Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

Mao, Tuo; Mihăiţă, Adriana-Simona; Chen, Fang; Vu, Hai L.

doi:10.1109/tits.2021.3066958

Cited by 37 publications

(12 citation statements)

References 43 publications

(39 reference statements)

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“…Overall, this research lays the foundation stone of bi-level predictive methodologies regarding the traffic incident duration and can provide accurate information for both the end-user route choice modelling as well as for the operational centres which need to optimise their operations under non-recurrent traffic congestion. Moreover, this work contributes to our ongoing objective to build a real-time platform for predicting traffic congestion and to evaluate the incident impact during peak hours (see our previous works published in [38]- [42]- [37]).…”

Section: Challenges and Contributionmentioning

confidence: 99%

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Grigorev¹,

Mihăiţă²,

Lee³

et al. 2022

Preprint

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Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events. The ability to accurately predict how long accidents will last can provide significant benefits to both end-users in their route choice and traffic operation managers in handling of non-recurrent traffic congestion. This paper presents a novel bi-level machine learning framework enhanced with outlier removal and intra-extra joint optimisation for predicting the incident duration on three heterogeneous data sets collected for both arterial roads and motorways from Sydney, Australia and San-Francisco, U.S.A. Firstly, we use incident data logs to develop a binary classification prediction approach, which allows us to classify traffic incidents as short-term or long-term. We find the optimal threshold between short-term versus long-term traffic incident duration, targeting both class balance and prediction performance while also comparing the binary versus multi-class classification approaches using quantiled duration groups and varying threshold split. Secondly, for more granularity of the incident duration prediction to the minute level, we propose a new Intra-Extra Joint Optimisation algorithm (IEO-ML) which extends multiple baseline ML models tested against several regression scenarios across the data sets. Final results indicate that: a) 40-45 min is the best split threshold for identifying short versus longterm incidents and that these incidents should be modelled separately, b) our proposed IEO-ML approach significantly outperforms baseline ML models in 66% of all cases showcasing its great potential for accurate incident duration prediction. Lastly, we evaluate the feature importance and show that time, location, incident type, incident reporting source and weather at among the top 10 critical factors which influence how long incidents will last.

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Section: Challenges and Contributionmentioning

confidence: 99%

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Grigorev¹,

Mihăiţă²,

Lee³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…To improve the efficiency of the transport system at a large scale, the encouragement of a travel behaviour change and active mode shift is an encouraging option studied recently [2]. Many other research studies reinforce this initiative by providing substantial evidence via data-driven, or simulationbased approaches [3], [4], [5]. The data-driven approaches capture the real traffic behaviour before and after disruptions, and some applications are used in programs such as: INPHORMM, TAPESTRY or Travel Smart [6].…”

Section: A Background and Motivationmentioning

confidence: 99%

Traffic disruption modelling with mode shift in multi-modal networks

Zhang¹,

Mihăiţă²,

Ou³

et al. 2022

Preprint

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A multi-modal transport system is acknowledged to have robust failure tolerance and can effectively relieve urban congestion issues. However, estimating the impact of disruptions across multi-transport modes is a challenging problem due to a dis-aggregated modelling approach applied to only individual modes at a time. To fill this gap, this paper proposes a new integrated modelling framework for a multi-modal traffic state estimation and evaluation of the disruption impact across all modes under various traffic conditions. First, we propose an iterative trip assignment model to elucidate the association between travel demand and travel behaviour, including a multi-modal origin-to-destination estimation for private and public transport. Secondly, we provide a practical multi-modal travel demand re-adjustment that takes the mode shift of the affected travellers into consideration. The pros and cons of the mode shift strategy are showcased via several scenariobased transport simulating experiments. The results show that a well-balanced mode shift with flexible routing and early announcements of detours so that travellers can plan ahead can significantly benefit all travellers by a delay time reduction of 46%, while a stable route assignment maintains a higher average traffic flow and the inactive mode-route choice help relief density under the traffic disruptions.

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“…Traffic control optimizations combining Machine Learning (ML) and Genetic Algorithms (GA) are also efficient. In the work of Mao et al [160], the Extreme-Gradient Decision-Tree (XGBT) and Genetic Algorithm (GA) were combined to reduce the total travel time by almost half when used under incident conditions.…”

Section: • Boosted Genetic Algorithmmentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

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Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

Cited by 37 publications

References 43 publications

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Traffic disruption modelling with mode shift in multi-modal networks

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

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