Context-Aware Online Spatiotemporal Traffic Prediction

Xu, Jie; Deng, Dingxiong; Demiryurek, Ugur; Shahabi, Cyrus; Schaar, Mihaela van der

doi:10.1109/icdmw.2014.102

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…However, there are few existing studies on the development of spatial correlations, which makes sense for the construction of short-term traffic state evolution models, especially at the city-wide or network-wide level; therefore, there is a need for further studies in this area. The related literature provides sufficient evidence to support the idea that the incorporation of spatial correlations can enhance short-term traffic state prediction [ 115 , 116 , 117 ]. However, properly capturing these correlations is difficult because they typically do not follow a simple distance rule.…”

Section: Discussionmentioning

confidence: 99%

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Chen

Wang

Hua

et al. 2022

Sensors

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Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications.

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Section: Discussionmentioning

confidence: 99%

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Chen

Wang

Hua

et al. 2022

Sensors

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“…Given that the previously discussed categories of approaches are not universally likely to generate high quality predictions, the research community has created scope for hybridising methods from different groups. To that effect, a simple Bayesian Classifier and a support vector machine are dynamically alternated, based on live traffic conditions, to produce the optimal vehicle flow model [1]. For this purpose, a "regret metric" is used to penalise under-performing predictors.…”

Section: Hybrid Modelsmentioning

confidence: 99%

“…In 2014, 83.4% of all inland passenger transport across EU states was accomplished by car, 9.1% by coaches, buses and trolleys, leaving only 7.6% to cleaner alternatives such as train travel. 1 European freight transportation mainly occurs on roads, namely 74.9%, leaving only a share of 18.4% to be fulfilled on rail and 6.7% on inland waterways. 2 Furthermore, in 2015, there were 25 regions within the European Union where over one fifth of the workforce commuted to work.…”

Section: Introductionmentioning

confidence: 99%

“…2 Furthermore, in 2015, there were 25 regions within the European Union where over one fifth of the workforce commuted to work. 3 These European Commission statistics provide strong evidence in support of the fact that industrialisation and overpopulation have pushed urban infrastructures to and beyond capacity [1], stemming severe problems around traffic safety, road maintenance and development costs as well as air and noise pollution, to name only a few. The smart cities vision [2,3,4,5,6,7] offers a potential solution to all these issues, in the form of Intelligent Transport Systems (ITS) [8,9,10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

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Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data

Patelli,

Lush,

Ekart

et al. 2020

Preprint

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The continuous expansion of the urban traffic sensing infrastructure has led to a surge in the volume of widely available road related data. Consequently, increasing effort is being dedicated to the creation of intelligent transportation systems, where decisions on issues ranging from city-wide road maintenance planning to improving the commuting experience are informed by computational models of urban traffic instead of being left entirely to humans. The automation of traffic management has received substantial attention from the research community, however, most approaches target highways, produce predictions valid for a limited time window or require expensive retraining of available models in order to accurately forecast traffic at a new location. In this article, we propose a novel and accurate traffic flow prediction method based on symbolic regression enhanced with a lag operator. Our approach produces robust models suitable for the intricacies of urban roads, much more difficult to predict than highways. Additionally, there is no need to retrain the model for a period of up to 9 weeks. Furthermore, the proposed method generates models that are transferable to other segments of the road network, similar to, yet geographically distinct from the ones they were initially trained on. We demonstrate the achievement of these claims by conducting extensive experiments on data collected from the Darmstadt urban infrastructure.

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