Refined Taxi Demand Prediction with ST-Vec

Zhou, Yirong; Wu, Ye; Wu, Jiangjiang; Chen, Luo; Jun, Li

doi:10.1109/geoinformatics.2018.8557158

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Attempts were performed on modeling vector representations based on spatial proximity between points of interest in cities for place type similarity analysis [26,27] and functional region identification [28]. Regarding embeddings generated from human motion, their study is primarily focused on urban road systems and city-level mobility [29][30][31]. Recent research on social media data also includes multi-context embedding models for personalized recommendation systems, representing different features such as user personal behaviors, place categories and points of interest, in the same high dimensional space [32][33][34].…”

Section: Related Workmentioning

confidence: 99%

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

Crivellari

Beinat

2019

IJGI

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The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations’ connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people.

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Section: Related Workmentioning

confidence: 99%

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

Crivellari

Beinat

2019

IJGI

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“…They also studied how taxi demand is influenced by factors like socioeconomic, traffic, and land‐use data. Zhou and others [22] proposed a method called ST‐Vec in which they predicted taxi demand at vital destinations for a given region of New York City. The ST‐Vec method maps regions with dense, low‐dimensional vectors such that the vectors of more‐likely destination regions will be nearer, and hence, the spatiotemporal relationships among zones can be obtained from the similarities between these vectors.…”

Section: Literature Reviewmentioning

confidence: 99%

Taxi‐demand forecasting using dynamic spatiotemporal analysis

2022

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Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

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“…Moreover, the work in [12] considered taxi demand records as time series and extracted features like closeness trend and period. In [9,27], taxi records were used to compose an origin-destination matrix representing the passenger flows within the city of interest.…”

Section: Related Workmentioning

confidence: 99%

“…Whereas the former dealt with pick-up records, the latter embedded other spatio-temporal features. In some cases, NNs were just used to embed certain features, so as to be further analyzed by other models like Support Vector Regression (SVR) [27].…”

Section: Related Workmentioning

confidence: 99%

QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Terroso-Sáenz

Muñoz

Cecilia

2019

Sensors

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Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

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Refined Taxi Demand Prediction with ST-Vec

Cited by 5 publications

References 8 publications

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

Taxi‐demand forecasting using dynamic spatiotemporal analysis

QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

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