Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

Fusco, Gaetano; Colombaroni, Chiara; Comelli, Luciano; Isaenko, Natalia

doi:10.1109/mtits.2015.7223242

Cited by 60 publications

(33 citation statements)

References 12 publications

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“…Two well-known supervised machine learning models, i.e., artificial neural network (ANN) and support vector machine (SVM), are used for forecasting in this paper, as they are all capable of solving complicated non-linear problems and are frequently used for traffic forecasting [9,[14][15][16]28,29]. …”

Section: Results Evaluationmentioning

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: Results Evaluationmentioning

confidence: 99%

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

Kang

Gao

Qiu

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…Recently, the most prominent approaches have revolved around Bayesian Networks and, above all, Neural Networks. Fusco et al [14] use mobile GPS-based data on travel speeds and concentrate on the application of Bayesian Networks and Neural Networks to resolve short-term traffic predictions. Morris, Antoniades and Took [15] have car accident data and combine Bayesian Networks and Neural Networks specifically for the prediction of traffic accidents.…”

Section: Related Workmentioning

confidence: 99%

Robust methods and conditional expectations for vehicular traffic count analysis

Kilpi

Norros

Kuusela

et al. 2020

Eur. Transp. Res. Rev.

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We study the problem of making algorithmic statistical inferences about the dynamics of city traffic. Our data is based on loop detector counts of observed vehicles in various roads in the city of Tampere, Finland. We show that meaningful correlations can be found between traffic asymmetries at different measurement locations. The traffic asymmetry is the difference of the traffic counts of the opposite directions of a road. The correlations can be further quantified by estimating how much they effect on the average values of the traffic asymmetries at the neighbouring locations. Conditional expectations, both sample and binormal model-based versions are useful tools for quantifying this effect. The uncertainty bounds of conditional expectations of the binormal model distribution are extremely useful for outlier detection. Furthermore, conditional expectations of the multinormal distribution model can be used to recover missing data with bounds to uncertainty.

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“…Ramezani and Geroliminis () applied Markov chains to estimate the probability distribution of arterial route travel time. Apart from these probabilistic graphical models, many other machine learning methods have also been applied to this problem, including artificial neural networks (Fusco, Colombaroni, Comelli, & Isaenko, ), regression tree (Wang, Cao, Xu, & Li, ), and support vector machine (SVM) model (Yao et al., ).…”

Section: Literature Reviewmentioning

confidence: 99%

A spatio‐temporal ensemble method for large‐scale traffic state prediction

Liu

et al. 2019

Computer aided Civil Eng

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How to effectively ensemble multiple models while leveraging the spatio‐temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio‐temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio‐temporal ensemble net. The proposed method is suitable for grid‐based spatio‐temporal prediction in dense urban areas. Experiments demonstrate that through spatio‐temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.

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Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

Cited by 60 publications

References 12 publications

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

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

Robust methods and conditional expectations for vehicular traffic count analysis

A spatio‐temporal ensemble method for large‐scale traffic state prediction

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