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Documents authored by Cheng, Tao

Document
DOI: 10.4230/LIPIcs.GIScience.2025.3
Enriching Location Representation with Detailed Semantic Information

Authors: Junyuan Liu, Xinglei Wang, and Tao Cheng

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prioritize spatial proximity while underutilizing fine-grained contextual information from places. To address this limitation, we introduce CaLLiPer+, an extension of the CaLLiPer model that systematically integrates Point-of-Interest (POI) names alongside categorical labels within a multimodal contrastive learning framework. We evaluate its effectiveness on two downstream tasks - land use classification and socioeconomic status distribution mapping - demonstrating consistent performance gains of 4% to 11% over baseline methods. Additionally, we show that incorporating POI names enhances location retrieval, enabling models to capture complex urban concepts with greater precision. Ablation studies further reveal the complementary role of POI names and the advantages of leveraging pretrained text encoders for spatial representations. Overall, our findings highlight the potential of integrating fine-grained semantic attributes and multimodal learning techniques to advance the development of urban foundation models.
Cite as

Junyuan Liu, Xinglei Wang, and Tao Cheng. Enriching Location Representation with Detailed Semantic Information. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{liu_et_al:LIPIcs.GIScience.2025.3,
  author =	{Liu, Junyuan and Wang, Xinglei and Cheng, Tao},
  title =	{{Enriching Location Representation with Detailed Semantic Information}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{3:1--3:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.3},
  URN =		{urn:nbn:de:0030-drops-238322},
  doi =		{10.4230/LIPIcs.GIScience.2025.3},
  annote =	{Keywords: Location Embedding, Contrastive Learning, Pretrained Model}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.58
Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements (Short Paper)

Authors: Mustafa Can Ozkan and Tao Cheng

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
The quest for identifying feasible routes holds immense significance in the realm of transportation, spanning a diverse range of applications, from logistics and emergency systems to taxis and public transport services. This research area offers multifaceted benefits, including optimising traffic management, maximising traffic flow, and reducing carbon emissions and fuel consumption. Extensive studies have been conducted to address this critical issue, with a primary focus on finding the shortest paths, while some of them incorporate various traffic conditions such as waiting times at traffic lights and traffic speeds on road segments. In this study, we direct our attention towards historical data sets that encapsulate individuals' route preferences, assuming they encompass all traffic conditions, real-time decisions and topological features. We acknowledge that the prevailing preferences during the recorded period serve as a guide for feasible routes. The study’s noteworthy contribution lies in our departure from analysing individual preferences and trajectory information, instead focusing solely on macro-level measurements of each road segment, such as traffic flow or traffic speed. These types of macro-level measurements are easier to collect compared to individual data sets. We propose an algorithm based on Q-learning, employing traffic measurements within a road network as positive attractive rewards for an agent. In short, observations from macro-level decisions will help us to determine optimal routes between any two points. Preliminary results demonstrate the agent’s ability to accurately identify the most feasible routes within a short training period.
Cite as

Mustafa Can Ozkan and Tao Cheng. Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 58:1-58:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{ozkan_et_al:LIPIcs.GIScience.2023.58,
  author =	{Ozkan, Mustafa Can and Cheng, Tao},
  title =	{{Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{58:1--58:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.58},
  URN =		{urn:nbn:de:0030-drops-189536},
  doi =		{10.4230/LIPIcs.GIScience.2023.58},
  annote =	{Keywords: routing, reinforcement learning, q-learning, data mining, macro-level patterns}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.80
The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis (Short Paper)

Authors: Xinglei Wang, Xianghui Zhang, and Tao Cheng

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
As an important part of the economic and social fabric of urban areas, high streets were hit hard during the COVID-19 pandemic, resulting in massive closures of shops and plunge of footfall. To better understand how high streets respond to and recover from the pandemic, this paper examines the performance of London’s high streets, focusing on footfall-based clustering analysis. Applying time series clustering to longitudinal footfall data derived from a mobile phone GPS dataset spanning over two years, we identify distinct groups of high streets with similar footfall change patterns. By analysing the resulting clusters' footfall dynamics, composition and geographic distribution, we uncover the diverse responses of different high streets to the pandemic disruption. Furthermore, we explore the factors driving specific footfall change patterns by examining the number of local and nonlocal visitors. This research addresses gaps in the existing literature by presenting a holistic view of high street responses throughout the pandemic and providing in-depth analysis of footfall change patterns and underlying causes. The implications and insights can inform strategies for the revitalisation and redevelopment of high streets in the post-pandemic era.
Cite as

Xinglei Wang, Xianghui Zhang, and Tao Cheng. The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 80:1-80:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{wang_et_al:LIPIcs.GIScience.2023.80,
  author =	{Wang, Xinglei and Zhang, Xianghui and Cheng, Tao},
  title =	{{The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{80:1--80:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.80},
  URN =		{urn:nbn:de:0030-drops-189754},
  doi =		{10.4230/LIPIcs.GIScience.2023.80},
  annote =	{Keywords: High street, performance, footfall, clustering analysis, COVID-19}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.92
Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation (Short Paper)

Authors: Xianghui Zhang and Tao Cheng

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Decarbonising transport is crucial in addressing climate change and achieving the Net Zero target. However, limitations arising from traditional data sources and methods obstruct the provision of individual travel information with comprehensive travel modes, high spatiotemporal granularity and updating frequency for achieving transport decarbonisation. Mobile phone application data, an essentially new form of data, can provide valuable travel information after effective mining and assist in progress monitoring, policy evaluation, and system optimisation in transport decarbonisation. This paper proposes a standardised methodology to unlock the power of mobile phone application data for supporting transport decarbonisation. Three typical cases are employed to demonstrate the capabilities of the generated individual multimodal dataset, including monitoring Londoners’ 20-minute active travel target, transport GHGs emissions and their contributors, and evaluating small-scale transport interventions. The paper also discusses the limitations of mobile phone application data, such as issues surrounding data privacy and regulation.
Cite as

Xianghui Zhang and Tao Cheng. Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 92:1-92:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.92,
  author =	{Zhang, Xianghui and Cheng, Tao},
  title =	{{Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{92:1--92:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.92},
  URN =		{urn:nbn:de:0030-drops-189873},
  doi =		{10.4230/LIPIcs.GIScience.2023.92},
  annote =	{Keywords: Transport decarbonisation, Mobile phone application data, Application, London}
}
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