As road transportation energy use and environmental impact are globally rising at an alarming pace, authorities seek in research and technological advancement innovative solutions to increase road ...traffic sustainability. The unclear and partial correlation between road congestion and environmental impact is promoting new research directions in traffic management. This paper aims to review the existing modeling approaches to accurately represent traffic behavior and the associated energy consumption and pollutant emissions. The review then covers the transportation problems and control strategies that address directly environmental performance criteria, especially in urban networks. A discussion on the advantages of the different methods and on the future outlook for the eco-traffic management completes the proposed survey.
This work focuses on evaluating the potential of variable speed limits (VSLs) in a synthetic urban network to improve both environmental sustainability and traffic performance. The traffic system is ...modeled using the microscopic traffic simulator SUMO, and a physical fuel consumption and NOx emission model is used to assess the vehicles' energy efficiency. Speed limits are controlled through a nonlinear model predictive control (NMPC) approach, in which the traffic evolution and fuel consumption are respectively predicted with a macroscopic traffic model, namely the cell transmission model (CTM), and a pre-calibrated artificial neural network (ANN). The results reveal that in transient phases between different levels of congestion, the proposed eco-VSL controller is faster to decongest the network, resulting in an improvement of the environmental sustainability and the traffic performance both in the controlled network, and at its boundary roads.
This paper proposes an estimation method of the traffic flow in a road network, based on topographic characteristics, temporal variables, and population statistics. It provides an alternative to ...conventional traffic modeling approaches by defining a data-based low-computational and low-input model that can estimate and predict day-long variations in traffic flow throughout a road network. The approach is based on a succession of statistical, machine learning, and optimization modules. The optimization is introduced in order to ensure spatial coherence and traffic flow continuity over neighboring road-links. Overall, the proposed model was calibrated with loop-detectors measurements by learning correlations between traffic flow (as output) and open-source data (as input). The approach can therefore be easily applicable to any road network, without the need for local traffic flow measurements. An application to a different road network and a different time period is proposed in order to assess the model ability to extrapolate in time and space, and promising results are obtained.
Today, Floating Car Data (FCD), which are obtained from the GPS sensor measurements of vehicles, represent a particularly interesting source of data for road traffic managers. Indeed, they give ...information on the traffic flow and the average speed on a whole territory, enabling many applications such as congestion or air quality prediction. However, this information is partial in the sense that only the vehicles equipped with GPS are counted. Models are therefore necessary to convert the partial FCD count into the total traffic volume. In this study, we propose to cross the FCD with loop detectors data, which capture the real traffic flows but only at certain locations, in order to train a flow prediction model. The proposed approach is based on multiple linear regressions, the weight of each regression being determined by a Random Forest (RF) algorithm. The methodology is calibrated and tested on data from Lyon, France, collected in 2021.
This paper introduces a new model depicting electric vehicles (EVs) mobility and the evolution of their State-of-Charge (SoC) in urban traffic networks. The model couples the vehicles' mobility ...described by a set of dynamic equations over a graph capturing the Origin-Destination motion, with the energy consumption associated with the EVs mobility patterns. Additionally, power inputs from charging stations are included in the model. A model calibration method based on multi-source public data is also provided. Finally, several experiments are conducted through simulation to evaluate the appropriateness of the current charging station infrastructure under an increasing EVs penetration rate in the whole metropolitan area of Grenoble, France.
The problem of improving traffic sustainability and traffic efficiency in an urban road network, by implementing variable speed limits (VSL), is addressed in this paper. A nonlinear model predictive ...control (NMPC) design based on a first-order macroscopic traffic flow model is proposed for the speed limits optimization in each segment of the road network. Simulation results show the effectiveness of the proposed control approach, compared to reference cases in which the speed limits are constantly set to 30 km/h or 50 km/h. In the particular case of congested traffic conditions, the controller is capable of reducing both energy consumption and travel time, without delaying users waiting at the network boundaries.
This work focuses on comparing the ecological potential of variable speed limits (VSLs) and signalized access control. A synthetic two-region network composed of an urban and a peri-urban area is ...considered. This study aims at improving the energy efficiency in both areas. A microscopic traffic simulator (SUMO) is used to model the dynamics of the system. It is controlled by a nonlinear model predictive control (NMPC) framework based on a macroscopic traffic model, which is an adapted version of the cell transmission model (CTM). The controller is coupled with an artificial neural network (ANN) to predict the fuel consumption. Finally, microscopic physical energy and NO X models are used to evaluate the performance of both control actuators. The results reveal that VSLs are more promising due to the smoother variation of the densities.
Injuries of the biliary tract and complex injuries involving vascular and parenchymal tissue can be detrimental despite the improved use of laparoscopy. Complex biliary injuries are variable ...depending on the type of injury as well as patient and surgeon factors. We present four cases of complex biliary injuries at our tertiary referral center with hepatobiliary expertise: biliary stenosis with obstruction, double duct system anatomy, combined right hepatic arterial transection and biliary duct injury, and a complete pedicle injury. Early identification and specialized repair of complex biliary injuries is essential to minimize patient morbidity. Notably, consulting a specialist intraoperatively in case of difficult dissection and visualization or a suspected injury and considering bail-out strategies such as a subtotal cholecystectomy or conversion are safe approaches to minimize complex biliary injuries. Earlier recognition and repair of complex biliary injuries improves outcomes when immediate intraoperative repair can be performed rather than delayed postoperatively.
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