Real-time Traffic Flow Data Research Articles

Short-Term Traffic Flow Prediction Based on a K-Nearest Neighbor and Bidirectional Long Short-Term Memory Model

In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to predict the spatio-temporal characteristics of short-term traffic flow by combining the k-nearest neighbor algorithm and bidirectional long short-term memory network model. By selecting the real-time traffic flow data observed on high-speed roads in the United Kingdom, the K-nearest neighbor algorithm is used to spatially screen the station data to determine the points with high correlation and then input the BILSTM model for prediction. The experimental results show that compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models, the model proposed in this paper has better prediction accuracy, and its performance has been improved by 77%, 19%, 18%, 22%, and 13%, respectively. The proposed K-nearest neighbor-bidirectional long short-time memory model shows better prediction performance.

Analysis of the influence of expressway emergencies on transmission speeds and travel delays

Expressway emergencies tend to cause traffic congestion, and understanding the travel time delays of on-road vehicles under different combinations of event scenarios and road traffic conditions is valuable for guiding the accurate emer-gency dispatch services. Most existing studies used methods that combine the Lighthill–Whitham–Richards (LWR) theory and basic traffic diagrams to solve this problem, but the discrete traffic flow characteristics caused by the pres-ence of heavy vehicles have not been considered, thus affecting the applicability of those results to road traffic charac-teristics in China. Moreover, there is a lack of systematic research on multiple combinations of unexpected event sce-narios and traffic conditions, and the guidance value of the previously obtained results is limited. In order to improve the applicability of the prediction model and accurately predict the severity of emergencies, based on a logistic model that is applicable to emergencies, a velocity–density model is constructed to describe discrete traffic flow characteris-tics. Based on LWR theory, the internal driving force of expressway traffic state evolution under emergency conditions is explored. Combined with real-time traffic flow data, the parameters of the logistic model are calibrated, and a lo-gistic velocity–density model is constructed using a goodness-of-fit test and a marching method, including the free-flow velocity, turning density and heavy vehicle mixing ratio. Thus, the problem that existing models lack applicability to road traffic characteristics in China is solved. Travel time delay is associated with the impact range of an emergency, and it is an effective index for evaluating the severity of emergency incidents. Thus, the travel time delays under differ-ent scenarios, different numbers of blocked lanes and different orthogonal combinations of approximate saturation conditions are explored, and the impacts of lane blockage on emergency incidents and travel time delays are obtained. The conclusions show that the presented logistic velocity–density model constructed based on discrete traffic flow characteristics can properly quantify the impact of the presence of heavy vehicles. Additionally, the results can provide theoretical support for handling emergencies and emergency rescues.

Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

Proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.

Navigating to safety: Necessity, requirements, and barriers to considering safety in route finding

Automotive navigation systems are one of the driver assistance technologies with the primary objective of directing drivers to their desired destinations. Although recent versions of automotive navigation systems help users minimize their travel time, there are certain situations in which the shortest route is not necessarily the safest one. Navigating through local roads that carry higher risks of crashes—roads with poor geometric designs, drainage problems, lack of illumination, wildlife crossing danger, and interruptions in traffic flow—is an example of the unintended consequences of routing to ensure minimum travel time. This study is designed to examine the safety of the fastest routes suggested by navigation systems. Road network connecting five metropolitan areas in Texas, including more than 29,000 road segments, is studied. The results of comparing the safest and shortest route between pairs of origins and destinations showed that the shortest route can differ from the safest, where taking a route to decrease travel time by 8% was associated with a 23% higher risk of being involved in a crash. The findings indicate the safest route varies according to different weather conditions. To incorporate safety in route-finding, a centralized, predictive algorithm is introduced for static and dynamic safe route-finding that can complement the existing navigation systems. The requirements for implementing such a system are identified as: (1) availability of real-time traffic flow and incident data for dynamic route-finding systems, (2) more accurate crash prediction models, and (3) a methodology for dealing with the tradeoffs between travel time and safety to find the optimal route.

Development of a Dynamic Calibration Method of Traffic Simulation Parameters Based on Bayes Theorem and Real-Time Traffic Flow Data

Development of a Dynamic Calibration Method of Traffic Simulation Parameters Based on Bayes Theorem and Real-Time Traffic Flow Data

Data Interpolation of Traffic Flow Algorithm Using Wavelet Transform for Traffic Generative Modeling

The real-time data acquisition is an important foundation for Parallel Transportation Systems (PTSs). In practical application, due to the sensor or communication faults, it leads to traffic flow data missing, which will affect the computational experiments and decision-making of the parallel transportation system. Inspired by the idea of parallel intelligence, in this work, we use discrete wavelet transform (DWT) to decompose the complete traffic flow data into low-frequency data and high-frequency data. These decomposed sequences are used as training data for Generative Adversarial Network (GAN) to generate two sequences with different frequencies. A Denoising Autoencoder (DAE) is introduced to interpolate the missing traffic flow data. And it is trained by the dataset through combining the real-time traffic flow data and the generated data. The computational experiments are carried out by using the PeMS dataset. The experimental results show that our algorithm can generate more realistic traffic flow data and improve the performance of data interpolation for missing traffic flow data.

Application of risky driving behavior in crash detection and analysis

Traffic crash detection is a promising and challenging research topic. Due to the limitations of data collection, previous studies mainly used traffic flow variables to establish a traffic crash detection model, and the contribution of risky driving behavior to the traffic crash detection model was not clear. The widespread application of traffic detectors and in-vehicle AutoNavigator software make it possible to collect and update real-time traffic flow data and risky driving behavior data in a short period of time. These data lay the foundation for this study, which aims to quantify the improvement degree of risky driving behavior in a traffic crash detection model and then analyze the coupling effect of risky driving behavior and traffic operation state on the impact of traffic crashes. In this research, we investigated real-time and dynamic traffic flow data and risky driving behavior data by using eXtreme Gradient Boosting (XGBoost) and the logistic regression algorithm, respectively. In addition, SHapley Additive exPlanation (SHAP) was employed to analyze the results and the importance of individual features. The results indicate that the model with the combined inputs has increased accuracy of 8% and nearly a 5% reduction in the false alarm rate. The results of feature importance analysis show that in the variables of risky driving behavior and traffic flow, the most important feature influencing traffic crashes is sharp deceleration. In addition, the characteristics of risky driving behavior increase or decrease the probability of traffic crashes caused by traffic flow characteristics. The results of this paper will help with real-time crash detection and relevant policy-making.

Resource management scheme for full-duplex device-to-device vehicular communication using hypergraph clustering and interference limited area theory

In cellular vehicle-to-everything systems, the rapid movement of vehicles leads to the network topology instability of device-to-device (D2D) links. The frequent reconfiguration of network topology increases the complexity of the same frequency interference problem and the amount of signaling overhead for the management of network topology, which result in the waste of the computation ability and bandwidth resources of the base station (BS). In this paper, we establish a full-duplex device-to-device (FD-D2D) vehicular communication model with cluster as the core and propose a resource management scheme for FD-D2D vehicular communication by using hypergraph clustering and interference limited area theory. In the proposed scheme, the degree of link dependence and the computation ability of vehicle users are utilized to improve the cluster survival time and reduce the BS overhead. Meanwhile, dynamic power control and sharing area mapping are used to assist the BS in allocating resources, reduce the co-frequency interference between vehicular device-to-device (V-D2D) links and between V-D2D links and cellular links that reuse the same frequency, and improve spectrum efficiency. Real-time traffic flow data are generated by the traffic simulation modeling software VISSIM to verify the proposed resource management scheme. Simulation results show that the network topology management workload of the BS is reduced by at least 50%. Relative to that in traditional cellular D2D vehicle communication, the average frequency of spectrum sharing in FD-D2D communication increases by more than 0.5 times, and the spectrum efficiency increases by 10%.

Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia

The impact of inclement weather on traffic flow has been extensively studied in the literature. However, little research has unveiled how local weather conditions affect real-time traffic flows both spatially and temporally. By analysing the real-time traffic flow data of Traffic Signal Controllers (TSCs) and weather information in Brisbane, Australia, this paper aims to explore weather’s impact on traffic flow, more specifically, rainfall’s impact on traffic flow. A suite of analytic methods has been applied, including the space-time cube, time-series clustering, and regression models at three different levels (i.e., comprehensive, location-specific, and aggregate). Our results reveal that rainfall would induce a change of the traffic flow temporally (on weekdays, Saturday, and Sunday and at various periods on each day) and spatially (in the transportation network). Particularly, our results consistently show that the traffic flow would increase on wet days, especially on weekdays, and that the urban inner space, such as the central business district (CBD), is more likely to be impacted by inclement weather compared with other suburbs. Such results could be used by traffic operators to better manage traffic in response to rainfall. The findings could also help transport planners and policy analysts to identify the key transport corridors that are most susceptible to traffic shifts in different weather conditions and establish more weather-resilient transport infrastructures accordingly.

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.

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).

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.

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.

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

Traffic flow data compression considering burst components

Many recent applications of intelligent transportation systems require both real-time and network-wide traffic flow data as input. However, as the detection time and network size increase, the data volume may become very large in terms of both dimension and scale. To address this concern, various traffic flow data compression methods have been proposed, which archive the low-dimensional subspace rather than the original data. Many studies have shown the traffic flow data consist of different components, i.e. low-dimensional intra-day trend, Gaussian type fluctuation and burst components. Existing compression methods cannot compress the burst components well and provide very limited choices of compression ratio (CR). A better compression method should have the ability to archive all the dominant information in different components of traffic flow data. In this study, the authors compare the influence of different data reformatting, archive the bursts defined before in descending order with respect to the absolute value of the burst points and propose a flexible compression framework to balance between burst components and low-dimensional intra-day trend. Experimental results show that the proposed framework promotes the reconstruction accuracy significantly. Moreover, the proposed framework provides more flexible choices with respect to CR, which can benefit a variety of applications.

Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles

Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

Effective Urban Structure Inference from Traffic Flow Dynamics

Mobility in a city is represented as traffic flows in and out of defined urban travel or administrative zones. While the zones and the road networks connecting them are fixed in space, traffic flows between pairs of zones are dynamic through the day. Understanding these dynamics in real time is crucial for real time traffic planning in the city. In this paper, we use real time traffic flow data to generate dense functional correlation matrices between zones during different times of the day. Then, we derive optimal sparse representations of these dense functional matrices, that accurately recover not only the existing road network connectivity between zones, but also reveal new latent links between zones that do not yet exist but are suggested by traffic flow dynamics. We call this sparse representation the time-varying effective traffic connectivity of the city. A convex optimization problem is formulated and used to infer the sparse effective traffic network from time series data of traffic flow for arbitrary levels of temporal granularity. We demonstrate the results for the city of Doha, Qatar on data collected from several hundred bluetooth sensors deployed across the city to record vehicular activity through the city's traffic zones. While the static road network connectivity between zones is accurately inferred, other long range connections are also predicted that could be useful in planning future road linkages in the city. Further, the proposed model can be applied to socio-economic activity other than traffic, such as new housing, construction, or economic activity captured as functional correlations between zones, and can also be similarly used to predict new traffic linkages that are latently needed but as yet do not exist. Preliminary experiments suggest that our framework can be used by urban transportation experts and policy specialists to take a real time data-driven approach towards urban planning and real time traffic planning in the city, especially at the level of administrative zones of a city.

Crash risk analysis during fog conditions using real-time traffic data

This research investigates the changes of traffic characteristics and crash risks during fog conditions. Using real-time traffic flow and weather data at two regions in Florida, the traffic patterns at the fog duration were compared to the traffic patterns at the clear duration. It was found that the average 5-min speed and the average 5-min volume were prone to decreasing during fog. Based on previous studies, a “Crash Risk Increase Indicator (CRII)” was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash risks with traffic flow characteristics. The results suggested that the proposed indicator worked well in evaluating the increase of crash risk under fog condition. It was indicated that the crash risk was prone to increase at ramp vicinities in fog conditions. Also, the average 5-min volume during fog and the lane position are important factors for crash risk increase. The differences between the regions were also explored in this study. The results indicated that the locations with heavier traffic or locations at the lanes that were closest to the median in Region 2 were more likely to observe an increase in crash risks in fog conditions. It is expected that the proposed indicator can help identify the dangerous traffic status under fog conditions and then proper ITS technologies can be implemented to enhance traffic safety when the visibility declines.

Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data

This study aimed to develop a secondary crash risk prediction model on freeways using real-time traffic flow data. The crash and traffic data were collected on the I-880 freeway for five years in California, United States. The secondary crashes were identified by a method based on speed contour plot. The random effect logit model was used to link the probability of secondary crashes with the real-time traffic flow conditions, primary crash characteristics, environmental conditions, and geometric characteristics. The results showed that real-time traffic variables significantly affect the likelihood of secondary crashes. These traffic variables include the traffic volume, average speed, standard deviation of detector occupancy, and volume difference between adjacent lanes. In addition, the primary crash characteristics, environmental conditions and geometric characteristics also significantly affect the risks of secondary crashes. The model evaluation results showed that the predictive performance of the developed model was deemed satisfactory. The inclusion of traffic flow variables and random effect increases prediction accuracy by 16.6% and 7.7%, respectively. These results have the potential to be used in advanced traffic management systems to develop proactive traffic control strategies to prevent the occurrences of secondary crashes on freeways.