12,909 research outputs found
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs
An increasing amount of location-based service (LBS) data is being
accumulated and helps to study urban dynamics and human mobility. GPS
coordinates and other location indicators are normally low dimensional and only
representing spatial proximity, thus difficult to be effectively utilized by
machine learning models in Geo-aware applications. Existing location embedding
methods are mostly tailored for specific problems that are taken place within
areas of interest. When it comes to the scale of a city or even a country,
existing approaches always suffer from extensive computational cost and
significant data sparsity. Different from existing studies, we propose to learn
representations through a GCN-aided skip-gram model named GCN-L2V by
considering both spatial connection and human mobility. With a flow graph and a
spatial graph, it embeds context information into vector representations.
GCN-L2V is able to capture relationships among locations and provide a better
notion of similarity in a spatial environment. Across quantitative experiments
and case studies, we empirically demonstrate that representations learned by
GCN-L2V are effective. As far as we know, this is the first study that provides
a fine-grained location embedding at the city level using only LBS records.
GCN-L2V is a general-purpose embedding model with high flexibility and can be
applied in down-streaming Geo-aware applications
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and
developed separately. However, there is now cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In the
present paper, we introduce the importance of urban planning from the
sustainability, living, economic, disaster, and environmental perspectives. We
review the fundamental concepts of urban planning and relate these concepts to
crucial open problems of machine learning, including adversarial learning,
generative neural networks, deep encoder-decoder networks, conversational AI,
and geospatial and temporal machine learning, thereby assaying how AI can
contribute to modern urban planning. Thus, a central problem is automated
land-use configuration, which is formulated as the generation of land uses and
building configuration for a target area from surrounding geospatial, human
mobility, social media, environment, and economic activities. Finally, we
delineate some implications of AI for urban planning and propose key research
areas at the intersection of both topics.Comment: TSAS Submissio
City2City: Translating Place Representations across Cities
Large mobility datasets collected from various sources have allowed us to
observe, analyze, predict and solve a wide range of important urban challenges.
In particular, studies have generated place representations (or embeddings)
from mobility patterns in a similar manner to word embeddings to better
understand the functionality of different places within a city. However,
studies have been limited to generating such representations of cities in an
individual manner and has lacked an inter-city perspective, which has made it
difficult to transfer the insights gained from the place representations across
different cities. In this study, we attempt to bridge this research gap by
treating \textit{cities} and \textit{languages} analogously. We apply methods
developed for unsupervised machine language translation tasks to translate
place representations across different cities. Real world mobility data
collected from mobile phone users in 2 cities in Japan are used to test our
place representation translation methods. Translated place representations are
validated using landuse data, and results show that our methods were able to
accurately translate place representations from one city to another.Comment: A short 4-page version of this work was accepted in ACM SIGSPATIAL
Conference 2019. This is the full version with details. In Proceedings of the
27th ACM SIGSPATIAL International Conference on Advances in Geographic
Information Systems. AC
Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding
Understanding intrinsic patterns and predicting spatiotemporal
characteristics of cities require a comprehensive representation of urban
neighborhoods. Existing works relied on either inter- or intra-region
connectivities to generate neighborhood representations but failed to fully
utilize the informative yet heterogeneous data within neighborhoods. In this
work, we propose Urban2Vec, an unsupervised multi-modal framework which
incorporates both street view imagery and point-of-interest (POI) data to learn
neighborhood embeddings. Specifically, we use a convolutional neural network to
extract visual features from street view images while preserving geospatial
similarity. Furthermore, we model each POI as a bag-of-words containing its
category, rating, and review information. Analog to document embedding in
natural language processing, we establish the semantic similarity between
neighborhood ("document") and the words from its surrounding POIs in the vector
space. By jointly encoding visual, textual, and geospatial information into the
neighborhood representation, Urban2Vec can achieve performances better than
baseline models and comparable to fully-supervised methods in downstream
prediction tasks. Extensive experiments on three U.S. metropolitan areas also
demonstrate the model interpretability, generalization capability, and its
value in neighborhood similarity analysis.Comment: To appear in Proceedings of the Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20
Key challenges in agent-based modelling for geo-spatial simulation
Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web
Current âInternet of Thingsâ concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3Câs Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where driversâ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun
- âŠ