Multisource Data Framework for Road Traffic State Estimation

Grau, Josep Maria Salanova; Mitsakis, Evangelos; Tzenos, Panagiotis; Stamos, Iraklis; Selmi, Luigi; Aifadopoulou, Georgia

doi:10.1155/2018/9078547

Cited by 15 publications

(9 citation statements)

References 29 publications

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“…A specific algorithm then, that considers the network topology (monographs, types of roads, etc. ), each record (point) is mapped to the part of the road network to which it is most likely to belong with the highest degree of certainty [4] . Lastly, for each section (segment) of the road network, proper statistical analysis is performed to provide a safe estimation of the average speed at which vehicle traffic is conducted on it.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Open data from taxis and Bluetooth detectors to extract congestion and mobility patterns in Thessaloniki

et al. 2023

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Open data from taxis and Bluetooth detectors to extract congestion and mobility patterns in Thessaloniki

et al. 2023

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“…There was a need to address the spatial features as well as a need to investigate with different data aggregated levels that would act as an additional input and contribute towards the prediction of speed in a more accurate consistent manner. Salavona Grau et al [1] proposed the framework that collects data from conventional(cameras, radars, and loops) as well as innovative (FCD and Bluetooth devices) technologies and analyzes the ETA and traffic flow on road networks. They are focused on data collection, filtration, and fusion of big data.…”

Section: Related Workmentioning

confidence: 99%

“…Where the real-time availability of heterogeneous data generated from a huge number of sensors has brought about novel possibilities, a new set of challenges has also emerged, which is primarily related with the need of methodologies and capabilities to optimally harness the power of heterogeneity of data. Unveiling new interpretations by first integrating various forms of heterogeneous data and then applying intelligent algorithms to it is one of the most exciting possibilities of the notion of the Connected World [1].…”

Section: Introductionmentioning

confidence: 99%

Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas

Zafar

Haq

Chughtai

et al. 2022

Sensors

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With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial–temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM–GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.

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“…It has an average density of 665 inhabitants per km 2 [27]. The mobility data sources in Thessaloniki include conventional traffic data sources, such as traffic radars, cameras, and loops, as well as probe data, both stationary and from the bluetooth detector network of the city and Floating Car Data (FCD), which were acquired from a 1200 taxi fleet [28].…”

Section: Data Collectionmentioning

confidence: 99%

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

et al. 2019

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Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.

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Multisource Data Framework for Road Traffic State Estimation

Cited by 15 publications

References 29 publications

Open data from taxis and Bluetooth detectors to extract congestion and mobility patterns in Thessaloniki

Open data from taxis and Bluetooth detectors to extract congestion and mobility patterns in Thessaloniki

Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

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