Traffic Control Applications Research Articles

Traffic operation analysis for underground and ground roads using microscopic traffic simulation

This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design.

Development and validation of a driving simulator for traffic control using field data

This paper presents the development and validation of a driving simulator for ramp traffic control on expressways using a traffic simulator and control (TSC). The TSC consists of two main components: car-following model (CFM), and traffic controller (TC). The CFM simulates the car-following behavior and delivers aggregated traffic parameters to the TC to derive control actions. The CFM and TC are harmonized and integrated in a close-loop control manner, where the effects of the control by the TC are fed-back as inputs for the CFM in real-time applications. Although the following behavior of individual vehicles is simulated, the aggregated outputs such as average speed and flow rate from the model are the parameters of interest. For simplicity in the model development and validation and to capture lane-changing effects, the traffic in the multi-lane expressway where the data were obtained was equivalently represented as a single-lane system. The validation of the CFM was performed at macroscopic level where aggregated outputs from the model were compared to observed data in a segment of the Pan Island Expressway of Singapore under various traffic conditions. The result shows that the simulated speed is not significantly different from the actual speed at 5% significance level, and the aggregated flow rate discrepancies fall in a small range, from 2.21% to 3.15%. This shows that the TSC model is a reliable model for traffic simulation and control applications.

Theory and Experiment of Cooperative Control at Multi-Intersections in Intelligent Connected Vehicle Environment: Review and Perspectives

A heterogeneous traffic flow consists of regular vehicles, and intelligent connected vehicles having interactive functions is updating the composition of the current urban-road network traffic flow. It has been a growing trend and will continue to be so. Because of the urgent demand, the research focused on three main parts of cooperative control methods under intelligent connected vehicles environment, typical traffic control application scenarios and experimental validation in intelligent connected vehicles conditions, and intersection-oriented hybrid traffic control mechanism for urban road. For heterogeneous interrupted traffic flow of intelligent connected vehicles, to analyze the characteristics and information extraction method of heterogeneous traffic flow of intelligent connected vehicles under different conditions, the research examined driving modes of regular vehicles and intelligent connected vehicles, including car following and lane changing. This study summarized control modes of traffic-signal control, active control of intelligent connected vehicles, and indirect control of regular vehicles through intelligent vehicles to study the active control mechanism and multi-intersection coordinated control strategy for intelligent connected vehicle heterogeneous traffic flow. With the combination of coordinated control theory, this work overviewed integrated experiment of information interaction and coordinated control under intelligent-connected-vehicle heterogeneous traffic-flow environments.

Signalized Intersection Control in Mixed Autonomous and Regular Vehicles Traffic Environment—A Critical Review Focusing on Future Control

The recent advancement in industrial technology has offered new opportunities to overcome different problems of stochastic driving behavior of humans through effective implementation of autonomous vehicles (AVs). Optimum utilization of driving behavior and advanced capabilities of the AVs has enabled researchers to propose autonomous cooperative-based methods for signalized intersection control under an AV traffic environment. In the future, AVs will share road networks with regular vehicles (RVs), representing a dynamic mixed traffic environment of two groups of vehicles with different characteristics. Without compromising the safety and level of service, traffic operation and control of such a complex environment is a challenging task. The current study includes a comprehensive review focused on the signalized intersection control methods under a mixed traffic environment. The different proposed methods in the literature are based on certain assumptions, requirements, and constraints mainly associated with traffic composition, connectivity, road infrastructures, intersection, and functional network design. Therefore, these methods should be evaluated with appropriate consideration of the underlying assumptions and limitations. This study concludes that the application of adaptive traffic signal control can effectively optimize traffic signal plans for variations of AV traffic environments. However, artificial intelligence approaches primarily focusing on reinforcement learning should be considered to better utilization of the improved AV characteristics.

Dynamic Capacity Estimation of Mixed Traffic Flows with Application in Adaptive Traffic Signal Control

Dynamic Capacity Estimation of Mixed Traffic Flows with Application in Adaptive Traffic Signal Control

Code-based Sequential Aggregate Signature Scheme

This paper proposes the first code-based quantum immune sequential aggregate signature (SAS) scheme and proves the security of the proposed scheme in the random oracle model. Aggregate signature (AS) schemes and sequential aggregate signature schemes allow a group of potential signers to sign different messages respectively, and all the signatures of those users on those messages can be aggregated into a single signature such that the size of the aggregate signature is much smaller than the total size of all individual signatures. Because of the aggregation of many signatures into a single short signature, AS and SAS schemes can reduce bandwidth and save storage; moreover, when a SAS is verified, not only the valid but also the order in which each signer signed can be verified. AS and SAS schemes can be applied to traffic control, banking transaction and military applications. Most of the existing AS and SAS schemes are based either on pairing or Rivest–Shamir–Adleman (RSA), and hence, can be broken by Shor’s quantum algorithm for Integer Factoring Problem (IFP) and Discrete Logarithm Problem (DLP). There are no quantum algorithms to solve syndrome decoding problems. Hence, code-based cryptography is seen as one of the promising candidates for post-quantum cryptography. This paper shows how to construct quantum immune sequential aggregate signatures based on coding theory. Specifically, we construct our scheme with the first code based signature scheme proposed by Courtois, Finiasz and Sendrier (CFS). Compared to the CFS signature scheme without aggregation, the proposed sequential aggregate signature scheme can save about 90% storage when the number of signers is asymptotically large.

A self-organising organisational paradigm for using multi-agent systems in traffic control application of VANETs

A self-organising organisational paradigm for using multi-agent systems in traffic control application of VANETs

Modeling the traffic signal control system at an isolated intersection using queuing systems

As the population grows in cities worldwide, the number of vehicles present on the roadways also increases, resulting in slow-moving and congested traffic. Therefore, a widespread problem in large cities concerns the traffic in the streets. Traffic signals are one of the most powerful tools available to city authorities for urban traffic control. Their proper installation can improve both traffic flow and the safety of all road users. Extensive research has been conducted to reduce the impacts of long car queues, based mainly on traffic signal timing optimization. This paper estimates the average waiting time at an isolated intersection and optimizes the timing of the green and red phases using an analysis of queueing systems. The control system is assumed to be the fixed-time type, and the Poisson process is considered for the arrivals. The proposed model is applied to real traffic data at a two-phase intersection in Bojnurd, Iran. It needs to be noted that the current situation at the intersection under study reduces average waiting time only for one side, but the analytic model can reduce average waiting time for the whole intersection. Moreover, simulation experiments are carried out, the results of which verify the capabilities of the proposed methodology in traffic signal control applications.

Adaptive tracking of uncertain nonlinear systems under different types of input delays with urban traffic perimeter control application

AbstractPragmatic design method of direct model reference adaptive control (MRAC) is developed for a class of uncertain systems, where various input channels might have different delays and taking into account the effect of control saturation. To overcome the difficulty of directly predict the plant state and cope with different kinds of input delays, the proposed control design relies on implicit time‐delay prediction by using auxiliary linear Smith‐like dynamic units with adjustable gains. The design is unified to different time delay categories, that is, constant, time‐varying, or dependent on the current plant state. Within the context of the proposed approach, various adaptive control feedback strategies are designed for problems of continuous and sample‐and‐hold control implementations. Simulation results of a perimeter control for two interacting aggregate urban traffic regions are presented.

Guest Editorial Advanced Sensing and Sensor Fusion for Intelligent Transportation Systems

The seamless integration of sensor technologies with the transportation infrastructure has leveraged the sensing and communication capabilities to achieve a sustainable intelligent transportation system through which safety, traffic control, and infotainment applications can benefit from multiple sensors deployed in different elements of the transportation system. Being one of the most active research fields in both academia and industry, sensor technologies enable various types of vehicular applications, such as autonomous driving, precise fleet management, intelligent traffic and congestion management, etc. A combination of low cost and reliable passive road sensors as well as traffic sensors (e.g., piezoelectric or magnetic micro-electro-mechanical system (MEMS) sensors) along with the IoT and fog computing provide an enormous potential for improving traffic efficiency, driving safety, and ride comfort.

Improving the accuracy and efficiency of online calibration for simulation-based Dynamic Traffic Assignment

Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states, which necessitates online calibration. A widely used solution approach for online calibration is the Extended Kalman Filter (EKF), which – although appealing in its flexibility to incorporate any class of parameters and measurements – poses several challenges with regard to calibration accuracy and scalability, especially in congested situations for large-scale networks. This paper addresses these issues in turn so as to improve the accuracy and efficiency of EKF-based online calibration approaches for large and congested networks. First, the concept of state augmentation is revisited to handle violations of the Markovian assumption typically implicit in online applications of the EKF. Second, a method based on graph-coloring is proposed to operationalize the partitioned finite-difference approach that enhances scalability of the gradient computations.Several synthetic experiments and a real world case study demonstrate that application of the proposed approaches yields improvements in terms of both prediction accuracy and computational performance. The work has applications in real-world deployments of simulation-based dynamic traffic assignment systems.

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling

We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our proposed scheme is equivalent to running regular temporal difference learning with linear function approximation, albeit with samples picked uniformly from a given dataset. Our method results in an O(d) improvement in complexity in comparison to LSTD, where d is the dimension of the data. We provide non-asymptotic bounds for our proposed method, both in high probability and in expectation, under the assumption that the matrix underlying the LSTD solution is positive definite. The latter assumption can be easily satisfied for the pathwise LSTD variant proposed by Lazaric (J Mach Learn Res 13:3041–3074, 2012). Moreover, we also establish that using our method in place of LSTD does not impact the rate of convergence of the approximate value function to the true value function. These rate results coupled with the low computational complexity of our method make it attractive for implementation in big data settings, where d is large. A similar low-complexity alternative for least squares regression is well-known as the stochastic gradient descent (SGD) algorithm. We provide finite-time bounds for SGD. We demonstrate the practicality of our method as an efficient alternative for pathwise LSTD empirically by combining it with the least squares policy iteration algorithm in a traffic signal control application. We also conduct another set of experiments that combines the SA-based low-complexity variant for least squares regression with the LinUCB algorithm for contextual bandits, using the large scale news recommendation dataset from Yahoo.

Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace

For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms.

Application of area traffic control using the max-pressure algorithm

ABSTRACT This paper proposes an application of max-pressure control for network-wide signal control at Bandung, Indonesia. The max-pressure approach is employed for a specific disturbed network system synthetic scenario, created with the aim to simulate spillback conditions which causing long congestion across road segments in real traffic conditions. The max-pressure controller is implemented for a network of six signalized intersections in PTV Vissim, a traffic micro-simulation platform. The validated model is generated before implementing in the Vissim traffic simulation. Three types of controller are studied: the currently implemented controller (fixed time controller), cycle-based max-pressure and slotted-based max-pressure. The simulation results show that max-pressure control is more powerful than the currently implemented technique in terms of the capability to avoid congestion by spreading vehicles to other road segments with respect to some events that can be seen as a disturbance.

Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency

Obtaining accurate information about future traffic flows of all links in a traffic network is of great importance for traffic management and control applications. This research studies two particular problems in traffic forecasting: (1) capture the dynamic and non-local spatial correlation between traffic links and (2) model the dynamics of temporal dependency for accurate multiple steps ahead predictions. To address these issues, we propose a deep learning framework named Spatial-Temporal Sequence to Sequence model (STSeq2Seq). This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information. Moreover, STSeq2Seq defines and constructs Pattern-aware Adjacency Matrix (PAM) based on pair-wise similarity of the recent traffic patterns on traffic links and integrates it into graph convolution operation. It also deploys a novel seq2sesq architecture which couples a convolutional encoder and a recurrent decoder with attention mechanism for dynamic modeling of long-range dependence between different time steps. We conduct extensive experiments using two publicly-available large-scale traffic datasets and compare STSeq2Seq with other baseline models. The numerical results demonstrate that the proposed model achieves state-of-the-art forecasting performance in terms of various error measures. The ablation study verifies the effectiveness of PAMs in capturing dynamic non-local spatial correlation and the superiority of proposed seq2seq architecture in modeling non-stationary temporal dependency for multiple steps ahead prediction. Furthermore, qualitative analysis is conducted on PAMs as well as the attention weights for model interpretation.

Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data

License-plate recognition (LPR) data are emerging data sources in urban transportation systems which contain rich information. Large-scale LPR systems have seen rapid development in many parts of the world. However, limited by privacy considerations, LPR data are seldom available to the research community, which lead to huge research gap in data-driven applications. In this study, we propose a complete solution using LPR data for link-based traffic state estimation and prediction for arterial networks. The proposed integrative data-driven framework provides the inference of both cycle maximum queue length states and average travel times of links using LPR data from a subset of intersections in an arterial network. The framework contains three novel data-driven sub-components that are highly customized based on the characteristics of LPR data, including: a traffic signal timing inference model to find signal timing information from the LPR timestamp sequences; a light-weighted queue length approximation model to estimate lane-based cycle maximum queue lengths and a network-wide traffic state inference model to perform network-level estimation and prediction using partially observed data. This study exploits and utilizes the unique features of LPR data and other similar vehicle re-identification data for urban network-wide link-based traffic state estimation and prediction. A six days’ LPR dataset from a small road network in the city of Langfang in China and a more comprehensive link-level field experiment dataset are used to validate the model. Numerical results show that the framework provides good estimation and prediction accuracy. The proposed framework is efficient and calibration-free, which can be easily implemented in urban networks for various real-time traffic monitoring and control applications.

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.

Detection of the vehicle license plate using a kernel density with default search radius algorithm filter

With the contemporary improvements in intelligent transportation systems, automatic car License Plate Detection and Recognition (LPDR) has involved extensive research interests. It has a variety of potential applications in security and traffic control, and much work has been completed on the subject of LPDR. But, majority of prevailing procedures work well either at measured circumstances or with sophisticated image capture systems. It is still a challenging task to read license plates precisely in an open environment. The problem exists in extreme assortment in pattern of characters, such as dissimilar sizes, letterings, misrepresentation, obstruction or blurring, as well as high intricate backgrounds, similar to the common fonts in boards of shops, windows, handrails or construction blocks. In this study, a hybrid methodology has been established in recognizing license plates of candidates in a specific area. This research proposes a technique called kernel density function along with the binary technique that has been utilized for pre-processing. By means of the filtered binary value of the image, the position of car plate can be found by multiplying the binary value and original value of the image. The proposed method outperforms advanced techniques by a huge margin concerning about accuracy in detection as well as efficiency of run-time. The proposed method achieved 98.1 % accuracy to detect the License Plate (LP) and the computation cost is about 0.452 s.

Research on self-organising control method of urban intelligent traffic signal based on vehicle networking

In order to overcome the problem of poor application of traditional urban intelligent traffic signal self-organisation control, a method of urban intelligent traffic signal self-organisation control based on vehicle network is proposed. A signal self-organising control system based on on-demand distribution is constructed, in which the fixed unit module RSU receives vehicle traffic data through sensors. RDU is used to monitor vehicle data and construct signal adaptive control strategy, which can reduce vehicle waiting time and realise urban intelligent traffic signal self-organising control. Simulation results show that the average number of stops at the intersection at the same time point is less than 0.3. The average stopping time is 8.728s, which is obviously lower than other methods. The average pass rate at the intersection is 98.65%, which is obviously higher than other methods and feasible.