Traffic Measurements Research Articles

Traffic flow measurement for smart traffic light system design

Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.

Cuckoo Counter: Adaptive Structure of Counters for Accurate Frequency and Top-k Estimation

Frequency estimation and top-k flows identification are fundamental problems in network traffic measurement. Sketch, as a basic probabilistic data structure, has been extensively investigated and used in different management applications. However, few of them is suitable for both estimating frequency and finding top-k flows due to the unbalanced distribution of real-world network streams. By introducing a pre-filtering stage to isolate elephant and mice flows, the recently proposed Augmented Sketch (ASketch) significantly improves accuracy for both tasks. However, it suffers from serious performance degradation because of frequent flow exchanges. In this paper, we propose Cuckoo Counter (CC), an adaptive structure that consists of several buckets organized in a specific way. The size of the entry in each bucket is carefully designed to match the actual distribution of streams. During processing, CC hashes a flow to buckets and uses the idea of cuckoo hashing to relocate the flow if an overflow or collision happens, which contributes to fully utilizing memory. Therefore, the replacement strategy helps CC precisely record elephant flows and cover more mice flows, and also guarantees the throughput. Extensive experimental results show that CC has the highest (Freq.) accuracy, excellent (Heavy hitter / change) accuracy, highest (Top-k) precision, and competitive throughput compared to the state-of-the-art. Specifically, CC improves the throughput and accuracy by around 1 and 2 orders of magnitude respectively compared to the well-known ASketch.

Server load estimation by Burr distribution mixture analysis of TCP SYN response time

Server load estimation is key in balancing traffic between servers when optimizing data center resources. Intrusive methods are sometimes difficult or impossible to implement. Therefore, non-intrusive estimation methods are the best alternative in these cases. The objective of this paper is to present a server load estimation method based on external network traffic measurements obtained in a vantage point close to the server. Statistical distributions of TCP SYN response time, that is, the time from SYN to SYN+ACK segments at the server side, are used to fit Burr Type XII heavy tail distribution mixtures. The fitting algorithm, based on maximum likelihood estimation, is developed in detail in this paper. Experimental data shows that the median of the fitted distribution correlates within the 95% confidence interval of the server load figures and, thus, it can be used as a non-intrusive and accurate method to measure it. This new method can be applied to almost any existing load balancing algorithm, as it does not make any assumption about the server, which is considered a black box.

Reliability of smart noise pollution map

AbstractThe problem of noise pollution in Baghdad, the capital city of Iraq, is getting worse every day as a result of the increased volume of traffic. This presents a considerable risk, particularly on the main roads that connect densely populated neighborhoods such as the Al-Sadr City district with the central neighborhoods of the capital. In order to inform decision-makers in urban development and environmental policy about the high values of noise pollution that require remediation and regulation, noise maps are produced. However, two fundamental problems are generally faced in creating a more reliable noise map in the shortest possible time: the excessive time requirements for measuring noise and determining the method of map creation. Therefore, the role of geographic information system (GIS) software in producing noise maps is evident due to the difficulty of increasing the spatial density of measurements and integrating them with spatial information. Hence, an appropriate interpolation method is required. In this article, Moran’sIindex was calculated to assess the spatial autocorrelation of measured traffic noise points. A comparison was made between the Smart Map Plugin ordinary kriging (OK) and the inverse distance weighting (IDW) deterministic interpolation method to determine the best method for producing noise maps for the main entrance and exit roads of Al-Sadr City. The noise values were modeled using the best-performing method. Furthermore, the predictive raster data are displayed in the spatial context as a starting point and reference for identifying and understanding the levels of traffic noise in the selected study area. The locations of selected points for measuring traffic noise values were determined in an organized and homogeneous manner, where noise points for the main entrance and exit roads were opposite each other, and the distance between consecutive noise points on each road was 100 m. Traffic noise measurements were carried out at each selected point using the SVAN977 sound and vibration analyzer. At each measurement point, three noise values (LAeq, Max, Min) were obtained during the three peak times, 7–9 AM, 12–2 PM, and 4–6 PM. QGIS software was used to compare the two interpolation methods, with its strength lying in the use of plugins that facilitate spatial analysis, processing tools, and algorithms. The Smart Map Plugin provided facilities to choose the appropriate semi-variogram in the OK interpolation method. The root mean square error was used to compare the two interpolation methods in order to determine the most suitable method for producing traffic noise maps in the study area. The results indicated that the Smart Map Plugin using OK outperformed the IDW method, as spatial distribution pattern and homogeneity affect the accuracy of interpolation. Moreover, based on the analysis of the three noise attributes (LAeq, Max, Min), the performance of the Smart Map Plugin (OK) was found to be better than IDW when the Moran’sIvalue was high.

Lightweight Per-Flow Traffic Measurement Using Improved LRU List

Per-flow traffic measurement is extremely important for various applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , flow size distribution, flow cardinality and DDos detection. Previous works, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , Elastic Sketch and FlowRadar, incur massive per-packet processing overhead and/or large memory consumption. In this paper, we present a novel least-recently-used (LRU) list based solution, called LRU-TX, for lightweight and accurate per-flow traffic measurement. Different from the classic LRU list with one exit at the tail, we maintain another exit at a certain position <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> (not the tail) in the list. Many terminated flows will be removed from the exit at position <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> as soon as possible with their counters less than a given threshold, which helps to decrease both the amortized processing overhead and the memory consumption. We also formally analyze the impact of different parameters ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , the exit's position, the threshold) on the measurement performance. Experimental results and extensive simulation results show that LRU-TX can increase the throughput by 30% at least, and reduce the memory cost by 50%, compared with the state-of-the-art solutions under the same measurement error conditions.

Calibration of a Microscopic Traffic Simulation in an Urban Scenario Using Loop Detector Data

Travel demand is an essential input for the creation of traffic models. However, estimating travel demand to accurately represent traffic behaviour usually requires the collection of extensive sets of data on traffic behaviour. Traffic counts are a comparably cost effective and reproducible source of information on travel demand. The utilisation of traffic counts to estimate demand is commonly found in the literature as the static and dynamic O-D estimation problem. A variety of approaches have been developed over recent decades to tackle this problem. Usually initial estimates of the O-D matrix are calibrated by utilising traffic counts and considering different assignment models. Other approaches for the estimation of travel demand solely based on traffic measurements can be found in the simulation software SUMO. The present work demonstrates the systematic development of a network model in SUMO in the inner city of Munich. In a sample network the estimation of travel demand through the tools flowrouter and routeSampler is tested by utilising flow measurements from induction loop detectors. The tests delivered unsatisfactory results, which is proven through observations of traffic flows in the resulting simulations as well as comparisons to historic traffic counts. The lack of sufficient detector data and the complexity of the sample network are discussed as the main reasons for the results. It is concluded that the applied tools should be tested in future studies with a more extensive dataset to perform a more comprehensive review of both tools. Therefore, we deliver specific requirements based on the network example of Munich.

Application of Wireless Magnetic Sensors in the Urban Environment and Their Accuracy Verification.

In a smart city, sensors are essential elements-the source of up-to-date traffic information. This article deals with magnetic sensors connected to wireless sensor networks (WSNs). They have a low investment cost, a long lifetime, and easy installation. However, it is still necessary to disturb the road surface locally during their installation. All road lanes to and from the city center of Žilina have sensors that send data at five-minute intervals. They send up-to-date information about the traffic flow's intensity, speed, and composition. The LoRa network ensures the data transmission, but in the event of failure, the 4G/LTE modem realizes the backup transmission. The disadvantage of this application of sensors is their accuracy. The research task was to compare the outputs from the WSN with a traffic survey. The appropriate method for the traffic survey on the selected road profile is a video recording and speed measurement using the Sierzega radar. The results show distorted values, mainly for short intervals. The most accurate output from magnetic sensors is the number of vehicles. On the other hand, traffic flow composition and speed measurement are relatively inaccurate because it is not easy to identify vehicles based on dynamic length. Another problem with sensors is frequent communication outages, which cause an accumulation of values after the outage ends. The secondary objective of the paper is to describe the traffic sensor network and its publicly accessible database. In the end, there are several proposals for data usage.

Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion

Traffic matrices (TMs) are essential for managing networks. Getting the whole TMs is difficult because of the high measurement cost. Several recent studies propose sparse measurement schemes to reduce the cost, which involve taking measurements on only a subset of origin and destination pairs (OD pairs) and inferring data for unmeasured OD pairs through matrix completion. However, existing sparse network measurement schemes suffer from the problems of high computation costs and low recovery quality. This paper investigates the coherence feature of real traffic flow data traces (Abilene and GÈANT). Both data sets are high coherence, with column coherence greater than row coherence. According to the coherence feature of both data sets, we propose our Redundant Row Subspace-based Matrix Completion (RRS-MC). RRS-MC involves several techniques. Firstly, we design an algorithm to identify subspace rows (OD pairs) from historical data. Secondly, based on the identified subspace rows, we design our sampling scheduling algorithm, which takes full measurement samples in subspace rows while taking partial measurement samples in the remaining rows. Moreover, we propose a redundant sampling rule prevent the recovery accuracy decrease caused by the subspace rows varying. Finally, we design a completion algorithm to recover the partially measured rows. We conduct extensive experiments. Results indicate that the proposed scheme is superior to the state-of-the-art sampling and completion scheme in computation costs and recovery accuracy.

Alternative unobserved heterogeneity models to analyze injury severity of expressway crashes in different tunnel types

This study examines the non-transferability of the determinants affecting injury severities of tunnels of three grades classified by different lengths. Based on tunnel crash data in Shanxi Province during (2012–2018), three possible crash injury severity categories (no injury, minor injury, and severe injury) are estimated. Two alternate models were used to account for unobserved heterogeneity: a random parameters multinomial logit (RPML) model with heterogeneity in means and variances and a latent-class multinomial logit (LCML) model with class probability functions. Non-transferability in the effects of explanatory variables is confirmed using two series of likelihood ratio tests based on the two alternate models. A wide variety of variables is observed to determine pedestrian-injury severities. The estimation results show significant non-transferability across the three grades in both RPML and LCML models. In addition, several explanatory variables produce relatively transferable effects, which can provide some valuable insights to implement effective countermeasures. Moreover, out-of-sample predictions are simulated to confirm the non-transferability, while the LC models produce higher differences for the non-transferability than RP models. According to estimation results, several practical measurements concerning education programs, traffic enforcement, emergency response systems, and traffic safety measurements are implemented to eliminate tunnel crash outcomes. Understanding and depth comparing the estimation results, likelihood ratio tests and out-of-sample predictions using alternate models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated.

Application of a 3D Sensor as a Vehicle Detector

Vehicle detectors are required to operate in various environments. Current detectors employ various types of sensors including ultrasonic, loop coil, and image sensors. However, there exist some problems such as temperature and wind effects, maintenance problems, and accuracy degradation during nighttime. To solve these problems, we propose a vehicle detector that utilizes a 3D sensor based on near infrared laser and Time-of-Flight. This sensor can acquire a three-dimensional image that includes the distance data, unaffected by the external factors mentioned earlier. Therefore, our proposed vehicle detector design can be applied to traffic volume measurement, vehicle classification, multiple lane detection, etc. This paper discusses the application of the proposed vehicle detector in several scenarios.

Globally Optimal Robust Radar Calibration in Intelligent Transportation Systems

Radar is among the most popular sensors in modern Intelligent Transportation Systems (ITSs), enabling weather-robust perception. The orientation and position of the traffic radar relative to the ITS coordinate system are necessary for the perception fusion in ITSs. However, due to the unknown target association, sparseness and noisiness of traffic radar measurements, the robust and accurate extrinsic calibration of traffic radar is challenging. In this paper, we propose a targetless traffic radar calibration method based on GPS to overcome the inconvenience during ITS operation, because the installation of a dedicated calibration target on the highway is impractical and dangerous. On the other hand, the high-precision GPS device installed on the moving vehicle can provide traffic radar with accurate positioning information of the detection target. Furthermore, during the optimization process of extrinsic calibration, we propose a globally optimal registration method, which is robust to noise and outliers in radar measurements, and is called Gaussian Mixture Robust Branch and Bound (GMRBnB). Specifically, we first construct the robust objective function by utilizing the Gaussian Mixture Model (GMM). Then, we derive novel relaxation bounds and present the GMRBnB algorithm that overcomes the susceptibility to local minima and the dependence on initialization of traditional optimization methods. Compared with existing methods, extensive experiments in synthetic and real-world data demonstrate that our method is not only globally optimal, but also more accurate and robust.

Self-Adaptive Sampling Based Per-Flow Traffic Measurement

Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, which complements sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, leading to the waste in storage and communication resources. In practice, different flows often require different sampling rates to meet the same accuracy constraint. This paper presents self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose three algorithms, SAS-LC, SAS-LOG, and SAS-HYB. SAS-LC and SAS-LOG are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. SAS-HYB combines the advantages of SAS-LC and SAS-LOG, showing higher efficiency when both small flows and large flows are interested. We implement our estimators in hardware using NetFPGA. Experimental results based on real Internet traces show that, compared to the state-of-the-art in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. In per-flow size estimation, SAS-LOG can save 40% on-chip space and reduce up to 96% communication costs for large flows. Moreover, SAS-HYB’s on-chip memory usage will not be larger than SAS-LC or SAS-LOG and can save up to 19% on-chip space than SAS-LOG when both small flows and large flows are interested.

Automated Traffic Surveillance Using Existing Cameras on Transit Buses.

Millions of commuters face congestion as a part of their daily routines. Mitigating traffic congestion requires effective transportation planning, design, and management. Accurate traffic data are needed for informed decision making. As such, operating agencies deploy fixed-location and often temporary detectors on public roads to count passing vehicles. This traffic flow measurement is key to estimating demand throughout the network. However, fixed-location detectors are spatially sparse and do not cover the entirety of the road network, and temporary detectors are temporally sparse, providing often only a few days of measurements every few years. Against this backdrop, previous studies proposed that public transit bus fleets could be used as surveillance agents if additional sensors were installed, and the viability and accuracy of this methodology was established by manually processing video imagery recorded by cameras mounted on transit buses. In this paper, we propose to operationalize this traffic surveillance methodology for practical applications, leveraging the perception and localization sensors already deployed on these vehicles. We present an automatic, vision-based vehicle counting method applied to the video imagery recorded by cameras mounted on transit buses. First, a state-of-the-art 2D deep learning model detects objects frame by frame. Then, detected objects are tracked with the commonly used SORT method. The proposed counting logic converts tracking results to vehicle counts and real-world bird's-eye-view trajectories. Using multiple hours of real-world video imagery obtained from in-service transit buses, we demonstrate that the proposed system can detect and track vehicles, distinguish parked vehicles from traffic participants, and count vehicles bidirectionally. Through an exhaustive ablation study and analysis under various weather conditions, it is shown that the proposed method can achieve high-accuracy vehicle counts.

Estimating individual vehicle emission factors from near-road measurements in India

Over the years, vehicular emissions have been the main contributor to the deterioration of urban air quality. However, quantification of real-world vehicular emissions is quite limited in low- and middle-income countries like India. Developing real-world vehicle emission factors (EFs) using reference-grade instruments requires a significant amount of resources. This study aims to develop the individual and fleet vehicle EFs and the fraction of high-emitting vehicles using high-time resolution, low-cost sensors from near-road measurements – a first-of-its-kind study in India. Traffic and air pollutant measurements were conducted at the kerbside of a street canyon in Mumbai. The individual vehicle fuel-based EFCO and EFNOx were estimated using the plume-identification technique coupled with the information obtained from the vehicle registration number plates. The fleet mean (±SD) EFCO, EFNO, and EFNO2 were 6.30 (±3.16), 1.38 (±1.17), and 0.43 (±0.32) g/kg, respectively, while for EFPM1, EFPM2.5, EFPM2.5-10, and EFPM10 were 0.70 (±0.34), 1.19 (±0.57), 0.90 (±0.65), and 2.09 (±1.05) g/kg, respectively. The developed individual vehicle EFCO and EFNOx were greatly varied within each vehicle type due to differences in emission control technology, engine size, and the prevalence of “super-emitters”. There was no substantial difference in EFCO and EFNOx among the different BS emission standards across almost all vehicle types. The reconstructed fleet EFCO and EFNOx from the developed single-vehicle EFs were 1.4 and 1.9 times higher than the recorded fleet EFs. Approximately 14% of vehicles in the fleet were identified as super-emitters, responsible for 37–54% of total emissions, primarily from private passenger vehicles such as cars and two-wheelers. The EFCO and EFNOx from these high-emitters were 3–30 times greater than the laboratory-reported emissions. Our study suggests that improving emission standards alone is not enough to decrease tailpipe emissions from vehicles. Proper vehicle inspection and maintenance programs are crucial in controlling these emissions.

ALSketch: An adaptive learning-based sketch for accurate network measurement under dynamic traffic distribution

Learning-based sketch improves accuracy and reduces the space overhead of traditional sketches by using machine learning to recognize input data patterns. However, most learning-based sketches adopts offline learning to train a learning model, which shows poor accuracy in measuring dynamic network traffic. To accommodate unpredictable changes in network traffic, it is urgent to study a learning-based sketch that not only supports timely updating recognition rules but also keeps accurate and efficient network traffic measurement. We propose a novel adaptive learning-based sketch (ALSketch) to accurately and efficiently measure dynamic network traffic with online machine learning. ALSketch incrementally constructs a learner to identify hot and cold flows and store them separately. Such a strategy reduces hash conflicts and improves estimated accuracy under limited memory usage. We perform extensive comparative experiments to verify the performance of ALSketch. Experimental results reveal that the average relative error of ALSketch is approximately 1.86∼5.25 times lower than that of traditional sketches under the same memory conditions.

Intersection analysis using computer vision techniques with SUMO

Abstract This paper presents intersection analysis using computer vision techniques with Simulation of Urban MObility (SUMO). First, an efficient deep-visual tracking pipeline is proposed by using the off-the-shelf YOLO object detection architecture and cascading it with a discriminative correlation filter to produce reliable trajectories for traffic analysis of vehicles and pedestrians. While a variety of traffic measurements can be directly estimated from the extracted trajectories (e.g., speed, turning movement count), a method of incorporating turning movement count (TMC) within SUMO is proposed in order to mimic a realistic traffic flow for an observed intersection and its comprehensive analysis. Experimental evaluations on the developed tracking system implies that the YOLOv5 variant is the best for traffic cameras and, after appropriate fine-tuning using the University of Nevada, Las Vegas pedestrian data set, the YOLOv5 performance manifested a significant improvement with a recall value of 0.62. The tracking system is further employed for monitoring three other intersections in the downtown area of Las Vegas and turning movement counts were estimated for peak hours in the morning and evening of one day (7:00–9:00 and 16:00–18:00) at 15-min intervals. Finally, the intersection design, including traffic signals with estimated TMC, is used to calibrate SUMO to provide critical parameters (e.g., lane density, travel time, occupancy) for traffic signal performance evaluation and comprehensive intersection analysis. The signal design treatment demonstrates a significant improvement in travel times and simulation results indicate that the turning-left ratio is a crucial factor affecting the travel time of vehicles on each intersection leg.

Resources Relocation Support Strategy Based on a Modified Genetic Algorithm for Bike-Sharing Systems

In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in bike-sharing service systems were permanently improved, there would still be some issues that needed new and more efficient solutions. One of these refers to the rebalancing operations that follow the bike depletion phenomenon that affects most stations during shorter or longer time periods. Current work develops a two-step method to perform effective rebalancing operations in bike-sharing. The core elements of the method are a fuzzy logic-controlled genetic algorithm for bike station prioritization and an inference mechanism aiming to do the assignment between the stations and trucks. The solution was tested on traffic data collected from the Citi Bike New York bike-sharing system. The proposed method shows overall superior performance compared to other algorithms that are specific to capacitated vehicle routing problems: standard genetic algorithm, ant colony optimization, Tabu search algorithm, and improved performance compared to Harris Hawks optimization for some scenarios. Since the algorithm is independent of past traffic measurements, it applies to any other potential bike-sharing system.

Speed and temperature superposition on traffic speed deflectometer measurements

The traffic speed deflectometer (TSD) is capable of conducting deflection surveys at traffic speeds and is an efficient tool for collecting network-level pavement structural data. Due to the viscoelastic nature of asphalt concrete (AC), the raw TSD deflections need to be corrected to the reference temperature and speed for baseline comparisons with data from different tests. Although the topic of temperature correction for deflections has been extensively discussed, the correction of traffic speed on pavement deflection has rarely been investigated. According to the principle of time–temperature superposition, the time (speed) effect on pavement responses can be evaluated by the concept of “pseudo temperature”. Combined with the test temperature, the time and temperature dependence of AC materials can be integrated into a single factor, referred to as the effective temperature. By correcting raw TSD deflections from the effective temperature to the reference temperature, speed correction and temperature correction can be achieved simultaneously. It also provides some insights into eliminating the need for temperature correction by artificially presetting the TSD speed so that the TSD deflection collected at the preset speed and test temperature is equal to the TSD deflection at the reference speed and reference temperature. Results from the present analysis suggest that TSD deflections are more sensitive to test temperatures than to test speeds. The equivalence between speed and temperature depends on the activation energy of the AC material. For normal TSD operating speeds, a 10 mph increase in test speed is approximately equivalent to a 1 °C drop in temperature. In general, a practical preset speed range of 20–60 mph can accommodate temperature variations of approximately 4 °C.

Ensemble Learning Traffic Model for Sofia: A Case Study

Traffic models have gained much popularity in recent years, in the context of smart cities and urban planning, as well as environmental and health research. With the development of Machine Learning (ML) and Artificial Intelligence (AI) some limitations imposed by the traditional analytical, numerical and statistical methods have been overcome. The present paper shows a case study of traffic modeling with scarce reliable data. The approach we propose resorts on the advantages of ensemble learning using a large number of related features such as road and street categories, population density, functional analysis, space syntax, previous traffic measurements and models, etc. We use advanced regression models such as Random Forest, XGBoost, CatBoost etc., ranked according to the chosen evaluation metrics and stacked in a weighted ensemble for optimal fitting. After a series of consecutive data imputations we estimate the annual average daily traffic distribution in the street and road network of Sofia city and the metropolitan municipality for 2018 and 2022, and the NO2 levels for 2021 with accuracy resp. 78%, 74% and 92%, using AutoGluon and Scikit-Learn.

A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.