Ye Hong

Last Name

Hong

First Name

Ye

ORCID

0000-0002-8996-3748

Organisational unit

03901 - Raubal, Martin / Raubal, Martin

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Publications 1 - 10 of 29

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.
Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism
Yao, Yao; Guo, Zijin; Dou, Chen; et al. (2023)
Computers, Environment and Urban Systems
Predicting the next location of human mobility and its semantic information can support recommendations for location-based services and trajectory mining, such as human mobility pattern recognition and sequential anomaly detection. Previous studies have ignored the implicit correlation between location and spatiotemporal information thereby limiting the model performance in terms of location prediction accuracy. In this study, we propose a GEMA-BiLSTM (Geographical Embedding and Multilayer Attention -Bidirectional Long Short-Term Memory) model to predict the next-location in users' mobility. The model combines location and spatiotemporal information to extract the semantics of human mobility. The results show that the model can accurately predict the next location with a high accuracy of 87.63%. Compared with BiLSTM-CNN, LSTM, CNN, and Markov, the location prediction accuracy of the model improved by 2.28%, 9.72%, 11.53%, and 17.64%, respectively. In addition, the model has the highest semantic prediction accuracy (75.35%). Compared with the BiLSTM-CNN model, the our method improves the semantic prediction accuracy for residential and industrial function areas by 4.79% and 5.37%, respectively. The accuracy of location prediction for different time periods indicates that the next location of human activity during morning rush and evening rush hours is the most difficult to predict, which corresponds to the increase in human travel demand. Moreover, weekday human activity patterns indicate that the commercial area is still very active at night, which may be linked to nighttime economic policies. This study could improve the accuracy of recommendations for location-based service applications.
Spatiotemporal distribution of human trafficking in China and predicting the locations of missing persons
Yao, Yao; Liu, Yifei; Guan, Qingfeng; et al. (2021)
Computers, Environment and Urban Systems
In China, the illegal adoption of missing persons and especially of missing children is a major public safety issue that affects social and family stability. Recent work has established a trafficking information network developed from a volunteer-managed database of missing persons that identifies and locates node cities and critical paths of illegal adoption. In order to evaluate locations where trafficking can be identified and provide direct advice for affected families, this study analyses the temporal and spatial distribution of the missing population and explores factors that affect their transfer. We use spatiotemporal information to construct multiple random forest (RF) models for predicting the locations of missing persons transfer on a larger spatial scale. The proposed independent RF models, namely, provinces potentially entered, destination grids, relative distances and relative directions models, achieve high levels of accuracy. Moreover, an integrated RF-based city-level prediction model can effectively locate the city a missing person was trafficked to. From our driving factor analysis, the transfer paths are strongly correlated with source provinces and grids. The study also shows that the transfer of missing persons is driven by multiple factors rather than by a single element. © 2020 Elsevier Ltd.
Evaluating geospatial context information for travel mode detection
Hong, Ye; Stüdeli, Emanuel; Raubal, Martin (2023)
Journal of Transport Geography
Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the significance of these context features, hindering the development of an efficient model. Here, we identify context representations from related work and propose an analytical pipeline to assess the contribution of geospatial context information for travel mode detection based on a random forest model and the SHapley Additive exPlanation (SHAP) method. Through experiments on a large-scale GNSS tracking dataset, we report that features describing relationships with infrastructure networks, such as the distance to the railway or road network, significantly contribute to the model's prediction. Moreover, features related to the geospatial point entities help identify public transport travel, but most land-use and land-cover features barely contribute to the task. We finally reveal that geospatial contexts have distinct contributions in identifying different travel modes, providing insights into selecting appropriate context information and modeling approaches. The results from this study enhance our understanding of the relationship between movement and geospatial context and guide the implementation of effective and efficient transport mode detection models.
Is a 15-Minute City Within Reach? Measuring Multimodal Accessibility and Carbon Footprint in 12 Major American Cities
Jin, Tanhua; Wang, Kailai; Xin, Yanan; et al. (2024)
Land Use Policy
Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms. The concept of a 15-minute city, emphasizing proximity-based planning, holds promise in reducing unnecessary travel and progressing towards carbon neutrality. However, a critical research question remains inadequately explored: to what extent is the 15-minute city concept feasible for American cities? This paper presents a comprehensive framework to evaluate the 15-minute city concept using SafeGraph Point of Interest (POI) check-in data across 12 major American cities. Our findings suggest a prevailing reliance on cars among residents due to the spatial distribution of essential activities beyond convenient walking, cycling, and public transit distances. Nevertheless, there exists significant promise for realizing the 15-minute city vision, given that most residents' daily activities can be accommodated within a 15-minute radius by low-emission modes transportation modes. When comparing cities, it appears that achieving a 15-minute walking city is more feasible for metropolises like New York City, San Francisco, Boston, and Chicago, while proving to be challenging for cities such as Atlanta, Dallas, Houston, and Phoenix. In examing inter-group comparisons, neighborhoods with higher proportion of White residents and higher median incomes tend to have more accessible POIs, with a substantial percentage of activities concentrated within a 15-minute radius. This demographic also shows a greater propensity to fulfill daily activities through walking, cycling, or public transit trips within a 15-minute travel time, thus presenting a greater potential in CO2 reduction compared to African Americans. This study can offer policymakers insight into how far American cities are away from the 15-minute city concept. It also highlights the potential CO2 emissions reductions that could be achieved through successful implementation.
Trackintel: An open-source Python library for human mobility analysis
Martin, Henry; Hong, Ye; Wiedemann, Nina; et al. (2023)
Computers, Environment and Urban Systems
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-source Python library for human mobility analysis. Trackintel is built on a standard data model for human mobility used in transport planning that is compatible with different types of tracking data. We introduce the main functionalities of the library that covers the full life-cycle of human mobility analysis, including processing steps according to the conceptual data model, read and write interfaces, as well as analysis functions (e.g., data quality assessment, travel mode prediction, and location labeling). We showcase the effectiveness of the Trackintel library through a case study with four different tracking datasets. Trackintel can serve as an essential tool to standardize mobility data analysis and increase the transparency and comparability of novel research on human mobility. The library is available open-source at https://github.com/mie-lab/trackintel.
Graph-ResNets for short-term traffic forecasts in almost unknown cities
Martin, Henry; Bucher, Dominik; Hong, Ye; et al. (2020)
Proceedings of Machine Learning Research ~ Proceedings of Machine Learning Research, NeurIPS 2019 Competition and Demonstration Track, 8-14 December 2019, Vancouver, CA
The 2019 IARAI traffic4cast competition is a traffic forecasting problem based on traffic data from three cities that are encoded as images. We developed a ResNet-inspired graph convolutional neural network (GCN) approach that uses street network-based subgraphs of the image lattice graphs as a prior. We train the Graph-ResNet together with GCN and convolutional neural network (CNN) benchmark models on Moscow traffic data and use them to first predict the traffic in Moscow and then to predict the traffic in Berlin and Istanbul. The results suggest that the graph-based models have superior generalization properties than CNN-based models for this application. We argue that in contrast to purely image-based approaches, formulating the prediction problem on a graph allows the neural network to learn properties given by the underlying street network. This facilitates the transfer of a learned network to predict the traffic status at unknown locations.
Conserved quantities in human mobility
Hong, Ye; Martin, Henry; Xin, Yanan; et al. (2022)
Arbeitsberichte Verkehrs- und Raumplanung
Quantifying intra-person variability in travel choices is essential for the comprehension of activity-travel behaviour. Due to a lack of appropriate datasets and methods, there is limited understanding of how an individual’s travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ∼ 3800 individuals to analyze individuals’ activity-travel behaviour over the long term. The general movement patterns of the considered population are characterised using mobility indicators. Despite the differences in the mobility patterns, we find that individuals from both datasets maintain a conserved quantity in the number of essential travel mode and activity location combinations over time, resulting from a balance between exploring new choice combinations and exploiting existing options. A typical individual maintains ∼ 15 mode-location combinations, of which ∼ 7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well modelled using a power-law fit that slows down over time. Our findings enrich the understanding of the long-term intra-person variability in activity-travel behaviour and open new possibilities for designing mobility simulation models.
How do you go where?: improving next location prediction by learning travel mode information using transformers
Hong, Ye; Martin, Henry; Raubal, Martin (2022)
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options. Here, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes, which are behaviour dimensions often overlooked in previous work. In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning. For evaluation, we apply this approach to two large-scale and long-term GPS tracking datasets involving more than 600 individuals. Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods by a large margin (8.05% and 5.60% relative increase in F1-score for the two datasets, respectively). We conduct an extensive ablation study that quantifies the influence of considering temporal features, travel mode information, and the auxiliary task on the prediction results. Moreover, we experimentally determine the performance upper bound when including the next mode prediction in our model. Finally, our analysis indicates that the performance of location prediction varies significantly with the chosen next travel mode by the individual. These results show potential for a more systematic consideration of additional dimensions of travel behaviour in human mobility prediction tasks. The source code of our model and experiments is available at https://github.com/mie-lab/location-mode-prediction.
Context-aware multi-head self-attentional neural network model for next location prediction
Hong, Ye; Zhang, Yatao; Schindler, Konrad; et al. (2023)
Transportation Research Part C: Emerging Technologies
Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.

Publications 1 - 10 of 29

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