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Analysis of traffic flow signals plays an important role in traffic prediction and management. As an intrinsic property, the singular point of a traffic flow signal labels a new nonsteady status. Therefore, detecting the singular point is an effective approach to determine the moment of traffic flow prediction. In this paper, an improved wavelet transform is proposed to detect singular points of real-time traffic flow signals. The number of detected singular points is output via the heuristic selection of multiple scales. Then, a weighted similarity measurement of historical traffic flow signals is utilized to predict the next singular point. The position of the next singular point decides the duration of prediction adaptively. The detected and predicted singular points are applied to dynamically update the unequal interval prediction of traffic flow. Furthermore, a Vasicek model is used to predict the traffic flow by minimizing the sum of the relative mean standard error (RMSE) between the traffic flow increment in the predicted interval and the sampled increments of previous intervals. A decomposition method is used to solve the unequal matrix problem. Based on the scenario and traffic flow imported from the real-world map, the simulation results show that the proposed algorithm outperforms existing approaches with high prediction accuracy and much lower computing cost. [ABSTRACT FROM AUTHOR]

Featured Application: Optimizing urban traffic/logistics; estimating pollution due to combustion engines; studying the relation between urban pollution and respiratory/cardiovascular problems. 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 NO 2 levels for 2021 with accuracy resp. 78 % , 74 % and 92 % , using AutoGluon and Scikit-Learn. [ABSTRACT FROM AUTHOR]

A noise reduction strategy was proposed for Panyu District of Guangzhou City based on its traffic noise map, which considered both road and railway traffic noise. Commercial software was applied to compute the noise maps with and without noise barriers based on the field traffic flow measurements. Noise compliance maps were further developed to assess the effects of noise barriers on the quality of the sound environment. The change in populations exposed to unhealthy traffic noise with noise barriers was calculated, and the group noise indicators Gdn and Gnight, which indicate the populations of highly annoyed and sleep-disturbed people, were evaluated. The results showed that the traffic noise level was significantly decreased after the implementation of noise barriers. It was predicted that with the strategy of applying suitable noise barriers, the overall areas under heavy noise pollution could be reduced by 24.5 and 24.3 km2 during daytime and nighttime, respectively. Meanwhile, the total areas that could meet the regulated standard for noise levels were increased by 97.4, and 66.9 km2, corresponding to compliance rate increments of 18.38% and 12.62%, respectively, in daytime and nighttime. The results further revealed that the installation of noise barriers could significantly reduce the population of highly annoyed people, while the reduction of the population experiencing sleep disturbances was insignificant. [ABSTRACT FROM AUTHOR]

Recently, increasing interest in managing pedestrian and bicycle flows has been demonstrated by cities and transportation professionals aiming to reach community goals related to health, safety, and the environment. Precise forecasting of pedestrian and bicycle traffic flow is crucial for identifying the potential use of bicycle and pedestrian infrastructure and improving bicyclists' safety and comfort. Advances in sensory technology enable collecting massive traffic flow data, including road traffic, bicycle, and pedestrian traffic flow. This paper introduces a novel deep hybrid learning model with a fully guided-attention mechanism to improve bicycles and pedestrians' traffic flow forecasting. Notably, the proposed approach extends the modeling capability of the Variational Autoencoder (VAE) by merging a long short-term memory (LSTM) model with the VAE's decoder and using a self-attention mechanism at multi-stage of the VAE model (i.e., decoder and before data resampling). Specifically, LSTM improves the VAE decoder's capacity in learning temporal dependencies, and the guided-attention units enable selecting relevant features based on the self-attention mechanism. This proposed deep hybrid learning model with a multi-stage guided-attention mechanism is called GAHD-VAE. Proposed methods were validated with traffic measurements from six publicly available pedestrian and bicycle traffic flow datasets. The proposed method provides promising forecasting results but requires no assumptions that the data are drawn from a given distribution. Results revealed that the GAHD-VAE methodology can efficiently enhance the traffic forecasting accuracy and achieved better performance than the deep learning methods VAE, LSTM, gated recurrent units (GRUs), bidirectional LSTM, bidirectional GRU, convolutional neural network (CNN), and convolutional LSTM (ConvLSTM), and four shallow methods, linear regression, lasso regression, ridge regression, and support vector regression. [ABSTRACT FROM AUTHOR]

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy. [ABSTRACT FROM AUTHOR]

The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process. [ABSTRACT FROM AUTHOR]

There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented. [ABSTRACT FROM AUTHOR]

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%. [ABSTRACT FROM AUTHOR]

Traffic noise is a major pollution problem in Hong Kong and many large cities throughout the world. Indeed, traffic noise and traffic flow patterns interact, however, there is a very little research effort studying it. To the best knowledge of the authors, the present research is the first on-site measurement study about the effect of a traffic signal on the noise level of moving vehicles. Various cases of "stop and go" and "non-stop" were considered in the traffic noise measurements, where the terms "stop and go" and "non-stop" represent a traffic road with and without traffic signals, respectively. The L10 and Leq noise levels were recorded and compared in the present study. From the results, it was found that the stop time and traffic flow are factors that also affect the sound level properties. [ABSTRACT FROM AUTHOR]

Fatigue load models for road bridges given in the Eurocode EN1991-2 have been calibrated considering real traffic measurements that became available around 1990. Since then, traffic composition has evolved considerably, also considering the issuing of the 96/53/EC Directive, which legitimated member states, on an equal and not discriminatory basis, to allow the circulation of Long and Heavy Vehicles (LHVs). Thus, the appropriateness of fatigue load models to cover also the effects of these vehicles, which are longer, heavier and potentially more damaging than common Heavy Goods Vehicles (HGVs), became an issue. The aim of the study is to assess how the evolution of European traffic influences the fatigue assessment of bridges. To capture the essence of the problem, three different real traffic measurements are compared in terms of fatigue damage: the Auxerre (FR) traffic, adopted to define fatigue load models in EN1991-2; the Moerdijk (NL) traffic, characterized by a high percentage of LHVs; and the Igualada (ES) traffic. To assess the current relevance of fatigue load models LM2 and LM4 of EN1991-2, the aptitude of these models to adequately reproduce the effects caused by LHVs is discussed in detail. The results demonstrate that the Auxerre traffic is still the most onerous; that the Moerdijk traffic is generally more severe than the Igualada traffic, and that the fatigue load models of Eurocode do not require major updates. The study is further supplemented by investigating the suitability of the formulae provided in the Eurocodes for the damage equivalence factors λ 2 and λ 3 to express the influence of the total lorry volume on the fatigue damage. In that latter case, the conclusion is that the formulae proposed in the Eurocodes, based on the assumption of a linear fatigue strength S – N curve with constant conventional slope m , could lead to erroneous, even unsafe, estimates of the fatigue life, especially when details are characterized by constant amplitude fatigue limit Δ σ D , thus calling for further improvements of the formulae themselves. [ABSTRACT FROM AUTHOR]

Fatigue assessments of bridges depend on vehicle interactions, occurring when several vehicles travel simultaneously on the bridge or when two individual stress histories, caused by vehicles traveling in different times, generate a more damaging combined stress history. When interactions are significant, stress histories cannot be directly determined using conventional fatigue load models provided in Codes, unless suitable theoretical models for vehicle interactions are available. In the paper, original approaches are proposed to study different aspects of the problem. Concerning interactions due to simultaneity, the novelty is to consider the bridge a service system. Since the process of vehicle arrivals is a Markov process, vehicle interactions can be studied in the framework of the queuing theory. In this way, in the appropriate context, interacting vehicles are equivalent to queued requests (vehicles) in the service system. The method considers two subcases, to be tackled in the given sequence, so that the solution is noticeably simplified. The first subcase refers to vehicles traveling simultaneously in one lane; the second subcase to vehicle and vehicle convoys traveling simultaneously on two or more lanes. In the first subcase the problem is solved considering each lane as a single channel system with a waiting queue, where the number of vehicles in the queue and the waiting time, depending on the number of vehicles in the queue, are limited. A modified vehicle flow on each bridge lane is thus obtained, composed by vehicles and vehicle convoys separately traveling the lane, which is, if relevant, the input for the second subcase. In the second subcase the multilane bridge is modeled as a multichannel system without the waiting queue. When the number of requests exceeds the number of channels, r , the surplus is lost and cannot reenter the system. The results regarding simultaneity are much more relevant than it appears at the first sight: Two relevant examples demonstrated that they can be fruitfully used to implement optimized Monte Carlo algorithms for artificial traffic generation, as well for adaptation of traffic measurements, when flows are modified. Finally, a "non-interacting traffic flow" is obtained, whose elements (vehicle, vehicle convoy, cluster of vehicles) travel individually on the bridge. The global stress history results thus a mere random assembly of the stress histories induced by each element of the non-interacting traffic flow. These stress histories can only combine, as simultaneity interactions are excluded for them. Combination of stress histories is a complex issue, especially when, as in the Eurocodes, fatigue load models are composed by a set of standardized lorries. In fact, questions concerning: Conditions for the combination; stress history which can combine; expected number of occurrences of combined stress histories and of the remaining individual ones; are still open. Really, they can be tackled resorting to sophisticated and time-consuming simulations based on Monte Carlo methods, but elementary solutions have not been proposed so far. The original method proposed here, whose practical application is illustrated referring to an important case study, allows to solve the problem providing simple recursive formulae. Finally, two relevant examples illustrate, with specific reference to the Eurocodes, some important implications of the study. [ABSTRACT FROM AUTHOR]

For satisfactory traffic management of an intelligent transport system, it is vital that traffic microwave radar detectors (TMRDs) can provide real-time traffic information with high accuracy. In this study, we develop several information-aided smart schemes for traffic detection improvements of TMRDs in multiple-lane environments. Specifically, we select appropriate thresholds not only for removing noise from fast Fourier transforms (FFTs) of regional lane contexts but also for reducing FFT side lobes within each lane. The resulting FFTs of reflected vehicle signals and those of clutter are distinguishable. We exploit FFT and lane-/or time stamp-related information for developing smart schemes, which mitigate adverse effects of lane-crossing FFT side lobes of a vehicle signal. As such, the proposed schemes can enhance the detection accuracy of both lane vehicle flow and directional traffic volume. On-site experimental results demonstrate the advantages and feasibility of the proposed methods, and suggest the best smart scheme. [ABSTRACT FROM AUTHOR]