Traffic Changes Research Articles

Identification and quantification of additives in bituminous binders based on FTIR spectroscopy and multivariate analysis methods

Bitumen is a crucial building material in road construction, which is exposed to continuously higher stresses due to higher traffic loads and changing climatic conditions. Therefore, various additives are increasingly being added to the bitumen complicating the characterisation of the bituminous binder, especially concerning the reuse of reclaimed asphalt. Therefore, this work aimed to demonstrate that the combination of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflexion (ATR) technique and multivariate evaluation is a very well-suited method to reliable identify and quantify additives in bituminous binders. For this purpose, various unmodified and modified binders, directly and extracted from laboratory and reclaimed asphalts, were investigated with FTIR-ATR spectroscopy. The determined spectra, pre-processed by standard normal variate (SNV) transformation and the determination of the 1st derivation, were evaluated using factor analysis (FA), linear discriminant analysis (LDA) and partial least square regression (PLSR). With this multivariate evaluation, first, a significant model with a very high hit rate of over 90% was developed allowing for the identification of styrene-butadiene copolymers (SBC), ethylene-copolymer bitumen (ECB) and different waxes (e.g. amide and Fischer-Tropsch wax) even if the additives do not show any additional peaks or the samples are multi-modified. Second, a quantification of the content is possible for SBC, ECB, and amide wax with a mean error of RMSE ≤ 0.4 wt% and a coefficient of determination of R2 > 90%. Based on these results, FTIR identification and quantification of additives in bituminous binders is a very promising method with a great potential.

An Intelligent Route Computation Approach Based on Real-Time Deep Learning Strategy for Software Defined Communication Systems

Software Defined Networking (SDN) is regarded as the next generation paradigm as it simplifies the structure of the data plane and improves the resource utilization. However, in current Software Defined Communication Systems (SDCSs), the maximum or minimum metric value based routing strategies come from traditional networks, which lack the ability of self-adaptation and do not efficiently utilize the computation resource in the controllers. To solve these problems, in this paper, we utilize the deep learning technique to conduct the routing computation for the SDCSs. Specifically, in our proposal, the considered Convolutional Neural Networks (CNNs) are adopted to intelligently compute the paths according to the input real-time traffic traces. To reduce the computation overhead of the central controller and improve the adaptation of CNNs to the changing traffic pattern, we consider an online training manner. Analysis shows that the computation complexity can be significantly reduced through the online training manner. Moreover, the simulation results demonstrate that our proposed CNNs are able to compute the appropriate paths combinations with high accuracy. Furthermore, the adopted periodical retraining enables the deep learning structures to adapt to the traffic changes.

Changes in Underwater Noise and Vessel Traffic in the Approaches to Halifax Harbor, Nova Scotia, Canada

Over the past two years, researchers at Fisheries and Oceans Canada have been running an acoustic monitoring project at multiple study sites throughout Nova Scotia, Canada to investigate baleen whale presence and levels of underwater noise. At the onset of the COVID-19 pandemic, a passive acoustic monitor (PAM) was in place in the study site located in the approaches to Halifax Harbor, a major Canadian port. This provided a unique opportunity to determine if changes in vessel noise levels occurred after pandemic restrictions were put in place. To investigate this, we analyzed and compared acoustic data collected from March 28 to April 28 and August 6 to October 22 in both 2019 and 2020. We also investigated possible changes in vessel traffic from February 1 through April 28 and July 1 through July 28 in 2019 and 2020 using terrestrial-based automatic identification system (AIS) data provided by the Canadian Coast Guard and cargo information provided by the Port of Halifax. The acoustic data were analyzed in 1/3 octave frequency bands. For the 89.1–112 Hz frequency band, we found an 8.4 dB increase in the daily minimum sound pressure level (SPL) in April 2020 compared to April 2019 due the presence of a large crane vessel stationed near the mooring site. For the period of August to October, we found an approximately 1.7 dB reduction in the same metric from 2019 to 2020. The most noticeable change in vessel composition was the dramatic decrease in the number and occurrence of pleasure craft in July 2020 compared to the same period in 2019. While this analysis looked at only a single PAM and a limited amount of data, we observed changes in sound levels in the frequency band known to be associated with shipping as well as changes in vessel traffic; we conclude that these observed changes may be related to pandemic restrictions.

Deep Q‐network‐based auto scaling for service in a multi‐access edge computing environment

SummaryIn 5G networks, it is necessary to provide services while meeting various service requirements, such as high data rates and low latency, in response to dynamic network conditions. Multi‐access edge computing (MEC) is a promising concept to meet these requirements. The MEC environment enables service providers to deploy their low latency services that are composed of multiple components. However, operating a service manually and attempting to satisfy the quality of service (QoS) requirements is difficult because many factors need to be considered in an MEC scenario. In this paper, we propose an auto‐scaling method using deep Q‐networks (DQN), which is a reinforcement learning algorithm, to resize the number of instances assigned to service. In our evaluation, compared to other baseline methods, the proposed approach maintains the appropriate number of instances effectively in response to dynamic traffic change while satisfying QoS and minimizing the cost of operating the service in the MEC environment. The proposed method was implemented as a module running in OpenStack and published as open‐source software.

Distributed Controller-Switch Assignment in 5G Networks

Software defined networking (SDN) is a promising technology in fifth generation wireless networks (5G) where due to the adoption of a centralized SDN-controller, resources such as processing and storage, can be utilized in an optimal manner. Although SDN was first considered with a logically centralized controller, due to delay, reliability, and scalability challenges, moving towards multiple distributed controllers is inevitable. In distributed control schemes, an assignment that associates a controller with each switch leads to three challenges of (1) Computational complexity, since the assignment is an NP-hard problem, (2) Resource and energy efficiency, to obtain an assignment with the lowest number of controllers in order to reduce resource and energy consumption, and (3) Dynamicity, where a dynamic approach of assignment is required to adapt to the network’s traffic changes. In this paper, we investigate the controller-switch assignment problem given the aforementioned challenges, and propose efficient algorithms for static and dynamic scenarios, that even achieve quantitative optimality guarantees in special cases. As shown through simulations, the proposed lower complexity algorithms not only outperform earlier works but also approach the performance of exhaustive search schemes, in some scenarios.

The potential geographical accessibility to public hospitals for the population in Bucharest’ proximity

Access to health services is essential for ensuring a fundamental human right: the right to health care. Traditional accessibility methods do not consider traffic changes and possible infrastructure works and do not always reflect the actual travel time to the nearest hospital. This study tries to measure the potential access to hospitals of the population from the proximity of Bucharest, an area overlapping Ilfov County, using API (Application Program Interface). Two scenarios were considered. The first represents accessibility in the morning, an interval with heavy traffic, and the second represents accessibility in the evening, an interval without heavy traffic. The results confirm that Ilfov County has good accessibility to public hospitals, with over 60% of the population travelling less than 30 minutes to the nearest hospital. The proximity of Bucharest makes it possible for residents to access hospitals in that area quickly. The findings provide a scientific basis for local authorities to optimize access to hospitals and planning resources.

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

Air traffic and contrail changes over Europe during COVID-19: a model study

Abstract. The strong reduction of air traffic during the COVID-19 pandemic provides a unique test case for the relationship between air traffic density, contrails, and their radiative forcing of climate change. Here, air traffic and contrail cirrus changes are quantified for a European domain for March to August 2020 and compared to the same period in 2019. Traffic data show a 72 % reduction in flight distance compared with 2019. This paper investigates the induced contrail changes in a model study. The contrail model results depend on various methodological details as discussed in parameter studies. In the reference case, the reduced traffic caused a reduction in contrail length. The reduction is slightly stronger than expected from the traffic change because the weather conditions in 2020 were less favorable for contrail formation than in 2019. Contrail coverage over Europe with an optical depth larger than 0.1 decreased from 4.6 % in 2019 to 1.4 % in 2020; the total cirrus cover amount changed by 28 % to 25 %. The reduced contrail coverage caused 70 % less longwave and 73 % less shortwave radiative forcing but, because of various nonlinearities, only 54 % less net forcing in this case. The methods include recently developed models for performance parameters and soot emissions. The overall propulsion efficiency of the aircraft is about 20 % smaller than estimated in earlier studies, resulting in 3 % fewer contrails. Considerable sensitivity to soot emissions is found, highlighting fuel and engine importance. The contrail model includes a new approximate method to account for water vapor exchange between contrails and background air and for radiative forcing changes due to contrail–contrail overlap. The water vapor exchange reduces available ice supersaturation in the atmosphere, which is critical for contrail formation. Contrail–contrail overlap changes the computed radiative forcing considerably. Comparisons to satellite observations are described and discussed in a parallel publication.

HAFTA: Highly adaptive fault‐tolerant routing algorithm for two‐dimensional network‐on‐chips

AbstractLearning‐based routing algorithms are good candidates for two‐dimensional Network‐on‐Chip (NoC) architectures since they can give good path selection decisions by combining current and past state of the network traffic. On the other hand, they generally send packets through nonminimal paths in order to detour congested areas in an attempt to minimize the communication cost. Since adapting to the traffic changes takes time to gather enough feedback information by the applied learning model, non‐minimal paths may take more time than the congested minimal paths. In this work, we incorporate a probabilistic method to a Q‐learning‐based NoC routing algorithm for selecting a minimal or nonminimal path to minimize the negative effect of learning duration. We also consider the errors in the architecture and propose a fault‐tolerance mechanism that detects both transient and permanent link errors. We compared our method against a standard Q‐learning‐based routing algorithm on several traffic models in terms of throughput and latency. The results show that our method outperforms its counterpart up to 30% in latency and 7% in throughput.

COVID-19 and consumer demand for local meat products in South Carolina

The emergence of the novel coronavirus (COVID-19) pandemic and the associated economic disrup­tions have challenged local food producers, distributors, retailers, and restaurants since March 2020. COVID-19 was a stress test for the U.S. local food supply chain, exposing vulnerabilities whose impacts have varied by region and sector. Some local producers saw sales fall in 2020 due to COVID-19 restric­tions and consumer foot traffic changes (O’Hara, Woods, Dutton, & Stavely, 2021). In other areas, local food producers were able to pivot from collapsing market channels by finding opportunities elsewhere (Thilmany, Canales, Low, & Boys, 2020).

Optimization of load balancing routing algorithm based on extended localized link states in low earth orbit satellite networks

SummaryIt has been observed for a long time that there exists a conflict between the un‐uniform terrestrial traffic distribution and the regular satellite deployed in low earth orbit (LEO) satellite networks. This can result in the link congestion in some areas and the network performance degradation. Load balancing routing is an effective method to solve this problem. This paper discusses the optimization of this kind of routing algorithms and focuses on the local optimization problem due to the routing decision based on localized link states. The extended localized link state load balancing routing algorithm (LB‐ELS) is proposed. It makes use of the path congestion states to determine routes avoiding the local optimization and reduces the overhead by the on‐demand route computing and link congestion state exchange mechanism. The triggered local congestion notification mechanism (TLCN) is also presented, which ensures the timely response to the burst traffic changes and further alleviation of the traffic gathering in congestion links. Simulation results show that this algorithm can disperse the traffic better and improve the network performance greatly with an acceptable delay increase and a small overhead.

How PPP Renegotiation Behaviors Evolve with Traffic Changes: Evolutionary Game Approach

High risk in actual traffic demand often causes a serious imbalance in interests between the public and private sectors in transportation public–private partnership (PPP) projects. The party who suffers has the incentive to breach the contract and hopes to make up for their loss by renegotiating the PPP concessions. The high frequency of such concession renegotiations has raised questions about the viability of the PPP approach. To study how concession renegotiation behaviors evolve when actual traffic volume is lower or higher than expected, this study introduces traffic changes as a quantitative financial indicator. Moreover, it develops two evolutionary game models in which the private and public sectors initiate the renegotiation. The thresholds for renegotiation by the private and public sectors can be defined based on the models and provide decision support for the governance of concession renegotiation behaviors in transportation PPP projects to reduce the incidence and incentives to renegotiate PPP contracts.

A fast all-packets-based DDoS attack detection approach based on network graph and graph kernel

DDoS attack detection methods play a very important role in protecting computer network security. However, the existing flow-based DDoS attack detection methods face the non-negligible time delay and are not general for different types of DDoS attacks at different rates. In order to fill this research gap, a fast all-packets-based DDoS attack detection approach (FAPDD) is proposed. The FAPDD firstly designs a new time series network graph model to effectively simplify the processing of network traffic handling compared with the flow-based detections. Furthermore, it is the first time that the directed Weisfeiler-Lehman graph kernel is built for measuring the divergence between the current network graph and the normalization network graphs. Due to the new graph model and kernel measurement method to judge network changes, the different types and rates of DDoS attacks can be especially detected. In addition, the dynamic threshold and freezing mechanism are constructed to display standard traffic changes and prevent the pollution of attack traffic to the standard network. Finally, a number of real DDoS attack datasets are applied to evaluate the effectiveness of the proposed method, as well as the overall time efficiency and detection effect. Compared with other methods, the FAPDD can better meet the real-time requirements and achieve good detection effects in different types of DDoS attacks with different attack rates.

Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network

The rapid development of internet of things (IoT) and in-stream big data processing technology has brought new opportunities for the research of intelligent transportation systems. Traffic forecasting has always been a key issue in the smart transportation system. Aiming at the problem that a fixed model cannot adapt to multiple environments in traffic flow prediction and the problem of model updating for data flow, a traffic flow prediction method is proposed based on variable structure dynamic Bayesian network. Based on the complex event processing and event context, this method divides historical data through context clustering and supports cluster update through online clustering of event streams. For different clustered data, a search-scoring method is used to learn the corresponding Bayesian network structure, and a Bayesian network is approximated based on a Gaussian mixture model. When forecasting online, a suitable model or combination of models is selected according to the current context for prediction.

Online Estimation and Prediction of Large-Scale Network Traffic From Sparse Probe Vehicle Data

Network traffic prediction based on probe vehicle data is important for traffic management and route recommendation and has been intensively studied. Previous traffic prediction methods mainly focused on recurring traffic congestion. Predicting non-recurring traffic congestion, caused by events and accidents, is significantly more important; however, it has not been intensively studied. To predict non-recurring traffic congestion using probe data, we need to estimate the current traffic conditions based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse</i> observations for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large</i> traffic networks to track traffic changes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> . Conventional traffic forecasting methods have not been able to solve all of these problems. To address these problems, we propose a data assimilation method using a state space neural network (SSNN) with an incorporated topology of road networks. The SSNN model can easily model network traffic and can easily estimate its states and parameters by data assimilation using Bayesian filtering. In this study, we adopted a decoupled extended Kalman filter (DEKF) based data assimilation, which is scalable and applicable to large-scale network traffic, to estimate the states and parameters online. We evaluate the proposed method using an open dataset that includes a road network comprising over 30000 road segments. The results show that our method achieves higher prediction accuracy for predicting unknown traffic congestion and is more robust against data sparsity than conventional state estimation methods.

Traffic change point detection and analysis

Traffic change point detection and analysis

Jointly Optimizing Throughput and Content Delivery Cost Over Lossy Cache Networks

Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric - the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential-based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.

Discovering Traffic Anomaly Propagation in Urban Space Using Enhanced Traffic Change Peaks

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as Outlier-Tree, are not accurate to find out the trend of abnormal traffic for two reasons. First, they discover the propagation pattern based on the detected traffic anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal outlier neighborhoods rather than considering from a global perspective, and thus cannot form a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using the mesh data and enhanced traffic change peaks (en-TCP) to visualize the change of traffic anomalies (e.g., an area where vehicles are gathering or evacuating) and thus accurately capture traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time bin can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level ([Formula: see text]) of pixels. An enhanced adaptive filter is developed to generate traffic change graph sequences from grid traffic images in consecutive periods, and clustering en-TCP in a continuous period is to discover the propagation of traffic anomalies. The accuracy and effectiveness of the proposed method have been demonstrated using a real-world GPS trajectory dataset.

Efficient execution plan for egress traffic engineering

The main objective of Egress Peer Engineering (EPE) is to steer traffic exiting one Autonomous System (AS) to another in the most cost-effective way by assigning network traffic flows of different destinations to internal routes specific to every AS. The traffic assignment process is carried out to satisfy network operators’ objectives ,which include optimizing resource utilization, minimizing monetary costs and avoiding overloading the peer links. Due to network traffic dynamicity and unpredictability, traffic assignments should be constantly updated so that the aforementioned objectives are satisfied. Each of these updates results in traffic assignment changes that transition the network to a more optimized state. Executing these changes all at once is detrimental to the internal network infrastructure. To tackle this issue, this work targets finding execution plans involving several intermediate steps whereby each step includes a subset of traffic assignment changes. While executing these steps, the network operator’s objectives need to be guaranteed. To that end, an oracle algorithm that generates all the possible balanced subsets of traffic changes as execution plans is formulated. This algorithm is compared to two heuristics that are designed based on an analytical study of the problem itself. To effectively evaluate these algorithms, evaluation criteria are devised that encompass several technical and design quality metrics of the desired execution plan. These three approaches are evaluated on network configurations of small size networks, and the results obtained show that one of the heuristics outperforms the oracle implementation in terms of execution time while producing comparable results based on the evaluation criteria. For big networks, the best performing heuristic satisfies the quality metrics and generates its best execution plan in a short period of time.

Estimating cycle-level real-time traffic movements at signalized intersections

Real-time traffic movements at intersections is vital for transportation and traffic engineering. It helps in providing intersection traffic data and optimizing signal control plans. This study attempts to extend the data coverage by developing algorithms to estimate through and left-turn movements in real-time at signalized intersections. This study is the first attempt to estimate short-term traffic movement counts at signalized intersections at the cycle-level for signal control. Real-time data were collected from 19 intersections along two main corridors in Orange county, Florida. A framework was proposed to identify different variables considering the characteristics of the cycle-level traffic movement estimation. To develop generic algorithms, a proposed approach was utilized to generalize the traffic movement estimation at the corridor level. Signal timing and movement counts at the upstream and downstream intersections were utilized to estimate through and left-turn movements at target intersections. An extensive comparison study was carried out based on the processed data. The experimental results show that the proposed Gradient Boosting model outperformed the baseline models with Mean Absolute Percentage Error 9.53% and 4.6% for the through and left-turn movements, respectively. In addition, the transferability of the developed models for the abnormal traffic conditions was validated and the estimated model proved that it could instantaneously capture the traffic change due to an accident. It is expected that the proposed method could reduce the sensor cost and extend the movement data coverage, which could help in developing efficient signal control plans at signalized intersections.