Recent scholars have developed a number of stochastic car-following models that successfully captured driver behavior uncertainties and reproduced traffic oscillation propagation. Whereas elegant frequency domain analytical methods are available for stability analysis classic deterministic linear models, there lacks an method quantifying the performance their peer theoretically proving features observed in real world. To fill this methodological gap, study proposes novel measures magnitudes...

Self-driving technology companies and the research community are accelerating pace of use machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews current state art in mMP, with an exclusive focus on its impact traffic congestion. The identifies availability congestion scenarios datasets, summarizes required features training mMP. For methods, major methods both imitation non-imitation surveyed. emerging technologies adopted by some leading AV...

Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet quantified. This paper compares the performance between and exact optimization models. We investigate a core problem as building block numerous problems, i.e., guiding movements of connected automated vehicles on one-lane highway when arrival departure times velocity are given. To apply SH algorithm to this problem, we adapt...

The connected automated vehicle (AV) technologies provide unprecedented opportunities for precisely controlling and optimising trajectories to improve traffic performance from the aspects of travel time reduction, driving comfort improvement, fuel consumption emission savings safety enhancement. Recently, (CAV) trajectory optimisation research has become a hot topic. This study provides an overview studies on CAV in road context, with focus literature past decade. Rather than exhausting all...

In order to mitigate risks from road tests for autonomous-driving vehicles, reduce costs and accelerate development, a virtual reality (VR)-based test platform vehicles was built combined with the AirSim system UE4 engine by establishing model library which contains vehicle dynamics model, sensor models traffic environment model. The controller-in-the-loop simulation method implemented complete autonomous under different driving conditions results were used optimise control system. actual...

Intersections in the urban network are potential sources of traffic flow inefficiency. Existing intersection control mostly adopts “cross” pattern model, while use roundabout circular is rather sparse. Connected and autonomous vehicle (CAV) technologies can enable roundabouts to better compete with traditional designs terms performance. This study proposes a strategy for CAVs enhance performance ensuring safety. A hierarchical framework developed decouple flow-level objective vehicle-level...

This paper deals with traffic signal control finite queue capacities in a discrete-time and stochastic setting. A so-called "pressure releasing policy" (PRP) is introduced to optimally release pressure at every time slot, where the each intersection incorporates knowledge of turning ratios information neighboring ingress queues. PRP does not require arrival rates. Moreover, it employs set weights satisfying given condition handle downstream spillover, an algorithm provided generate one...

The construction sector is responsible for almost 30% of the world’s total energy consumption, with a significant portion this being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people’s thermal comfort. In practical applications, conventional approach HVAC management in buildings typically involves manual control temperature setpoints facility operators. Nevertheless, implementation real-time alterations that are based on comfort levels humans inside building...

Motivated by the observation that traffic flows in urban networks may vary significantly both temporally and spatially, this paper introduces a new version of extended back-pressure (EBP) signal control algorithm. This algorithm can be responsive to different tuning parameters over time location, it tends minimize delay at low using set so-called stage weights. We show under mild conditions, EBP with time-varying achieve maximum throughput, i.e., when is adopted, expected long-term average...

To utilize the utmost capacities of junctions with traffic signals, having preemptive and predictive capabilities integrated sensors are important elements for an intelligent signal control system. Recently, efficient strategy known as iterative tuning (IT) has been developed by exploring repetitive characteristic demand on working days, whereas weekends or days special events, severe nonrepetitive disturbances limit performance IT practically. This paper proposes a methodology, i.e.,...

Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The used by current PV prediction methods makes it challenging to accurately perceive movements. obstruction of sun clouds will lead significant decrease actual power generation. network model cannot respond time, resulting accuracy. In order overcome this problem, paper develops visual transformer for prediction, which target sensor and surrounding auxiliary are as input data. By...

Connected and autonomous vehicles (CAVs) can be leveraged to enable cooperative platooning control alleviate traffic oscillations. However, in the near future, CAVs human-driven (HDVs) will coexist on roads, creating a mixed-flow environment. In traffic, CAV platoons would inevitably encounter lane changes by HDVs adjacent lanes. These generate disturbances oscillations upstream, jeopardizing performance of control. Hence, it is necessary explore interactions between lane-change process,...

Due to the existence of predicting errors in power systems, such as solar power, wind and load demand, economic performance systems can be weakened accordingly. In this paper, we propose an adaptive forecasting (ASPF) method for precise forecasting, which captures characteristics revises predictions accordingly by combining data clustering, variable selection, neural network. The proposed ASPF is thus quite general, does not require any specific original method. We first framework ASPF,...

This paper introduces Iterative Tuning (IT) strategy for urban traffic signal control. is motivated by people's daily repetitive travel patterns between homes and working places. Statistical analysis of a real network shows that flows junctions are with small variations on weekly basis. The main idea IT that, schedules tuned anticipation demands. In this paper, only phase split iteratively to balance the demands from all directions in junction. Each junction has its own controller these...

Urban traffic networks are often choked due to recurrent congestion. Heavy economic costs, environmental pollution and severe noise can arise from the lack of valid Traffic Signal Control (TSC) strategy. In this paper, assuming cycle time lights phase order a signal fixed, Deep Reinforcement Learning (DRL) algorithm called Twin Delayed Deterministic Policy Gradient (TD3) is first investigated control by optimizing splits. Unlike widely used Q-learning based TSC strategies, action space TD3...

With the rapid development of industrial technology, application fields AGV are constantly expanding. In this article, a differential vehicle is selected to construct dynamic model and establish co-simulation platform MATLAB/Simulink ADAMS, which fully considers nonlinear friction between wheels ground, body mass its own moment inertia during steering, simulates actual motion trajectory under different paths, compares ideal with ADAMS output, generally consistent theory, basic path trend...

Facing large-scale urban traffic network, countless effort has been made toward intelligent and efficient control system to better use existing infrastructures. Recently, a novelty pre-timed signal strategy known as iterative tuning (IT) developed by exploiting repetitive characteristic of junction's vehicle throughput on working days, which is sufficiently in under-saturated conditions. This study further improves IT saturated conditions with consideration demand including residual queued...

Vehicle automation and connectivity enable the cooperative platooning control of connected autonomous vehicles (CAVs), which can enhance traffic safety alleviate oscillations. However, CAVs human-driven (HDVs) will coexist on roads prior to pure CAV environment, creating a mixed-flow environment. Mixed-flow introduces challenges for operations in terms lane-change maneuvers HDVs adjacent lanes, generate oscillations, jeopardizing performance control. Hence, there is need explore interactions...

Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow due to the token-by-token process. We ask an ambitious question: can a pre-trained AR model be adapted generate outputs just one or two steps? If successful, this would significantly advance development deployment of models. notice that existing works try speed up by generating multiple tokens at once fundamentally cannot capture output distribution conditional dependencies...

This paper’s research is based on the model of BDI Agent, carries out modeling and simulating Virtual Enterprise’s partner selection using Swarm simulation methodology under background analyzes results to study relationship between micro macro behavior in this system. The time duration, number virtual enterprises ability transforming information affect its some extent. will play a reference role modern enterprise alliances.