Adaptive Traffic Control Algorithm Research Articles

HumanLight: Incentivizing ridesharing via human-centric deep reinforcement learning in traffic signal control

Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in information technologies create opportunities for smart solutions that incentivize ridesharing and mode shift to higher occupancy vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we present HumanLight, a novel decentralized adaptive traffic signal control algorithm designed to optimize people throughput at intersections. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times. Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window. The proposed algorithm showcases significant headroom and scalability in different network configurations considering multimodal vehicle splits at various scenarios of HOV adoption. Improvements in person delays and queues range from 15% to over 55% compared to vehicle-level SOTA controllers. We quantify the impact of incorporating active vehicles in the formulation of our RL model for different network structures. HumanLight also enables regulation of the aggressiveness of the HOV prioritization. The impact of parameter setting on the generated phase profile is investigated as a key component of acyclic signal controllers affecting pedestrian waiting times. HumanLight’s scalable, decentralized design can reshape the resolution of traffic management to be more human-centric and empower policies that incentivize ridesharing and public transit systems.

Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods.

Cooperative Control of Traffic Signals and Vehicle Trajectories

The transportation system is one of the most important parts of the country's economy. At the same time, the growth in road traffic has a significant negative impact on the economic performance of the industry. One of the ways to increase the efficiency of using the transportation infrastructure is to manage traffic flows, incl. by controlling traffic signals at signalized intersections. One of the trends in the development of intelligent transportation systems is the creation of vehicular ad hoc networks that allow the exchange of information between vehicles and infrastructure, as well as the development of autonomous vehicles. As a result, it becomes possible to formulate the problem of cooperative control of vehicle trajectories and traffic signals to increase the capacity of intersections and reduce fuel consumption and travel time. This paper presents a method for managing traffic flow at an intersection, which consists of the cooperative control of traffic signals and trajectories of connected/autonomous vehicles. The developed method combines an algorithm for the adaptive control of traffic signals based on a deterministic model for predicting the movement of vehicles and a two-stage algorithm for constructing the trajectory of vehicles. The objective optimization function used to construct the optimal trajectories takes into account fuel consumption, travel time on the road lane, and waiting time at the intersection. Experimental studies of the developed method were carried out in the microscopic traffic simulation package SUMO using three simulation scenarios, including two synthetic scenarios and a scenario in a real urban environment. The results of experimental studies confirm the effectiveness of the developed method in terms of fuel consumption, travel time, and waiting time in comparison with the adaptive traffic signal control algorithm.

A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow

The operational and safety performance of intersections is the key to ensuring the efficient operation of urban traffic. With the development of automated driving technologies, the ability of adaptive traffic signal control has been improved according to data detected by connected and automated vehicles (CAVs). In this paper, an adaptive traffic signal control was proposed to optimize the operational and safety performance of the intersection. The proposed algorithm based on Q-learning considers the data detected by loop detectors and CAVs. Furthermore, a comprehensive analysis was conducted to verify the performance of the proposed algorithm. The results show that the average delay and conflict rate have been significantly optimized compared with fixed timing and traffic actuated control. In addition, the performance of the proposed algorithm is good in the test of the irregular intersection. The algorithm provides a new idea for the intelligent management of isolated intersections under the condition of mixed traffic flow. It provides a research basis for the collaborative control of multiple intersections.

Real-Time Crash-Risk Optimization at Signalized Intersections

Over the past few decades, numerous adaptive traffic signal control (ATSC) algorithms have been proposed to alleviate traffic congestion and optimize traffic mobility using real-time traffic data, such as data from connected vehicles (CVs). However, most of the existing ATSC algorithms do not consider optimizing traffic safety, likely because of the lack of tools to evaluate safety in real time. In this paper, we propose a novel ATSC algorithm for real-time safety optimization. The algorithm utilizes a traditional Reinforcement Learning approach (i.e., Q-learning) as well as recently developed extreme value theory (EVT) real-time crash prediction models. The algorithm was validated using real-world traffic video data collected from two signalized intersections in British Columbia. The results indicated that, compared with an existing fully actuated signal controller, the developed algorithm can significantly reduce the real-time crash risk by 43% to 45% at the intersection’s approaches even at low CVs market penetration rates.

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm.

Adaptive traffic signal control algorithms based on probe vehicle data

In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information.

Dynamic Local Vehicular Flow Optimization Using Real-Time Traffic Conditions at Multiple Road Intersections

Dynamic management of vehicular traffic congestion to maximize throughput in urban areas has been drawing increased attention in recent years. For that purpose, a number of adaptive control algorithms have been proposed for individual traffic lights based on the in-flow rate. However, little attention has been given to the traffic throughput maximization problem considering real-time road conditions from multiple intersections. In this paper, we formulate such a problem as maximum integer multi-commodity flow by considering incoming vehicles that have different outgoing directions. Then, we propose a novel adaptive traffic light signal control algorithm which opts to maximize traffic flow through and reduce the waiting time of vehicles at an intersection. The proposed algorithm adjusts traffic light signal phases and durations depending on real-time road condition of local and neighboring intersections. Via SUMO simulation, we demonstrate the effectiveness of the proposed algorithm in terms of traffic throughput and average travel time.

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

By widely introducing information technology tools in the field of traffic control, it is possible to increase the capacity of hubs and reduce vehicle delays. Adaptive traffic light control is one of such tools. Its effectiveness can be assessed through traffic flow simulation. The aim of this study is to create a simulation model of a signal-controlled intersection that can be used to assess the effectiveness of adaptive control in various traffic situations, including the presence or absence of pedestrian traffic through an intersection. The model is based on a numerical experiment conducted using the Monte Carlo method. As a result of the study, vehicle delays, queue length and duration of traffic light cycles are calculated subject to different intensities of incoming traffic flows, and the presence or absence of pedestrian traffic.

High performance adaptive traffic control for efficient response in vehicular ad hoc networks

Nowadays, with the invention of CUDA, a parallel computing platform and programming model, there is a dramatic increase in computing performance by harnessing the power of the GPU. GPU computing with CUDA can be utilised to find efficient solutions for many real world complex problems. One such problem is traffic signal control problem, which takes care of conflicting movements at the intersections to avoid accidents and ensure smooth flow of traffic for the commuters in safe and efficient manner. Adaptive traffic control (ATC) algorithm is used in literature to reduce the average queue length at the intersections. This algorithm has serial implementation on a single CPU and hence takes large computation time. In this paper, we propose a high performance adaptive traffic control for proving efficient responses and hence reducing average queue length that results in decrease in the overall waiting time at the intersections. We tested our proposed approach with varying number of vehicles for two real world networks. The performance of the proposed algorithm is compared with its serial counterpart.

Implementing an adaptive traffic signal control algorithm in an agent-based transport simulation

This paper describes the implementation of the fully traffic adaptive signal control algorithm by Lämmer2 in the agent-based transport simulation MATSim. The implementation is tested at an illustrative, single intersection scenario and compared to the results of Lammers MATLAB simulation. Plausibility of the self-controlled signals and overall results can be confirmed. Small deviations can be explained by differences in flow simulation and resolution of simulation time steps. In the simulation of the illustrative intersection, the adaptive control is proved to be stable and, overall, superior to a fixed-time control. Constant vehicle arrivals are simulated to show the performance of the control and its underlying sub-strategies. The expected behavior of the algorithm and its implementation are validated by analyzing queue lengths over time. The adaptive control significantly outperforms the fixed-time control for stochastic demand, where its ability to dynamically react to changes in flow becomes important.

Parameter Identification and Model Validation of a Macroscopic Traffic Model

Adaptive traffic control algorithms require an underlying traffic model. A suitable macroscopic traffic model has previously been developed by the authors. In this work the proposed method of parameter identification and the traffic model are validated with real live traffic data PeMS (Performance Measurement System) provided by the California Institute of Transportation. The proposed method of parameter identification utilizes a genetic algorithm (GA) where the difference between measurement data and simulation data are minimized. Parameter sensitivity and identifiability are investigated via the Fisher Information Matrix. The macroscopic traffic model based on the identified parameters is used to simulate traffic for a given time period at the test field. Results are presented and cross-validated with both the provided data of the PeMS data base and simulation results of the stochastic cell transmission model.

Traffic network micro‐simulation model and control algorithm based on approximate dynamic programming

This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system.

Improving Energy Efficiency by Advanced Traffic Control Systems

The problem of traffic congestion is particularly acute in urban areas in which the possibilities for the physical increase of capacities are limited or nonexistent. Traffic congestion has a direct impact on the emission, energy efficiency and fuel consumption of personal vehicles. Several projects in the European Union are focused on solving this problem (both at the physical level – automotive industry, as well as at the traffic management level). This paper explores the possibility of the implementation of advanced traffic control systems in urban areas in which driving behavior involves a multitude of stopand-go actions, lower speeds in lower vehicle gears. Since this type of driving behavior affects vehicle fuel consumption and emission, relevant evaluation parameters were defined (queue length, average vehicle speed, etc.). A demonstration corridor in the city of Zagreb was chosen and a simulation model based on the traffic data collected in real traffic situations developed. The basis for further research is laid down to allow the application of the proposed model and adaptive traffic control algorithms to the greater urban traffic network.

Earliest-Deadline-Based Scheduling to Reduce Urban Traffic Congestion

One of the major problems, caused by traffic congestion, owes its existence to the unwanted delay experienced by the priority vehicles. The evaluation of two scheduling algorithms as adaptive traffic control algorithms has been proposed here to reduce this unwanted delay. One of these algorithms is the earliest deadline first (EDF) algorithm, whereas the other is the fixed priority (FP) algorithm. The performance of both algorithms as adaptive traffic light control algorithms is evaluated for isolated traffic intersections. A comparative study is performed here, where the performance of these algorithms is compared against a fixed static traffic light controller. Moreover, their performance is also compared against each other. Conclusive results from the simulation of the algorithms reveal that the number of stops, average delay, and mean trip time of the priority vehicles is significantly reduced by the implementation of these algorithms. Furthermore, it has been shown that the overall performance of EDF is much better than FP in terms of improvement of different performance measures for congestion reduction of priority vehicles.

Traffic Signal Control with Connected Vehicles

The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a microscopic simulation driven by present vehicle positions, headings, and speeds. The algorithm is relatively simple, does not require point detectors or signal-to-signal communication, and is completely responsive to immediate vehicle demands. To ensure drivers' privacy, the algorithm does not store individual or aggregate vehicle locations. Results from a simulation showed that the algorithm maintained or improved performance compared with that of a state-of-the-practice coordinated actuated timing plan optimized by Synchro at low and midlevel volumes, but that performance worsened under saturated and oversaturated conditions. Testing also showed that the algorithm had improved performance during periods of unexpected high demand and the ability to respond automatically to year-to-year growth without retiming.

Single-Intersection Evaluation of Real-Time Adaptive Traffic Signal Control Algorithms

Adaptive traffic signal control systems present the potential to significantly reduce delay, travel time, stops, and fuel consumption relative to current arterial traffic management practices. However, this technology—now referred to as adaptive-control software (ACS) by FHWA—has not been widely deployed in the United States, in part because traffic engineers are unconvinced of the incremental benefits of ACS over those of well-maintained time-of-day plans. This study identifies three specific deficiencies in the current generation of adaptive-control algorithms. Primarily, the inability to optimize timing in general multiring configurations prevents current adaptive-control algorithms from consistently out-performing properly calibrated dual-ring vehicle-actuated controllers. Limitations in real-time computational capabilities are identified as the main roadblock to overcoming this and other deficiencies. This limitation motivates a novel simulation study focused on real-time computational capabilities of adaptive-control algorithms used in OPAC, PRODYN, ALLONS-D, and COP, the intersection control algorithm of RHODES. The goal is to determine which algorithmic techniques are best suited for extension or reformulation to overcome the identified deficiencies. Benchmarks for vehicle delay performance include Webster's optimized fixed-time control, vehicle-actuated control, and an adaptive fixed-time policy derived from SCOOT. In conclusion, certain aspects of algorithmic techniques from OPAC, PRODYN, and ALLONS-D are unsuitable for much more computationally demanding tasks, including future extension to full optimization of a multiring controller. Also, specific features of the COP-97 and ALLONS-D algorithms present significant advantages in computational complexity and reduced traffic delay, respectively.