Adaptive Signal Control System Research Articles

Integrating machine learning techniques with IoT sensors and connected vehicles to enable real-time traffic monitoring and Adaptive signal control systems

This research investigates machine learning algorithms to traffic monitoring and adaptive signal control coordinated in the context of the Internet of things sensors and connected vehicles for real-time traffic supervision. It measures the efficiency of machine learning algorithms, simulates adaptive control strategies for signal systems, and addresses the challenges of deploying signal system changes. The performance metrics suggest better traffic flow, leading to a decrease in traffic congestion. Further study should be made towards parametrization of algorithms, improvement of communication standards and training of integration models.

Fusing crowdsourced data to an adaptive wireless traffic signal control system architecture

In this study, a novel wireless adaptive traffic signal control System architecture is proposed, in which crowdsourced data is integrated to detect the traffic state on each approach of an intersection. Under the wireless architecture, the green signal timing is calculated using a travel time-based Max Pressure control algorithm, which is performed on a single-board computer (SBC, RPi 4). The resulting green timings are sent from SBC to the master controller (ESP32) over serial UART and then to the slave (ESP32) wirelessly using ESP-NOW; slave ESP32 is placed on each approach of the intersection. It integrates a fail-safe mechanism to avoid any vehicular conflicts. The proposed system architecture also embeds a crowdsourced data-based pre-emption of intersections for efficient movement of emergency vehicles. The efficacy of the proposed system is compared using a simulation tool with real-world data. With respect to actuated and Max Pressure based on traffic flow, the proposed algorithm reduces the total delay by 54.8% and 22.96%, respectively. Further, the total queue dissipation time decreases by 8.32% and 5.45%, respectively. It outperforms the actuated signal control as well as Max Pressure based on traffic flows. The capability of transforming existing traffic signal controllers into wireless adaptive traffic signal control systems makes it a very affordable and scalable solution.

Managing mixed traffic at signalized intersections: An adaptive signal control and CAV coordination system based on deep reinforcement learning

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs) at a signalized intersection has become a concern of researchers. However, the performances of most existing control methods are limited, especially when CAV penetration rate is low, since they fail to make a better trade-off between safety and operational efficiency for both CAVs and HVs. To this end, this study proposes a deep reinforcement learning (DRL) powered control system for the mixed traffic at signalized intersections, which aims to optimize operational efficiency of both CAVs and HVs while assuring safety and reducing interference on HVs’ driving habits. The system adopts an adaptive traffic signal control strategy and an efficient CAV control policy with a passing rule proposed as a link in between. The traffic signal control strategy allows traffic light to adaptively adjust its phase and duration based on real-time traffic information, while the CAV control policy permits the CAVs meeting certain safety constraints to form platoons and pass through the intersection in a coordinated manner regardless of traffic signals. As an efficient DRL algorithm, Deep Q-Network (DQN) is adopted to adaptively control the signals and implement CAV coordination. The proposed system is examined on Simulation of Urban Mobility (SUMO), given different CAV penetration rates and traffic conditions. It is found that the proposed system not only outperforms the state-of-the-art control methods on reducing travel time and fuel consumption under low CAV penetration rate, but also enlarges its advantages with the increase of CAV penetration rate. In certain traffic scenarios, the proposed system can even achieve a maximum reduction of travel time by 37.33% and fuel consumption by 15.95%, in comparison to the existing method with the best performance. Besides, to some extent, the comparisons between the performances of CAVs and HVs demonstrate certain benefits of introducing CAVs into the mixed traffic.

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle's bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections.

A physics-informed road user safety field theory for traffic safety assessments applying artificial intelligence-based video analytics

The rapid technological advancements in video analytics and the availability of big data have made traffic conflict techniques a viable tool for road safety assessments. They can potentially overcome many major limitations of conventional road safety practices that use crash-data analyses. However, the current traffic conflict techniques flag serious concerns regarding the context-dependence of the relationship between traffic conflicts and crashes, the lack of consideration of road user and vehicle heterogeneities in their formulation, and the exclusion of crash severity estimation from the analysis process. To overcome these limitations, this study proposes a novel application of the safety field theory to estimate crash risk and severity by modeling the safety-aware interactions of various road users in a road traffic environment. The safety field theory borrows from the Physics concept of electromagnetic fields to mathematically define the safety “buffers” that road users typically maintain around them while moving in traffic. Additionally, the model formulation explicitly accounts for exceptional circumstances (crashes and extreme conflicts) and integrates severity in the risk estimation framework to provide a holistic safety assessment framework. The proposed safety field theory application was tested by analyzing a total of 196 h of traffic movement videos collected from three signalized intersections in Brisbane, Australia and extracting the required road user trajectory information through artificial intelligence-based video analytics. Extreme value modeling of the tail distribution of the risk force generated by the interacting road user safety fields showed that it could predict the crash frequency and outcome severity more accurately than the prevalent traffic conflict indicators. Thus, the proposed approach provides a single, unified, and efficient method of accurately estimating crash risk and injury severities that can be adapted for various application contexts. The study results significantly improve the effectiveness of automated safety analysis for transport facilities and could elevate the safety prediction algorithms of real-time applications like adaptive signal control systems and Connected and Automated Vehicles.

Grey models for short-term queue length predictions for adaptive traffic signal control

Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adjust the signal phasing and timings for different traffic movements at a signalized intersection with ASCS. The accuracy of the queue length prediction model varies based on many factors, such as the stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather, and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and saturated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to develop short-term queue length prediction models for signalized intersections that can be leveraged by adaptive traffic signal control systems using six variations of Grey systems: (i) the first-order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst model (GVM), (iv) GVM with Fourier error corrections (EGVM), (v) the Grey model with cosine term (GM(1,1|cos(ωt) or GMC), and (vi) GMC with Fourier error corrections (EGMC). The efficacy of the Grey models is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they can adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with adaptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models were compared with linear, nonlinear time series models, Adaboost (ADA), Bagging (BAG), Gradient Boost (GB), Neural Network (NN), Random Forest (RF), Support Vector Regression (SVR), and Long Short Term Memory (LSTM) models. Based on our analyses, we found that GMC can produce better or competing performance compared to more complex (i.e., LSTM and NN models) in predicting average and maximum queue lengths in terms of root mean squared and mean absolute errors. GMC model needs only 4 data values and can be used in real-time as the computational time to generate a prediction in less than 0.001 s.

Assessment of Operational Effectiveness of SynchroGreen Adaptive Signal Control System in South Carolina

An adaptive signal control system (ASCS) can adjust signal timings in real time based on traffic demands. The operational benefits of ASCS vary depending on the type of ASCS, corridor characteristics, and geographical area. This paper evaluates the operational performance of 11 ASCS corridors located throughout South Carolina. These corridors are operated using SynchroGreen, one of several types of ASCS, developed by TrafficWare. Based on the operational analysis, it is found that when SynchroGreen is operational, it reduces the travel time on the corridor by an average of 6.4% and improves travel time reliability by an average of 31.4% compared with when the conventional traffic signal control system (e.g., pre-timed and actuated signal control) is operational. SynchroGreen reduces travel time on a corridor on average 61% of the time during a day and on average 77% of the time during peak periods. Additionally, SynchroGreen improves travel time reliability on average 53% of the time during a day and on average 52% of the time during peak periods. The operational effectiveness of SynchroGreen in reducing travel time and improving travel time reliability is consistent in both directions on an hourly basis for eight corridors and five corridors, respectively. Lastly, SynchroGreen is found to produce greater operational benefits by reducing travel time if the average speed of a corridor is lower than or equal to 35 mph and the number of signals on a corridor is more than 10.

Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy

In contemporary urban areas, the construction speed of urban roads lags far behind the growth rate of the number of vehicles. The traffic delay caused by excessive vehicles is a major challenge for modern transportation systems. In this paper, we design an adaptive coordination traffic signal control system based on a three-level framework. In the framework, we propose a hierarchical strategy, which is helpful in avoiding possible offset conflicts and configuring the offsets reasonably. In addition, we establish a multi-intersection traffic signal control model with the goal of minimizing traffic delays. To solve the proposed model, we propose a new algorithm, called the Immune-Fireworks algorithm (IM-FWA), on the basis of the artificial immune and fireworks algorithms. Inspired by the antibody maintenance mechanism, diversity mechanism and communication mechanism of the artificial immune algorithm, IM-FWA can effectively overcome the shortcomings of the fireworks algorithm, such as its limited search range and lack of interaction among fireworks. The experiments show that the proposed model and algorithm have good practicability and that our control system can obtain a better signal timing schedule to effectively reduce traffic delays.

Adaptive and multi-path progression signal control under connected vehicle environment

Through wireless communications, enriched information from connected vehicles (CVs) can describe traffic information near an intersection and would supplement a data source for an effective signal control. This paper proposes an adaptive traffic signal control system in a CV environment. At the intersection level, an adaptive control model is developed to assign optimal green times by minimizing the total vehicle delay. When the CV penetration rate is low, a method depending on limited CV data is presented to estimate the vehicle arrival information. At the corridor level, a real-time optimization model is formulated to design the dynamic progression plan for critical paths (i.e. paths with high flows). These two optimization models are solved by the dynamic programming technique. A real-world arterial is modeled in VISSIM to evaluate the effectiveness and efficiency of the proposed traffic signal control system. Simulations with various CV penetration rates and demand levels are conducted to compare the proposed system with fixed coordination and adaptive signal control systems. Results indicate that the proposed system outperforms both base systems by reducing 15.67% and 13.81% of the average delay, respectively, when the CV penetration rate is relatively high. Moreover, the proposed system can improve the arterial performance under all tested scenarios with various CV penetration rates and demand levels. Since the tested demand scenarios can reflect the real traffic fluctuations, it can be proved that the proposed control system could be applied in the field to boost the overall traffic efficiency along the arterial.

Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes

Introduction: Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash. Method: The method includes four steps: (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered. Results and Conclusions: Findings from the logistic regression modeling reveal significant reduction in the likelihood of secondary crashes for one freeway section (i.e., Charleston I-26 E) with ASCS deployed on alternate route. Other factors such as rear-end crash, dark or limited light, peak period, and annual average daily traffic contribute to the likelihood of freeway secondary crashes. Furthermore, random-parameter logistic regression model results for Charleston I-26 E reveal that unobserved heterogeneity of ASCS effect exists across the observations and ASCS are associated with the reduction of the likelihood of freeway secondary crashes for 84% of the observations (i.e., primary crashes). Location of the primary crash on the freeway is observed to affect the benefit of ASCS toward freeway secondary crash reduction as the primary crash’s location determines how many upstream freeway vehicles will be able to take the alternate route. Practical Applications: Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.

Investigating the impacts of crash prediction models on quantifying safety effectiveness of Adaptive Signal Control Systems

Introduction: Adaptive Signal Control System (ASCS) can improve both operational and safety benefits at signalized corridors. Methods: This paper develops a series of models accounting for model forms and possible predictors and implements these models in Empirical Bayes (EB) and Fully Bayesian (FB) frameworks for ASCS safety evaluation studies. Different models are validated in terms of the ability to reduce the potential bias and variance of prediction and improve the safety effectiveness estimation accuracy using real-world crash data from non-ASCS sites. This paper then develops the safety effectiveness of ASCS at six different corridors with a total of 65 signalized intersections with the same type of ASCS, in South Carolina. Results: Validation results show that the FB model that accounts for traffic volume, roadway geometric features, year factor, and spatial effects shows the best performance among all models. The study findings reveal that ASCS reduces crash frequencies in the total crash, fatal and injury crash, and angle crash for most of the intersections. The safety effectiveness of ASCS varies with different intersection features (i.e., AADT at major streets, number of legs at an intersection, the number of through lanes on major streets, the number of access points on minor streets, and the speed limit at major streets). Conclusions: ASCS is associated with crash reductions, and its safety effects vary with different intersection features. Practical Applications: The findings of this research encourage more ASCS deployments and provide insights into selecting ASCS deployment sites for reducing crashes considering the variation of the safety effectiveness of ASCS.

Investigating hierarchical effects of adaptive signal control system on crash severity using random-parameter ordered regression models incorporating observed heterogeneity

By handling conflicting traffic movements and establishing dynamic coordination between intersections in real-time, the Adaptive Signal Control System (ASCS) can potentially improve the operation and safety of signalized intersections on a corridor. This study identifies the hierarchical effects of ASCS on the crash severity by exploring the heterogeneous effect of ASCS on the crash severity. Four different random-parameter ordered regression models (two ordered probit models, and two ordered logit models) are developed and compared. The analysis reveals that the random-parameter ordered probit and logit models (ROP and ROL) with observed heterogeneity perform better than the random-parameter ordered probit and logit models (RP and RL) without observed heterogeneity in terms of the Akaike information criteria and the goodness of fit of the model. The ROP model performs better than the ROL model in terms of classification model performance measures. The ROP model enables parameters (i.e., the coefficients of the explanatory variables) to vary as a function of explanatory variables as well as across observations, thus accounting for both observed (captured by available explanatory variables) and unobserved (not captured by available explanatory variables) heterogeneity. The analysis reveals that the presence of ASCS is associated with lower crash severity. In this study, observed heterogeneity of ASCS effects on the crash severity is captured by variables related to the intersection and corridor features. Other contributing factors besides ASCS, such as annual average daily traffic, speed limit, lighting, peak period, crash type (rear-end, angle), and pedestrian involvements, are also associated with the probability of crash severity. Unobserved heterogeneity of the effect of angle crash type on the crash severity is found to exist across the observations. The findings of this research have practical implications for establishing ASCS implementation guidelines in lowering the probability of higher crash severity.

An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework

A paradigm shift towards agile and adaptive traffic signal control empowered with the massive growth of Big Data and Internet of Things (IoT) technologies is emerging rapidly for Intelligent Transportation Systems. Generally, an adaptive signal control system fine-tunes signal timing parameters based on pre-defined control hyperparameters using instantaneous traffic detection information. Once traffic pattern changes, those hyperparameters (e.g., maximum and minimum green times) need to be adjusted according to the evolution of traffic dynamics over a very short-term period. Such adjustment processes are usually conducted by professional and experienced traffic engineers. Here we present a human-in-the-loop parallel learning framework and its utilization in an end-to-end recommendation system that mimics and enhances professional signal control engineers' behaviors. The system has been deployed into a real-world application for an extended period in Hangzhou, China, where signal control hyperparameters are recommended based on large-scale multidimensional traffic datasets. Experimental evaluations demonstrate significant improvements in traffic efficiency through the use of our signal recommendation system.

A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

With the development of connected vehicles (CVs) technology, it has become a new research topic to capture the dynamic traffic system by using CVs data. The traditional vehicle arrival prediction models are limited to the fixed detectors, which can only recognize the passing information of vehicles, but cannot identify the state of vehicles. This paper proposed a new vehicle arrival prediction model of traffic signal control in a connected vehicle environment. First, the vehicle's identification number (ID), position, velocity, and acceleration were obtained in a CV environment. Second, a new vehicle arrival prediction model was developed by the joint probability distribution calibrated based on CV data. Then, the simulation experiment was designed for analyzing influence factors on model performance. The results show that the average relative error of each factor is less than 10%. Finally, the reliability of the proposed model was verified based on an adaptive signal control algorithm. Therefore, the proposed model can be used in the adaptive signal control system.

Coordinated control method of intersection traffic light in one-way road based on V2X

One-way roads have potential for improving vehicle speed and reducing traffic delay. Suffering from dense road network, most of adjacent intersections' distance on one-way roads becomes relatively close, which makes isolated control of intersections inefficient in this scene. Thus, it is significant to develop coordinated control of multiple intersection signals on the one-way roads. This paper proposes a signal coordination control method that is suitable for one-way arterial roads. This method uses the cooperation technology of the vehicle infrastructure to collect intersection traffic information and share information among the intersections. Adaptive signal control system is adopted for each intersection in the coordination system, and the green light time is adjusted in real time based on the number of vehicles in queue. The offset and clearance time can be calculated according to the real-time traffic volume. The proposed method was verified with simulation results by VISSiM traffic simulation software. The results compared with other methods show that the coordinated control method proposed in this paper can effectively reduce the average delay of vehicles on the arterial roads and improve the traffic efficiency.

Dynamic platoon dispersion model based on real‐time link travel time

Adaptive signal control system aims to improve the operation efficiency of intersections, in which the platoon dispersion model is one of the key component modules. The problem of the traditional Robertson's model is that they are based on historical data, which cannot effectively capture the dynamic characteristics of traffic flow and be further applied to adaptive signal control systems. Thanks to the unprecedented availability of real-time travel time data provided by the modern intelligent transportation systems, there is an opportunity to calibrate Robertson's model online using real-time travel time data. This study proposes a dynamic Robertson's platoon dispersion model based on real-time travel times. Then, based on field observations, the prediction error and computational efficiency of the traditional Robertson's model, the Gaussian model, and the proposed model are compared and analysed. The results show that the proposed model can better capture the law of dynamic dispersion for traffic flow. The reduced average mean square error of prediction can be as much as 16.86 and 7.07% compared with the traditional Robertson's model and the Gaussian model, respectively. In addition, the average computational time of the proposed model is 0.46 s, which can be applied in the dynamic predictive signal control systems.

A dynamic optimization method for adaptive signal control in a connected vehicle environment

In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic flow changes. This study presents a dynamic optimization method for adaptive signal control in a connected vehicle environment. First, the proposed method developed a dynamic platoon dispersion model to predict vehicle arrivals by using connected vehicle data. Then, a signal timing optimization model is constructed by regarding the minimization of average vehicle delay as the optimization objective, and setting the green time duration of each phase as a constraint. To achieve real-time adaptive signal control, a genetic algorithm is adopted to solve the optimization model through rolling optimization. Finally, a real-world road network was modeled in Vissim to validate the proposed method. Simulation results show that compared with the classical adaptive signal control algorithm, the proposed method is able to reduce vehicle delays and queue lengths at least 50% penetration rates. At 100% penetration rate, the proposed method improved the average vehicle delay and the average queue length by 22.7% and 24.8%, respectively. Moreover, it catered to all directions in a balanced manner.

Estimation of Turning Ratio at Intersections Based on Detector Data using Kalman Filter

Abstract Adaptive signal control system shows strong flexibility and high performance in traffic signal control compared with other control systems. Even though it still has weaknesses such as in turning ratio estimation on so called mixed lanes, on which traffic streams turn into different directions. This may lead to wrong estimation of platoon intensities and as a consequence to non-optimal and unreliable signal control. Therefore, this study proposes an extended Kalman filter (EKF) based on extended cell transmission model (ECTM), to estimate those turning ratios using local detector data of the related intersectons and its neighbors. The ECTM models the traffic situation in signalized urban area macroscopically. The ECTM was developed by Zeng (2012) and Papapanagiotou (2013) based on the orignal cell transimission model (CTM) but overcomes some disadvantages of the original CTM according to urban signalized intersections. In this contribution the ECTM along with the measurement model is organized in a compact state-space form, based on which a turning ratio estimator is designed using EKF. While creating the turning ratio estimator, attention has been especially paid to the linearization of ECTM, in order to organize it into the state-space model and make sure that the EKF is able to interpret the traffic information properly. For estimating the turning ratios, the cell occupancies of the ECTM are also estimated and corrected. Therefor the ECTM based EKF provides a traffic state estimation besides turning ratio estimation. In this study, traffic situation is simulated with Vissim and it is taken as a substitution of reality. The estimated turning ratio using EKF based on ECTM is compared with the turning ratios configured in Vissim and evaluated from two aspects: the mechanism analysis and the sensitivity analysis. In summary, the estimation of turning ratio by ECTM based EKF provides a reliable traffic state estimation. Therefore, it promises a better optimization of signals control, for increasing efficiency in traffic management.

Vulnerability of Traffic Control System Under Cyberattacks with Falsified Data

Existing traffic control systems are mostly deployed in private wired networks. With the development of wireless technology, vehicles and infrastructure devices will be connected through wireless communications, which might open a new door for cyberattackers. It is still not clear what types of cyberattacks can be performed through infrastructure-to-infrastructure and vehicle-to-infrastructure communications, whether such attacks can introduce critical failure to the system, and what the impacts are of cyberattacks on traffic operations. This paper investigates the vulnerability of traffic control systems in a connected environment. Four typical elements, including signal controllers, vehicle detectors, roadside units, and onboard units, are identified as the attack surfaces. The paper mainly focuses on attacking actuated and adaptive signal control systems by sending falsified data, which is considered as an indirect but realistic attack approach. The objective of an attacker is to maximize system delay with constraints such as budget and attack intensity. Empirical results show that different attack scenarios result in significant differences in delay, and some ineffective attacks may even improve the system performance. Simulation results from a real-world corridor show that critical intersections, which have a higher impact on network performance, can be identified by analyzing the attack locations. Identification of such intersections can be helpful in designing a more resilient transportation network.

Evaluation of Actuated, Coordinated, and Adaptive Signal Control Systems: A Case Study

AbstractAs urban traffic congestion grows, traffic engineers must find ways to maximize the efficiency of traffic signal control. Different control strategies, including actuated, coordinated, and ...