Real-time Traffic Flow Research Articles

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.

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.

Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network

Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network, which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.

On-Road Safety Prompt Framework for Platoon

In recent years, researchers are very interested in the development of autonomous vehicle platoons that can be commercialized on a large scale. The research progress in this field raises the new demand for real-time traffic flow information and safety prediction. Therefore, an On-road Safety Prompt Framework (OSPF) that combines the free space detection and lane change prediction has beed developed by this paper. This OSPF can predict the lane-change decision and lane-change trajectory around in a short period of time in the future according to the current state and historical trajectory of the surrounding vehicles, and combine these prediction information to provide the autonomous vehicle platoon with the predicted free space in the near future. The free space provided by this OSPF includes three types: continuing driving according to the current state or changing lanes to the left or right. At the same time, it also provides the required recommended driving trajectory. The evaluation test of this ADAS has been done through a simulation testbed of the NGSIM dataset built with Eclipse SUMO (Lopez et al. (2018)).

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.

Density Based Traffic Control System with Convolutional Neural Network

We present a real-time traffic flow controller structure that can keep traffic under control using image processing techniques. In this way, a camera is used in every section of the robot to take pictures of the traffic where traffic jams will appear. The number of vehicles in these images is designed using image processing tools. In the proposed image, green and red signals are represented using LEDs and the diminished green signal supervisor is signified by a specific presentation.

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.

A hybrid machine learning model for predicting Real-Time secondary crash likelihood

Secondary crashes usually occur within the spatio-temporal impact ranges of primary crashes, which could cause traffic disturbance and increase traffic safety problems. However, existing studies only focused on predicting the likelihood of crashes leading to secondary crashes without considering the likelihood of the occurrence of secondary crashes. In addition, previous studies did not consider the real-time implementation of secondary crash likelihood prediction models and included too many features that were not available in real-time. A real-time secondary crash likelihood prediction model aims to predict the likelihood of secondary crashes in a short period (e.g., 5–10 min) and update every minute. The main objective of this paper is to develop a machine learning model to predict secondary crash likelihood in real-time. Two XGBoost models were developed for predicting the likelihood of crashes leading to secondary crashes and the likelihood of the occurrence of secondary crashes, respectively. A hybrid model was proposed to combine the results from the two developed models. Results indicated that the proposed hybrid model significantly improved the accuracy of secondary crash likelihood prediction. The proposed model has the potential to be applied in proactive traffic safety management system and prevent the occurrence of secondary crashes. Moreover, experimental results suggested that several features related to real-time traffic flow conditions were crucial for predicting secondary crash likelihood, such as the average traffic volume and average occupancy.

A low-power and low cost smart streetlight system based on Internet of Things technology

Street lighting, as the most essential and universal component of the urban lighting system, accounts for a large portion of public electricity usage. Therefore, improving street lamps working efficiency is vital for energy savings. This paper demonstrates the design of a smart street lighting system supported by the combination of NB-IoT and LoRa communication technology. By adopting an optimized street lamp control algorithm, the system can realize the automatic control of street lights according to the real-time traffic flow information. This system has been installed on Luyang Avenue, Lucheng City, Shanxi Province, China in May 2019. It managed to reduce the electricity consumption in this region significantly in June 2019, which was 18% lower than that in April 2019 and 19.7% lower than that in June 2018. By illustrating the unique advantages of this system in energy savings and cost reduction, this paper displays its potential for further application in the construction of smart cities on a large scale.

Real-time traffic flow topology sensing in partial vehicular ad hoc network: a deep learning solution

In the near future, road traffic is expected to be a mixture of CAVs (connected and autonomous vehicles) and HDVs (human-driven vehicles). CAVs can be monitored by the system. By contrast, the information of HDVs is not readily available. Since full traffic flow topology information is valuable for applications such as data routing, this study proposes a method to infer the information of HDVs using CAV trajectories in partial VANET (Vehicular Ad Hoc Network). Specifically, two deep networks, i.e. VexSen and VelSen, are constructed to infer the existence and location of HDVs. Then, the networks are combined and fused into one sensing procedure. The proposed method was validated by conducting numerous experiments. The results show that HDV existence inferrence accuracy can reach 80%, whereas for the case of multiple CAVs at least the information of 20% HDVs can be inferred given the CAVs penetration rate of 20%.

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

PurposeEfficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.Design/methodology/approachThis paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.FindingsResults show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.Research limitations/implicationsThe incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.Practical implicationsThe framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.Originality/valueThis study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

Traffic-induced atmospheric pollution during the COVID-19 lockdown: Dispersion modeling based on traffic flow monitoring in Turin, Italy

The COVID-19 pandemic, as a worldwide threat to public health, has led many governments to impose mobility restrictions and adopt partial or full lockdown strategies in many regions to control the disease outbreak. Although these lockdowns are imposed to save public health by reducing the transmission of the virus, rather significant improvements of the air quality in this period have been reported in different areas, mainly as a result of the reduction in vehicular trips. In this research, the city of Turin in the northern part of Italy has been considered as the study area, because of its special meteorology and geographic location in one of the most polluted regions in Europe, and also its high density of vehicular emissions. A Lagrangian approach is applied to illustrate and analyze the effect of imposing full lockdown restrictions on the reduction of traffic-induced air pollution in the city. To do this, the real-time traffic flow during the lockdown period is recorded, and by utilizing CALPUFF version 7, the dispersion of PM2.5, Total Suspended Particulate (TSP), Benzo(a)pyrene (BaP), NOx, and Black Carbon (BC) emitted from all circulating vehicles during and before the lockdown period are compared. Results indicate that the concentration of pollutants generated by road traffic sources (including passenger cars, busses, heavy-duty vehicles, light-duty vehicles, mopeds, and motorcycles) reduced at least 70% (for PM2.5) up to 88.1% (for BaP) during the studied period. Concentration maps show that the concentration reduction varied in different areas of the town, mainly due to the characteristics and strength of the emission sources and the geophysical features of the area.

BusBeat: Early Event Detection with Real-Time Bus GPS Trajectories

Large-scale events attracting many participants might have a strong negative impact on productivity, mobility, comfort, and safety in a city. Within the few years, serious accidents led by congestion have occurred, especially during sports events, religious ceremonies, festivals, and so on. To alleviate these serious accidents, predicting the occurrence of a large-scale event is much significant. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this paper, we present an early event detection technique named BusBeat. BusBeat uses GPS trajectory data collected from periodic-cars that are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. BusBeat interpolates the missing GPS data by using the features of the periodic-cars. In addition, BusBeat uses the network-based analysis with a Time-dependent Congestion Network (TCN) in order to detect geo-spatial events. BusBeat achieves early event detection without incurring any privacy invasion, by using the continuous trajectories of the periodic-cars that provide the real-time traffic flow and speed. Since traffic towards an event venue would be slow before the event starts, BusBeat detects the geo-spatial events before the attendees gather. We evaluate our BusBeat using over 7,000-bus data collected in Beijing for 5 months and compare with the check-in data collected from a social network service.

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.

How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm.

Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

A Novel Cost-Effective Controller Placement Scheme for Software-Defined Vehicular Networks

Energy costs have dramatically increased in data center networks as an increasing number of large-scale Internet applications are used. In software-defined vehicular networks (SDVN), the communication delay between two vehicles and between vehicles and the controller will dramatically climb up as the number of vehicles increases. This requires more controllers to provide communication service to minimize the latency. More controllers lead to high energy costs. Therefore, the number of controllers and their placement, the so-called controller placement problem (CPP), should be addressed. The appropriate placement of controllers can decrease the energy cost, enabling green communication in SDVN. Although CPP has been studied for static networks, it has not been effectively solved in highly dynamic and complex networks. In this article, a novel cost-effective CPP scheme for SDVN is proposed. First, our proposed minimum controller selection mechanism (MOSA) can reduce the number of controllers and guarantee the coverage of the area. Besides, an improved multiobjective artificial bee colony algorithm (IMABC) is proposed based on the original artificial bee colony algorithm. The IMABC can judge which controller should be switched on for data transmission based on real-time traffic flow. A route computation mechanism is proposed to evaluate the performance of our CPP scheme. The experimental results confirm that compared to other existing CPP schemes, our scheme can achieve a higher packet delivery ratio while greatly reducing energy consumption and latency.

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

QA2: QoS-Guaranteed Access Assistance for Space–Air–Ground Internet of Vehicle Networks

Space–air–ground Internet of Vehicle networks is a promising network paradigm to support diverse vehicular services. Exploiting the unique advantages of spatial, aerial, and terrestrial network segments, the Quality of Service (QoS) of different services can be met by smart access network selection. However, the integrated network inevitably has to face many challenges, such as dynamic network topology, heterogeneous resources, and long propagation latency, which can seriously degrade the QoS performance. In order to provide QoS guarantee to vehicles, we propose a novel architecture, called QoS-guaranteed access assistance (QA2) serve vehicles with access assistance. To be specific, a virtualized layer is established in the network, which contains the logical resources of ground network infrastructures. Using that layer, Access Assistants are flexibly deployed at a dynamic set of ground infrastructures, and then help vehicles to acquire network services with satisfied QoS requirements. Furthermore, to accommodate temporal–spatial-varying network demands of vehicles, QA2 adopts a cost-effective deployment scheme, which updates the locations of Assistants based on the real-time traffic flow with the minimum operation cost. The evaluation results demonstrate the effectiveness of QA2, which increases request success rate by more than 50%. The results also prove that the cost-effective deployment scheme can efficiently adapt to the change of traffic flow and achieve a good balance between QoS guarantee and cost saving. The operation cost is saved by more than 25% and the QoS performance is improved by more than 22%.

Optimal operation for coupled transportation and power networks considering information propagation

The growing number of electric vehicles (EVs) and the rapid development of wireless charging technology couple the transportation network (TN) and power distribution network (PDN). When local traffic events happen, transportation parameters are changed and drivers will change their driving behaviors accordingly. However, information spread by vehicle-to-vehicle communication takes time. Information asymmetry among EVs will break the original traffic equilibrium. Therefore, a transient state is caused between two steady states in TN. Meanwhile, the variation of traffic flow caused by information propagation will also influence the power flow in PDN. This paper investigates the interactive mechanism of coupled transportation network and power network during the transient and steady state. Considering road parameters, electricity price and information flow among vehicles, a model that combines traffic assignment model and clustered epidemiological differential equations is proposed to formulate spatio-temporal distribution of EVs. Distflow equations are employed and relaxed to a second-order cone problem. Then the locational marginal price (LMP) is formulated. To avoid massive parameters transfer between two networks, we use a best-response decomposition algorithm to iteratively calculate the electricity price and real-time traffic flow. The results of two coupled networks justify the necessity to take information propagation among vehicles into account to coordinate the operation of coupled TN and PDN.