Real-time Traffic Flow Research Articles

Dynamic luminance tuning method for tunnel lighting based on data mining of real-time traffic flow

Tunnel lighting constitutes one of the major expenses incurred in transportation lighting, and hence substantial research has been conducted to improve the efficiency of lighting and thus to minimize operating costs. This paper investigates an intelligent method for adjusting tunnel lighting with dynamic control based on data mining of traffic flow distribution, traffic composition, and vehicle speed distribution. Field monitoring data of traffic flow in five real expressway tunnels, which are in HeDa expressway, Jilin Province, China, was used in the analysis. The K-MEANS clustering algorithm was used to group (or cluster) the distribution of daily traffic volume into six-time periods, in which the traffic volume includes two peak periods (8:01–11:23 and 14:31–19:01). A dynamic luminance regulation method is proposed that distinguishes operational strategies under different time periods. Furthermore, the impact of tunnel length and traffic flow on the effect of energy-saving and system sustainability of the proposed method was assessed. The results show that when using the proposed method, the energy-savings in tunnel lighting could be between about 50% and 60% for a daily traffic volume between 750 and 2500 vehicles. The results also show that the switching frequency of the lighting system is significantly reduced, which would significantly enhance the sustainability of the lighting system.

Urban expressway parallel pattern recognition based on intelligent IOT data processing for smart city

With the sustained and rapid development of the social economy and the rapid growth of urban vehicles, urban expressway has developed rapidly. As the roadmap of urban traffic, the urban expressway has a relatively high and stable driving speed, and also bears a large amount of urban traffic. However, in recent years, with the expansion of the city scale, the congestion of urban expressways has become increasingly severe. In various areas of the merged area, due to various factors such as mismatched traffic capacity and reduced driving speed, it is easy to cause deterioration of road conditions in a short period of time and cause more secondary accidents. In order to reduce the incidence of expressway traffic accidents and to avoid as much as possible the casualties and property losses caused by accidents, Intelligent Transportation Systems (ITS) supported by information technology, data communication transmission technology, control technology and traffic engineering were introduced. With the rise of artificial intelligence and the continuous development of ITS, the video acquisition methods of real-time traffic flow data and the image recognition ability of video sequences continue to improve, providing theoretical basis and technical support for the research of urban expressway parallel pattern recognition. Aiming at the shortcomings of traditional pattern recognition methods, such as weak anti-interference to complex traffic environment and low correct recognition rate, this paper studies the pattern recognition method based on image processing, and selects the fuzzy C-means clustering in the currently used clustering methods (FCM) algorithm Because the FCM algorithm cannot obtain the global optimal solution and need to determine the number of cluster categories in advance, this paper uses ReliefF algorithm and Particle Swarm Optimization (PSO) to compare the feature weight and number of clusters of traditional FCM algorithm. Make improvements. Through the experimental analysis of the acquired video images, the results show that the improved FCM algorithm based on the proposed method has better real-time and accuracy in the application of urban expressway parallel pattern recognition.

Intelligent Traffic-Lights Management by Exploiting Smart Antenna Technology (ITSAT)

Existing traffic lights’ timings and switching patterns do not operate with real-time traffic conditions. This results in wastage of time, fuel, and higher emission of CO2. This paper proposes an intelligent traffic-lights management system by exploiting Smart Antenna Technology (already installed on cellular Base Stations). With the proposed approach, the traffic lights’ duration adapts to the real-time traffic flow. Our proposed approach does not delve into improvements of Smart Antenna Technology but suggests to extract cell-phones’ location information, which translates it into traffic density and use the information to control traffic lights’ timings. The work is based on the assumption that the cell-phones density corresponds to the vehicles’ density on the roadways. We also propose a Notifier integrated into the TDC. It alerts the driver about the signal transition from red to green, and thus helps drivers use their cell phones serenely for the transitory duration the light stays the red. The analysis shows that the proposed approach not only improves the Average Waiting Time (AWT) by 59.3% but also saves 3.42 gallons of fuel, and reduces CO2 emission by 67 pounds per year for each commuter.

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.

Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining

This article aims at discussing problems such as complex object types, large amount of data collection, high demand for transmission and calculation, and weak real-time scheduling and control ability in the construction of modern intelligent traffic information physical fusion network, cloud-based control system theory, modern intelligent traffic control network as the research object, and the physical design of the intelligent transportation information fusion cloud control system scheme. The scheme includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on intelligent traffic flow data, in the center of the cloud control management server using deep learning and overrun learning machine intelligence study methods, such as the forecast of traffic flow data for training, to predict urban road short-term traffic flow and congestion. Further up in the air by using intelligent optimization scheduling algorithm for real-time traffic flow control strategy, the simulation results show the effectiveness of the proposed method.

Research on GSTAR-SVM Traffic Prediction Model Based on Wavelet Transform

With the continuous advancement of urbanization in China, the number of urban vehicles has increased rapidly, and traffic congestion has become an urgent problem for modern cities. Therefore, accurate real-time traffic flow prediction is of great significance for solving this problem. However, traffic flow, as a special type of prediction object, has the characteristics of complexity, uncertainty and nonlinearity, which bring great difficulties to the prediction, and the external spatial correlation also has a great influence on the prediction results. In this paper, based on the above problems, the GSTAR-SVM traffic prediction model based on wavelet transform is constructed to predict the short-term traffic flow, and the model is verified based on the data. The experimental results show that the proposed model has higher prediction accuracy.

SDN-based real-time urban traffic analysis in VANET environment

Accurate and real-time traffic flow prediction plays a central role for efficient traffic management. Software Defined Networking (SDN) is one of the key concerns in networking that has caught much attention in recent years. Extensive advancement in software-based configurable equipment has made ready for a new networking paradigm called software-defined vehicular networks (SDVNs). In this paper, we propose a data-driven approach for implementing an artificially intelligent model for vehicular traffic behavior prediction. We combine the flexibility, scalability, and adaptability leveraged by the SDVN architecture along with the machine learning algorithms to model the traffic flow efficiently. First, we introduce an ingenious approach to find congestion sensitive spots in the VANET by means of clustering algorithm and then predicting the future traffic densities for each spot by recurrent neural networks (RNNs). Neural networks (NNs) have been extensively used to model short-term traffic prediction in the past years. We construct a long short-term memory neural network (LSTM-NN) architecture which overcomes the issue of back-propagated error decay through memory blocks for spatiotemporal traffic prediction with high temporal dependency. Due to such learning ability, LSTM can capture the stochastic characteristics and non-linear nature of the traffic flow. The performance evaluation of the proposed approach shows that it has the potential to predict real-time traffic trends accurately with Mean Squared Error (MSE) of 3.0e−3 in the scaled metrics, which is equivalent to 97% accuracy in traffic density prediction.

Virtual Controller Interface Device for Hardware-in-the-Loop Simulation of Traffic Signals

Microscopic traffic simulation has been widely used for the design and/or evaluation of a new signal control strategy prior to field implementation. One of the most popular simulation approaches is the Hardware-in-the-Loop Simulation (HILS), which employs a Controller Interface Device (CID) to connect a physical signal controller to the simulation software to improve the accuracy of simulation. This paper documents the development and performance evaluation of a Virtual Controller Interface Device (VCID) based HILS. Different from the traditional physical CIDs, VCID provides wireless communication between signal controllers and simulation software through the National Transportation Communications for ITS Protocol (NTCIP). Controlled simulation experiments were conducted to compare the performance of VCID-based HILS with the Emulator-in-the-Loop Simulation (EILS), Software-in-the-Loop Simulation (SILS), and the traditional HILS. Queue length, delay, and vehicle trajectories were employed as performance measures. Simulation results show that VCID is an accurate and reliable tool for traffic signal simulation. In addition, since VCID-based HILS enables real-time updating of signal controller parameters during the simulation through the Simple Network Management Protocol (SNMP), it can efficiently assess the adaptive signal control algorithms that are developed based on the real-time traffic flow data collected by Connected Vehicles (CV) or Intelligent Transportation Systems (ITS).

Short-Term Prediction of Signal Cycle on an Arterial With Actuated-Uncoordinated Control Using Sparse Time Series Models

Traffic signals as part of intelligent transportation systems can play a significant role in making cities smart. Conventionally, most traffic lights are designed with fixed-time control, which induces a lot of slack time (unused green time). Actuated traffic lights control traffic flow in real time and are more responsive to the variation of traffic demands. For an isolated signal, a family of time series models, such as autoregressive integrated moving average (ARIMA) models, can be beneficial for predicting the next cycle length. However, when there are multiple signals placed along a corridor with different spacing and configurations, the cycle length variation of such signals is not just related to each signal’s values, but it is also affected by the platoon of vehicles coming from neighboring intersections. In this paper, a multivariate time series model is developed to analyze the behavior of signal cycle lengths of multiple intersections placed along a corridor in a fully actuated setup. Five signalized intersections have been modeled along a corridor, with different spacing among them, together with multiple levels of traffic demand. To tackle the high-dimensional nature of the problem, a penalized least-squares method is utilized in the estimation procedure to output sparse models. Two proposed sparse time series methods captured the signal data reasonably well and outperformed the conventional vector autoregressive model—in some cases up to 17%—as well as being more powerful than univariate models, such as ARIMA.

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.

Integration of Model-Based Signal Control and Queue Spillover Control for Urban Oversaturated Signalized Intersection

An improved model-based predictive control approach integrating model-based signal control and queue spillover control is proposed in this paper, which includes three modules: model-based signal control, queue spillover identification, and spillover control to deal with the problem of traffic congestion for urban oversaturated signalized intersection. The main steps are as follows. First of all, according to the real-time traffic flow data, the green time splits for all intersections will be solved online by the model-based signal control controller whose optimization model is based on model-predictive control (MPC) strategy. Second, the queue spillover identification module will be used to detect the potential queue spillover. If potential queue spillover is detected, the spillover control module will be activated to prevent vehicles from the upstream link of the link with possible spillover from entering the downstream link to avoid traffic congestion. The experiment is performed on a simulated road network. The results verify that the proposed scheme can significantly decrease the delay which reflects the overall performance of the studied intersection.

Floating Car Data Technology

The limiting conditions of traffic in cities, together with the complex and dynamic traffic flows, require an efficient and systematic management and information provision for the traffic participants, with the goal to achieve better utilisation of traffic resources and preserve sustainable mobility. In that context, it is important to identify the traffic flow location features, which requires data and information. This paper presents the application of mobile vehicles for the collection of real time traffic flow data. Such data have become an important source of traffic data, since they can be collected in a simple and cost-efficient way, enabling higher coverage than the conventional approaches, despite the reliability issues. The term referring to that type of data collection, commonly used in scientific and professional literature is FCD (Floating Car Data) and “Probe vehicle”. The efficiency presentation of applying this extensive data source for retrieving necessary parameters and information related to the achievement of sustainable mobility is the final objective of this paper. A description of modern technologies that serve as a basis for probe vehicle data collection has been provided: a geographical information system (GIS), global navigation satellite system (GNSS) and related wireless communication. Within the key technologies review, the development possibilities of data collection by mobile sensors have also been presented.

Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction

Accurate estimation of the traffic state can help to address the issue of urban traffic congestion, providing guiding advices for people’s travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm based on an adaptive multi-kernel support vector machine (AMSVM) with spatial–temporal correlation, which is named as AMSVM-STC. First, we explore both the nonlinearity and randomness of the traffic flow, and hybridize Gaussian kernel and polynomial kernel to constitute the AMSVM. Second, we optimize the parameters of AMSVM with the adaptive particle swarm optimization algorithm, and propose a novel method to make the hybrid kernel’s weight adjust adaptively according to the change tendency of real-time traffic flow. Third, we incorporate the spatial–temporal correlation information with AMSVM to predict the short-term traffic flow. We evaluate our algorithm by doing thorough experiment on real data sets. The results demonstrate that our algorithm can do a timely and adaptive prediction even in the rush hour when the traffic conditions change rapidly. At the same time, the proposed AMSVM-STC outperforms the existing methods.

Real-Time Traffic Flow Pattern Matching to Improve Predictive Performance of Online Simulation-Based Dynamic Traffic Assignment

In real-time simulation-based dynamic traffic assignment, selection of the most suitable demand from the library of demands calibrated offline improves the accuracy of the prediction. In the era of data explosion, relying on contextual and rule-based pattern matching logic does not seem sufficient. A rolling horizon scheme for real-time pattern matching is introduced using two pattern matching frameworks. The hard matching algorithm chooses the closest pattern at each evaluation interval, while soft matching calculates the probability of being a match for each pattern. To make sure the pattern switch does not happen because of short-lived interruptions in traffic conditions, a persistency index is introduced. The results show that the number of switches in hard matching is bigger than soft matching but the error of real-time matching for both cases is low. The importance of the results is twofold: First, any observation that is not similar to only one pattern in the library can be mimicked using multiple available patterns; second, more advanced algorithms can match the patterns existing in the library, without any contextual logics for pattern matching.

Scalable Models for Redundant Data Flow Analysis in Online Social Networks

Sharing and reception of identical video contents amongst the users of various online social media applications induce enormous amount of redundant data traffic flow across Internet layer, various planes of Service provider’s network and also increase in the data consumption of the end users. The primary aim of this research is to reduce the routing of redundant data across social media by means of a novel procedure based on the validation of pre-existence of contents at the receiver’s end prior to the actual video content transmission. A proactive middleware is proposed to handle the enormous growth of social media traffic transparently and hence a distributed Web based framework has been implemented for real-time traffic flow analysis. As part of the Service Oriented Architectural model, various services are defined to fetch a frame from each initiated video content, to validate the pre-existence at receiver’s end and to take appropriate decision for acceptance or deferral of the actual video transfer. The middleware which has been developed for redundant data flow traffic analysis is further extended to cloud services to address the scalability issues. The statistical analysis carried out by the proposed models depicted 27% of reduction in the total initiated traffic with adequate amount of data saving for the end users. The overhead associated in the transmission of the first frame of the actual video is also analyzed and it is observed that overhead is led by the substantial amount of gains achieved by the proposed model.

Smart DPF Regenerations - A Case Study of a Connected Powertrain Function

<div class="section abstract"><div class="htmlview paragraph">The availability of connectivity and autonomy enabled resources, within the automotive sector, has primarily been considered for driver assist technologies and for extending the levels of vehicle autonomy. It is not a stretch to imagine that the additional information, available from connectivity and autonomy, may also be useful in further improving powertrain functions. Critical powertrain subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. Unfortunately, the adoption of this new information resource has been slow within the powertrain community and has typically been limited to the obvious problem choices such as battery charge management for electric vehicles and efforts related to fuel economy benefits from adaptive/coordinated cruise control. In this paper we discuss the application of connectivity resources in the management of an aftertreatment sub-system, the Diesel Particulate Filter (DPF). Standard DPF regenerations are scheduled on an inferred soot load based on indirect indicators of system state, such as exhaust gas flow rate and pressure drop across the DPF and/or empirical models of engine out soot. Soot load estimation approaches such as these are necessary since a reliable method of a direct soot load measurement in a DPF is currently not available. In addition to model uncertainty it is also well known that regeneration control also suffers from uncertainty related to the drive routes, driver behavior, and traffic flow over the driven routes. These uncertainties force a conservative regeneration scheme, that does not fully exploit the soot trapping capacity of the DPF. This makes it difficult to guarantee any measure of uniform optimality over all vehicles. It is evident, however, that by leveraging information that allows a reduction in driver and traffic related uncertainties it may be possible to better schedule DPF regenerations and achieve some degree of performance benefit related to the overall efficiency of the regeneration process over the life of the vehicle. In this paper we present some initial results from such an effort that leverages cloud based real time traffic flow information from a traffic provider in making smart decisions related to the soot management over a DPF.</div></div>

Investigating the factors affecting secondary crash frequency caused by one primary crash using zero-inflated ordered probit regression

The contributing factors to secondary crashes have been investigated by a number of studies in recent years. However, previous studies generally considered only the first secondary crash after the primary crash. Existing studies have seldom considered the multiple secondary crashes caused by one primary crash and the effects of real-time traffic flow conditions. This paper aimed to investigate the effects of real-time traffic flow conditions on the frequency of secondary crashes caused by one primary crash on freeways. The zero-inflated ordered probit (ZIOP) regression model was developed to link the probability of multiple secondary crashes after the occurrence of one primary crash with real-time traffic flow, geometric, weather and primary crash characteristics. The ZIOP regression model analyzed the probability of secondary crash frequency after one primary crash by separating it into two states. One is a secondary-crash-free state that determines whether the occurrence of a crash will lead to one or more secondary crashes, and the other is a secondary-crash-prone state that determines the secondary crash frequency caused by one primary crash. The average speed, average traffic volume, and the difference between the numbers of on-ramp and off-ramp are the significant variables in the secondary-crash-free state. In the secondary-crash-prone state, the significant variables affecting the probability of multiple secondary crashes include average detector occupancy, rainy weather, primary crash severity, and hit-and-run primary crash. The ROC curves were used to test predictive performance of the ZIOP model. The test results suggested that the ZIOP model provide reasonably good predictive accuracy of multiple secondary crashes caused by one primary crash.

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

Junction Conditions for Hamilton–Jacobi Equations for Solving Real-Time Traffic Flow Problems

In this paper, we propose junction conditions for discontinuities due to local perturbation, diverging, merging, and multi-in-multi-out junctions. Traffic flows on junctions can be described by a system of coupled Hamilton-Jacobi equations. At their connection points, it is necessary to propose appropriate junction conditions to close the system. Then, we provide an effective numerical method to compute approximate solutions to these Hamilton-Jacobi equations on junctions. The numerical boundary conditions to close the Hamilton-Jacobi system are also proposed. Numerical tests demonstrate the effectiveness of both the proposed junction conditions and the numerical method.