Space–time modeling of traffic flow

Kamarianakis, Yiannis; Prastacos, Poulicos

doi:10.1016/j.cageo.2004.05.012

Cited by 263 publications

(113 citation statements)

References 19 publications

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“…For instance, many studies have reinforced that traffic forecasting accuracy not only depends on forecasting models, but also on the spatio-temporal inputs [4,[7][8][9][10][11][12][13]. Identifying in travel routes or they frequently share common upstream and/or downstream segments in travel routes.…”

Section: Introductionmentioning

confidence: 99%

Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes

Kang

Gao

Qiu

et al. 2017

IJGI

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Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topological distance or the correlation between their traffic variables. However, the distance-based methods neglect the spatial heterogeneity of roads' traffic interactions, while the correlation-based methods cannot capture the non-linear dependency between two roads' traffic variables. In this paper, we propose a novel approach called Road2Vec to quantify the implicit traffic interactions among roads based on large-scale taxi operating route data using a Word2Vec model from the natural language processing (NLP) field. First, the analogy between transportation elements (i.e., road segment, travel route) and NLP terms (i.e., word, document) is established. Second, the real-valued vectors for road segments are trained from massive travel routes using the Word2Vec model. Third, the traffic interaction between any pair of roads is measured by the cosine similarity of their vectors. A case study on short-term traffic forecasting is conducted with artificial neural network (ANN) and support vector machine (SVM) algorithms to validate the advantages of the presented method. The results show that the forecasting achieves a higher accuracy with the support of the Road2Vec method than with the topological distance and traffic correlation based methods. We argue that the Road2Vec method can be effectively utilized for quantifying complex traffic interactions among roads and capturing underlying heterogeneous and non-linear properties.

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Section: Introductionmentioning

confidence: 99%

Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes

Kang

Gao

Qiu

et al. 2017

IJGI

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“…Parametric models assume that the data follow a particular parametric distribution, and that the future values of the process can be modelled as a (usually linear) combination of its past values. Examples of parametric space-time forecasting models include the space-time autoregressive integrated moving average (STARIMA) modelling framework ( (Pfeifer and Deutsch, 1980); (Kamarianakis and Prastacos, 2005), statespace models ( (Stathopoulos and Karlaftis, 2003), Bayesian networks (Sun et al, 2005) to name but a few.…”

Section: Space-time Forecastingmentioning

confidence: 99%

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Haworth

Cheng

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London's road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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“…Regression imputation and MI have been proved to www.intechopen.com be more effective than the others, especially the latter one (Ni et al, 2005;Zhong et al, 2004). State space methodology is found to be extremely significant to ensure more accurate results in nearest nonparametric regression (Kamarianakis & Prastakos, 2005). The amelioration including the historical information in the state space may further improve imputation accuracy.…”

Section: Data Imputationmentioning

confidence: 99%