Real-Time Estimation of Critical Vehicle Accumulation for Maximum Network Throughput

Perimeter traffic flow control has recently been found to be a practical and efficient control scheme in mitigating traffic congestion in urban road networks. This control scheme aims at stabilising the accumulation of vehicles of the socalled network fundamental diagram near critical accumulation to achieve maximum network throughput. Nevertheless, the maximum throughput in urban road networks may be observed over a range of accumulation-values. In this work, an adaptive perimeter flow control strategy is proposed that allows the automatic monitoring of the critical accumulation to help maintain the accumulation near the optimal range of accumulation-values, while network's throughput is maximised. To this end, we design a Kalman filter-based estimation scheme that utilises real-time measurements of circulating flow and accumulation of vehicles to produce estimates of the currently prevailing critical accumulation. We use real data from an urban area with 70 sensors and show that the area exhibits a network fundamental diagram with low scatter. We demonstrate that the fundamental diagram is reproduced under different days but its shape and critical occupancy depend on the applied semi-real-time signal control and the distribution of congestion in the network. Results from the application of the estimation algorithm to the experimental data indicate good estimation accuracy and performance, and rapid tracking behaviour

Irregular ventricular tachycardia underdetected by implantable cardioverter defibrillator device

A case of sustained monomorphic ventricular tachycardia underdetected by a single chamber implantable cardioverter defibrillator because of RR interval irregularity is presented. The programmed stability criterion is responsible for the underdetection. Special attention must be paid when it comes to programming this detection parameter. (Cardiol J 2008; 15: 281-283

Real-time Estimation of Critical Values of the Macroscopic Fundamental Diagram for Maximum Network Throughput

Perimeter flow control or gating has recently been found to be a practical and efficient control scheme in mitigating traffic congestion in urban road networks. This control scheme aims at stabilising the accumulation of vehicles (or a proxy of accumulation, e.g. average occupancy or density) of the macroscopic or network fundamental diagram near critical accumulation to achieve maximum network throughput. Nevertheless, the maximum throughput (capacity flow) in urban road networks may be observed over a range of accumulation-values. In this work, an extension of a previously proposed real-time feedback perimeter flow control strategy is proposed that allows the automatic monitoring of the critical accumulation to help maintain the accumulation near the optimal range of accumulation-values, while network’s throughput is maximised. To this end, we design a Kalman filter-based estimation algorithm that utilises real-time measurements of circu- lating flow and accumulation of vehicles to produce estimates of the currently prevailing critical accumulation. The developed strategy may be valuable whenever the network fundamental dia- gram is not well defined and the critical accumulation cannot accurately be specified or is subject change due to traffic-responsive signal control, traffic composition (e.g. cars versus buses), or non- recurrent day-to-day traffic patterns. We use real experimental data from an urban area with 70 sensors and show that the area exhibits a fundamental diagram with low scatter. We demonstrate that the fundamental diagram is reproduced under different days but its shape and critical occu- pancy depend on the applied semi-real-time signal control and the distribution of congestion in the network. Preliminary results from the application of the estimation algorithm to the experimental data indicate good estimation accuracy and performance, and rapid tracking behaviour

Real-time estimation of critical values of the macroscopic fundamental diagram for maximum network throughput

Perimeter flow control or gating has recently been found to be a practical and efficient control scheme in mitigating traffic congestion in urban road networks. This control scheme aims at stabilising the accumulation of vehicles (or a proxy of accumulation, e.g. average occupancy or density) of the macroscopic or network fundamental diagram near critical accumulation to achieve maximum network throughput. Nevertheless, the maximum throughput (capacity flow) in urban road networks may be observed over a range of accumulation-values. In this work, an extension of a previously proposed real-time feedback perimeter flow control strategy is proposed that allows the automatic monitoring of the critical accumulation to help maintain the accumulation near the optimal range of accumulation-values, while network’s throughput is maximised. To this end, we design a Kalman filter-based estimation algorithm that utilises real-time measurements of circulating flow and accumulation of vehicles to produce estimates of the currently prevailing critical accumulation. The developed strategy may be valuable whenever the network fundamental diagram is not well defined and the critical accumulation cannot accurately be specified or is subject change due to traffic-responsive signal control, traffic composition (e.g. cars versus buses), or non- recurrent day-to-day traffic patterns. We use real experimental data from an urban area with 70 sensors and show that the area exhibits a fundamental diagram with low scatter. We demonstrate that the fundamental diagram is reproduced under different days but its shape and critical occupancy depend on the applied semi-real-time signal control and the distribution of congestion in the network. Preliminary results from the application of the estimation algorithm to the experimental data indicate good estimation accuracy and performance, and rapid tracking behaviour

Real-time estimation of critical accumulation in perimeter flow control for maximum network throughput

Perimeter flow control or gating has recently been found to be a practical and efficient control scheme in mitigating traffic congestion in urban road networks. This control scheme aims at stabilising the accumulation of vehicles (or a proxy of accumulation, e.g. average occupancy or density) of the macroscopic or network fundamental diagram near critical accumulation (set point) to achieve maximum network throughput. Nevertheless, the maximum throughput (capacity flow) in urban road networks may be observed over a range of accumulation-values in contrast to motorway traffic where capacity flow is deemed to occur for a (more or less) specific density value. In this work, an extension of a previously proposed real-time feedback perimeter flow control strategy is proposed that allows the automatic monitoring of the critical accumulation to help maintain the accumulation near the optimal range of accumulation-values, while network’s throughput is maximised. To this end, we design a Kalman filter-based estimation algorithm that utilise real-time measurements of circulating flow and accumulation of vehicles to produce estimates of the currently prevailing critical accumulation. The developed strategy may be valuable whenever the network fundamental diagram is not well defined and the critical accumulation cannot accurately be specified or is subject change due to traffic-responsive signal control, traffic composition (e.g. cars versus buses), or non-recurrent day-to-day traffic patterns. Preliminary results indicate good estimation accuracy and performance, and rapid tracking behaviour

Real-time Estimation of Critical Values of the Macroscopic Fundamental Diagram for Maximum Network Throughput

Perimeter flow control or gating has recently been found to be a practical and efficient control scheme in mitigating traffic congestion in urban road networks. This control scheme aims at stabilising the accumulation of vehicles (or a proxy of accumulation, e.g. average occupancy or density) of the macroscopic or network fundamental diagram near critical accumulation to achieve maximum network throughput. Nevertheless, the maximum throughput (capacity flow) in urban road networks may be observed over a range of accumulation-values. In this work, an extension of a previously proposed real-time feedback perimeter flow control strategy is proposed that allows the automatic monitoring of the critical accumulation to help maintain the accumulation near the optimal range of accumulation-values, while network’s throughput is maximised. To this end, we design a Kalman filter-based estimation algorithm that utilises real-time measurements of circu- lating flow and accumulation of vehicles to produce estimates of the currently prevailing critical accumulation. The developed strategy may be valuable whenever the network fundamental dia- gram is not well defined and the critical accumulation cannot accurately be specified or is subject change due to traffic-responsive signal control, traffic composition (e.g. cars versus buses), or non- recurrent day-to-day traffic patterns. We use real experimental data from an urban area with 70 sensors and show that the area exhibits a fundamental diagram with low scatter. We demonstrate that the fundamental diagram is reproduced under different days but its shape and critical occu- pancy depend on the applied semi-real-time signal control and the distribution of congestion in the network. Preliminary results from the application of the estimation algorithm to the experimental data indicate good estimation accuracy and performance, and rapid tracking behaviour

Energy-based assessment and driving behavior of ACC systems and humans inside platoons

Evidence in the literature shows that automated and human driving modes demonstrate different driving characteristics, i.e., headway policy, spacing policy, reaction time, comfortable acceleration, and others. These differences alter observed traffic dynamics and have an impact on energy consumption. This paper assesses the energy footprint of commercially implemented adaptive cruise control (ACC) systems and human drivers in car-following formation via different models using empirical observations on very similar driving cycles and/or routes. Most importantly, it initiates a critical discussion of the findings under the behavioral properties of each mode. Findings show that: ACC systems propagate an increasing energy consumption upstream, while human drivers do not; they succeed in maintaining a constant time-headway policy, operating very reliably; they develop a strong bond with their leader compared to their human counterparts; the two modes (humans and ACCs) are operating in different phase-space areas with room for improvement. Overall, findings show that ACC systems must be optimized to achieve a trade-off between functional requirements and eco-driving instructions.ISSN:1524-9050ISSN:1558-001

Energy-based assessment and driving behavior of ACC systems and humans inside platoons

Evidence in the literature shows that automated and human driving modes demonstrate different driving characteristics, i.e., headway policy, spacing policy, reaction time, comfortable acceleration, and others. These differences alter observed traffic dynamics and have an impact on energy consumption. This paper assesses the energy footprint of commercially implemented adaptive cruise control (ACC) systems and human drivers in car-following formation via different models using empirical observations on very similar driving cycles and/or routes. Most importantly, it initiates a critical discussion of the findings under the behavioral properties of each mode. Findings show that: ACC systems propagate an increasing energy consumption upstream, while human drivers do not; they succeed in maintaining a constant time-headway policy, operating very reliably; they develop a strong bond with their leader compared to their human counterparts; the two modes (humans and ACCs) are operating in different phase-space areas with room for improvement. Overall, findings show that ACC systems must be optimized to achieve a trade-off between functional requirements and eco-driving instructions

Commercially implemented adaptive cruise control systems are not evil

Vehicle automation is regarded as one of the most promising technologies in transportation networks to alleviate congestion, improve safety and energy efficiency. Adaptive cruise control (ACC) systems, which serve as the first step of automation, are already standard equipment in many commercially available vehicles. Therefore, the observation-based assessment of such systems individually and in platoon formations, is very appealing. This paper assesses the impact of ACC systems on energy and fuel consumption car-following scenarios. High-resolution data from three experimental car-following campaigns consisted of platoons with ACC-equipped vehicles, are collected. Two driving modes are considered, human- and ACC-driven vehicles. Results are presented with four independent models estimating energy and fuel consumption. The findings reveal that an upstream energy propagation was observed inside the platoon by the ACC participants, indicating that ACC systems are less efficient than human drivers. On the positive side, commercial ACC systems do not generally fail inside a platoon, keeping steady time-gaps. They seem to operate based on a constant headway policy and their performance is conditioned to the environment. ACC drivers in protected environments and campaigns might perform better but should be (ideally) tested in adverse environments. Overall, commercially implemented ACC systems must be optimized to realize a trade-off between functional specifications in terms of time-gap policies and safe and eco-driving instructions