Search

The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards. [ABSTRACT FROM AUTHOR]

We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud. [ABSTRACT FROM AUTHOR]

The increasing use of road vehicles has caused a number of transport and environmental issues throughout the world. To cope with them, traffic calming schemes are being increasingly implemented in built-up areas. An example of such schemes are Tempo-30 zones. The traffic calming measures applied as part of this scheme must be carefully planned in terms of location and design details in order to obtain the desired reduction in speed, traffic volume and exhaust emissions and, last but foremost, to increase the safety and facilitate the movement of vulnerable road users. The coexistence and combined effect of these measures and their design details must also be taken into account. The purpose of this study was to investigate whether the applied traffic calming measures had a considerable bearing on the reduction in speed to the desired level, as assumed in the traffic calming plan. Three street sections starting and ending with different intersection types were chosen to examine the synergy of the applied traffic calming measures. The numbers and speeds of vehicles were measured in three day-long continuous surveys. As it was expected, the amount of speed reduction depended on the hourly traffic volume on a one-way street and various other traffic engineering aspects. The obtained results may be used to modify the existing speed profile models and can guide traffic engineers in choosing the most effective traffic calming measures. [ABSTRACT FROM AUTHOR]

Lightning discharge becomes a serious source of interference and damage for electronic and electrical power systems. Safe and reliable operation of railway traffic control systems requires proper protection against the effects of lightning. However, the current standards on lightning protection, PN-EN/EN/IEC 62305, do not cover railway objects. Moreover, there are no other standards or recommendations dedicated to the railway. The paper is an attempt to apply the procedure of lightning risk management according to PN-EN 62305-2 to select the proper protection measures in railway objects. A case study for the signal box with installed relaying and digital stations of the railway traffic control system is analyzed. The analysis comprises calculations based on the current standard PN-EN 62305-2:2012 but including the issues specific to railway traffic control. The risks of lightning losses have been calculated for two cases: without lightning protection measures and with protection measures proposed to decrease the risks below the tolerable values. The results show that a practically effective solution to reduce the risks is applying surge protective devices with proper characteristics. Another way is replacing unshielded incoming lines with shielded ones of given shield bonding way, and supplementing it with surge protective devices when necessary. [ABSTRACT FROM AUTHOR]

Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research. [ABSTRACT FROM AUTHOR]

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling. [ABSTRACT FROM AUTHOR]

Ship detection from synthetic aperture radar (SAR) imagery is a fundamental and significant marine mission. It plays an important role in marine traffic control, marine fishery management, and marine rescue. Nevertheless, there are still some challenges hindering accuracy improvements of SAR ship detection, e.g., complex background interferences, multi-scale ship feature differences, and indistinctive small ship features. Therefore, to address these problems, a novel quad feature pyramid network (Quad-FPN) is proposed for SAR ship detection in this paper. Quad-FPN consists of four unique FPNs, i.e., a DEformable COnvolutional FPN (DE-CO-FPN), a Content-Aware Feature Reassembly FPN (CA-FR-FPN), a Path Aggregation Space Attention FPN (PA-SA-FPN), and a Balance Scale Global Attention FPN (BS-GA-FPN). To confirm the effectiveness of each FPN, extensive ablation studies are conducted. We conduct experiments on five open SAR ship detection datasets, i.e., SAR ship detection dataset (SSDD), Gaofen-SSDD, Sentinel-SSDD, SAR-Ship-Dataset, and high-resolution SAR images dataset (HRSID). Qualitative and quantitative experimental results jointly reveal Quad-FPN's optimal SAR ship detection performance compared with the other 12 competitive state-of-the-art convolutional neural network (CNN)-based SAR ship detectors. To confirm the excellent migration application capability of Quad-FPN, the actual ship detection in another two large-scene Sentinel-1 SAR images is conducted. Their satisfactory detection results indicate the practical application value of Quad-FPN in marine surveillance. [ABSTRACT FROM AUTHOR]

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm's performance on a data set comprising five years of real data measurements at the Croatian bridge "Krk" and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model. [ABSTRACT FROM AUTHOR]

A driving cycle is a time series of a vehicle's speed, reflecting its movement in real road conditions. In addition to certification and comparative research, driving cycles are used in the virtual design of drive systems and embedded control algorithms, traffic management and intelligent road transport (traffic engineering). This study aimed to develop an adaptive driving cycle for a known route to optimize the energy consumption of an electric vehicle and improve the driving range. A novel distance-based adaptive driving cycle method was developed. The proposed algorithm uses the segmentation and iterative synthesis procedures of Markov chains. Energy consumption during driving is monitored on an ongoing basis using Gaussian process regression, and speed and acceleration are corrected adaptively to maintain the planned energy consumption. This paper presents the results of studies of simulated driving cycles and the performance of the algorithm when applied to the real recorded driving cycles of an electric vehicle. [ABSTRACT FROM AUTHOR]

In this article, a way to employ the diffusion approximation to model interplay between TCP and UDP flows is presented. In order to control traffic congestion, an environment of IP routers applying AQM (Active Queue Management) algorithms has been introduced. Furthermore, the impact of the fractional controller P I γ and its parameters on the transport protocols is investigated. The controller has been elaborated in accordance with the control theory. The TCP and UDP flows are transmitted simultaneously and are mutually independent. Only the TCP is controlled by the AQM algorithm. Our diffusion model allows a single TCP or UDP flow to start or end at any time, which distinguishes it from those previously described in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-zone service control scheme to maximize the performance of each service zone when a large number of cellular service zones and Device-to-Device (D2D) service zones are composed into the 5G cellular network. This paper also improves performance of service zone by dividing traffic into real-time traffic and non-real-time traffic in order to minimize traffic interference. Real-time traffic and non-real-time traffic have a significant impact on communication performance. We propose a new self-detection traffic interference control technique to improve the Quality of Service (QoS) and throughput of D2D and Cellular-to-Device (C2D) communication in a cellular network, Self-detecting Traffic Interference Control Scheme (STICS). The proposed STICS mechanism distinguishes between short-term traffic congestion process and long-term traffic congestion process according to traffic characteristics to detect and control traffic. When the proposed scheme is applied to the 5G-based cellular network environment, it is expected that the traffic type will be efficiently classified by self-detecting the traffic according to the flow. Such classified traffic is less sensitive to communication between the D2D and C2D links, thereby reducing traffic overload. We evaluate the performance of the proposed scheme through simulation and show that the proposed scheme is more efficient than other comparison schemes. [ABSTRACT FROM AUTHOR]

Recent research works on intelligent traffic signal control (TSC) have been mainly focused on leveraging deep reinforcement learning (DRL) due to its proven capability and performance. DRL-based traffic signal control frameworks belong to either discrete or continuous controls. In discrete control, the DRL agent selects the appropriate traffic light phase from a finite set of phases. Whereas in continuous control approach, the agent decides the appropriate duration for each signal phase within a predetermined sequence of phases. Among the existing works, there are no prior approaches that propose a flexible framework combining both discrete and continuous DRL approaches in controlling traffic signal. Thus, our ultimate objective in this paper is to propose an approach capable of deciding simultaneously the proper phase and its associated duration. Our contribution resides in adapting a hybrid Deep Reinforcement Learning that considers at the same time discrete and continuous decisions. Precisely, we customize a Parameterized Deep Q-Networks (P-DQN) architecture that permits a hierarchical decision-making process that primarily decides the traffic light next phases and secondly specifies its the associated timing. The evaluation results of our approach using Simulation of Urban MObility (SUMO) shows its out-performance over the benchmarks. The proposed framework is able to reduce the average queue length of vehicles and the average travel time by 22.20% and 5.78%, respectively, over the alternative DRL-based TSC systems. [ABSTRACT FROM AUTHOR]

The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of sensors do not offer all the information currently required for exhaustive and comprehensive traffic control. Thus, this paper presents the design, implementation, and configuration of laser systems to obtain 3D profiles of vehicles, which collect more precise information about the state of the roads. Nevertheless, to obtain reliable information on vehicle traffic by means of these systems, it is fundamental to correctly carry out a series of preliminary steps: choose the most suitable type of laser, select its configuration properly, determine the optimal location, and process the information provided accurately. Therefore, this paper details a series of criteria to help make these crucial and difficult decisions. Furthermore, following these guidelines, a complete laser system implemented for vehicle detection and classification is presented as result, which is characterized by its versatility and the ability to control up to four lanes in real time. [ABSTRACT FROM AUTHOR]

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes. [ABSTRACT FROM AUTHOR]

In smart cities, it is expected that transport, communication as well as the movement of people and goods will take place in the shortest possible time while maintaining a high level of safety. In recent years, due to the significant increase in the number of passengers and vehicles on the road and the capacity limitations of transport networks, it has become necessary to use new technologies for intelligent control and traffic management. Intelligent transport systems use advanced technologies in the field of data gathering, information processing, and traffic control to meet current transport needs. To be able to effectively control and manage road traffic, it is necessary to have reliable mathematical models that allow for a faithful representation of the real traffic conditions. Models of this type are usually the basis of complex algorithms used in practice in road traffic control. The application of appropriate models reflecting the behavior of road users contributes to the reduction of congestion, the vehicles travel time on the transport network, fuel consumption and the emissions, which in turn support broadly understood energy savings. The article proposes a model that allows for the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue). This model takes into account the most important traffic characteristics of the vehicles forming this queue. The verification allowed for the conclusion that the proposed model is characterized by high compliance with the actual traffic and road conditions at the intersections with signal controllers located in built-up areas in Poland. The obtained compliance confirms the possibility of using the model for practical applications in calculating the maximum back-of-queue at signal-controlled intersections located in built-up areas in Poland. [ABSTRACT FROM AUTHOR]

Mutations in LRRK2 play a critical role in both familial and sporadic Parkinson's disease (PD). Up to date, the role of LRRK2 in PD onset and progression remains largely unknown. However, experimental evidence highlights a critical role of LRRK2 in the control of vesicle trafficking, likely by Rab phosphorylation, that in turn may regulate different aspects of neuronal physiology. Here we show that LRRK2 interacts with Sec8, one of eight subunits of the exocyst complex. The exocyst complex is an evolutionarily conserved multisubunit protein complex mainly involved in tethering secretory vesicles to the plasma membrane and implicated in the regulation of multiple biological processes modulated by vesicle trafficking. Interestingly, Rabs and exocyst complex belong to the same protein network. Our experimental evidence indicates that LRRK2 kinase activity or the presence of the LRRK2 kinase domain regulate the assembly of exocyst subunits and that the over-expression of Sec8 significantly rescues the LRRK2 G2019S mutant pathological effect. Our findings strongly suggest an interesting molecular mechanism by which LRRK2 could modulate vesicle trafficking and may have important implications to decode the complex role that LRRK2 plays in neuronal physiology. [ABSTRACT FROM AUTHOR]

KV1.5 channel function is modified by different regulatory subunits. KVβ1.3 subunits assemble with KV1.5 channels and induce a fast and incomplete inactivation. Inhibition of PKC abolishes the KVβ1.3-induced fast inactivation, decreases the amplitude of the current KV1.5–KVβ1.3 and modifies their pharmacology likely due to changes in the traffic of KV1.5–KVβ1.3 channels in a PKC-dependent manner. In order to analyze this hypothesis, HEK293 cells were transfected with KV1.5–KVβ1.3 channels, and currents were recorded by whole-cell configuration of the patch-clamp technique. The presence of KV1.5 in the membrane was analyzed by biotinylation techniques, live cell imaging and confocal microscopy approaches. PKC inhibition resulted in a decrease of 33 ± 7% of channels in the cell surface due to reduced recycling to the plasma membrane, as was confirmed by confocal microscopy. Live cell imaging indicated that PKC inhibition almost abolished the recycling of the KV1.5–KVβ1.3 channels, generating an accumulation of channels into the cytoplasm. All these results suggest that the trafficking regulation of KV1.5–KVβ1.3 channels is dependent on phosphorylation by PKC and, therefore, they could represent a clinically relevant issue, mainly in those diseases that exhibit modifications in PKC activity. [ABSTRACT FROM AUTHOR]

The protein phosphatase PP2A is essential for the control of integrated eukaryotic cell functioning. Several cellular and developmental events, e.g., plant growth regulator (PGR) mediated signaling pathways are regulated by reversible phosphorylation of vesicle traffic proteins. Reviewing present knowledge on the relevant role of PP2A is timely. We discuss three aspects: (1) PP2A regulates microtubule-mediated vesicle delivery during cell plate assembly. PP2A dephosphorylates members of the microtubule associated protein family MAP65, promoting their binding to microtubules. Regulation of phosphatase activity leads to changes in microtubule organization, which affects vesicle traffic towards cell plate and vesicle fusion to build the new cell wall between dividing cells. (2) PP2A-mediated inhibition of target of rapamycin complex (TORC) dependent signaling pathways contributes to autophagy and this has possible connections to the brassinosteroid signaling pathway. (3) Transcytosis of vesicles transporting PIN auxin efflux carriers. PP2A regulates vesicle localization and recycling of PINs related to GNOM (a GTP–GDP exchange factor) mediated pathways. The proper intracellular traffic of PINs is essential for auxin distribution in the plant body, thus in whole plant development. Overall, PP2A has essential roles in membrane interactions of plant cell and it is crucial for plant development and stress responses. [ABSTRACT FROM AUTHOR]

Since the 2013 Nobel Prize was awarded for the discovery of vesicle trafficking, a subgroup of nanovesicles called exosomes has been driving the research field to a new regime for understanding cellular communication. This exosome-dominated traffic control system has increased understanding of many diseases, including cancer metastasis, diabetes, and HIV. In addition to the important diagnostic role, exosomes are particularly attractive for drug delivery, due to their distinctive properties in cellular information transfer and uptake. Compared to viral and non-viral synthetic systems, the natural, cell-derived exosomes exhibit intrinsic payload and bioavailability. Most importantly, exosomes easily cross biological barriers, obstacles that continue to challenge other drug delivery nanoparticle systems. Recent emerging studies have shown numerous critical roles of exosomes in many biological barriers, including the blood–brain barrier (BBB), blood–cerebrospinal fluid barrier (BCSFB), blood–lymph barrier (BlyB), blood–air barrier (BAB), stromal barrier (SB), blood–labyrinth barrier (BLaB), blood–retinal barrier (BRB), and placental barrier (PB), which opens exciting new possibilities for using exosomes as the delivery platform. However, the systematic reviews summarizing such discoveries are still limited. This review covers state-of-the-art exosome research on crossing several important biological barriers with a focus on the current, accepted models used to explain the mechanisms of barrier crossing, including tight junctions. The potential to design and engineer exosomes to enhance delivery efficacy, leading to future applications in precision medicine and immunotherapy, is discussed. [ABSTRACT FROM AUTHOR]

This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21 % and 41 % , total delay ranging between 40 % and 55 % , and emission levels/fuel consumption ranging between 12 % and 20 % . The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers. [ABSTRACT FROM AUTHOR]

The need for drone traffic control management has emerged as the demand for drones increased. Particularly, in order to control unauthorized drones, the systems to detect and track drones have to be developed. In this paper, we propose the drone position tracking system using multiple Bluetooth low energy (BLE) receivers. The proposed system first estimates the target's location, which consists of the distance and angle, while using the received signal strength indication (RSSI) signals at four BLE receivers and gradually tracks the target based on the estimated distance and angle. We propose two tracking algorithms, depending on the estimation method and also apply the memory process, improving the tracking performance by using stored previous movement information. We evaluate the proposed system's performance in terms of the average number of movements that are required to track and the tracking success rate. [ABSTRACT FROM AUTHOR]

Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented. [ABSTRACT FROM AUTHOR]

An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike's Information Criterion technique (AIC) has also shown that the proposed model has a higher performance. [ABSTRACT FROM AUTHOR]

Visual traffic surveillance using computer vision techniques can be noninvasive, automated and cost effective. Traffic surveillance systems with the ability to detect, count and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. This works well in daylight when the road users are clearly visible to the camera, but it often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze and fog. Therefore, in this paper, we design a dual input faster region-based convolutional neural network (RCNN) to make full use of the complementary advantages of color and thermal images to detect traffic objects in bad weather. Different from the previous detector, we used halfway fusion to fuse color and thermal images for traffic object detection. Besides, we adopt the polling from multiple layers method to adapt the characteristics of large size differences between objects of traffic targets to accurately identify targets of different sizes. The experimental results show that the present method improves the target recognition accuracy by 7.15% under normal weather conditions and 14.2% under bad weather conditions. This exhibits promising potential for implementation with real-world applications. [ABSTRACT FROM AUTHOR]

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on Y O L O V 3 , (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance. [ABSTRACT FROM AUTHOR]

Traffic microsimulation models use the movement of individual driver-vehicle-units (DVUs) and their interactions, which allows a detailed estimation of the traffic noise using Common Noise Assessment Methods (CNOSSOS). The Dynamic Traffic Noise Assessment (DTNA) methodology is applied to real traffic situations, then compared to on-field noise levels from measurement campaigns. This makes it possible to determine the influence of certain local traffic factors on the evaluation of noise. The pattern of distribution of vehicles along the avenue is related to the logic of traffic light control. The analysis of the inter-cycles noise variability during the simulation and measurement time shows no influence from local factors on the prediction of the dynamic traffic noise assessment tool based on CNOSSOS. A multifractal approach of acoustic waves propagation and the source behaviors in the traffic area are implemented. The novelty of the approach also comes from the multifractal model's freedom which allows the simulation, through the fractality degree, of various behaviors of the acoustic waves. The mathematical backbone of the model is developed on Cayley–Klein-type absolute geometries, implying harmonic mappings between the usual space and the Lobacevsky plane in a Poincaré metric. The isomorphism of two groups of SL(2R) type showcases joint invariant functions that allow associations of pulsations–velocities manifolds type. [ABSTRACT FROM AUTHOR]

Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles' movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method's ability for classifying a greater variety of traffic regulator classes. [ABSTRACT FROM AUTHOR]

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control. [ABSTRACT FROM AUTHOR]

Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver's mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations. [ABSTRACT FROM AUTHOR]

In recent years, people have witnessed numerous Internet of Things (IoT)-based attacks with the exponential increase in the number of IoT devices. Alongside this, the means to secure IoT-based applications are maturing slower than our budding dependence on them. Moreover, the vulnerabilities in an IoT system are exploited in chains to penetrate deep into the network and yield more adverse aftereffects. To mitigate these issues, this paper gives unique insights for handling the growing vulnerabilities in common IoT devices and proposes a threat architecture for IoT, addressing threats in the context of a three-layer IoT reference architecture. Furthermore, the vulnerabilities exploited at the several IoT attack surfaces and the challenges they exert are explored. Thereafter, the challenges in quantifying the IoT vulnerabilities with the existing framework are also analyzed. The study also covers a case study on the Intelligent Transportation System, covering road transport and traffic control specifically in terms of threats and vulnerabilities. Another case study on secure energy management in the Smart Grid is also presented. This case study covers the applications of Internet of Vulnerable Things (IoVT) in Smart energy Grid solutions, as there will be tremendous use of IoT in future Smart Grids to save energy and improve overall distribution. The analysis shows that the integration of the proposed architecture in existing applications alarms the developers about the embedded threats in the system. [ABSTRACT FROM AUTHOR]

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment. [ABSTRACT FROM AUTHOR]

In the lane-based signal optimization model, permitted turn directions in the form of lane markings that guide road users to turn at an intersection are optimized with traffic signal settings. The spatial queue requirements of approach lanes should be considered to avoid the overdesigning of the cycle, effective red, and effective green durations. The point-queue system employed in the conventional modeling approach is unrealistic in many practical situations. Overflow conditions cannot be modeled accurately, while vehicle queues are accumulated that block back upstream intersections. In a previous study, a method was developed to manually refine the traffic signal settings by using the results of lane-based optimization. However, the method was inefficient. In the present study, new design constraint sets are proposed to control the effective red and effective green durations, such that traffic enters the road lanes without overflow. The reduced cycle times discharge the accumulated vehicles more frequently. Moreover, queue spillback and residual queues can be avoided. One of the most complicated four-arm intersections in Hong Kong is considered as a case study for demonstration. The existing traffic signal settings are ineffective for controlling the observed traffic demand, and overflow occurs in short lanes. The optimized traffic signal settings applied to the proposed optimization algorithm effectively avoided traffic overflow. The resultant queuing dynamics are simulated using TRANSYT 15 Cell Transmission Model (CTM) to verify the proposed model. The model application is extended to handle the difficult residual queue scenario. It is found that the proposed model can optimize the traffic signal settings in cases where there are short initial residual queues. [ABSTRACT FROM AUTHOR]

Fruit ripening and abscission are the results of the cell wall modification concerning different components of the signaling network. However, molecular-genetic information on the cross-talk between ripe fruit and their abscission zone (AZ) remains limited. In this study, we investigated transcriptional and hormonal changes in olive (Olea europaea L. cv Picual) pericarp and AZ tissues of fruit at the last stage of ripening, when fruit abscission occurs, to establish distinct tissue-specific expression patterns related to cell-wall modification, plant-hormone, and vesicle trafficking in combination with data on hormonal content. In this case, transcriptome profiling reveals that gene encoding members of the α-galactosidase and β-hexosaminidase families associated with up-regulation of RabB, RabD, and RabH classes of Rab-GTPases were exclusively transcribed in ripe fruit enriched in ABA, whereas genes of the arabinogalactan protein, laccase, lyase, endo-β-mannanase, ramnose synthase, and xyloglucan endotransglucosylase/hydrolase families associated with up-regulation of RabC, RabE, and RabG classes of Rab-GTPases were exclusively transcribed in AZ-enriched mainly in JA, which provide the first insights into the functional divergences among these protein families. The enrichment of these protein families in different tissues in combination with data on transcript abundance offer a tenable set of key genes of the regulatory network between olive fruit tissues in late development. [ABSTRACT FROM AUTHOR]

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion. [ABSTRACT FROM AUTHOR]

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic. [ABSTRACT FROM AUTHOR]

Recognition of antigens displayed on the surface of an antigen-presenting cell (APC) by T-cell receptors (TCR) of a T lymphocyte leads to the formation of a specialized contact between both cells named the immune synapse (IS). This highly organized structure ensures cell–cell communication and sustained T-cell activation. An essential lipid regulating T-cell activation is diacylglycerol (DAG), which accumulates at the cell–cell interface and mediates recruitment and activation of proteins involved in signaling and polarization. Formation of the IS requires rearrangement of the cytoskeleton, translocation of the microtubule-organizing center (MTOC) and vesicular compartments, and reorganization of signaling and adhesion molecules within the cell–cell junction. Among the multiple players involved in this polarized intracellular trafficking, we find sorting nexin 27 (SNX27). This protein translocates to the T cell–APC interface upon TCR activation, and it is suggested to facilitate the transport of cargoes toward this structure. Furthermore, its interaction with diacylglycerol kinase ζ (DGKζ), a negative regulator of DAG, sustains the precise modulation of this lipid and, thus, facilitates IS organization and signaling. Here, we review the role of SNX27, DAG metabolism, and their interplay in the control of T-cell activation and establishment of the IS. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is increasingly creating new market possibilities in several industries' sectors such as smart homes, smart manufacturing, and smart cities, to link the digital and physical worlds. A key challenge in an IoT system is to ensure network performance and cost-efficiency when a plethora of data is generated and proliferated. The adoption of Network Function Virtualization (NFV) technologies within an IoT environment enables a new approach of providing services in a more agile and cost-efficient way. We address the problem of traffic engineering with multiple paths for an NFV enabled IoT system (vIoT), taking into account the fluctuation of traffic volume in various time periods. We first formulate the problem as a mixed linear integer programming model for finding the optimal solution of link-weight configuration and traffic engineering. We then develop heuristic algorithms for a vIoT system with a large number of devices. Our solution enables a controller to adjust a link weight system and update a flow table at an NFV switch for directing IoT traffic through a service function chain in a vIoT system. The evaluation results under both synthetic and real-world datasets of network traffic and topologies show that our approach to traffic engineering with multiple paths remarkably improves several performance metrics for a vIoT system. [ABSTRACT FROM AUTHOR]

In this work, a 2-D gridded air pollution map with a high resolution of 50 × 50 m2 was proposed to help the exposure assessment studies focusing on the association between air pollutants and their health effects. To establish a reliable air pollution map in a 2 × 2 km2 urban area, a mobile monitoring procedure and a data process were developed. Among the various vehicle-related air pollutants, the particle-bound polycyclic aromatic hydrocarbon (pPAH) was chosen as a sensitive indicator. The average pPAH concentration on major roads (293.1 ng/m3) was found to be 35 times higher than that at a background location (8.4 ng/m3). Based on the cell-based pPAH concentrations, the 50 × 50 m2 cells in the air pollution map were categorized into five pollution levels. The higher air pollution levels were generally shown by the cells close to the major traffic emission points. The proposed map can be used to make various policies regarding land use and traffic flow control in urban areas. Estimation of the personal exposure level to air pollutants is possible at a reliable location using the highly resolved 2-D gridded air pollution map in exposure assessment studies. [ABSTRACT FROM AUTHOR]

The real-time vehicle detection and counting plays a crucial role in traffic control. To collect traffic information continuously, the access to information from traffic video shows great importance and huge advantages compared with traditional technologies. However, most current algorithms are not adapted to the effects of undesirable environments, such as sudden changes in illumination, vehicle shadows, and complex urban traffic conditions, etc. To address these problems, a new vehicle detection and counting method was proposed in this paper. Based on a real-time background model, the problem of sudden illumination changes could be solved, while the vehicle shadows could be removed using a detection method based on motion. The vehicle counting was built on two types of ROIs—called Normative-Lane and Non-Normative-Lane—which could adapt to the complex urban traffic conditions, especially for non-normative driving. Results have shown that the methodology we proposed is able to count vehicles with 99.93% accuracy under the undesirable environments mentioned above. At the same time, the setting of the Normative-Lane and the Non-Normative-Lane can realize the detection of non-normative driving, and it is of great significance to improve the counting accuracy. [ABSTRACT FROM AUTHOR]

The present paper is a review on efficiency issues related to three important sectors of the transportation systems: railways, electrical vehicles, and marine. For the three sectors, the authors, in reference of their knowledge and research area, show the results of a wide literature analysis, in order to highlight which are the measures, in terms of technological solutions and management techniques, which are recently investigated and implemented, for improving the three transportation systems, from the point of view of efficiency. In particular, for the railway transportation sector, a wide analysis is presented, detecting which are the main measures adopted for improving the efficiency, related to the power systems for supplying trains and to the train traffic control, with a focus on the storage system integration. For electric road vehicles the analysis is focused on the plug-in electrical vehicles and on the infrastructure for their recharge, with an emphasis on how these vehicles can support the grid, e.g., through Vehicle to Grid (V2G) applications. Finally, for the marine transport service the review is related to the propulsion systems and on how the different solutions can meet the objective of efficiency. [ABSTRACT FROM AUTHOR]

In this paper, which is based on a real-life motivation, we present an algebraic theory of automata and multi-automata. We combine these (multi-)automata using the products introduced by W. Dörfler, where we work with the cartesian composition and we define the internal links among multiautomata by means of the internal links' matrix. We used the obtained product of n-ary multi-automata as a system that models and controls certain traffic situations (lane shifting) for autonomous vehicles. [ABSTRACT FROM AUTHOR]

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems. [ABSTRACT FROM AUTHOR]

Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods. [ABSTRACT FROM AUTHOR]

Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management. [ABSTRACT FROM AUTHOR]

With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal. [ABSTRACT FROM AUTHOR]

Intersections are the bottlenecks of the road network. The capacity of signalized intersections restricts the operation of the road network. Dynamic estimation of capacity is necessary for signalized intersections refined management. With the development of technology, more and more detectors were installed near the intersection. It had been the information-rich environment, which provided support for dynamic estimation of capacity. A dynamic estimation method for a saturation flow rate based on a neural network was developed. It would grasp the dynamic change of saturation flow rates and influencing factors. The measure data at three scenarios (through lanes, shared right-turn and through lanes, shared left-turn and through lanes) of signalized intersections in Beijing were taken as examples to validate the proposed method. Firstly, the traffic flow characteristics of the three scenarios and factors affecting the saturation flow rate were analyzed. Secondly, neural network models of the three scenarios were established. Then the hyperparameters of neural network models were determined. After training, the neural network structure and parameters were saved. Lastly, the test set data was validated by the training model. At the same time, the proposed method was compared with the Highway Capacity Manual (HCM) method and the statistical regression method. The results show that both regression models and neural network models have better accuracy than HCM models. In a simple scenario, the neural network models are not much different from the regression models. With the increase of complexity of scenarios, the advantages of neural network models are highlighted. In through-left lane and through-right lane scenarios, the estimated saturation flow rates used by the proposed method were 7.02%, 4.70%, respectively. In the complexity of traffic scenarios, the proposed method can estimate the saturation flow rate accurately and timely. The results could be used for signal control schemes optimizing and operation managing at signalized intersections subtly. [ABSTRACT FROM AUTHOR]

Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates. [ABSTRACT FROM AUTHOR]

This paper introduces a new methodology for reconstructing vehicle densities of freeway segments by utilizing the limited data collected by traffic-counting sensors and developing a macroscopic traffic stream model formulated as a switched reduced-order state observer design problem with unknown or partially known inputs. Specifically, the traffic network is modeled as a hybrid dynamic system in a state space that incorporates unknown inputs. For freeway segments with traffic-counting sensors installed, vehicle densities are directly computed using field traffic count data. A reduced-order state observer is designed to analyze traffic state transitions for freeway segments without field traffic count data to indirectly estimate the vehicle densities for each freeway segment. A simulation-based experiment is performed applying the methodology and using data of a segment of Beijing Jingtong freeway in Beijing, China. The model execution results are compared with the field data associated with the same freeway segment, and highly consistent results are achieved. The proposed methodology is expected to be adopted by traffic engineers to evaluate freeway operations and develop effective management strategies. [ABSTRACT FROM AUTHOR]