Yanan Xin

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Xin

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Yanan

ORCID

0000-0003-3866-821X

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Publications1 - 10 of 28

Influence of tracking duration on the privacy of individual mobility graphs
Wiedemann, Nina; Martin, Henry; Suel, Esra; et al. (2023)
Journal of Location Based Services
Location graphs, compact representations of human mobility without geocoordinates, can be used to personalise location-based services. While they are more privacy-preserving than raw tracking data, it was shown that they still hold a considerable risk for users to be re-identified solely by the graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match the new tracking data of a user to a pool of previously recorded mobility profiles, and we analyse the dependence of the re-identification performance on the tracking duration. We find that the re-identification accuracy varies between 0.41% and 20.97% and is affected by both the pool duration and the test-user tracking duration, it is greater if both have the same duration, and it is not significantly affected by socio-demographics such as age or gender, but can to some extent be explained by different mobility and graph features. Overall, the influence of tracking duration on user privacy has clear implications for data collection and storage strategies. We advise data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.
Vehicle-to-grid for car sharing - A simulation study for 2030
Wiedemann, Nina; Xin, Yanan; Medici, Vasco; et al. (2024)
Applied Energy
The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologies - a facet that has received limited attention in previous research. In this study, we analyze the potential of V2G in car sharing by designing future scenarios for a national-scale service in Switzerland. We propose an agent-based simulation pipeline that considers population changes as well as different business strategies of the car sharing service, and we demonstrate its successful application for simulating scenarios for 2030. To imitate car sharing user behavior, we develop a data-driven mode choice model. Our analysis reveals important differences in the examined scenarios, such as higher vehicle utilization rates for a reduced fleet size as well as in a scenario featuring new car sharing stations. These disparities translate into variations in the power flexibility of the fleet available for ancillary services, ranging from 12 to 50 MW, depending on the scenario and the time of the day. Furthermore, we conduct a case study involving a subset of the car sharing fleet, incorporating real-world electricity pricing data. The case study substantiates the existence of a sweet spot involving monetary gains for both power grid operators and fleet owners. Our findings provide guidelines to decision makers and underscore the pressing need for regulatory enhancements concerning power trading within the realm of car sharing.
Connecting metros with shared electric scooters: Comparisons with shared bikes and taxis
Ma, Qingyu; Xin, Yanan; Yang, Hong; et al. (2022)
Transportation Research Part D: Transport and Environment
The rapid rise of shared electric scooter (E-Scooter) systems offers urban areas a new micro-mobility solution. The focus on short-distance travel has made it a competitive option for addressing first-/last-mile travel needs. Nevertheless, its role as a first-/last-mile solution was understudied due to the lack of fine-grained trip data. This study aims at exploring the integration of shared E-Scooters with public transportation systems. Specifically, it compared the use of shared E-Scooters against shared bikes and taxis for connecting trips from/to metro stations. We analyzed massive amounts of trip-related data extracted through APIs. Multinomial logistic regression models were developed to uncover how the mode choices from/to metro stations vary in different contexts. The results show that the use of shared E-Scooters to connect trips from/to metro stations can be notably different from shared bikes and taxis. The preference of shared E-Scooters will vary depending on the land use and time period.
Vehicle-To-Grid for Car Sharing
Wiedemann, Nina; Xin, Yanan; Nespoli, Lorenzo; et al. (2023)
Car-sharing services can effectively reduce the number of privately owned cars and thereby CO2 emissions. Furthermore, their batteries may be relevant to providing ancillary services with so-called “vehicle-to-grid” (V2G) technology, i.e., charging and discharging the vehicle dependent on power demand. In this project, we aim to quantify the potential gains of V2G for car sharing owners and grid operators in future scenarios for 2030.
Spatially-aware station based car-sharing demand prediction
Mühlematter, Dominik J.; Wiedemann, Nina; Xin, Yanan; et al. (2024)
Journal of Transport Geography
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms that offer high predictive performance as well as interpretability. Our study utilizes a rich set of socio-demographic, location-based (e.g., POIs), and car-sharing-specific features as input, extracted from a large proprietary car-sharing dataset and publicly available datasets. We first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance with an R-squared score of 0.87 on test data. While a local linear model, Geographically Weighted Regression, performs almost on par in terms of out-of-sample prediction accuracy. We further leverage the models to identify spatial and socio-demographic drivers of car-sharing demand. An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role. In addition, MGWR yields exciting insights into the multiscale heterogeneous spatial distributions of factors influencing car-sharing behaviour. Together, our study offers insights for selecting effective and interpretable methods for diagnosing and planning the placement of car-sharing stations.
Urban Mobility Analytics
Jonietz, David; Sester, Monika; Stewart, Kathleen; et al. (2022)
Dagstuhl Reports
This report documents the program and the outcomes of Dagstuhl Seminar 22162 "Urban Mobility Analytics". The seminar brought together researchers from academia and industry who work in complementary ways on urban mobility analytics. The seminar especially aimed at bringing together ideas and approaches from deep learning research, which is requiring large datasets, and reproducible research, which is requiring access to data.
MATSim in the Open Digital Twin Platform
Grübel, Jascha; Vivar Rios, Carlos; Zuo, Chenyu; et al. (2023)
CSFM Working Paper Series
MATSim simulations play a crucial role for research, industry, and governance. However, simulating mobility systems within MATSim requires large-scale efforts of data and software preparation, data transformation, computing hardware and data visualization. Digital twin offers a novel paradigm on how to manage the data and infrastructure efficiently and make the simulation results available in a reproducible manner with a low barrier to entry. We introduce the first prototype of a digital twin integrating MATSim to enable a common baseline for transport research and beyond. The Open Digital Twin Platform (ODTP) generates specific digital twins dynamically using containerization, loose coupling, and micro-services. In our first iteration, the prototype provides simulations for Switzerland called “CH on the move” consisting of an easy-to-use version of the eqasim pipeline for MATSim. We make it possible to initiate simulations of Switzerland with one click. Future versions of ODTP-based digital twins aim to include a rich set of relevant data sources and analytical pipelines related to transport and mobility and make them easily accessible to research, industry, and governance.
National-scale bi-directional EV fleet control for ancillary service provision
Nespoli, Lorenzo; Wiedemann, Nina; Suel, Esra; et al. (2023)
Energy Informatics
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.
Influence of tracking duration on the privacy of individual mobility graphs
Martin, Henry; Wiedemann, Nina; Suel, Esra; et al. (2022)
Proceedings: 17th International Conference on Location Based Services (LBS2022)
Location graphs are a compact representation of individual mobility that can be used as a mobility profile to personalize location-based services. While location graphs are more privacy-preserving than raw tracking data, it was shown that there is still a considerable risk for users to be re-identified by their mobility graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match new tracking data of a user to a pool of previously recorded mobility profiles, and we analyze the dependence of the re-identification performance on the tracking duration. For our experiment, we use a one-year long tracking dataset of 137 users divided into subsets of varying durations (4, 8, 16, 20, 24, and 28 weeks). We find that the re- identification performance increases with growing pool- and test-user tracking duration, and even the smallest tested duration allows to match users significantly better than random. The provided evidence for a tracking duration dependency of user privacy has clear implications for the data collection and storage strategies. It is advised for data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.
CH on the move
Grübel, Jascha; Vivar Rios, Carlos; Balac, Milos; et al. (2023)
Mobility simulations are crucial for research, industry, and governance. However, simulating mobility systems with reasonable accuracy requires large-scale efforts of data collection, data transformation, data analysis, and data visualization. The paradigm of the digital twin offers a novel perspective on how to manage the data efficiently and make the simulations available more steadily at a lower cost. We introduce the first prototype of the “CH on the move” digital twin that is designed to be openly available to all interested parties to enable a common baseline for transport research in Switzerland. This digital twin is based on the extensible Open Digital Twin Platform (ODTP) that uses containerization, loose coupling, and micro-services to provide dynamically composable digital twins. In its first iteration, “CH on the move” provides an easy-to-use version of the eqasim pipeline for MATSim making it possible to initiate simulations of Switzerland with one click. Future versions of “CH on the move” aim to include a rich set of relevant data sources and analytical pipelines related to transport and mobility and make them easily accessible to research, industry, and governance.

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Yanan Xin

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