Modeling Arterial Traffic Dynamics With Actuated Signal Control Using a Simplified Shockwave Model

An integrated macroscopic traffic model is proposed, which integrates a macroscopic urban traffic flow model with a microscopic traffic emission model for individual vehicles. As a macroscopic model, the integrated model is fast enough for on-line control purposes. Nevertheless, the model can still capture the traffic emissions for vehicles in different states compared with using a macroscopic traffic emission model, because of the accuracy of the microscopic traffic emission model. Model Predictive Control is applied to control urban traffic areas based on the integrated traffic model, aiming at reducing both travel delays and traffic emissions.

Macroscopic traffic models describe the traffic behaviour at a high level of aggregation, i.e. the traffic dynamics is expressed through aggregate variables, such as traffic density, mean speed and flow. Macroscopic models rely on the analogy between the flow of vehicles and the flow of fluids or gases and are based on a limited number of equations that are relatively easy to handle. This chapter is devoted to describe a very relevant class of macroscopic models, i.e. first-order traffic flow models, which capture the dynamics of only one aggregate variable, namely, the traffic density. A very important first-order macroscopic model is the Lighthill–Whitham–Richards model, developed in the 50s, but still of interest nowadays both for theoretical analysis and practical applications. It is a continuous model, which describes the dynamics of the macroscopic variables through partial differential equations. The most famous discretised version of the Lighthill–Whitham–Richards model is the so-called Cell Transmission Model, developed in the 90s and very widespread in the communities of mathematicians and traffic engineers.

The macroscopic evaluation of road traffic characteristics is vital as they will alter significantly with the emergence of Connected and Autonomous Vehicles (CAVs). The macroscopic second-order traffic flow models are emerging as essential tools to observe the evolutionary dynamics of traffic flow. These models are significantly improved to describe the dynamics of traffic flow in view of Human-Driven Vehicles (HDVs). However, proficient macroscopic second-order traffic flow models are indispensable in comprehending and elucidating the descriptions of traffic flow dynamics, particularly in the view of evolving CAVs. Therefore, we propose a macroscopic second-order traffic model that distinguishes CAVs from HDVs by meticulously considering their distinct driving characteristics. The proposed model incorporates lengthways between the vehicles, and the reaction time of the CAVs, and is evaluated on a 20,000 m ring road. The findings show that with smaller lengthways and reaction times of CAVs, traffic evolves quickly during transitions. An increase in average traffic velocity is observed as the reaction time is significantly small. It has been demonstrated that CAVs can significantly limit variations in velocity resulting in smoother flow operations.

This paper presents a real-time freeway network traffic surveillance tool RENAISSANCE. Based on a stochastic macroscopic freeway network traffic flow model and the extended Kalman filter, RENAISSANCE is fed with a limited amount of real-time traffic measurements to enable a number of freeway network traffic surveillance tasks, including traffic state estimation and prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction (queue tracking), and incident alarm. The paper first introduces the stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which a complete dynamic system model for freeway network traffic is established, with a special attention to the handling of some important model parameters. The addressed traffic surveillance tasks are described along with the functional architecture of RENAISSANCE. A simulation test was conducted for the tool with respect to a hypothetical freeway network example, while the traffic state estimator of RENAISSANCE was also tested with real traffic measurement data collected from a Bavarian freeway.

Macroscopic network-level traffic models: Bridging fifty years of development toward the next era

Evacuation is a crucial policy to mitigate wildfire impacts. Understanding traffic dynamics during a wildfire evacuation can help authorities to improve in improving emergency management plans, thus improving life safety. In this study, we developed a methodology to extract historical traffic data from vehicle detector stations and automate the analysis of traffic dynamics for actual wildfire evacuations. This has been implemented in an open-access tool called Traffic Dynamic Analyser (TDA) which generates speed-density and flow-density relationships from data using both commonly used macroscopic traffic models as well as machine learning techniques (e.g., support vector regression). The use of the methodology is demonstrated with a case study of the 2020 Glass Fire in California, USA. The results from TDA showed a slight reduction in speeds and flows on US Highway 101 during the evacuation scenario, compared with the routine scenario. Moreover, background traffic has been shown to play a key role in the 2020 Glass Fire compared with previous wildfire evacuation scenarios (e.g., the 2019 Kincade fire). The case study showed that the methodology implemented in the TDA can be used to understand traffic evacuation dynamics in wildfire scenarios and to validate evacuation models.

To reduce travel time, the actuated signal controls have been implemented at urban intersections. However, the safety impacts of actuated signal controls thus far have rarely been examined. In this assessment of the safety impact of urban intersections with semi-actuated signal controls, the safety performance functions and EB approaches were applied. The semi-actuated signal controls have increased injuries and total crashes in all crash types by around 5.9% and 3.8%, respectively. Regarding the most common crash types, such as angle, sideswipe & rear-end, and head-on crashes, semi-actuated signal controls have been seen to decrease injuries by 7.7%. Total crashes have been reduced by over 9.2% through the use of semi-actuated signal controls. This may be result of optimal signal timings considering traffic conditions during peak time periods. In conclusion, safety impact factors which have been established in this study can be used to improve safety and minimize travel times using semi-actuated signal controls.

This paper presents the estimation of travel time on highways based on macroscopic modelling. The focus is on real-time values as compared to average or static values. The macroscopic models are used for distributed and time/space lumped settings and corresponding travel time estimation functions and algorithms are developed. The implications of these algorithms for the implementation of various incident management and traffic control strategies are also discussed.

We introduce microscopic and macroscopic stochastic traffic models including traffic accidents. The microscopic model is based on a Follow-the-Leader approach whereas the macroscopic model is described by a scalar conservation law with space dependent flux function. Accidents are introduced as interruptions of a deterministic evolution and are directly linked to the traffic situation. Based on a Lax-Friedrichs discretization convergence of the microscopic model to the macroscopic model is shown. Numerical simulations are presented to compare the above models and show their convergence behaviour.

The state of the art of macroscopic vehicle traffic flow models is discussed and the traffic flow stability of these models is analyzed. A nonlinear traffic flow stability criterion is investigated using the wavefront expansion technique. Qualitative relationships between traffic flow stability and model parameters are derived for an entire class of second-order macroscopic traffic flow models. Numerical results axe obtained using the CLAWPACK package for the well-known Payne-Whitham model, in order to illustrate the stability criterion. The newly derived stability results are consistent with previous reported results obtained using both microscopic models and approximate linearization methods. Moreover, the stability criteria derived in the paper can provide more refined information regarding the propagation of traffic flow perturbations and shock waves in second-order models than previously established methodologies.

Adaptive signal control is a method that dynamically adjusts signal phases based on real-time traffic conditions, aiming to improve the efficiency of intersection light control. However, in practical applications, obtaining complete traffic spatiotemporal states at intersections is challenging given the limited data sources. To address this issue, this paper presents a novel deep reinforcement learning model, namely 3DQN-PSTER, which combines Dueling Deep Q Network, Double Deep Q Network, and Priority SumTree Experience Replay technology. This model effectively extracts traffic states from sampled floating car data (FCD) and achieves optimal timing schemes. To evaluate its performance, the proposed model is tested in a simulated intersection environment using actual intersection data. The model interacts with the environment by adjusting signal phases, updating traffic states, and obtaining optimal timing schemes. Simulation results demonstrate that the proposed model exhibits higher convergence efficiency and outperforms benchmark models. When applied, the reinforcement learning single control (RLSC) based on the 3DQN-PSTER model outperforms fixed-time signal control schemes and actuated signal control (ASC) schemes. Even in scenarios with low penetration ratios of FCD, RLSC still significantly outperforms ASC. Therefore, the signal timing optimization method proposed in this paper could greatly enhance intersection operation efficiency in both ideal overall data environments and partial states.

Traffic congestion has become an inevitable problem in modern society, and the signal control of intersection plays an important role to ensure the effective operation of urban transport.The paper proposes an improved actuated signal control model to improve the running efficiency of intersection.In order to verify the effectiveness of the improved algorithm，the paper uses VISSIM to simulate the operating condition of different durations under timing control, actuated signal control and improved actuated signal control based on actual traffic flow, signal timing and other data of intersection.The simulation result shows that travel time and delay time are both reduced under the control of actuated signal in different time periods.And the improved actuated signal control is more effective than other signal controls.

Today, the issue of video data processing is very urgent. For example, determining the purpose of human behavior, analyzing economic networks, identifying and eliminating traffic jams on the streets, etc. Nowadays, the increase in the number of vehicles moving on the roads creates traffic jams on the city streets. This makes it urgent to eliminate traffic jams, especially in tourist cities. First of all, the causes of traffic congestion should be identified, traffic congestion can have various causes, such as insufficient capacity, unrestricted demand, traffic light delays, pedestrians crossing the roads at the wrong place , wrong location of sidewalks and etc. Therefore, there is a need to simulate and optimize traffic control to better meet this growing demand. In this research paper, microscopic and macroscopic traffic flow models are reviewed and their properties are discussed to determine which one is suitable for use in this study.

Macroscopic traffic flow modelling of large-scale freeway networks with field data verification: State-of-the-art review, benchmarking framework, and case studies using METANET

Traffic congestion and delays at signalized intersections remain a challenge in urban mobility. To minimize congestion, adaptive traffic signal control systems have been adopted across the United States. However, for these systems to function at their best, systems require an accurate estimation of traffic states, such as traffic density and queue length. This paper implements a relatively novel method, Physics-Informed Neural Networks (PINNs), for real-time traffic density estimation to enhance adaptive intersection signal control. The methodology leverages PINNs, which integrate both data-driven learning and physical constraints imposed by the Lighthill-Whitham-Richards (LWR) macroscopic traffic flow model. A simulation of a four-way intersection was used to generate realistic spatio-temporal traffic data to evaluate the performance of the model. Separate PINNs models, one trained for each incoming approach using simulation-based spatio-temporal density data, provide segment-level density predictions. These real-time density estimates are then employed within an adaptive traffic signal control algorithm designed to adjust green light phasing based on detected demand. The proposed PINNs control strategy was evaluated through simulation, comparing its performance against the Webster traffic timing method using key metrics such as average vehicle delay at the intersection and average vehicle travel time. The findings show an approximate 18% decrease in average vehicle delay and more than a 6% reduction in average travel time when utilizing adaptive traffic control based on the PINNs method for determining density values. These findings highlight the potential of leveraging PINNs for accurate, real-time traffic state estimation, which can effectively inform and improve the performance of adaptive traffic signal control systems, leading to reduced congestion and enhanced mobility at intersections.