Capacity Area B1 deals with increasing mobility energy efficiency from a systemic perspective. This approach takes all aspects of mobility into account, i.e. mobility technology, infrastructure and users, and relates them to mobility patterns, urban planning and environmental data. One focus lies on designing and optimizing the infrastructure for renewable energy carriers (supply of charging stations, hydrogen filling stations and logistics). On the user level, research deals with assessing new IT and information service technologies to foster energy-saving mobility choices. Capacity Area B1 interlinks mobility choices and patterns with environmental and spatial planning to develop a decision support tool for consumers, municipalities and policy makers leading to energy demand reduction.
Prof. Dr. Martin Raubal
Chair of Geoinformation Engineering at ETH Zürich
mraubal@ethz.ch / 044 633 30 26
ETH Zürich
Chair of Geoinformation Engineering, IKG
Prof. Dr. Martin Raubal, Coordinator
ETH Zürich
Institut für Umweltingenieurwissenschaften, IfU-ESD
Prof. Dr. Stefanie Hellweg, Deputy Coordinator
Berner Fachhochschule BFH
Architektur, Holz und Bau, AHB
Prof. Dr. Joachim Huber
ETH Zürich
Aerothermochemistry and Combustion Systems Laboratory, LAV
Dr. Gil Georges
ETH Zürich
Institut für Verkehrsplanung und Transportsysteme, IVT
Prof. Dr. Kay Axhausen
ETH Zürich
Institut für Verkehrsplanung und Transportsysteme, IVT
Prof. Dr. Francesco Corman
ETH Zürich
Power Systems Laboratory
Prof. Dr. Gabriela Hug
SUPSI
Dalle Molle Institute for Artificial Intelligence
Prof. Dr. Luca Maria Gambardella
Hochschule Luzern HSLU (phase I)
Center of Competence IIEE, Efficient Energy Systems, IIEE/ES
Prof. Vinzenz Haerri
Multimodal Vehicle Integration and Charging Infrastructure (B1.1)
Spatio-temporal Data Acquisition & Analysis, Monitoring Devices and User Communication (B1.2)
Personalized energy mobility app prototype summary
Contact: M. Raubal
Urban Planning & Environmental Impact (B1.3)
Optimizing energy efficiency and infrastructure usage of railway operation
Optimal fleet composition and energy infrastructures for road-based mobility
Capacities of energy infrastructures with emphasis on the electric grid
Assessment of mobility choices in a geographic and socio-economic context
Information service for sustainable mobility choices
Prototype of transport need matching system
Household consumption modelling
Simulation of prospective household mobility behaviour
Impact of urban structures and planning activities on mobility
Erkenntnisse aus einem digitalen Modell (findings of a digital model)
Zwischenstand des laufenden Forschungsprojekts (intermediate results of the ongoing research project)
research conducted by SCCER Mobility CA B1 members Prof. Dr. Joachim Huber and Michael Walczak,
Berne University of Applied Sciences, Architecture, Wood and Civil Engineering
More information www.dencity.ch
Documents
Brochure (in german)
Analysis-diagram-spirit
Fact sheet
e-MIP - electro-Mobility-Information Planning
The innovative e-MIP project aims to optimize bus routes by using coherent, quantitative and spatial simulation and evaluation based on big-data. This collaborative effort between Dencity, ETHZ and HESS AG will promote improved land use in urban living spaces and reduced or neutral CO2 emissions in these areas.
Contact Joachim Huber
Documents
Fact sheet e-MIP (pdf)
Kick-off meeting e-MIP (pdf)
Balac, M., Hörl, S., & Axhausen, K. W. (2020). Fleet Sizing for Pooled (Automated) Vehicle Fleets. Transportation Research Record: Journal of the Transportation Research Board, 2674(9), 168–176. https://doi.org/10.1177/0361198120927388
Bucher, D., Martin, H., Hamper, J., Jaleh, A., Becker, H., Zhao, P., & Raubal, M. (2020). Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles. AGILE: GIScience Series, 1, 1–23. https://doi.org/10.5194/agile-giss-1-2-2020
Bucher, D., Martin, H., Jonietz, D., Raubal, M., & Westerholt, R. (2020). Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements. Leibniz International Proceedings in Informatics, LIPIcs, 177. https://doi.org/10.4230/LIPIcs.GIScience.2021.I.2
Froemelt, A., Buffat, R., & Hellweg, S. (2020). Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts. Journal of Industrial Ecology, 24(3), 639–652. https://doi.org/10.1111/jiec.12969
Livingston, C., Hörl, S., Bruns, F., Fischer, R., & Axhausen, K. W. c. (2020). Forecasting a future with automated vehicles in Switzerland Exploring the urban-rural divide and system effects. Arbeitsberichte Verkehrs- Und Raumplanung, 1541. https://doi.org/10.3929/ethz-b-000429755
Martin, H., Bucher, D., Hong, Y., Buffat, R., Rupprecht, C., Raubal, M., … Hadsell, R. (2020). Graph-ResNets for short-term traffic forecasts in almost unknown cities. In Proceedings of Machine Learning Research (Vol. 123). http://proceedings.mlr.press/v123/martin20a.html
Molloy, J., Tchervenkov, C., Hintermann, B., & Axhausen, K. W. (2020). Tracing the Sars-CoV-2 impact The first month in Switzerland ETH Library. Transport Findings. https://doi.org/10.3929/ethz-b-000424218
Raubal, M. (2020). Spatial data science for sustainable mobility. Journal of Spatial Information Science, 20(20), 109–114. https://doi.org/10.5311/JOSIS.2020.20.651
Raubal, M., Bucher, D., & Martin, H. (2020). Geosmartness for personalized and sustainable future urban mobility. In Urban Informatics. Retrieved from https://www.research-collection.ethz.ch/handle/20.500.11850/410300
Sessa, P. G., De Martinis, V., Bomhauer-Beins, A., Weidmann, U. A., & Corman, F. (2020). A hybrid stochastic approach for offline train trajectory reconstruction. Public Transport, 1–24. https://doi.org/10.1007/s12469-020-00230-4
Stiasny, J., Zufferey, T., Pareschi, G., Toffanin, D., Hug, G., & Boulouchos, K. (2020). Sensitivity analysis of electric vehicle impact on low-voltage distribution grids. Electric Power Systems Research, 191, 106696. https://doi.org/10.1016/j.epsr.2020.106696
Tchervenkov, C., Hörl, S., Balac, M., Dubernet, T., & Axhausen, K. W. (2020). An improved replanning strategy for congested traffic conditions in MATSim. Procedia Computer Science, 170, 779–784. https://doi.org/10.1016/j.procs.2020.03.156
Zhao, P., Liu, X., Shi, W., Jia, T., Li, W., & Chen, M. (2020). An empirical study on the intra-urban goods movement patterns using logistics big data. International Journal of Geographical Information Science, 34(6), 1089–1116. https://doi.org/10.1080/13658816.2018.1520236
Zhao, P., Xu, Y., Liu, X., & Kwan, M. P. (2020). Space-time dynamics of cab drivers’ stay behaviors and their relationships with built environment characteristics. Cities, 101, 102689. https://doi.org/10.1016/j.cities.2020.102689
Zufferey, T., Renggli, S., & Hug, G. (2020). Probabilistic State Forecasting and Optimal Voltage Control in Distribution Grids under Uncertainty. Electric Power Systems Research, 188, 106562. https://doi.org/10.1016/j.epsr.2020.106562
Balać, M., Hörl, S., & Axhausen, K. W. (2019). Fleet sizing for pooled automated vehicle fleets. Arbeitsberichte Verkehrs- Und Raumplanung, 1455. https://doi.org/10.3929/ETHZ-B-000357297
Bucher, D., Buffat, R., Froemelt, A., & Raubal, M. (2019). Energy and greenhouse gas emission reduction potentials resulting from different commuter electric bicycle adoption scenarios in Switzerland. Renewable and Sustainable Energy Reviews, 114, 109298. https://doi.org/10.1016/j.rser.2019.109298
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. https://doi.org/10.1016/J.TBS.2018.09.005
Buffat, R., Heeren, N., Froemelt, A., & Raubal, M. (2019). Impact of CH2018 Climate Change Scenarios for Switzerland on today’s Swiss building stock. Journal of Physics: Conference Series, 1343(1), 12004. https://doi.org/10.1088/1742-6596/1343/1/012004
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
Cellina, F., Bucher, D., Veiga Simão, J., Rudel, R., & Raubal, M. (2019). Beyond Limitations of Current Behaviour Change Apps for Sustainable Mobility: Insights from a User-Centered Design and Evaluation Process. Sustainability, 11(8), 2281. https://doi.org/10.3390/su11082281
Chin, J. X., Zufferey, T., Shyti, E., & Hug, G. (2019). Load forecasting of privacy-aware consumers. 2019 IEEE Milan PowerTech, PowerTech 2019. https://doi.org/10.1109/PTC.2019.8810874
Cucurachi, S., Schiess, S., Froemelt, A., & Hellweg, S. (2019). Noise footprint from personal land‐based mobility. Journal of Industrial Ecology, 23(5), 1028–1038. https://doi.org/10.1111/jiec.12837
De Martinis, V., & Corman, F. (2019). Online microscopic calibration of train motion models. RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden, June 17th – 20th, 2019, 69, 917–932. https://doi.org/10.3929/ETHZ-B-000368489
Hörl, S., & Axhausen, K. W. (2019). Relaxation-discretization algorithm for spatially constrained secondary location assignment. 20–01108. https://doi.org/10.3929/ethz-b-000378016
Hörl, S., Becker, F., Dubernet, T. J. P., & Axhausen, K. W. (2019). Induzierter Verkehr durch autonome Fahrzeuge (Vol. 1650). Retrieved from Eidgenössisches Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK); Bundesamt für Strassen (ASTRA) website: https://www.research-collection.ethz.ch/handle/20.500.11850/346381
Huang, H., Bucher, D., Kissling, J., Weibel, R., & Raubal, M. (2019). Multimodal Route Planning with Public Transport and Carpooling. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3513–3525. https://doi.org/10.1109/TITS.2018.2876570
Huang, J., Liu, X., Zhao, P., Zhang, J., & Kwan, M.-P. (2019). Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival. ISPRS International Journal of Geo-Information, 8(10), 445. https://doi.org/10.3390/ijgi8100445
Marra, A. D., Becker, H., Axhausen, K. W., & Corman, F. (2019). Developing a passive GPS tracking system to study long-term travel behavior. Transportation Research Part C: Emerging Technologies, 104, 348–368. https://doi.org/10.1016/j.trc.2019.05.006
Martin, H., Hong, Y., Bucher, D., Rupprecht, C., & Buffat, R. (2019). Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab. ArXiv. https://doi.org/10.3929/ethz-b-000388707
Miller, H. J., Jaegal, Y., & Raubal, M. (2019). Measuring the Geometric and Semantic Similarity of Space–Time Prisms Using Temporal Signatures. Annals of the American Association of Geographers, 109(3), 730–753. https://doi.org/10.1080/24694452.2018.1484686
Molloy, J., Schmid, B., & Becker, F. (2019). mixl : An open-source R package for estimating complex choice models on large datasets. Wp, 1408. https://doi.org/10.3929/ethz-b-000334289
Pritchard, R., Bucher, D., & Frøyen, Y. (2019). Does new bicycle infrastructure result in new or rerouted bicyclists? A longitudinal GPS study in Oslo. Journal of Transport Geography, 77, 113–125. https://doi.org/10.1016/j.jtrangeo.2019.05.005
Pun, L., Zhao, P., & Liu, X. (2019). A Multiple Regression Approach for Traffic Flow Estimation. IEEE Access, 7, 35998–36009. https://doi.org/10.1109/ACCESS.2019.2904645
Raubal, M. (2019). It’s the Spatial Data Science, stupid! https://doi.org/10.3929/ethz-b-000378440
Sailer, C., Rudi, D., Kurzhals, K., & Raubal, M. (2019). Towards Seamless Mobile Learning with Mixed Reality on Head-Mounted Displays. World Conference on Mobile and Contextual Learning, 2019(1), 69–76. https://doi.org/10.3929/ethz-b-000365881
Sessa, P. G., De Martinis, V., & Corman, F. (2019). Filtering approaches for online train motion estimation with onboard power measurements. Computer-Aided Civil and Infrastructure Engineering. https://doi.org/10.1111/mice.12514
Toffanin, D., & Ulbig, A. (2019). Taming uncertainty in distribution grid planning – A scenario-based methodology for the analysis of impact of electric vehicles. 25thInternational Conference on Electricity Distribution. Retrieved from https://www.cired-repository.org/handle/20.500.12455/660
Zufferey, T., Lepouze, A., & Hug, G. (2019). Inadequacy of standard algorithms and metrics for short-term load forecasts in low-voltage grids. 2019 IEEE Milan PowerTech, PowerTech 2019. https://doi.org/10.1109/PTC.2019.8810430
Bucher, D., Mangili, F., Bonesana, C., Jonietz, D., Cellina, F., & Raubal, M. (2018). Demo Abstract: Extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior. Computer Science - Research and Development, 33(1–2), 267–268. https://doi.org/10.1007/s00450-017-0375-2
Bucher, D., Rudi, D., & Buffat, R. (2018). Captcha Your Location Proof—A Novel Method for Passive Location Proofs in Adversarial Environments. In Lecture Notes in Geoinformation and Cartography (pp. 269–291). Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_14
Buffat, R., Bucher, D., & Raubal, M. (2018). Using locally produced photovoltaic energy to charge electric vehicles. Computer Science - Research and Development, 33(1–2), 37–47. https://doi.org/10.1007/s00450-017-0345-8
Çabukoglu, E., Georges, G., Küng, L., Pareschi, G., & Boulouchos, K. (2018). Battery electric propulsion: An option for heavy-duty vehicles? Results from a Swiss case-study. Transportation Research Part C: Emerging Technologies, 88, 107–123. https://doi.org/10.1016/J.TRC.2018.01.013
De Martinis, V., & Corman, F. (2018). Data-driven perspectives for energy efficient operations in railway systems: Current practices and future opportunities. Transportation Research Part C: Emerging Technologies, 95, 679–697. https://doi.org/10.1016/J.TRC.2018.08.008
De Martinis, V., Toletti, A., Corman, F., Weidmann, U. A., & Nash, A. (2018). Feedforward Tactical Optimization for Energy-Efficient Operation of Freight Trains: The Swiss Case. Transportation Research Record: Journal of the Transportation Research Board, 2672(10), 278–288. https://doi.org/10.1177/0361198118776508
Frischknecht, R., Bauer, C., Froemelt, A., Hellweg, S., Biemann, K., Buetler, T., Cox, B., de Haan, P., Hoerl, S., Itten, R., Jungbluth, N., Ligen, Y., Mathys, N. A., Schiess, S., Schori, S., van Loon, P., Wang, J., & Wettstein, S. (2018). LCA of mobility solutions: approaches and findings—66th LCA forum, Swiss Federal Institute of Technology, Zurich, 30 August, 2017. The International Journal of Life Cycle Assessment, 23(2), 381–386. https://doi.org/10.1007/s11367-017-1429-1
Froemelt, A., Buffat, R., Heeren, N., & Hellweg, S. (2018). Assessing environmental impacts of individual households: A large-scale bottom-up LCA-model for Switzerland. SETAC Europe 28th Annual Meeting, Rome, Italy, May 13-17,2018, 147, 32–33. https://www.research-collection.ethz.ch/handle/20.500.11850/291071
Froemelt, A., Dürrenmatt, D. J., & Hellweg, S. (2018). Using Data Mining To Assess Environmental Impacts of Household Consumption Behaviors. Environmental Science & Technology, 52(15), 8467–8478. https://doi.org/10.1021/acs.est.8b01452
Jonietz, D., & Bucher, D. (2018). Continuous Trajectory Pattern Mining for Mobility Behaviour Change Detection. In Lecture Notes in Geoinformation and Cartography (pp. 211–230). Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_11
Jonietz, D., Bucher, D., Martin, H., & Raubal, M. (2018). Identifying and Interpreting Clusters of Persons with Similar Mobility Behaviour Change Processes (pp. 291–307). Springer, Cham. https://doi.org/10.1007/978-3-319-78208-9_15
Küng, L., Bütler, T., Georges, G., & Boulouchos, K. (2018). Decarbonizing passenger cars using different powertrain technologies: Optimal fleet composition under evolving electricity supply. Transportation Research Part C: Emerging Technologies, 95, 785–801. https://doi.org/10.1016/J.TRC.2018.09.003
Martin, H., Bucher, D., Suel, E., Zhao, P., & Perez-Cruz, F. (2018). Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data. NIPS Workshop, Nips, 1–6. https://doi.org/10.3929/ethz-b-000310251
Sessa, P. G., De Martinis, V., Bomhauer-Beins, A., Corman, F., & Weidmann, U. (2018). Hybrid stochastic approaches for train trajectory reconstruction. Conference on Advanced Systems in Public Transport and TransitData (CASPT 2018), Brisbane, Australia. https://www.research-collection.ethz.ch/handle/20.500.11850/281347
Urner, J., Bucher, D., Yang, J., Jonietz, D., 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, 7(5), 166. https://doi.org/10.3390/ijgi7050166
Valverde, G., Zufferey, T., Karagiannopoulos, S., & Hug, G. (2018). Estimation of voltage sensitivities to power injections using smart meter data. 2018 IEEE International Energy Conference (ENERGYCON), 1–6. https://doi.org/10.1109/ENERGYCON.2018.8398841
Zufferey, T., Toffanin, D., Toprak, D., Ulbig, A., & Hug, G. (2018). Generating Stochastic Residential Load Profiles from Smart Meter Data for an Optimal Power Matching at an Aggregate Level. 2018 Power Systems Computation Conference (PSCC), 1–7. https://doi.org/10.23919/PSCC.2018.8442470
Zufferey, T., Ulbig, A., Koch, S., & Hug, G. (2018). Unsupervised Learning Methods for Power System Data Analysis. Big Data Application in Power Systems, 107–124. https://doi.org/10.1016/B978-0-12-811968-6.00006-1
Bucher, D., Scheider, S., & Raubal, M. (2017). A Model and Framework for Matching Complementary Spatio-Temporal Needs. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL’17, 1–4. https://doi.org/10.1145/3139958.3140038
Buffat, R., Froemelt, A., Heeren, N., Raubal, M., & Hellweg, S. (2017). Big data GIS analysis for novel approaches in building stock modelling. Applied Energy, 208, 277–290. https://doi.org/10.1016/J.APENERGY.2017.10.041
Buffat, R., Schmid, L., Heeren, N., Froemelt, A., Raubal, M., & Hellweg, S. (2017). GIS-based Decision Support System for Building Retrofit. Energy Procedia, 122, 403–408. https://doi.org/10.1016/J.EGYPRO.2017.07.433
Ciari, F., & Becker, H. (2017). How Disruptive Can Shared Mobility Be? A Scenario-Based Evaluation of Shared Mobility Systems Implemented at Large Scale. https://doi.org/10.1007/978-3-319-51602-8_3
Frischknecht, R., Bauer, C., Froemelt, A., Hellweg, S., Biemann, K., Buetler, T., … Wettstein, S. (2018). LCA of mobility solutions: approaches and findings—66th LCA forum, Swiss Federal Institute of Technology, Zurich, 30 August, 2017. The International Journal of Life Cycle Assessment, 23(2), 381–386. https://doi.org/10.1007/s11367-017-1429-1
Froemelt, A., & Hellweg, S. (2017). Assessing Space Heating Demandon a Regional Level: Evaluation of a Bottom-Up Model in the Scope of a Case Study. Journal of Industrial Ecology, 21(2), 332–343. https://doi.org/10.1111/jiec.12438
Jonietz, D., Antonio, V., See, L., & Zipf, A. (2017). Highlighting Current Trends in Volunteered Geographic Information. ISPRS International Journal of Geo-Information, 6(7), 202. https://doi.org/10.3390/ijgi6070202
Toletti, A., De Martinis, V., & Weidmann, U. A. (2017). Enhancing energy efficiency in railway operation through RCG-based rescheduling. 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 1–6. https://doi.org/10.1109/EEEIC.2017.7977624
Zufferey, T., Ulbig, A., Koch, S., & Hug, G. (2017). Forecasting of Smart Meter Time Series Based on Neural Networks. https://doi.org/10.1007/978-3-319-50947-1_2
Jonietz, D. (2016). Personalizing Walkability: A Concept for Pedestrian Needs Profiling Based on Movement Trajectories (pp. 279–295). Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_16
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. https://doi.org/10.1007/s10707-015-0242-x
Ahas, R., Aasa, A., Yuan, Y., Raubal, M., Smoreda, Z., Liu, Y., … 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. https://doi.org/10.1080/13658816.2015.1063151
Allemann, D., & Raubal, M. (2015). Usage Differences Between Bikes and E-Bikes (pp. 201–217). Springer, Cham. https://doi.org/10.1007/978-3-319-16787-9_12
De Martinis, V., & Weidmann, U. A. (2015). Definition of energy-efficient speed profiles within rail traffic by means of supply design models. Research in Transportation Economics, 54, 41–50. https://doi.org/10.1016/J.RETREC.2015.10.024
Haerri, V. V., Lindegger, M., & Neumaier, M. (2015). A novel interior permanent synchronous motor for a high end ebike drive chain. In 2015 5th International Electric Drives Production Conference (EDPC) (pp. 1–6). IEEE. https://doi.org/10.1109/EDPC.2015.7323228
Toletti, A., De Martinis, V., & Weidmann, U. (2015). What about Train Length and Energy Efficiency of Freight Trains in Rescheduling Models? Transportation Research Procedia, 10, 584–594. https://doi.org/10.1016/J.TRPRO.2015.09.012
De Martinis, V., Weidmann, U., & Gallo, M. (2014). Towards a simulation-based framework for evaluating energy-efficient solutions in train operation. In U. Weidmann & M. Gallo (Eds.), WIT Transactions on The Built Environment (Vol. 135, pp. 721–732). WIT Press. https://doi.org/10.2495/CR140601
Saner, D., Vadenbo, C., Steubing, B., & Hellweg, S. (2014). Regionalized LCA-Based Optimization of Building Energy Supply: Method and Case Study for a Swiss Municipality. Environmental Science & Technology, 48(13), 7651–7659. https://doi.org/10.1021/es500151q
Yuan, Y., & Raubal, M. (2014). Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method. International Journal of Geographical Information Science, 28(3), 496–520. https://doi.org/10.1080/13658816.2013.854369