Traffic Flow Rate Research Articles

An empirical study for the traffic flow rate prediction-based anomaly detection in software-defined networking: a challenging overview

An empirical study for the traffic flow rate prediction-based anomaly detection in software-defined networking: a challenging overview

Stability Analysis of Mixed Traffic Flow Considering Personal Space under the Connected and Automated Environment

A traffic survey using an unmanned aerial vehicle on the Haixia road in Chongqing, China found a significant correlation between the velocity of the vehicle and the distance of the vehicle when moving forward and laterally. To mitigate driving disruptions owing to vehicle intrusion at a desired distance, personal space (PS) is introduced to analyze the car-following behavior of continuous mixed traffic flow formed by human-driven (HD) vehicles and cooperative adaptive cruise control (CACC) vehicles. PS is a virtual boundary that refers to the space where psychological tension is caused by the invasion of others. Further, an intelligent driver model (IDM) was used to establish a mixed traffic car-following PS-IDM. The stability of the disturbance transfer function analytical model in homogeneous and heterogeneous traffic flows was used to calculate the traffic flow stability region under different CACC vehicle permutations. The results show that the PS-IDM-based car-following model effectively improves the stability fitting effect of mixed traffic flow, and the driving comfort is increased by up to 20.7% when compared with that of the single car-following model. In addition, there is a negative correlation between the PS and the unstable velocity range of the traffic flow. Compared with the homogeneous HD traffic flow, the average intrusion rate of homogeneous CACC traffic flow is reduced by 4.6%, and the driving comfort of vehicles is improved by approximately 65%.

A Safe and Robust Autonomous Intersection Management System Using a Hierarchical Control Strategy and V2I Communication

Connected autonomous vehicles can significantly improve the safety and mobility of urban transportation systems. However, these systems are vulnerable to model uncertainties, wireless communication impairments, and external disturbances. In this article, we propose a new autonomous intersection management (AIM) system, called hierarchical model predictive control (HMPC). In HMPC, the intersection coordination unit (ICU) in a global centralized layer is responsible for assigning a safe speed to each vehicle while minimizing the system's cost. In the Local decentralized layer, each vehicle is responsible for tracking the reference speed assigned by the ICU, while avoiding collisions. In our method, each vehicle can use its own sensors to monitor its close surroundings, and take its own decisions on its movements, independent on the control decisions sent from the ICU. We investigate the safety, scalability and robustness of HMPC compared with two well-known AIM methods based on centralized and decentralized control strategies. For the evaluation, we use simulation of urban mobility (SUMO). Further, we study the scalability and performance of the algorithms in the presence of communication impairments associated with wireless channels. Our simulation results show that HMPC can safely handle high traffic flow rates. Also, HMPC is robust to uncertainties caused by the wireless communication.

Intersection capacity adjustments considering different market penetration rates of connected and automated vehicles

ABSTRACT To better prepare and guide both intersection planning and operations under different market penetration rates (MPRs) of connected and autonomous vehicles (CAVs) and traffic demands, this research estimates both the lane-level and intersection-level capacity. On the lane-level, adjustment factors for saturation headway and saturation traffic flow rate for each lane under different MPRs of CAVs are investigated. On the intersection-level, the maximum throughput function for different MPRs of CAVs is calibrated. With 100% CAVs, the saturation headways for the exclusive through lane, exclusive left-turn lane, and shared-right-and-through lane decrease by 55.8%, 48.9%, and 42.4%, respectively. The maximum throughput of the intersection with 100% CAVs increases by 70% compared to the scenario with only human driving vehicles (HDVs). Moreover, the maximum throughput increases rapidly after 60% MPRs of CAVs. The research could provide a solid reference for traffic engineers and planners in calculating the intersection capacity under different MPRs of CAVs.

Vehicle Routing Problem Model with Practicality

Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer linear programming model for the routing problem in truck platooning considering the deadline of vehicles, continuous-time units, different fuel reduction rates, traffic congestion avoidance, and heterogeneous vehicles. In addition, a forward–backward heuristic called the “greedy heuristic” is presented for reasonable computation time. To validate the model’s performance, several parameters, such as the percentage of fuel reduction, percentage of detour vehicles, and percentage of platooned links (road segments), are considered. Additionally, various cases are considered with varying fuel reduction rates, traffic flow rates, and time windows.

The Impact of Three Specific Collaborative Merging Strategies on Traffic Flow

On-ramps are considered to be one of the common traffic bottlenecks. In order to improve the operation efficiency of on-ramps, scholars worldwide have proposed various vehicle merging strategies. In this study, we designed different rules to express three collaborative strategies and studied their impact on on-ramp systems. Cellular automata models were used to simulate the systems under different situations, and the average speed and traffic flow rate of both the main roads and ramps were analyzed. The results show that (1) all the three merging strategies give excessive “priority” to the merging vehicle, leading to a severe reduction in the traffic performance of the main road; (2) nevertheless, these strategies have different effects on the entire system with a one-lane or two-lane main road. Due to the lane-changing behavior, the system with a two-lane main road has more advantages than that featured with a one-lane road, making the former system performing better than the latter under the same strategies; (3) the vehicles on the ramp and main road affect each other, and as the vehicle entering probabilities become large, the traffic flow rate on the main road decreases whereas that on the ramp increases. However, the effect is not unlimited, the flow rate on both roads finally reaches a stable level (forming a “platform”); and (4) large values of the merging safety distance parameter decrease the flow rate of the entire system. All the previous results provide a deep understanding of the impact of the three merging strategies on traffic flow, contributing to the design of on-ramp systems that have better operation efficiency and low levels of congestion.

Hourly forecasting of traffic flow rates using spatial temporal graph neural networks

Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better.

Development of alternatives for Signalized intersection- A Case study of Laxmi Mill junction, Coimbatore

Urbanization and industrialization are two of the most important features of modern civilization. This leads to traffic congestion at the major roads of urbanized areas. At intersections, particularly those where four roads converge, signalization is also regarded as a more challenging issue. It takes longer for cars to cross that intersection and move from one side of the roads to the other, especially during rush hour. This may lead to heavy traffic congestion at the junction and may cause accidents. This paper presents the proposed alternatives for signalized intersection at Laxmi mill junction, Coimbatore. To control the flow of heavy traffic and also make sure the vehicles are moving continuously without waiting at the intersection, following two alternatives have been proposed. a) To design the medians instead of the signals with pedestrian crossing bridge in junction b) To design the roundabout at the intersection with pedestrian bridge. By providing these alternatives, Junction traffic flow rates can be managed and accident rate also will get reduced in that junction

Modeling the Operation of Signal-Controlled Intersections with Different Lane Occupancy

In many cities of the world, the problem of traffic congestion on the roads remains relevant and unresolved. It is especially noticeable at signal-controlled intersections, since traffic signalization is among the most important factors that reduce the maximum possible value of the traffic flow rate at the exit of a street intersection. Therefore, the development of a methodology aimed at reducing transport losses when pedestrians move through signal-controlled intersections is a joint task for the research and engineering community and municipalities. This paper is a continuation of a study wherein the results produced a mathematical model of the influence of lane occupancy and traffic signalization on the traffic flow rate. These results were then experimentally confirmed. The purpose of this work is to develop a method for the practical application of the mathematical model thus obtained. Together with the obtained results of the previous study, as well as a systems approach, traffic flow theory, impulses, probabilities and mathematical statistics form the methodological basis of this work. This paper established possible areas for the practical application of the previously obtained mathematical model. To collect the initial experimental data, open-street video surveillance cameras were used as vehicle detectors, the image streams of which were processed in real time using neural network technologies. Based on the results of this work, a new method was developed that allows for the adjustment of the traffic signal cycle, considering the influence of lane occupancy. In addition, the technological, economic and environmental effects were calculated, which was achieved through the application of the proposed method.

STABILITY ANALYSIS FOR URBAN TRAFFIC EVOLUTION PROCESS USING TEMPORAL TRAFFIC STATE PATTERNS

Recognizing the stability of the traffic evolution process of urban traffic networks has been an important consideration in traffic evolution research. However, little work has been conducted on identifying and associating temporal Traffic State Pattern (TSP) with the traffic evolution process. By clustering multi-dimensional traffic time series, we attempted to map the traffic evolution process into massive transitions of consecutive TSPs. Through the statistics of the time distribution of the transitions, we then defined the stability coefficient to conduct a quantitative analysis of the traffic evolution process. An empirical study using 30 days of traffic flow rate data of multiple road sections from the network of Nanshan District (Shenzhen, China) was carried out. Numerical results indicated that the traffic evolution process experienced obvious nonlinear changes at different periods of the day, but presented a regular cycle characteristic from morning till night. Further, with consideration of different travel purposes and traffic features on weekday and weekend, more traffic dynamics was extracted, which would be conducive to understand the complex behaviour of traffic evolution process.

Traffic Queue Length Estimation at Permissive Left-Turn Signalized Intersections with Probabilistic Priority

Probabilistic yielding behaviors are often observed at permissive left-turn signalized intersections in some countries, which tend to affect the operational performance of intersections such as queue length of left-turn traffic. Based on queuing theory, this research developed an analytical queue length estimation model that took into account the probabilistic priority phenomenon at permitted left-turn signals. The service time distributions for both queued and nonqueued left-turn vehicles were derived, and the M/G2/1 queuing model was applied to determine the queue length of left-turn traffic. To validate the developed queue length model, stochastic simulations with different combinations of left-turn yielding rates, left-turn traffic saturation ratios, and through traffic flow rates were performed. It was found that the left-turn traffic saturation ratio is the most critical factor for the estimation of left-turn traffic queue length. In addition, for permitted intersections with a single through lane and a single left-turn lane, when the opposite through traffic flow rate is greater than 0.3 veh/s, the impact of left-turn yielding rate on left-turn queue length tends to be more significant.

Urban traffic air pollution - case study City of Vranje, Serbia

The expansion of urban areas with unequal, rapidly growing population results in higher vehicle numbers in the cities. Traffic in the urban environment is one of the main concerns, since it is one of the most prominent sources of air pollution, connected to the mortality rate. Regarding the Sustainable Development Goals (SDG) and SDG 11, urban areas should meet environmental needs following the process of environmental transition. The Vranje city, Serbia follows the global trend of vehicle number increment. Regarding the specific geomorphological and climate conditions, it is of importance to emphasize the traffic air pollution control in the urban environment. Numerous studies found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, queuing time in idle mode with a red signal time, traffic-flow rate, ambient conditions, etc. Traffic management has the key role, especially regarding the intersection of traffic flows, since there are time losses on the intersections’ driveways that increase the energy consumption to overcome certain conflict flows. The scope of this study should point out the importance of traffic planning and management, especially at the traffic flows for reducing air pollution in the urban environment.

A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses

In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies.

Identifying high crash risk segments in rural roads using ensemble decision tree-based models

Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial–temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, time headway), road characteristics (e.g. road type, number of lanes), weather data (e.g. daily rainfall, snow depth, temperature), and calendar variables (e.g. day of the week, public holidays, month, year) are used for model calibration. Three different decision tree algorithms, including, Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) have been employed to predict crash frequency. Results show that based on the traditional evaluation measures, the XGBosst is better for the explanation and interpretation of the factors affecting crash frequency, while the RF model is better for detecting trends and forecasting crash frequency. According to the results, the traffic flow rate, road type, year of the crash, and wind speed are the most influencing variables in predicting crash frequency on rural roads. Forecasting the high and medium risk segment-day in the rural network can be essential to the safety management plan. This risk will be sensitive to real traffic data, weather forecasts and road geometric characteristics. Seventy percent of high and medium risk segment-day are predicted for the case study.

Resource Reservation in Backhaul and Radio Access Network With Uncertain User Demands

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multi-path routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker (KKT) solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

Application analysis of highway traffic accident risk model based on geographically weighted negative binomial regression

The traditional generalized linear model (GLM) can not effectively analyze discrete road traffic accidents when analyzing road traffic accidents with spatial dependence and heterogeneity. Therefore, a risk analysis method of highway traffic accidents based on geographically weighted negative binomial regression model (GWNBR) is proposed. Using geographical weighted regression (GWR) model and negative binomial regression (NB) model, this paper makes a comparative analysis of highway traffic accidents in Xi’an, including local spatial geographical weighted Poisson regression (GWPR) model and two geographical weighted negative binomial regression (GWNBRg and GWNBR) models. The corresponding model bandwidth is determined, and the performance of the model is compared based on the data of traffic environment, road characteristics, crowd characteristics and road alignment. The experimental results show that compared with the single NB model, the proposed model can effectively reduce the interference of the spatial nonstationarity of the data, and can effectively extract the risk factors affecting the accident. The coefficients of GWNBRg model and GWNBR model are positive, which are better than GLM in the mean and likelihood of the residuals. The spatial autocorrelation of the residuals is significantly reduced, and the significance level is 5%, which reduces the spatial heterogeneity of the data. The over dispersion parameter value of GWNBRg model shows a downward trend from southwest to northeast in space, which can effectively reflect the spatial relationship between traffic flow and accident rate, indicating that GWNBR model has a good effect in traffic accident risk analysis of super discrete highway. Therefore, the application of geographical weighted negative binomial regression to highway traffic accident risk prediction has a good application effect, and can effectively reduce the probability of accidents as safety prevention and early warning.

Comprehensive Evaluation of Operational Efficiency of Intersections in Arterial Considering Pedestrians Yield Rule

Yield to pedestrians has become a new trend of civilized transportation in the metropolis. In order to evaluate the influence of yield behavior on the comprehensive operation efficiency of signalized intersections in arterial, the efficiency of the subject who gives way and the subject who is given up in the process of yielding to pedestrians was considered in this study, and the comprehensive operation evaluation of intersection in arterial was given. First, based on the rule of pedestrians yield, the concept of a safe headway gap of pedestrians was introduced in the process of conflict analysis of traffic flows at the intersection, and 3 situations were discussed, which are no yield, yield to 1 flow and 2 flows, to calculate the departure rate of traffic flow at the intersection. Furthermore, models of 3 evaluation indices were established, which are number of people passing per unit time, average delay per people, and average yield number per people at intersection. Moreover, the entropy weight method was taken to decide the weight of these 3 indices, and to calculate the comprehensive efficiency evaluation of intersection operation, with the standardized matrix. Finally, case study work was carried out to evaluate the comprehensive efficiency of 4 types of intersections in arterial considering pedestrians yield rule, which is the intersection between arterial and arterial (IAA), intersection between arterial and subarterial (IAS), intersection with one-time (IAA-1, IAS-1), and two-time (IAA-2, IAS-2) crossing of pedestrians. The relevant results show that the impact of an increase in the number of pedestrians on the combined efficiency of arterial intersections can vary dramatically in different scenarios. Therefore, in the implementation process of “yielding to pedestrians,” the flow fluctuation characteristics and channelization of each intersection should be taken into account, and the corresponding phase changes should be based on pedestrian and vehicle volumes to improve the efficiency of all parts, such as pedestrians and drivers.

A Novel Coordination Mechanism for Connected and Automated Vehicles in the Multi-Intersection Road Network

In recent years, connected automated vehicles (CAVs) have attracted much attention, and the coordination strategy of CAVs in isolated intersections has been widely discussed. However, these algorithms for isolated intersections cannot be directly applied in a multi-intersection road network (MiRN). The coordination strategy in the MiRN requires further investigation. This paper proposes a two-tier strategy for CAV coordination in the MiRN. First, we analyze the coordination problem in isolated intersections and formulate it as a mixed-integer programming problem. Then, for the MiRN, we propose a consensus prediction method to estimate the travel time for CAVs with different paths. Finally, a novel coordination approach is given, showing how to determine the optimal path for CAVs. The experimental results demonstrate the efficiency of the proposed strategy under various traffic flow rates. Compared with the fixed signal time assignment method and the actuated signal time assignment method, our method reduces the average travel time by about 74–83% under different flow rates. We also evaluate the impact of parameters on the strategy’s performance and provide some suggestions for setting these parameters.

Congestion Identification for Multilane Highway by Vehicle Lane Change as Driver Behavior


 
 
 
 This work introduces a new way to assess the condition of highways and classify them as congested or uncongested highways. This was done by recording a video and sorting the number of lane change orders instead of finding the traffic density, delay, and flow rate. Then extracting data from the video and performing a microscopic simulation for it to study the driver's behavior and create the statistical model. Thus, obtaining the mathematical relationships and coefficients that illustrate that process, after verifying the reliability and calibration of these variables.
 This research aims to facilitate the highway assessment process, as an alternative or supplementary method of highway evaluation by the Highway Capacity Manual 2010. It is adequate to record a video and count the number of vehicles that change their lanes and then compare them with the results that have been determined in the search to judge the highway is congested or not.
 The study area was achieved by choosing five segments of multilane arterial roads (six lanes divided) on the left coast of Mosul city. These segments were selected to be as similar as possible in the geometric configuration as well as like traffic flow. After that, cameras were installed according to criteria to record traffic movement and for a time not less than ten hours for each segment, and on the workdays for traffic movement. Then, the videos were uploaded using the Goodvision traffic data recognition program. These outputs were converted to be entered into the PTV-VISSIM traffic simulation program in addition to the other geometric information needed by the program to complete the analysis process and find traffic flow parameters. In the extraction process, the normal distribution and the uniform nature of the data representation of the observed event on the segments were examined using the Z-test and the K-S test, the results were normal and significant. Then the results were verified and the traffic parameters such as density and flow rate were checked to ensure the nature of the traffic flow. The process was also verified using the vehicle motion graphic representation method (Vehicle Trajectory), which is the initial examination to estimate the new variable.
 A new variable has been extracted, which is one of the traffic movement variables, which depends on the percentage of complete and proper lane change completion within the parameters imposed by the simulation program, it’s the Lane Change Occupancy factor (LnChOc.). Then the statistical model was derived using SPSS for the new variable. It describes traffic conditions on highways and the results are verified and calibrated in more than one way. It’s a simple statistical model was obtained to estimate the traffic movement from this variable and vice versa from the following equation:, Which gave results for critical traffic on multi-lane roads in the Mosul city as follows: -
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A grey convolutional neural network model for traffic flow prediction under traffic accidents

Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has become a trending topic in intelligent transportation systems. However, the occurrence of traffic accidents will cause great fluctuations of traffic flow in a short time, thus affecting the accuracy of flow prediction. This paper analyses the influence of traffic accident information on traffic flow, and proposes a grey convolutional neural network called G-CNN for traffic flow prediction. First, considering the small sample size of traffic accidents and the incomplete information description, the traffic flow and traffic speed change rate are defined to represent traffic accident grey information, and a grey fixed-weight clustering method for extracting the characteristics of traffic accident grey information is established. Second, the spatiotemporal characteristics of traffic flow are analysed, and a third-order tensor is developed to fuse them with the accident characteristics and serve as the input of the G-CNN. Then the G-CNN model is built to predict traffic flow in a traffic accident environment. The Canadian Whitemud Drive experiment on traffic flow without traffic accident information reveals that the developed G-CNN model has a better ability to predict future traffic flow with better accuracy and stability. Meanwhile, it is verified that the fluctuation of the traffic change rate will cause traffic accidents, and the probability of traffic accidents at a certain time point will be obtained through feature extraction of traffic accidents. Finally, the model is verified for the traffic flow of Hangzhou Viaduct in China, which demonstrates that the G-CNN model outperforms all of the compared models.