Urban Traffic Control System Research Articles

Going Global

An Israel-based company is looking to build on its local successes by taking its urban traffic control system, which promises to revolutionize traffic flow in small- and medium-sized cities, to the international market

A genetic timing scheduling model for urban traffic signal control

As urban traffic condition is diverse and complicated, maximizing the use of urban traffic signal control system to solve traffic issues becomes one of the hot and promising topics. Especially, how to control traffic signals at multiple intersections is a key challenge. To speed up urban traffic flow, this research presents a genetic timing scheduling model (GTSM) for urban traffic signal control. GTSM constructs cellular automata to update the timing cycles of traffic signals at multiple intersections, where state update functions are formulated to coordinate traffic signals. In addition, a proposed genetic optimization algorithm (GOA) in GTSM optimizes the timing cycles of traffic signals at multiple intersections in the dynamic timing optimization. The experimental results on urban road networks show that the performance of our model is excellent in various scenarios to control traffic signals to speed up urban traffic flow.

The impact of strategic transport policies on future urban traffic management systems

Urban traffic management and traffic signal control systems, denoted as Urban Traffic Control (UTC) systems, are used extensively worldwide by Local Government Authorities (LGAs) when implementing strategic transport policies. However, it is not clear how well the requirements imposed by LGA policy implementation will be met by UTC systems being developed for the future. Therefore, research was undertaken to analyse how delivery of urban transport policies over the next 5–10 years would shape LGAs' requirements for the next generation of UTC systems, and thereby to identify Key Performance Indicators (KPIs) to provide user-led guidance for future system development. A two-stage survey of LGA policy makers and implementers from around the world was conducted. The results produced consensus among the group of participants (n = 16) on 17 KPIs, representing a synthesis of expert opinions on the desired features of future UTC systems from a policy implementation perspective. The research makes an important contribution in eliciting the wide-ranging breadth of issues associated with delivering strategic transport policies and understanding how these issues affect the requirements LGAs have for future UTC systems. Aligning future UTC system capabilities with LGAs’ requirements will enable more effective implementation of strategic urban transport policies worldwide and allow the benefits to society associated with those policies to be realised.

Resilient multivariable perimeter control of urban road networks under cyberattacks

The Macroscopic Fundamental Diagram (MFD) concept has become a very valuable tool for modeling the aggregated dynamic of large-scale urban traffic networks. This advance in traffic modeling has fueled the development of many urban traffic control (UTC) policies based on the MFD concept. In particular, perimeter control policies with the aim of maintaining a large-scale urban traffic network operating at its maximum capacity by manipulating the transfer flow of vehicles between adjacent homogeneous regions. In addition to achieve a satisfactory performance level, it is desirable that the control system would maintain a reliable operation under unanticipated events such as cyberattacks. It is important to note that modern UTC systems extensively rely on information technology infrastructures making them vulnerable to cyberattacks. Due to the occurrence of real-world cyberattacks affecting controlled physical systems in different fields, the threat of cyberattacks is becoming a serious and increasing concern for the proper operation of UTC. This paper presents a resilient multivariable control framework for large-scale urban traffic networks subject to several types of cyberattacks such as deception attacks and denial of service (DoS) attacks. Additionally, a feedforward–feedback control scheme combining a time-optimal controller with the resilient multivariable controller is also proposed. The aim of this feedforward–feedback controller is to steer the accumulations to their desired values after a large deviation. Finally, the performance of the proposed resilient controller is demonstrated by means of some realistic simulations of urban traffic networks, including stochastic errors in the traffic demands and in the MFD model, in addition to cyberattacks.

Impact of the Link Length on the Delay in Two-Way Signal Coordination

Signal coordination plays an important role in urban traffic control systems. Studying the influence of some factors such as the link length on the potential benefits of signal coordination can result in more efficient traffic control. Researchers have observed a highly fluctuating or damped sine-wave type relationship between the link length and the delay in two-way coordinated control, but a clear explanation of this relationship is still lacking. This paper presents a thorough analysis of the mechanism by which link length affects the delay in two-way coordination. The impact of the link length involves two aspects. We first derived formulas for the delay using shockwave queuing profiles that included the impact of the offsets in the two directions. We then conducted numerical experiments employing Robertson’s platoon dispersion model to incorporate the impact of platoon dispersion. By comparing the results from the derived formulas and the numerical experiments, we concluded that the periodicity is due to the mutual relation between the offsets in the two directions, whereas the attenuation is due to platoon dispersion. The findings should provide valuable insights for developing a more reasonable correlation degree model for the coordination of adjacent intersections. In addition, we found that platoon dispersion has a negligible influence on the critical link lengths at which the delay cannot be reduced by signal coordination. This means that whether or not platoon dispersion is considered, it will not affect the choice between the simultaneous and the alternative progressions to minimize the delay under a given link length, which is very meaningful in practical work.

From traffic data to GHG emissions: A novel bottom-up methodology and its application to Valencia city

Sustainable cities will only be possible with effective local measures tackling Greenhouse Gas (GHG) emissions. Transport and mobility represent the main sources of these emissions, particularly in urban settings. National and local public administrations need accurate and more responsive tools to quantify GHG emissions. Digitisation and ICTs are key elements in the development of such tools, which, additionally, have to be based on robust methodologies validated by the scientific community. This research presents a bottom-up methodology for the quantification of road traffic's GHG emissions with higher levels of immediacy and spatial resolution when compared to other already existing methods. The methodology uses data from the urban traffic control and monitoring systems as a baseline to calculate emissions. A pilot test has been conducted in Valencia city (Spain). Its results show a highly detailed picture of GHG emission in the city with high temporal (hour) and space (street) resolutions. The emission patterns reflect the dynamics of the city and its citizenship mobility. Since the tools developed for the pilot test can be adapted to other cities, public decision-makers could benefit from a precise diagnosis system based on traffic data to offer and evaluate solutions to reduce road transport GHG emissions.

Research on Urban Traffic Signal Control Systems Based on Cyber Physical Systems

In this paper, we present a traffic cyber physical system for urban road traffic signal control, which is referred to as UTSC-CPS. With this proposed system, managers and researchers can realize the construction and simulation of various types of traffic scenarios, the rapid development, and optimization of new control strategies and can apply effective control strategies to actual traffic management. The advantages of this new system include the following. Firstly, the fusion architecture of private cloud computing and edge computing is proposed for the first time, which effectively improves the performance of software and hardware of the urban road traffic signal control system and realizes information security perception and protection in cloud and equipment, respectively, within the fusion framework; secondly, using the concept of parallel system, the depth of real-time traffic control subsystem and real-time simulation subsystem is realized. Thirdly, the idea of virtual scene basic engine and strategy agent engine is put forward in the system design, which separates data from control strategy by designing a general control strategy API and helps researchers focus on control algorithm itself without paying attention to detection data and basic data. Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. The standard protocol conversion middleware is skillfully designed, which decouples the field equipment from the system software and achieves the universality and reliability of the control strategy. To further demonstrate the advantages of the new system, we have carried out a one-year practical test in Weifang City, Shandong Province, China. The system has been proved in terms of stability, security, scalability, practicability and rapid practice, and verification of the new control strategy. At the same time, it proves the superiority of the simulation subsystem in the performance and simulation scale by comparing the different-scale road networks of Shunyi District in Beijing and Weifang City in Shandong Province. Further tests were conducted using real intersections, and the results were equally valid.

Traffic parameter estimation and control system based on machine vision

With the rapid development of urbanization in the world, it has brought enormous pressure on urban traffic management and control such as traffic congestion. An excellent urban traffic management and control system consists of three critical aspects: obtaining traffic parameters, developing traffic control scheme, and evaluating traffic control scheme. Intersection signal timing is one of the most important parts in urban traffic control. This paper proposed an intersection signal timing system based on traffic video which consists of three parts: acquisition of video-based traffic parameters, calculation of traffic flow-based signal timing scheme, and evaluation of intersection signal timing scheme. In the first part, we used advanced techniques such as deep learning and image processing to obtain traffic parameters such as traffic flow, vehicle type, composition of different vehicle types, and speed of vehicles passing through a scene in a traffic video. In the second part, we calculated the signal timing scheme of the video at the traffic scene through the obtained traffic flow information with Webster method. In the third part, the detailed traffic parameters and signal timing scheme were input into the VISSIM software for traffic microscopic simulation, which was used to evaluate the signal timing scheme. The experimental results show that the accuracy of the detailed traffic flow information obtained by the proposed system can reach more than 90%, the accuracy of composition of different vehicle types can be achieved more than 98%, and the vehicle speed accuracy can reach more than 95%. Therefore, the system improves the reliability and adaptability of the whole signal timing network. At the same time, the simulation results show that the proposed system integrates the acquisition of traffic parameters and the calculation and evaluation of signal timing schemes, and provides a good solution for solving research problems and actual needs such as signal timing optimization.

Agent Based Simulation of ITS for Urban Traffic Control Using GPS

Intelligent transportation systems (ITS), especially in metropolitan areas, can play a crucial role in reducing traffic and its flow and finally reducing the average travel time of vehicles. Due to the high capabilities of intelligent agents in modeling and simulation, they have been used significantly to model complex urban environments and traffic control systems in recent years. In fact, base operating systems are appropriate for modeling intelligent transportation systems, especially in changing urban spaces, and simulating related facilities and equipment. However, most researches in this area have not been comprehensive. As it was mentioned, in this research, a comprehensive agent-based modeling of intelligent urban transportation system is used to control and manage urban traffic using instantaneous traffic information of streets and finally various scenarios have been proposed and implemented. Also, all vehicles have been equipped with GPS and communication devices are simulated. In this study, more emphasis has been placed on intelligent traffic lights using traffic information than routing methods using traffic information and the results have shown high effect of the smartization of traffic lights compared to the change in vehicles routing methods in management and reduction of urban traffic in implemented scenarios.

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

Extended Observer for Urban Traffic Control Based on Limited Measurements From Connected Vehicles

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.

Resilient perimeter control of macroscopic fundamental diagram networks under cyberattacks

The perimeter control concept based on macroscopic fundamental diagram (MFD) modeling is now well-established in various literature. Recent research efforts are devoted to develop perimeter control schemes, which can be eventually applied to urban traffic control systems. Modern urban traffic control systems increasingly rely on information technology infrastructure, combining various network and physical facilities and use of communication technologies, which increases the vulnerability for cyberattacks. As witnessed in recent real-life events, modern urban traffic control systems are vulnerable and not protected from internal and external, malicious and accidental threats. Hence, applicable perimeter control algorithms should be robust not only against dynamic uncertainties, but in addition, it should be resilient against cyberattack issues for real future implementations.In this paper, a resilient perimeter control scheme is developed for a modified multi-region MFD system model. The developed perimeter control is a fully decentralized scheme, which operates based on online local regional information only. It achieves an a priori desired tracking performance with simultaneous compensation of (i) cyberattacks in the channels of control signals transmission, (ii) model parameter uncertainties, (iii) unknown but bounded disturbances, and (iv) control input constraints. A framework of adaptive control techniques is utilized to design the perimeter control. The proposed fault-tolerant perimeter control does not require on-line fault detection, as the fault effects caused by cyberattacks are continuously and adaptively compensated by the developed algorithm. Stability proof of the closed-loop system in terms of the tracking and parameter errors are introduced, and simulation results for three aggregate urban traffic regions are presented.

A New Multi-Agent Reinforcement Learning Method Based on Evolving Dynamic Correlation Matrix

Multi-agent reinforcement learning approaches can be roughly classified into two categories. One is the agent-based approach which can be implemented in real distributed systems, though most approaches of this type cannot provide meaningful theoretical verifications. The other can be seen as the more formalized approach, which can provide theoretical results. However, most of current algorithms usually require unrealistic global communication, which makes them impractical for real distributed systems. In this article, we propose a dynamic correlation matrix based multi-agent reinforcement learning approach where the meta-parameters are evolved using an evolutionary algorithm. We believe that our approach is able to fill the gap between the two kinds of traditional multi-agent reinforcement learning approaches by providing both agent-level implementation and system-level convergence verification. The basic idea of this approach is that agents learn not only from local environmental feedback, i.e., their own experiences and rewards, but also from other agents’ experiences. In this way, the agents’ learning speed can be increased significantly. The performance of the proposed algorithm is demonstrated on a number of application scenarios, including blackjack games, urban traffic control systems and multi-robot foraging.

Resilient Perimeter Control of Macroscopic Fundamental Diagram Networks under Cyberattacks

The perimeter control concept based on macroscopic fundamental diagram (MFD) modeling is now well-established in various literature. Recent research efforts are devoted to develop perimeter control schemes, which can be eventually applied to urban traffic control systems. Modern urban traffic control systems increasingly rely on information technology infrastructure, combining various network and physical facilities and use of communication technologies, which increases the vulnerability for cyberattacks. As witnessed in recent real-life events, modern urban traffic control systems are vulnerable and not protected from internal and external, malicious and accidental threats. Hence, applicable perimeter control algorithms should be robust not only against dynamic uncertainties, but in addition, it should be resilient against cyberattack issues for real future implementations. In this paper, a resilient perimeter control scheme is developed for a modified multi-region MFD system model. The developed perimeter control is a fully decentralized scheme, which operates based on online local regional information only. It achieves an a priori desired tracking performance with simultaneous compensation of (i) cyberattacks in the channels of control signals transmission, (ii) model parameter uncertainties, (iii) unknown but bounded disturbances, and (iv) control input constraints. A framework of adaptive control techniques is utilized to design the perimeter control. The proposed fault-tolerant perimeter control does not require on-line fault detection, as the fault effects caused by cyberattacks are continuously and adaptively compensated by the developed algorithm. Stability proof of the closed-loop system in terms of the tracking and parameter errors are introduced, and simulation results for three aggregate urban traffic regions are presented.

System Error

A new era in ITS is dawning. London’s Urban Traffic Control (UTC) system, the backbone for SCOOT, is reaching the end of its useful life. Its replacement will be revolutionary.

Optimizing multi-agent based urban traffic signal control system

Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods.

City‐wide emissions modelling using fleet probe vehicles

Five UK cities will implement clean air zones to improve air quality (AQ), specifically in relation to oxides of nitrogen, and 253 UK local authorities have declared AQ management areas. Extended range electric vehicles can offer zero emission (ZE) operation but cities lack the ability to monitor and control when vehicles can switch from engine to battery. Project Autonomous and Connected vehicles for CleaneR Air (ACCRA) aims to develop and demonstrate a system allowing hybrid vehicle to become part of a city's urban traffic management control system; monitoring the vehicles’ location and operational state, and controlling the strategy to ensure ZE operation through geofenced zones with poor AQ. This study demonstrates a new methodology to model emissions using state-of-the-art instantaneous vehicle emissions modelling, geographic information system (GIS) and a new method for capturing vehicle behaviour using global positioning system. Probe vehicles were used for the pilot study in Leeds, UK, to capture drive-cycle data and the emissions are modelled. A GIS interface is used to match these emissions to the network. Finally, a high-resolution emissions map is generated to be used as input for AQ dispersion models and the ACCRA decision-making engine for the generation of geofencing AQ zones.

SHORT TERM URBAN TRAFFIC FORECASTING USING DEEP LEARNING

Abstract. In today’s world, the number of vehicles is increasing rapidly in developing countries and China and remains stable in all other countries, while road infrastructure mostly remains unchanged, causing congestion problems in many cities. Urban Traffic Control systems can be helpful in counteracting congestion if they receive accurate information on traffic flow. So far, these data are collected by sensors on roads, such as Inductive Loops, which are rather expensive to install and maintain. A less expensive approach could be to use a limited number of sensors combined with Artificial Intelligence to forecast the intensity of traffic at any point in a city. In this paper, we propose a simple yet accurate short-term urban traffic forecasting solution applying supervised window-based regression analysis using Deep Learning algorithm. Experimental results show that is it possible to forecast the intensity of traffic with good accuracy just monitoring its intensity in the last few minutes. The most significant result, in our opinion, is that the machine can generate accurate predictions even with no knowledge of the current time, the day of the week or the type of the day (holiday, weekday, etc).

Collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles

In order to improve the comprehensive effect of Urban Traffic Control System (UTCS) and Urban Traffic Flow Guidance System (UTFGS), this paper puts forward a collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles (IOV). With consideration of dynamic constraints of UTCS and UTFGS, UTCS is taken as the fast variable, and UTFGS is taken as the slow variable in the collaboration optimization modeling. The conception of Variable Cycle Management (VCM) is presented to solve the mathematical modeling problem under the background of the two variables. A unified framework for VCM is proposed based on IOV. The delay and travel time are calculated based on lane-group-based cell transmission model (LGCTM). The collaboration optimization problem is abstracted into a tri-level programming model. The upper level model is a cycle length optimization model based on multi-objective programming. The middle level model is a dynamic signal control decision model based on fairness analysis. The lower level model is a user equilibrium model based on average travel time. A Heuristic Iterative Optimization Algorithm (HIOA) is set up to solve the tri-level programming model. The upper level model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA II), the middle level model and the lower level model are solved by Method of Successive Averages (MSA). A case study shows the efficiency and applicability of the proposed model and algorithm.

Centralised versus decentralised signal control of large‐scale urban road networks in real time: a simulation study

Recently, signal control strategies with decentralised logic have been developed to tackle the traffic congestion problems of urban road networks. Such strategies aim at network-wide traffic flow efficiency improvement via local actions, thus low design effort and infrastructure investment. This study presents, compares, and evaluates two such innovative approaches: the job scheduling algorithm comprising the local control component of the scalable urban traffic control (SURTRAC) system and the max- or back-pressure algorithm. The approaches are also compared against traffic-responsive urban control (TUC), a well-established strategy with centralised logic. Evaluation is based on the AIMSUN simulation model of the city centre of Chania, Greece. The study results indicate that the TUC and max-pressure retain performance independently of the prevailing traffic conditions, while also being computationally simpler than job scheduling. Both decentralised approaches require frequent (high-resolution) and relatively accurate measurements; on the other hand, TUC, although less demanding in this respect, calls for communication lines between the junction controllers and the central computer. Finally, compared with both decentralised approaches, the TUC provides a signal plan sequence with less excessive differences between each other, thus fewer disturbances to the common network users. Nevertheless, for more comprehensive conclusions, more investigations, including field trials, would be needed.