Real-Time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach

2016 ◽  
Vol 11 (2) ◽  
pp. 246-254 ◽  
Author(s):  
Yosuke Kawasaki ◽  
◽  
Yusuke Hara ◽  
Takuma Mitani ◽  
Masao Kuwahara
Keyword(s):  
Real Time ◽  
State Space Model ◽  
Measurement Data ◽  
Wave Theory ◽  
Variational Theory ◽  
Dynamic Traffic ◽  
Fundamental Diagram ◽  
Real Time Traffic ◽  
Probe Vehicle ◽  
Traffic Detector

The real-time traffic state estimation we propose uses a state-space model considering the variability of the fundamental diagram (FD) and sensing data. Serious congestion was caused by vehicle evacuation in many Sanriku coast cities following the great East Japan earthquake on March 11, 2011. Many of the vehicles in these congested queues were caught in the enormous tsunami after the earthquake [1]. Safe, efficient evacuation and rescue and restoration require that dynamic traffic states be monitored in real time especially in natural disasters. Variational theory (VT) based on kinematic wave theory is used for the system model, with probe vehicle and traffic detector data used to for measurement data. Our proposal agrees better with simulated benchmark traffic states than deterministic VT results do.

scholarly journals The Dynamic Control of the Light Signalling Device in Real-Time

2019 ◽  
Vol 292 ◽  
pp. 03014
Author(s):  
Jan Mrazek ◽  
Lucia Duricova Mrazkova ◽  
Martin Hromada ◽  
Jana Reznickova
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Traffic Density ◽  
Light Signaling ◽  
Dynamic Traffic ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Control Dynamic

The article is focused on the issue of interval on a light signaling device. Light signaling devices operate on different systems by means of which they are controlled. The control problem is a very static setting that does not respond to real-time traffic. Important variables for dynamic real-time control are traffic density in a selected area along with average speed. These variables are interdependent and can be based on dynamic traffic control. Dynamic traffic control ensures smoother traffic through major turns. At the same time, the number of harmful CO2 emitted from the means of transport should be reduced to the air. When used in low operation, power consumption should be reduced.

scholarly journals Real-time Monitoring of Dynamic Traffic States by State-Space Model

2017 ◽  
Vol 21 ◽  
pp. 42-55 ◽  
Author(s):  
Yosuke Kawasaki ◽  
Yusuke Hara ◽  
Masao Kuwahara
Keyword(s):  
State Space ◽  
Real Time ◽  
State Space Model ◽  
Real Time Monitoring ◽  
Dynamic Traffic ◽  
Space Model

scholarly journals Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1254
Author(s):  
Olatz Iparraguirre ◽  
Aiert Amundarain ◽  
Alfonso Brazalez ◽  
Diego Borro
Keyword(s):  
Real Time ◽  
Road Safety ◽  
Image Feature ◽  
Transport Systems ◽  
Traffic Sign ◽  
Feature Extraction Method ◽  
Traffic Efficiency ◽  
Real Time Traffic ◽  
Probe Vehicle ◽  
Infrastructure Maintenance

European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.

Active traffic management on road networks: a macroscopic approach

2010 ◽  
Vol 368 (1928) ◽  
pp. 4607-4626 ◽  
Author(s):  
Alex A. Kurzhanskiy ◽  
Pravin Varaiya
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Control Strategies ◽  
Continuous Process ◽  
Measurement Data ◽  
Road Networks ◽  
Operations Planning ◽  
Traffic Measurement ◽  
Traffic State ◽  
Real Time Traffic

Active traffic management (ATM) is the ability to dynamically manage recurrent and non-recurrent congestion based on prevailing traffic conditions in order to maximize the effectiveness and efficiency of road networks. It is a continuous process of (i) obtaining and analysing traffic measurement data, (ii) operations planning, i.e. simulating various scenarios and control strategies, (iii) implementing the most promising control strategies in the field, and (iv) maintaining a real-time decision support system that filters current traffic measurements to predict the traffic state in the near future, and to suggest the best available control strategy for the predicted situation. ATM relies on a fast and trusted traffic simulator for the rapid quantitative assessment of a large number of control strategies for the road network under various scenarios, in a matter of minutes. The open-source macrosimulation tool A urora R OAD N ETWORK M ODELER is a good candidate for this purpose. The paper describes the underlying dynamical traffic model and what it takes to prepare the model for simulation; covers the traffic performance measures and evaluation of scenarios as part of operations planning; introduces the framework within which the control strategies are modelled and evaluated; and presents the algorithm for real-time traffic state estimation and short-term prediction.

scholarly journals On the application of variational theory on networks

2020 ◽  
Author(s):  
Gabriel Tilg ◽  
Lukas Ambühl ◽  
S. F. A. Batista ◽  
Fritz Busch ◽  
Monica Menendez
Keyword(s):  
Computational Cost ◽  
Wave Theory ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Source Terms ◽  
Variational Theory ◽  
Microscopic Simulation ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram ◽  
State Estimations

The well-known Lighthill-Whitham-Richards (LWR) theory is the fundamental pillar for most macroscopic traffic models. In the past, many methods were developed to numerically derive solutions for LWR problems. Examples for such numerical solution schemes are the cell transmission model, the link transmission model, and the variational theory (VT) of traffic flow. So far, the latter framework found applications in the fields of traffic modelling, macroscopic fundamental diagram estimation, multi-modal traffic analyses, and data fusion. However, these studies apply VT only at the link or corridor level. To the best of our knowledge, there is no methodology yet to apply VT at the network level. We address this gap by developing a VT-based framework applicable to networks. Our model allows us to account for source terms (e.g. inflows and outflows at intersections) and the propagation of spillbacks between adjacent corridors consistent with kinematic wave theory. We show that the trajectories extracted from a microscopic simulation fit the predicted traffic states from our model for a simple intersection with both source terms and spillbacks. We also use this simple example to illustrate the accuracy of the proposed model. Additionally, we apply our model to the Sioux Falls network and again compare the results to those from a microscopic simulation. Our results indicate a close fit of traffic states, but with substantially lower computational cost. The developed methodology is useful for network-wide traffic state estimations in real-time, or other applications within a model-based optimization framework.

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