & Yuan, J. (2022). TransBigData: A Python package for transportation spatio-temporal big data processing, analysis and visualization. Journal of Open Source Software, 7(71), 4021.
  6. Wang, R., Li, N., & Wang, Y. (2021). Does the returners and explorers dichotomy in urban human mobility depend on the observation duration? An empirical study in Guangzhou, China. Sustainable Cities and Society, 69, 102862.
  7. Hensher, D. A., Ho, C. Q., & Reck, D. J. (2021). Mobility as a service and private car use: Evidence from the Sydney MaaS trial. Transportation Research Part A: Policy and Practice, 145, 17–33.
  8. Rout, A., Nitoslawski, S., Ladle, A., & Galpern, P. (2021). Using smartphone-GPS data to understand pedestrian-scale behavior in urban settings: A review of themes and approaches. Computers, Environment and Urban Systems, 90, 101705.
  9. Wang, R., Li, N., & Wang, Y. (2021). Does the returners and explorers dichotomy in urban human mobility depend on the observation duration? An empirical study in Guangzhou, China. Sustainable Cities and Society, 69, 102862.
  10. Schiermeier, Q. (2021). Climate change made North America’s deadly heatwave 150 times more likely. Nature. https://doi.org/10.1038/d41586-021-01869-0
  11. Calafiore, A., Palmer, G., Comber, S., Arribas-Bel, D., & Singleton, A. (2021). A geographic data science framework for the functional and contextual analysis of human dynamics within global cities. Computers, Environment and Urban Systems, 85, 101539.
  12. McKenzie, G., & Romm, D. (2021). Measuring urban regional similarity through mobility signatures. Computers, Environment and Urban Systems, 89, 101684.
  13. Liu, C., Cai, J., Wang, D., Tang, J., Wang, L., Chen, H., & Xiao, Z. (2021). Understanding the Regular Travel Behavior of Private Vehicles: An Empirical Evaluation and A Semi-supervised Model. IEEE Sensors Journal.
  14. Kumar, N., & Raubal, M. (2021). Applications of deep learning in congestion detection, prediction and alleviation: A survey. Transportation Research Part C: Emerging Technologies, 133, 103432.
  15. Jia, L., Gaüzère, B., & Honeine, P. (2021). graphkit-learn: A Python library for graph kernels based on linear patterns. Pattern Recognition Letters, 143, 113–121.
  16. Kumar, N., & Raubal, M. (2021). Applications of deep learning in congestion detection, prediction and alleviation: A survey. Transportation Research Part C: Emerging Technologies, 133, 103432.
  17. Jia, L., Gaüzère, B., & Honeine, P. (2021). graphkit-learn: A Python library for graph kernels based on linear patterns. Pattern Recognition Letters, 143, 113–121.
  18. Chen, Q., & Poorthuis, A. (2021). Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility. International Journal of Geographical Information Science, 35(7), 1425–1448. https://doi.org/10.1080/13658816.2021.1887489
  19. Liu, C., Cai, J., Wang, D., Tang, J., Wang, L., Chen, H., & Xiao, Z. (2021). Understanding the Regular Travel Behavior of Private Vehicles: An Empirical Evaluation and A Semi-supervised Model. IEEE Sensors Journal.
  20. Luca, M., Barlacchi, G., Lepri, B., & Pappalardo, L. (2021). A Survey on Deep Learning for Human Mobility. ACM Computing Surveys, 55(1), 7:1–7:44. https://doi.org/10.1145/3485125
  21. Smolak, K., Siła-Nowicka, K., Delvenne, J.-C., Wierzbiński, M., & Rohm, W. (2021). The impact of human mobility data scales and processing on movement predictability. Scientific Reports, 11(1), 1–10.
  22. Haidri, S., Haranwala, Y. J., Bogorny, V., Renso, C., da Fonseca, V. P., & Soares, A. (2021). PTRAIL–A python package for parallel trajectory data preprocessing. ArXiv Preprint ArXiv:2108.13202.
  23. Hensher, D. A., Ho, C. Q., & Reck, D. J. (2021). Mobility as a service and private car use: Evidence from the Sydney MaaS trial. Transportation Research Part A: Policy and Practice, 145, 17–33.
  24. Schiermeier, Q. (2021). Climate change made North America’s deadly heatwave 150 times more likely. Nature. https://doi.org/10.1038/d41586-021-01869-0
  25. Shenk, J., Byttner, W., Nambusubramaniyan, S., & Zoeller, A. (2021). Traja: A Python toolbox for animal trajectory analysis. Journal of Open Source Software, 6(63), 3202.
  26. Calafiore, A., Palmer, G., Comber, S., Arribas-Bel, D., & Singleton, A. (2021). A geographic data science framework for the functional and contextual analysis of human dynamics within global cities. Computers, Environment and Urban Systems, 85, 101539.
  27. McKenzie, G., & Romm, D. (2021). Measuring urban regional similarity through mobility signatures. Computers, Environment and Urban Systems, 89, 101684.
  28. Zhao, P., Jonietz, D., & Raubal, M. (2021). Applying frequent-pattern mining and time geography to impute gaps in smartphone-based human-movement data. International Journal of Geographical Information Science, 1–29. https://doi.org/10.1080/13658816.2020.1862126
  29. Alam, M. M., Torgo, L., & Bifet, A. (2021). A Survey on Spatio-temporal Data Analytics Systems. ACM Computing Surveys. https://doi.org/10/gpsv52
  30. Schläpfer, M., Dong, L., O’Keeffe, K., Santi, P., Szell, M., Salat, H., Anklesaria, S., Vazifeh, M., Ratti, C., & West, G. B. (2021). The universal visitation law of human mobility. Nature, 593(7860), 522–527. https://doi.org/10.1038/s41586-021-03480-9
  31. Moro, E., Calacci, D., Dong, X., & Pentland, A. (2021). Mobility patterns are associated with experienced income segregation in large US cities. Nature Communications, 12(1), 4633. https://doi.org/10.1038/s41467-021-24899-8
  32. Solomon, A., Livne, A., Katz, G., Shapira, B., & Rokach, L. (2021). Analyzing movement predictability using human attributes and behavioral patterns. Computers, Environment and Urban Systems, 87, 101596. https://doi.org/10.1016/j.compenvurbsys.2021.101596
  33. Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J. (2021). Mobility network models of COVID-19 explain inequities and inform reopening. Nature, 589(7840), 82–87. https://doi.org/10.1038/s41586-020-2923-3
  34. Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., & others. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272.
  35. Alessandretti, L., Aslak, U., & Lehmann, S. (2020). The scales of human mobility. Nature, 587(7834), 402–407. https://doi.org/10.1038/s41586-020-2909-1
  36. Luca, M., Barlacchi, G., Lepri, B., & Pappalardo, L. (2020). A Survey on Deep Learning for Human Mobility. ArXiv Preprint ArXiv:2012.02825.
  37. Reck, D. J., Hensher, D. A., & Ho, C. Q. (2020). MaaS bundle design. Transportation Research Part A: Policy and Practice, 141, 485–501. https://doi.org/10.1016/j.tra.2020.09.021
  38. Joo, R., Boone, M. E., Clay, T. A., Patrick, S. C., Clusella-Trullas, S., & Basille, M. (2020). Navigating through the R packages for movement. Journal of Animal Ecology, 89(1), 248–267. https://doi.org/10.1111/1365-2656.13116
  39. Alessandretti, L., Aslak, U., & Lehmann, S. (2020). The scales of human mobility. Nature, 587(7834), 402–407.
  40. Gössling, S. (2020). Why cities need to take road space from cars - and how this could be done. Journal of Urban Design, 25(4), 443–448. https://doi.org/10.1080/13574809.2020.1727318
  41. Evangeliou, N., Grythe, H., Klimont, Z., Heyes, C., Eckhardt, S., Lopez-Aparicio, S., & Stohl, A. (2020). Atmospheric transport is a major pathway of microplastics to remote regions. Nature Communications, 11(1), 1–11.
  42. Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., & others. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272.
  43. Evangeliou, N., Grythe, H., Klimont, Z., Heyes, C., Eckhardt, S., Lopez-Aparicio, S., & Stohl, A. (2020). Atmospheric transport is a major pathway of microplastics to remote regions. Nature Communications, 11(1), 1–11.
  44. Xin, Y., & MacEachren, A. M. (2020). Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data. International Journal of Geographical Information Science, 34(12), 2497–2516.
  45. Aslak, U., & Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. ArXiv Preprint ArXiv:2003.14370.
  46. Gössling, S. (2020). Why cities need to take road space from cars - and how this could be done. Journal of Urban Design, 25(4), 443–448. https://doi.org/10.1080/13574809.2020.1727318
  47. Reck, D. J., Hensher, D. A., & Ho, C. Q. (2020). MaaS bundle design. Transportation Research Part A: Policy and Practice, 141, 485–501. https://doi.org/10.1016/j.tra.2020.09.021
  48. Schwalb-Willmann, J., Remelgado, R., Safi, K., & Wegmann, M. (2020). moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R. Methods in Ecology and Evolution, 11(5), 664–669.
  49. Zimányi, E., Sakr, M., & Lesuisse, A. (2020). MobilityDB: A mobility database based on PostgreSQL and PostGIS. ACM Transactions on Database Systems (TODS), 45(4), 1–42.
  50. Xin, Y., & MacEachren, A. M. (2020). Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data. International Journal of Geographical Information Science, 34(12), 2497–2516.
  51. Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum, 1, 54–68. https://doi.org/10.1553/giscience2019_01_s54
  52. Cellina, F., Bucher, D., Mangili, F., Veiga Simão, J., Rudel, R., & Raubal, M. (2019). A Large Scale, App-Based Behaviour Change Experiment Persuading Sustainable Mobility Patterns: Methods, Results and Lessons Learnt. Sustainability, 11(9), 2674. https://doi.org/10.3390/su11092674
  53. Martin, H., Becker, H., Bucher, D., Jonietz, D., Raubal, M., & \mboxAxhausen, K. W. (2019). Begleitstudie SBB Green Class - Abschlussbericht. Working Paper No. 1439, Institute for Transport Planning and Systems, ETH Zürich. https://doi.org/10.3929/ethz-b-000353337
  54. Pappalardo, L., Simini, F., Barlacchi, G., & Pellungrini, R. (2019). scikit-mobility: A Python library for the analysis, generation and risk assessment of mobility data. ArXiv Preprint ArXiv:1907.07062.
  55. Bucher, D., Mangili, F., Cellina, F., Bonesana, C., Jonietz, D., & Raubal, M. (2019). From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behaviour and Society, 14, 43–56.
  56. Reed, T. (2019). INRIX Global Traffic Scorecard. https://trid.trb.org/view/1456836
  57. Ben-Gal, I., Weinstock, S., Singer, G., & Bambos, N. (2019). Clustering users by their mobility behavioral patterns. ACM Transactions on Knowledge Discovery from Data (TKDD), 13(4), 1–28.
  58. Bassolas, A., Barbosa-Filho, H., Dickinson, B., Dotiwalla, X., Eastham, P., Gallotti, R., Ghoshal, G., Gipson, B., Hazarie, S. A., Kautz, H., Kucuktunc, O., Lieber, A., Sadilek, A., & Ramasco, J. J. (2019). Hierarchical organization of urban mobility and its connection with city livability. Nature Communications, 10(1), 4817. https://doi.org/10.1038/s41467-019-12809-y
  59. Martin, H., Becker, H., Bucher, D., Jonietz, D., Raubal, M., & Axhausen, K. W. (2019). Begleitstudie SBB Green Class-Abschlussbericht. Arbeitsberichte Verkehrs-Und Raumplanung, 1439.
  60. Huang, H., Cheng, Y., & Weibel, R. (2019). Transport mode detection based on mobile phone network data: A systematic review. Transportation Research Part C: Emerging Technologies, 101, 297–312.
  61. Reed, T. (2019). INRIX Global Traffic Scorecard. https://trid.trb.org/view/1456836
  62. Konkol, M., Kray, C., & Pfeiffer, M. (2019). Computational reproducibility in geoscientific papers: Insights from a series of studies with geoscientists and a reproduction study. International Journal of Geographical Information Science, 33(2), 408–429.
  63. Pappalardo, L., Simini, F., Barlacchi, G., & Pellungrini, R. (2019). scikit-mobility: A Python library for the analysis, generation and risk assessment of mobility data. ArXiv Preprint ArXiv:1907.07062.
  64. Cellina, F., Bucher, D., Mangili, F., Veiga Simão, J., Rudel, R., & Raubal, M. (2019). A Large Scale, App-Based Behaviour Change Experiment Persuading Sustainable Mobility Patterns: Methods, Results and Lessons Learnt. Sustainability, 11(9), 2674. https://doi.org/10.3390/su11092674
  65. Ben-Gal, I., Weinstock, S., Singer, G., & Bambos, N. (2019). Clustering users by their mobility behavioral patterns. ACM Transactions on Knowledge Discovery from Data (TKDD), 13(4), 1–28.
  66. Miller, H. J., Dodge, S., Miller, J., & Bohrer, G. (2019). Towards an integrated science of movement: converging research on animal movement ecology and human mobility science. International Journal of Geographical Information Science, 33(5), 855–876.
  67. Toch, E., Lerner, B., Ben-Zion, E., & Ben-Gal, I. (2019). Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowledge and Information Systems, 58(3), 501–523.
  68. Manousakas, D., Mascolo, C., Beresford, A. R., Chan, D., & Sharma, N. (2018). Quantifying privacy loss of human mobility graph topology. Proceedings on Privacy Enhancing Technologies, 2018(3), 5–21.
  69. Manousakas, D., Mascolo, C., Beresford, A. R., Chan, D., & Sharma, N. (2018). Quantifying privacy loss of human mobility graph topology. Proceedings on Privacy Enhancing Technologies, 2018(3), 5–21.
  70. Toch, E., Lerner, B., Ben-Zion, E., & Ben-Gal, I. (2018). Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowledge and Information Systems, 58(3), 501–523. https://doi.org/10.1007/s10115-018-1186-x
  71. Di Clemente, R., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B., & González, M. C. (2018). Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature Communications, 9(1), 1–8.
  72. Keßler, C., & McKenzie, G. (2018). A geoprivacy manifesto. Transactions in GIS, 22(1), 3–19.
  73. Di Clemente, R., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B., & González, M. C. (2018). Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature Communications, 9(1), 1–8.
  74. Huang, H., Gartner, G., Krisp, J. M., Raubal, M., & Van de Weghe, N. (2018). Location based services: ongoing evolution and research agenda. Journal of Location Based Services, 12(2), 63–93. https://doi.org/10/ghx2v9
  75. Lovelace, R., & Ellison, R. (2018). stplanr: A package for transport planning. The R Journal, 10(2), 7–23.
  76. Alessandretti, L., Sapiezynski, P., Sekara, V., Lehmann, S., & Baronchelli, A. (2018). Evidence for a conserved quantity in human mobility. Nature Human Behaviour, 2(7), 485. https://doi.org/10.1038/s41562-018-0364-x
  77. Keßler, C., & McKenzie, G. (2018). A geoprivacy manifesto. Transactions in GIS, 22(1), 3–19. https://doi.org/10.1111/tgis.12305
  78. Urner, J., Bucher, D., Yang, J., & Jonietz, D. (2018). Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches. ISPRS International Journal of Geo-Information, 24.
  79. Keßler, C., & McKenzie, G. (2018). A geoprivacy manifesto. Transactions in GIS, 22(1), 3–19.
  80. Xu, Y., Çolak, S., Kara, E. C., Moura, S. J., & González, M. C. (2018). Planning for electric vehicle needs by coupling charging profiles with urban mobility. Nature Energy, 3(6), 484–493. https://doi.org/10.1038/s41560-018-0136-x
  81. Alessandretti, L., Sapiezynski, P., Sekara, V., Lehmann, S., & Baronchelli, A. (2018). Evidence for a conserved quantity in human mobility. Nature Human Behaviour, 2(7), 485–491. https://doi.org/10.1038/s41562-018-0364-x
  82. Frick, H., & Kosmidis, I. (2017). trackeR: Infrastructure for running and cycling data from GPS-enabled tracking devices in R. Journal of Statistical Software, 82, 1–29.
  83. Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.
  84. Jiang, S., Ferreira, J., & Gonzalez, 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), 208–219.
  85. Yan, X.-Y., Wang, W.-X., Gao, Z.-Y., & Lai, Y.-C. (2017). Universal model of individual and population mobility on diverse spatial scales. Nature Communications, 8(1), 1639. https://doi.org/10.1038/s41467-017-01892-8
  86. Luo, T., Zheng, X., Xu, G., Fu, K., & Ren, W. (2017). An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories. ISPRS International Journal of Geo-Information, 6(3), 63. https://doi.org/10.3390/ijgi6030063
  87. Wang, D., & Zhou, M. (2017). The built environment and travel behavior in urban China: A literature review. Transportation Research Part D: Transport and Environment, 52, 574–585. https://doi.org/10.1016/j.trd.2016.10.031
  88. Raubal, M., Jonietz, D., Ciari, F., Boulouchos, K., Hirschberg, S., Schenler, W., Cox, B., Kannan, R., Rudel, R., Cellina, F., & others. (2017). Towards an Energy Efficient and Climate Compatible Future Swiss Transportation System.
  89. Raubal, M., Jonietz, D., Ciari, F., Boulouchos, K., Hirschberg, S., Schenler, W., Cox, B., Kannan, R., Rudel, R., Cellina, F., & others. (2017). Towards an Energy Efficient and Climate Compatible Future Swiss Transportation System.
  90. Prelipcean, A. C., Gidófalvi, G., & Susilo, Y. O. (2017). Transportation mode detection–an in-depth review of applicability and reliability. Transport Reviews, 37(4), 442–464.
  91. Jiang, S., Ferreira, J., & Gonzalez, 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), 208–219.
  92. Yan, X.-Y., Wang, W.-X., Gao, Z.-Y., & Lai, Y.-C. (2017). Universal model of individual and population mobility on diverse spatial scales. Nature Communications, 8(1), 1639. https://doi.org/10.1038/s41467-017-01892-8
  93. Das, R. D., & Winter, S. (2016). A context-sensitive conceptual framework for activity modeling. Journal of Spatial Information Science, 2016(12), 45–85. https://doi.org/10.5311/JOSIS.2016.12.260
  94. Yang, D., Zhang, D., & Qu, B. (2016). Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 7(3), 1–23.
  95. El Mahrsi, M. K., Côme, E., Oukhellou, L., & Verleysen, M. (2016). Clustering smart card data for urban mobility analysis. IEEE Transactions on Intelligent Transportation Systems, 18(3), 712–728.
  96. El Mahrsi, M. K., Côme, E., Oukhellou, L., & Verleysen, M. (2016). Clustering smart card data for urban mobility analysis. IEEE Transactions on Intelligent Transportation Systems, 18(3), 712–728.
  97. Yang, D., Zhang, D., & Qu, B. (2016). Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 7(3), 1–23.
  98. 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. Transportation Research Part C: Emerging Technologies, 68, 285–299. https://doi.org/10.1016/j.trc.2016.04.005
  99. Raux, C., Ma, T.-Y., & Cornelis, E. (2016). Variability in daily activity-travel patterns: the case of a one-week travel diary. European Transport Research Review, 8(4), 1–14. https://doi.org/10.1007/s12544-016-0213-9
  100. 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. Transportation Research Part C: Emerging Technologies, 68, 285–299. https://doi.org/10.1016/j.trc.2016.04.005
  101. Pappalardo, L., Rinzivillo, S., & Simini, F. (2016). Human Mobility Modelling: Exploration and Preferential Return Meet the Gravity Model. Procedia Computer Science, 83, 934–939. https://doi.org/10.1016/j.procs.2016.04.188
  102. Das, R. D., & Winter, S. (2016). A context-sensitive conceptual framework for activity modeling. Journal of Spatial Information Science, 2016(12), 45–85. https://doi.org/10.5311/JOSIS.2016.12.260
  103. Weiser, P., Scheider, S., Bucher, D., Kiefer, P., & Raubal, M. (2016). Towards sustainable mobility behavior: Research challenges for location-aware information and communication technology. GeoInformatica, 20(2), 213–239.
  104. Weiser, P., Scheider, S., Bucher, D., Kiefer, P., & Raubal, M. (2016). Towards sustainable mobility behavior: Research challenges for location-aware information and communication technology. GeoInformatica, 20(2), 213–239.
  105. Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461.
  106. Creutzig, F., Jochem, P., Edelenbosch, O. Y., Mattauch, L., van Vuuren, D. P., McCollum, D., & Minx, J. (2015). Transport: A roadblock to climate change mitigation? Science, 350(6263), 911–912.
  107. Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3), 1–41.
  108. Yang, D., Zhang, D., Chen, L., & Qu, B. (2015). Nationtelescope: Monitoring and visualizing large-scale collective behavior in lbsns. Journal of Network and Computer Applications, 55, 170–180.
  109. Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., & Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature Communications, 6(1), 1–8.
  110. Castiglione, J., Bradley, M., & Gliebe, J. (2015). Activity-Based Travel Demand Models: A Primer. SHRP 2 Report, S2-C46-RR-1.
  111. Barbosa, H., de Lima-Neto, F. B., Evsukoff, A., & Menezes, R. (2015). The effect of recency to human mobility. EPJ Data Science, 4(1), 21. https://doi.org/10.1140/epjds/s13688-015-0059-8
  112. Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461.
  113. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
  114. Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., & Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature Communications, 6(1), 8166. https://doi.org/10.1038/ncomms9166
  115. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
  116. Ahas, R., Aasa, A., Yuan, Y., Raubal, M., Smoreda, Z., Liu, Y., Ziemlicki, C., Tiru, M., & Zook, M. (2015). Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. International Journal of Geographical Information Science, 29(11), 2017–2039.
  117. Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., & Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature Communications, 6(1), 1–8.
  118. Creutzig, F., Jochem, P., Edelenbosch, O. Y., Mattauch, L., van Vuuren, D. P., McCollum, D., & Minx, J. (2015). Transport: A roadblock to climate change mitigation? Science, 350(6263), 911–912.
  119. Yang, D., Zhang, D., Chen, L., & Qu, B. (2015). Nationtelescope: Monitoring and visualizing large-scale collective behavior in lbsns. Journal of Network and Computer Applications, 55, 170–180.
  120. Castiglione, J., Bradley, M., & Gliebe, J. (2015). Activity-Based Travel Demand Models: A Primer. SHRP 2 Report, S2-C46-RR-1.
  121. Susilo, Y. O., & Axhausen, K. W. (2014). Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index. Transportation, 41(5), 995–1011. https://doi.org/10.1007/s11116-014-9519-4
  122. Hong, J., Shen, Q., & Zhang, L. (2014). How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales. Transportation, 41(3), 419–440. https://doi.org/10.1007/s11116-013-9462-9
  123. Moiseeva, A., Timmermans, H., Choi, J., & Joh, C.-H. (2014). Sequence Alignment Analysis of Variability in Activity Travel Patterns through 8 Weeks of Diary Data. Transportation Research Record, 2412(1), 49–56. https://doi.org/10.3141/2412-06
  124. Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84), 20130246. https://doi.org/10.1098/rsif.2013.0246
  125. Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84), 20130246.
  126. Gillies, S. (2013). The shapely user manual. https://shapely.readthedocs.io/en/stable/manual.html
  127. Yuan, N. J., Zheng, Y., Zhang, L., & Xie, X. (2013). T-Finder: A Recommender System for Finding Passengers and Vacant Taxis. IEEE Transactions on Knowledge and Data Engineering, 25(10), 2390–2403. https://doi.org/10.1109/TKDE.2012.153
  128. Avineri, E. (2012). On the use and potential of behavioural economics from the perspective of transport and climate change. Journal of Transport Geography, 24, 512–521. https://doi.org/10.1016/j.jtrangeo.2012.03.003
  129. Calenge, C. (2011). Analysis of animal movements in R: the adehabitatLT package. R Foundation for Statistical Computing, Vienna.
  130. Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S. J., & Chong, S. (2011). On the Levy-Walk Nature of Human Mobility. IEEE/ACM Transactions on Networking, 19(3), 630–643. https://doi.org/10.1109/TNET.2011.2120618
  131. Song, C., Qu, Z., Blumm, N., & Barabasi, A.-L. (2010). Limits of Predictability in Human Mobility. Science, 327(5968), 1018–1021. https://doi.org/10.1126/science.1177170
  132. Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.
  133. Song, C., Koren, T., Wang, P., & Barabási, A.-L. (2010). Modelling the scaling properties of human mobility. Nature Physics, 6(10), 818–823. https://doi.org/10.1038/nphys1760
  134. Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.
  135. Zheng, Y., Xie, X., Ma, W.-Y., & others. (2010). Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., 33(2), 32–39.
  136. Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136–145.
  137. González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. https://doi.org/10.1038/nature06958
  138. Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.
  139. Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136–145.
  140. Chapman, L. (2007). Transport and climate change: a review. Journal of Transport Geography, 15(5), 354–367.
  141. Chapman, L. (2007). Transport and climate change: a review. Journal of Transport Geography, 15(5), 354–367.
  142. Fagiolo, G. (2007). Clustering in complex directed networks. Physical Review E, 76(2), 026107.
  143. Fagiolo, G. (2007). Clustering in complex directed networks. Physical Review E, 76(2), 026107.
  144. Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature, 439(7075), 462–465. https://doi.org/10.1038/nature04292
  145. Ratti, C., Frenchman, D., Pulselli, R. M., & Williams, S. (2006). Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33(5), 727–748.
  146. Ratti, C., Frenchman, D., Pulselli, R. M., & Williams, S. (2006). Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33(5), 727–748.
  147. Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature, 439(7075), 462–465.
  148. Calenge, C. (2006). The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling, 197(3-4), 516–519.
  149. Onnela, J.-P., Saramäki, J., Kertész, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71(6), 065103.
  150. Onnela, J.-P., Saramäki, J., Kertész, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71(6), 065103.
  151. Ralaivola, L., Swamidass, S. J., Saigo, H., & Baldi, P. (2005). Graph kernels for chemical informatics. Neural Networks, 18(8), 1093–1110.
  152. Ralaivola, L., Swamidass, S. J., Saigo, H., & Baldi, P. (2005). Graph kernels for chemical informatics. Neural Networks, 18(8), 1093–1110.
  153. Hildebrand, E. D. (2003). Dimensions in elderly travel behaviour: A simplified activity-based model using lifestyle clusters. Transportation, 30(3), 285–306.
  154. Hildebrand, E. D. (2003). Dimensions in elderly travel behaviour: A simplified activity-based model using lifestyle clusters. Transportation, 30(3), 285–306.
  155. Bhattacharya, A., & Das, S. K. (2002). LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks. Wireless Networks, 8(2), 121–135. https://doi.org/10.1023/A:1013759724438
  156. Hornsby, K., & Egenhofer, M. J. (2002). Modeling Moving Objects over Multiple Granularities. Annals of Mathematics and Artificial Intelligence, 36(1-2), 177–194. https://doi.org/10.1023/A:1015812206586
  157. Bhattacharya, A., & Das, S. K. (2002). LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks. Wireless Networks, 8(2), 121–135. https://doi.org/10.1023/A:1013759724438
  158. Hornsby, K., & Egenhofer, M. J. (2002). Modeling Moving Objects over Multiple Granularities. Annals of Mathematics and Artificial Intelligence, 36(1-2), 177–194. https://doi.org/10.1023/A:1015812206586
  159. Mirkin, B. (2001). Eleven ways to look at the chi-squared coefficient for contingency tables. The American Statistician, 55(2), 111–120.
  160. Mirkin, B. (2001). Eleven ways to look at the chi-squared coefficient for contingency tables. The American Statistician, 55(2), 111–120.
  161. Axhausen, K. W., & Gärling, T. (1992). Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems. Transport Reviews, 12(4), 323–341. https://doi.org/10.1080/01441649208716826
  162. Axhausen, K. W., & Gärling, T. (1992). Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems. Transport Reviews, 12(4), 323–341. https://doi.org/10.1080/01441649208716826
  163. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.
  164. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.
  165. Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
  166. Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
  167. Douglas, D. H., & Peucker, T. K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: the International Journal for Geographic Information and Geovisualization, 10(2), 112–122.
  168. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846–850.
  169. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846–850.
  170. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 50–60.
  171. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 50–60.
  172. Barbour, E., Davila, C. C., Gupta, S., Reinhart, C., Kaur, J., & González, M. C. (2019). Planning for sustainable cities by estimating building occupancy with mobile phones. Nature Communications, 10(1), 3736. https://doi.org/10.1038/s41467-019-11685-w
  173. Cuttone, A., Sune Lehmann, & González, M. C. (2018). Understanding predictability and exploration in human mobility. EPJ Data Science, 7(1), 2. https://doi.org/10.1140/epjds/s13688-017-0129-1
  174. Xu, Y., Zou, D., Park, S., Li, Q., Zhou, S., & Li, X. (2022). Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea. Computers, Environment and Urban Systems, 92, 101753. https://doi.org/10.1016/j.compenvurbsys.2021.101753

Conference Articles

  1. Hong, Y., Xin, Y., Martin, H., Bucher, D., & Raubal, M. (2021). A Clustering-Based Framework for Individual Travel Behaviour Change Detection. 11th International Conference on Geographic Information Science (GIScience 2021)-Part II.
  2. Hong, Y., Xin, Y., Martin, H., Bucher, D., & Raubal, M. (2021). A Clustering-Based Framework for Individual Travel Behaviour Change Detection. 11th International Conference on Geographic Information Science (GIScience 2021)-Part II.
  3. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021). “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–15.
  4. Kreil, D. P., Kopp, M. K., Jonietz, D., Neun, M., Gruca, A., Herruzo, P., Martin, H., Soleymani, A., & Hochreiter, S. (2020). The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task–Insights from the IARAI Traffic4cast Competition at NeurIPS 2019. NeurIPS 2019 Competition and Demonstration Track, 232–241.
  5. Kreil, D. P., Kopp, M. K., Jonietz, D., Neun, M., Gruca, A., Herruzo, P., Martin, H., Soleymani, A., & Hochreiter, S. (2020). The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task–Insights from the IARAI Traffic4cast Competition at NeurIPS 2019. NeurIPS 2019 Competition and Demonstration Track, 232–241.
  6. Zhao, P., Bucher, D., Martin, H., & Raubal, M. (2019). A clustering-based framework for understanding individuals’ travel mode choice behavior. International Conference on Geographic Information Science, 77–94.
  7. Zhao, P., Bucher, D., Martin, H., & Raubal, M. (2019). A clustering-based framework for understanding individuals’ travel mode choice behavior. International Conference on Geographic Information Science, 77–94.
  8. Martin, H., Bucher, D., Suel, E., Zhao, P., Perez-Cruz, F., & Raubal, M. (2018). Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data. NeurIPS 2018 Spatiotemporal Workshop.
  9. Martin, H., Bucher, D., Suel, E., Zhao, P., Perez-Cruz, F., & Raubal, M. (2018). Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data. NeurIPS 2018 Spatiotemporal Workshop.
  10. Jonietz, D., Bucher, D., Martin, H., & Raubal, M. (2018). Identifying and interpreting clusters of persons with similar mobility behaviour change processes. The Annual International Conference on Geographic Information Science, 291–307.
  11. Jonietz, D., & Bucher, D. (2018). Continuous trajectory pattern mining for mobility behaviour change detection. LBS 2018: 14th International Conference on Location Based Services, 211–230.
  12. Jonietz, D., Bucher, D., Martin, H., & Raubal, M. (2018). Identifying and interpreting clusters of persons with similar mobility behaviour change processes. The Annual International Conference on Geographic Information Science, 291–307.
  13. Sulis, P., & Manley, E. (2018). Exploring similarities and variations of human mobility patterns in the city of London. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 42(4/W11), 51–58.
  14. Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., & Jin, D. (2018). DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18, 1459–1468. https://doi.org/10.1145/3178876.3186058
  15. Sulis, P., & Manley, E. (2018). Exploring similarities and variations of human mobility patterns in the city of London. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 42(4/W11), 51–58.
  16. Bachir, D., Khodabandelou, G., Gauthier, V., El Yacoubi, M., & Vachon, E. (2018). Combining bayesian inference and clustering for transport mode detection from sparse and noisy geolocation data. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 569–584.
  17. Efstathiades, H., Antoniades, D., Pallis, G., & Dikaiakos, M. D. (2015). Identification of key locations based on online social network activity. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 218–225.
  18. Rinzivillo, S., Gabrielli, L., Nanni, M., Pappalardo, L., Pedreschi, D., & Giannotti, F. (2014). The purpose of motion: Learning activities from individual mobility networks. International Conference on Data Science and Advanced Analytics (DSAA), 312–318.
  19. Rinzivillo, S., Gabrielli, L., Nanni, M., Pappalardo, L., Pedreschi, D., & Giannotti, F. (2014). The purpose of motion: Learning activities from individual mobility networks. International Conference on Data Science and Advanced Analytics (DSAA), 312–318.
  20. Widhalm, P., Nitsche, P., & Brändie, N. (2012). Transport mode detection with realistic smartphone sensor data. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 573–576.
  21. Yuan, Y., & Raubal, M. (2012). Extracting dynamic urban mobility patterns from mobile phone data. International Conference on Geographic Information Science, 354–367.
  22. Yuan, Y., & Raubal, M. (2012). Extracting dynamic urban mobility patterns from mobile phone data. International Conference on Geographic Information Science, 354–367.
  23. McKinney, W. (2010). Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
  24. Ying, J. J.-C., Lu, E. H.-C., Lee, W.-C., Weng, T.-C., & Tseng, V. S. (2010). Mining user similarity from semantic trajectories. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, 19–26.
  25. Ying, J. J.-C., Lu, E. H.-C., Lee, W.-C., Weng, T.-C., & Tseng, V. S. (2010). Mining user similarity from semantic trajectories. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, 19–26.
  26. Zheng, Y., Zhang, L., Xie, X., & Ma, W.-Y. (2009). Mining interesting locations and travel sequences from GPS trajectories. Proceedings of the 18th International Conference on World Wide Web, 791–800.
  27. Shervashidze, N., Vishwanathan, S. V. N., Petri, T., Mehlhorn, K., & Borgwardt, K. (2009). Efficient graphlet kernels for large graph comparison. Artificial Intelligence and Statistics, 488–495.
  28. Shervashidze, N., Vishwanathan, S. V. N., Petri, T., Mehlhorn, K., & Borgwardt, K. (2009). Efficient graphlet kernels for large graph comparison. Artificial Intelligence and Statistics, 488–495.
  29. Zheng, Y., Zhang, L., Xie, X., & Ma, W.-Y. (2009). Mining interesting locations and travel sequences from GPS trajectories. Proceedings of the 18th International Conference on World Wide Web, 791–800.
  30. Zheng, Y., Li, Q., Chen, Y., Xie, X., & Ma, W.-Y. (2008). Understanding Mobility Based on GPS Data. Proceedings of the 10th International Conference on Ubiquitous Computing, 312–321. https://doi.org/10.1145/1409635.1409677
  31. Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., & Ma, W.-Y. (2008). Mining user similarity based on location history. Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 1–10.
  32. Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., & Ma, W.-Y. (2008). Mining user similarity based on location history. Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 1–10.
  33. Zheng, Y., Li, Q., Chen, Y., Xie, X., & Ma, W.-Y. (2008). Understanding Mobility Based on GPS Data. Proceedings of the 10th International Conference on Ubiquitous Computing, 312–321. https://doi.org/10.1145/1409635.1409677
  34. Vassilvitskii, S., & Arthur, D. (2006). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.
  35. Vassilvitskii, S., & Arthur, D. (2006). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.
  36. Borgwardt, K. M., & Kriegel, H.-P. (2005). Shortest-path kernels on graphs. Fifth IEEE International Conference on Data Mining (ICDM’05), 8–pp.
  37. Duckham, M., & Kulik, L. (2005). A formal model of obfuscation and negotiation for location privacy. Pervasive, 3468, 152–170.
  38. Duckham, M., & Kulik, L. (2005). A formal model of obfuscation and negotiation for location privacy. Pervasive, 3468, 152–170.
  39. Borgwardt, K. M., & Kriegel, H.-P. (2005). Shortest-path kernels on graphs. Fifth IEEE International Conference on Data Mining (ICDM’05), 8–pp.
  40. Hariharan, R., & Toyama, K. (2004). Project Lachesis: parsing and modeling location histories. International Conference on Geographic Information Science, 106–124.

Book Chapters

  1. Axhausen, K. W. (2007). Definition of movement and activity for transport modelling. In Handbook of transport modelling. Emerald Group Publishing Limited.
  2. Axhausen, K. W. (2007). Definition of movement and activity for transport modelling. In Handbook of transport modelling. Emerald Group Publishing Limited.

Technical Reports

  1. Martin, H., Reck, D. J., Axhausen, K. W., & Raubal, M. (2021). ETH Mobility Initiative Project MI-01-19 Empirical use and Impact analysis of MaaS. ETH Zurich.
  2. Boulouchos, K., Bach, C., Bauer, C., Bucher, D., Cerruti, D., Dehdarian, A., Filippini, M., Held, M., Hirschberg, S., Kannan, R., & others. (2021). Pathways to a net zero CO2 Swiss mobility system: SCCER Mobility Whitepaper. ETH Zurich.
  3. Martin, H., Reck, D. J., Axhausen, K. W., & Raubal, M. (2021). ETH Mobility Initiative Project MI-01-19 Empirical use and Impact analysis of MaaS. ETH Zurich.
  4. Boulouchos, K., Bach, C., Bauer, C., Bucher, D., Cerruti, D., Dehdarian, A., Filippini, M., Held, M., Hirschberg, S., Kannan, R., & others. (2021). Pathways to a net zero CO2 Swiss mobility system: SCCER Mobility Whitepaper. ETH Zurich.
  5. Pilzecker, A., Fernandez, R., Mandl, N., & Rigler, E. (2020). Annual European Union greenhouse gas inventory 1990–2018 and inventory report 2020. European Environment Agency.
  6. Pilzecker, A., Fernandez, R., Mandl, N., & Rigler, E. (2020). Annual European Union greenhouse gas inventory 1990–2018 and inventory report 2020. European Environment Agency.
  7. 2018 revision of world urbanization prospects. (2018). United Nations.
  8. 2018 revision of world urbanization prospects. (2018). United Nations.
  9. Wilson, A. T. (2014). TrackTable Trajectory Analysis. Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
  10. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab.
  11. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab.

software

  1. Jordahl, K., den Bossche, J. V., Fleischmann, M., McBride, J., Wasserman, J., Badaracco, A. G., Gerard, J., Snow, A. D., Tratner, J., Perry, M., Farmer, C., Hjelle, G. A., Cochran, M., Gillies, S., Culbertson, L., Bartos, M., Ward, B., Caria, G., Taves, M., … Wasser, L. (2021). geopandas/geopandas: v0.10.2 (Version v0.10.2). Zenodo. https://doi.org/10.5281/zenodo.5573592
  2. pandas development team, T. (2020). pandas-dev/pandas: Pandas (latest). Zenodo. https://doi.org/10.5281/zenodo.3509134

Books

  1. Programme, U. N. E. (2020). Emissions Gap Report 2020. United Nations Environment Programme.
  2. for Mobility and Transport (European Commission), D.-G. (2020). EU transport in figures: statistical pocketbook 2020. Publications Office of the European Union.
  3. for Mobility and Transport (European Commission), D.-G. (2020). EU transport in figures: statistical pocketbook 2020. Publications Office of the European Union.
  4. Programme, U. N. E. (2020). Emissions Gap Report 2020. United Nations Environment Programme.
  5. Friedman, J. H. (2017). The elements of statistical learning: Data mining, inference, and prediction. springer open.
  6. Friedman, J. H. (2017). The elements of statistical learning: Data mining, inference, and prediction. springer open.
  7. Schönfelder, S., & Axhausen, K. W. (2016). Urban rhythms and travel behaviour: spatial and temporal phenomena of daily travel. Routledge.
  8. Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39). Cambridge University Press Cambridge.
  9. Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39). Cambridge University Press Cambridge.

online

  1. Graser, A. (2020). Tools for the analysis of movement data. https://github.com/anitagraser/movement-analysis-tools

inbook

  1. Raubal, M., Bucher, D., & Martin, H. (2021). Geosmartness for Personalized and Sustainable Future Urban Mobility. In W. Shi, M. F. Goodchild, M. Batty, M.-P. Kwan, & A. Zhang (Eds.), Urban Informatics (pp. 59–83). Springer Singapore. https://doi.org/10.1007/978-981-15-8983-6_6
  2. Raubal, M., Bucher, D., & Martin, H. (2021). Geosmartness for Personalized and Sustainable Future Urban Mobility. In W. Shi, M. F. Goodchild, M. Batty, M.-P. Kwan, & A. Zhang (Eds.), Urban Informatics (pp. 59–83). Springer Singapore. https://doi.org/10.1007/978-981-15-8983-6_6

Miscellaneous

  1. Martin, H., Hong, Y., Wiedemann, N., Bucher, D., & Raubal, M. (2022). Trackintel: An open-source Python library for human mobility analysis. arXiv. https://doi.org/10.48550/ARXIV.2206.03593
  2. Martin, H., Hong, Y., Wiedemann, N., Bucher, D., & Raubal, M. (2022). Trackintel: An open-source Python library for human mobility analysis. arXiv. https://doi.org/10.48550/ARXIV.2206.03593
  3. Pappalardo, L., Simini, F., Barlacchi, G., & Pellungrini, R. (2019). scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data.
  4. Long, J. A., Weibel, R., Dodge, S., & Laube, P. (2018). Moving ahead with computational movement analysis. In International Journal of Geographical Information Science (Vol. 32, Number 7, pp. 1275–1281). Taylor & Francis.
  5. Long, J. A., Weibel, R., Dodge, S., & Laube, P. (2018). Moving ahead with computational movement analysis. In International Journal of Geographical Information Science (Vol. 32, Number 7, pp. 1275–1281). Taylor & Francis.
  6. A global comparison of the life-cycle greenhouse gas emissions of combustion engine and electric passenger cars \textbar International Council on Clean Transportation.
  7. A global comparison of the life-cycle greenhouse gas emissions of combustion engine and electric passenger cars \textbar International Council on Clean Transportation.