no code implementations • 20 Mar 2024 • Artur Grigorev, Khaled Saleh, Yuming Ou, Adriana-Simona Mihaita
By leveraging features generated by modern language models alongside conventional data extracted from incident reports, our research demonstrates improvements in the accuracy of severity classification across several machine learning algorithms.
no code implementations • 8 Jul 2023 • Bo wang, A. K. Qin, Sajjad Shafiei, Hussein Dia, Adriana-Simona Mihaita, Hanna Grzybowska
Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e. g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set.
no code implementations • 20 Sep 2022 • Khaled Saleh, Artur Grigorev, Adriana-Simona Mihaita
This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem.
1 code implementation • 19 Sep 2022 • Artur Grigorev, Adriana-Simona Mihaita, Khaled Saleh, Massimo Piccardi
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents.
1 code implementation • 10 May 2022 • Artur Grigorev, Adriana-Simona Mihaita, Seunghyeon Lee, Fang Chen
Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events.
1 code implementation • 26 Oct 2021 • Artur Grigorev, Tuo Mao, Adam Berry, Joachim Tan, Loki Purushothaman, Adriana-Simona Mihaita
This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge.
no code implementations • 11 Mar 2021 • Tuo Mao, Adriana-Simona Mihaita, Fang Chen, Hai L. Vu
Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework.
no code implementations • 26 Jun 2020 • Adriana-Simona Mihaita, Zac Papachatgis, Marian-Andrei Rizoiu
Traffic flow prediction, particularly in areas that experience highly dynamic flows such as motorways, is a major issue faced in traffic management.
no code implementations • 23 Jun 2020 • Adriana-Simona Mihaita, Haowen Li, Marian-Andrei Rizoiu
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling.
no code implementations • 15 Jul 2019 • Adriana-Simona Mihaita, Haowen Li, Zongyang He, Marian-Andrei Rizoiu
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling.
no code implementations • 11 Jun 2019 • Sajjad Shafiei, Adriana-Simona Mihaita, Chen Cai
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model.
no code implementations • 11 Jun 2019 • Tuo Mao, Adriana-Simona Mihaita, Chen Cai
Secondly, we apply the optimal signal timings previously found under severe incidents affecting the traffic flow in the network but without any further optimization.
no code implementations • 29 May 2019 • Adriana-Simona Mihaita, Zheyuan Liu, Chen Cai, Marian-Andrei Rizoiu
Predicting traffic incident duration is a major challenge for many traffic centres around the world.