Multimodal Vehicle Integration and Charging Infrastructure (B1.1)

• Analysis of overall optimization potential for alternative transport systems. | Contact: V.Härri
• Technical simulation for more efficient transport systems. summary deliverable
  Contact: U. Weidmann
• Pilot Project for Interconnection of Electrical Charging Devices and substations. Efficiency improvement and dissemination.
  Contact:V. Härri

Spatio-temporal Data Acquisition & Analysis, Monitoring Devices and User Communication (B1.2)

• System requirements and specifications for and ICT- and Sensor-Based Monitoring Framework | Contact: M. Raubal
• Implementation and extension of a model for real-time automatic matching of complementary transport needs
  Contact: M. Raubal
• Transport simulation implementation: Sharing and Autonomous Driving. | Contact: K. Axhausen

• Personalized energy mobility app prototype summary
  Contact: M. Raubal

Urban Planning & Environmental Impact (B1.3)

• Models and definitions: Decision-making process and spatial mobility model based on Building Information
  Modeling BIM [M6]; Stakeholders definition [M8].
• Inventory of spatial planning data, regulations, law and standards, LCiA [M12].
• Prototype of decision-making tool with limited function including home-work-leisure interaction and planning tool for e-mobility [M24]

Optimizing energy efficiency and infrastructure usage of railway operation

• Data collection of on-board monitoring data (first phase) [3, 2018]
• Calibration and validation models for railway simulation modelling [1, 2019]
• Energy efficient strategies in railway operation: towards autonomous driving in mixed traffic national rail networks [12, 2020]

Optimal fleet composition and energy infrastructures for road-based mobility

• Research proposal ready for submission [6, 2017]
• Release and publication of the optimization framework [12, 2017]
• Documentation or paper, proof of concept [1, 2018]
• Complete first example application based on micro-census [6, 2018]
• Documentation or paper, aggregated examples based on micro-census [12, 2018]
• Integration strategies for study region: final report case study 1 [6, 2020], and publication of generalization for other regions

Capacities of energy infrastructures with emphasis on the electric grid

• Modelling of the distribution grid of a benchmark distribution grid and, if possible of the concrete partnering region(s) including simulations of the respective grids typical usage patterns as given by load demand and local PV production time-series inside the grid platform DPG.sim [12, 2017]
• Technical report on distribution grid modelling and base-case simulation results [1, 2018]
• Development of techno-economic assessment concept for cost-effective distribution grid adaption due to electric mobility charging demands. Validation of the assessment concept based on simplified mobility-induced electric charging profiles inside the distribution grid simulation framework and development [12, 2018]
• Technical report or paper on electric mobility’s charging impacts and cost-effective distribution grid adaptation [1, 2019]
• Incorporation of detailed mobility-induced electric charging profiles into distribution grid simulation and adaptation framework [12, 2019]
• Final report or paper on electric mobility’s charging impacts and cost-effective distribution grid adaptation incl. generalizable grid adaptation recommendations for distribution grid operators [12, 2020]

Assessment of mobility choices in a geographic and socio-economic context

• Main reasons for and against choosing sustainable or non-sustainable mobility options in a particular geographic or socio-economic context identified [12, 2018]
• Model-driven questionnaire [12, 2017]
• Results of questionnaires and documentation [12, 2018]

Information service for sustainable mobility choices

• Sustainable mobility choice recommender system [12, 2020]
• Prototypical implementation of a mobility choice recommender system [12, 2020]

Prototype of transport need matching system

• Software prototype of transport need matching system & transfer to SUPSI team (within the framework of the NFP Go Eco! project) [12, 2017]
• Standards for web-based publication of transport needs [6, 2017]
• Algorithm for multi-modal and energy-efficient transportation modes [9, 2017]

Household consumption modelling

• Household consumption model quantifying environmental impacts of mobility and other consumption areas for all Swiss households [1, 2018]
• Analysis of causal relationships and trade-offs between mobility and other consumption areas [7, 2018]

Simulation of prospective household mobility behaviour

• Updated MATSim model (including integration of models from B1.2.1) [6, 2019]
• Documentation and open-source code for the updated MATSim model [6, 2019]
• Calibration results of updated MATSim model [12, 2019]
• Results of the simulation study integrating prospective household behaviour [11, 2020]
• Archive of the data and code used [12, 2020]
• Investigation of measures/incentive systems on the mobility impacts of individual households in future scenarios [4, 2020]

Impact of urban structures and planning activities on mobility

• Cluster analysis of settlement typologies inducing varying mobility behaviour is identified [10, 2018]
• Prototype for decision-making tool validated and implemented into external practice [10, 2020]

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)

2020

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

2019

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

2018

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

2017

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

2016

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

2015

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

2014

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