Traffic Blockage Research Articles

An Enhancement of Identity Based Conditional Privacy-preserving Authentication Process in Vehicular Ad Hoc Networks

In general, Vehicular Ad hoc Networks (VANETs) are permitting the communication between one vehicle with neighboring vehicles, infrastructure, and Road-Side Unit (RSU). In this, vehicle platoon is commonly known as the vehicle driving pattern it categorizes the batching of the vehicle in the on the trot fashion. It has been reviewed as an effective resolution to mitigate the reduction in traffic blockage and to widen the opulence of the travel. However, the malicious activities of any unauthorized person in VANET are increased the damage to authorized vehicles. In this manuscript, the Identity based Conditional Privacy-Preserving Authentication (ID-CPPA) signature scheme is proposed to detect the malignant command vehicle very efficiently by the consumer vehicle. In this, the proposed ID-CPPA method uses one-way hash functions for improving the efficiency of Road-Side Unit (RSU) signing and verification of a messages. In order to provide better concealment to the vehicle, Phase Truncated Fourier Transform based asymmetric encryption algorithm (PTFT-AE) is proposed. Thus, the proposed ID-CPPA-PTFT-AE approach has achieved 28.96%, 37.58%, 31.36% higher security rate and 25.8%, 37.9%, 42.6% lower delay than the existing MPDC-LPNS, PPSR-GS, and WCAA-TST methods respectively.

Innovative Imaging and Analysis Techniques for Quantifying Spalling Repair Materials in Concrete Pavements

Traditional spalling repair on concrete pavement roads is labor-intensive. It involves traffic blockages and the manual calculation of repair areas, leading to time-consuming processes with potential discrepancies. This study used a line scan camera to photograph road surface conditions to analyze spalling without causing traffic blockage in an indoor setting. By using deep learning algorithms, specifically a region-based convolutional neural network (R-CNN) in the form of the Mask R-CNN algorithm, the system detects spalling and calculates its area. The program processes data based on the Federal Highway Administration (FHWA) spalling repair standards. Accuracy was assessed using root mean square error (RMSE) and Pearson correlation coefficient (PCC) via comparisons with actual field calculations. The RMSE values were 0.0137 and 0.0167 for the minimum and maximum repair areas, respectively, showing high accuracy. The PCC values were 0.987 and 0.992, indicating a strong correlation between the actual and calculated repair areas, confirming the high calculation accuracy of the method.

INTERVENTION SOLUTIONS ON LANDSLIDES IN ROAD AREAS. CASE STUDY

In our country, there is a large variety of foundation soils, some of them having inferior characteristics to those necessary for the proper support of a communication path. This raises some of the most common problems in the field of road infrastructures, namely the sliding of slopes in the area related to road construction. This phenomenon appears more and more frequently, especially after some periods with accentuated atmospheric instability, sometimes leading to the partial inability to use the affected infrastructure sector. In some cases, the development of this phenomenon can lead to the traffic blockage and the isolation of the communities in the area, to the destruction of a well-defined road sector and even to the loss of human lives. In order to maintain the quality requirements imposed by the law and to limit the risk of reaching the limit state of normal operation, respectively of compromising the structure, the administrators and the staff who are directly involved in the maintenance and the expertise of the affected area, achieve direct examination works or investigation by means of specific observation and measurement. In the current practice, these works are part of the technical expertise of the affected area, being made by a specialized team, led by an authorized technical expert in the field.

Method of Assortment Control for Sector Boundary Traffic Signals Using Organic Computing

The research focuses on developing anassortment control procedure for traffic signals at sector boundaries using organic computing principles. This study lies at the intersection of urban traffic signal control and artificial intelligence. The proposed procedure comprises various modules, including traffic flow monitoring, self-optimization, self-modification, evolutionary learning, self-assessment, and self-adaptation. The objective is to achieve efficient assortment between traffic signals at sector boundaries, thus preventing congestion and traffic blockages in the intersecting areas.

Spatiotemporal Distributions and Vulnerability Assessment of Highway Blockage under Low-Visibility Weather in Eastern China Based on the FAHP and CRITIC Methods

In this study, the spatiotemporal distributions of highway blockage and the low-visibility weather events in eastern China are studied by taking Jiangsu Province as an example. Based on the record table data of highway-blocking events, a vulnerability evaluation model for the highway network in Jiangsu Province is established using the weight assignment methods of the fuzzy analytic hierarchy process (FAHP) and criteria importance though intercriteria correlation (CRITIC). By using the geographic information system, the vulnerability evaluation map of road network in low-visibility weather in Jiangsu Province is finally drawn. The results show that the monthly blockage events on Jiangsu highways are more frequent in the north than in the south and are more frequent along the coast than inland, with the highest occurrence number in winter and a second peak in May. There are basically no blockage events from July to October. Traffic blockage on Jiangsu highways mainly occurs between 22:00 and 08:00 Beijing time. In the afternoon, there are almost no highway-blocking events caused by low-visibility weather. The vulnerability of highway blockage in Jiangsu Province is high in the north and low in the south and high in coastal areas and relatively low in inland. The section K6-K99 of the G30 Lianhuo Highway is the most sensitive.

Analysis of Seismic Impact on Hailuogou Glacier after the 2022 Luding Ms 6.8 Earthquake, China, Using SAR Offset Tracking Technology

An Ms 6.8 earthquake struck Luding County, Ganzi Prefecture, Sichuan Province on 5 September 2022, with the epicenter about 10 km away from Hailuogou Glacier. How Hailuogou Glacier was affected by the earthquake was of major concern to society. Sentinel-1 SAR satellite imaging was used to monitor the glacier surface velocity during different periods before and after the Luding earthquake based on pixel offset tracking (POT) technology, which applies a feature-tracking algorithm to overcome the phase co-registration problems commonly encountered in large displacement monitoring. The results indicated that the velocity had a positive correlation with the average daily maximum temperature and the slope gradient on the small-slope surfaces. The correlation was not apparent on the steeper surfaces, which corresponded spatially with the identified ice avalanche region in the Planet images. It was deduced that this may be because of the occurrence of ice avalanches on surfaces steeper than 25°, or that the narrower front channel impeded the glacier’s movement. The Luding earthquake did not cause a significant increase in the velocity of Hailuogou Glacier within a large range, but it disturbed the front area of the ice cascade, where the maximum velocity reached 2.5 m/d. Although the possibility of directly-induced destruction by ice avalanches after the earthquake was low, and the buffering in the downstream glacier tongue further reduced the risk of ice avalanches, the risk of some secondary hazards such as debris flow increased. The proposed method in this study might be the most efficient in monitoring and evaluating the effects of strong earthquakes on glaciers because it would not be limited by undesirable weather or traffic blockage.

Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges

In recent years, autonomous vehicles have become more and more popular due to their broad influence over society, as they increase passenger safety and convenience, lower fuel consumption, reduce traffic blockage and accidents, save costs, and enhance reliability. However, autonomous vehicles suffer from some functionality errors which need to be minimized before they are completely deployed onto main roads. Pedestrian detection is one of the most considerable tasks (functionality errors) in autonomous vehicles to prevent accidents. However, accurate pedestrian detection is a very challenging task due to the following issues: (i) occlusion and deformation and (ii) low-quality and multi-spectral images. Recently, deep learning (DL) technologies have exhibited great potential for addressing the aforementioned pedestrian detection issues in autonomous vehicles. This survey paper provides an overview of pedestrian detection issues and the recent advances made in addressing them with the help of DL techniques. Informative discussions and future research works are also presented, with the aim of offering insights to the readers and motivating new research directions.

Joint maintenance planning of deteriorating co-located road and water infrastructures with interdependencies

Due to co-location and spatial proximity, deteriorating water infrastructure (WI) and transportation infrastructure (TI) have complex interdependencies, including both physical dependency from road to pipe, and operational dependency from pipe to road. On one hand, traffic load is not only a dominant factor for the physical degradation of road but may also be an important contributing factor to the sudden failure of the pipe underneath the co-located road due to the extreme propagated stress. On the other hand, the water pipe breaks and repairs will also cause traffic blockage, lane closure, and repaving of road surface, which influence both road user costs and road maintenance costs. Most of the existing maintenance work mainly considered WI and TI separately but neglected their complex interdependencies. This study proposes a joint proactive maintenance planning framework for the co-located road and pipe under their degradation uncertainties by explicitly taking into account both their physical and operational dependencies. In particular, the competing failure modes of both the traffic-induced sudden failure and the corrosion-induced gradual failure are simultaneously considered for the pipe to capture the physical dependency. Road maintenance costs and road user costs in the presence of pipe maintenance disruption are also formulated for the road to capture the operational dependency. A case study is further provided to comprehensively evaluate the performance of the proposed joint maintenance planning strategy over several existing benchmark strategies and demonstrate the cost-saving benefit of the proposed work. The impacts of interdependencies on the proposed maintenance planning decisions are also thoroughly investigated.

Traffic prediction using MSSBiLS with self‐attention model

AbstractDue to gathering and examining a tremendous amount of traffic stream data, traffic blockage is the most important issue in the intelligent transportation framework. The main aim of this research work is “to find the appropriate deep learning time series models, to predict the future traffic of Denmark Aarhus city.” In this article, multivariate sequential stacked bidirectional LSTM with self‐attention model (MSSBiLS‐SA) is proposed to prevent the long term dependencies and also to reduce the error rate. Based on the traffic state, the high, medium, and low range of traffic is predicted to give the best route to the users, and visualization is performed with the help of the Android application. The proposed MSSBiLS‐SA attains higher accuracy 94.56% and 95.36%, sensitivity 88.74% and 92.5%, specificity 93.5% and 94.5%, precision 94.23% and 95.5%, F‐score 94.5% and 94.5%, and lower time complexity 10.19% and 12.43% shows better performance when compared with the existing methods, such as prediction and classification of traffic location analysis using optimized graph convolutional recurrent neural network and prediction and classification of traffic location analysis based multilayer perceptron network using long short term memory. Finally, the proposed method predicts and classifies the traffic location very accurately.

A Study on Application of Stochastic Queuing Models for Control of Congestion and Crowding

Traffic congestion is a common day-to-day trouble in many countries, especially large cities. Gradually, even medium-sized and small towns are experiencing such problems and suffering from their negative effects. Importantly, traffic and crowd congestion largely spoils a region's economic and social status. For commuters, traffic congestion gives rise to excess travel costs, duration, and travel discomfort. From a traffic management perspective, traffic congestion leads to more expenses in the traffic system for handling inconvenient road networks. At the social level, traffic congestion causes road accidents, environmental damages and pollution. This article provides an overview of current time's traffic and crowd management system through a review of literature on stochastic queuing models for congestion and congestion control. In particular, we will focus on issues related to tackling the practical need and available features, and alleviation of daily traffic blockages. Dealing in terms of the concepts taken from Operation Research, the most important techniques are analytical queuing model application, stochastic optimality, and robust effective optimal solutions to common road congestion problems.

Traffic Data Collection and Analysis based on Wireless Sensor Network

The development of urban environments lead to many issues, one among them is the traffic blockage. For this speedy running world, adequate traffic measures should be taken with the help of modern technology. In the process of building smart cities, we should plan for the proper management of traffic. To control this traffic issue, we need effective computation and prediction of traffic flow. To achieve this, the traffic flow data should be collected and analyzed properly. Wireless sensor network (WSN) is an auto-configured group of sensors that monitor and record physical parameters of the environment like temperature, pressure, sound, motion, etc. and organize the data at a central location. These WSNs find their application in various fields these days like in the military for battlefield surveillance, in health monitoring systems, and many more. One such application can be used in the traffic management system. The traffic flow data can be collected using the wireless sensor network, it constitutes wireless modules and magnetometer sensors which gives the vehicle flow data along with their speed and location accurately. This system is cost-efficient, consumes less power, and requires less maintenance. The data is collected and transmitted to the cloud server using the mobile internet. The traffic flow data is then computed using a method based on incremental noise addition for chaotic identification. This method involves the addition of noise signals with different intensities incrementally by the Pseudo-periodic surrogate (PPS) method and the mean value of delayed mutual information (MD) is used to measure the complexity of the signal. Depending upon the trends of the changes in the noise mixed-signal, the characteristics can be determined. Hence, we conclude that this method helps in bringing out an organized traffic management system.

Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan

Larger types of small unmanned aerial vehicles (UAVs) are beginning to be used in the United States and Europe for commercial transportation. Additionally, some blood product transport systems have been commercialized in Rwanda and other countries and used in pandemic operations for the coronavirus disease 2019 (COVID-19) in infected areas. Conversely, implementing goods transportation for commercial purposes in Japan has been difficult, especially in urban areas, due to national legislation. This study examined UAV-assisted transportation in Japan, a natural disaster hotspot, with a focus on the potential uses of UAVs in situations where traffic blockages make ground transportation impossible. UAVs were used to transport 17 kg of medical supplies belonging to a disaster medical assistance team (DMAT), along with 100 emergency meals. We also transported insulin under controlled-temperature conditions, as well as many other emergency supplies. Using UAVs to transport emergency supplies could be an effective approach when dealing with disasters. This paper summarizes the effectiveness of this approach for medical care and disaster response activities. We present a method for using drones to bridge the gap between medical and firefighting personnel, such as DMAT personnel, who are engaged in life-saving activities at the time of a disaster, and those who are unable to transport necessary goods by land using terrestrial vehicles due to traffic interruptions.

Traffic Congestion Issues, Perceptions, Experience and Satisfaction of Car Drivers/Owners on Urban Roads

Traffic congestion is a major issue of Asian megacities which causes the irritation, anger and frustrations among drivers and owners of vehicles during driving in these cities. This paper aims at understanding traffic congestion issues from the perception and experiences of car drivers and owners and their satisfaction level concerning accessibility of circulation within urban roads of Karachi. The issues addressed in this respect are road construction, traffic flow, road crossing, shopping malls along roads, traffic blockage due to VIP movement, congestion during school timing, animal presence on roads, traffic police behavior, marriage halls along the roads, peak hour traffic jams, traffic jams during sociopolitical and religious rallies, road disputes, traffic congestion due to young inexperienced drivers, alternate road availability and pedestrian bridges. The method of investigating these issues is through 42 questionnaire surveys with car drivers and car owners from June to October 2018 on urban roads of Karachi. The analysis of feedback from respondents showed that, more than 90% respondents were satisfied with construction of pedestrian bridges and annoyed with sociopolitical and religious rallies on main urban roads. More than 80% respondents were satisfied with marriage halls along the roads, felt congestion during peak hours and preferred alternate routes to go home. More than 70% agreed that, young and inexperienced drivers cause traffic congestion and felt unsatisfied and displeased with disputes on the streets among residents, traffic police and drivers. More than 60% respondents were satisfied with the road construction but annoyed with VIP protocol and felt congestion during school timings and quite uncomfortable while crossing the road. More than 50% respondents were unsatisfied with shopping malls on both sides of the road, presence of animals on main roads and behavior of traffic police during traffic jams. Finally, concerning traffic flow the respondents were equally divided in their perception. Thus this research presents a detailed perspective of people regarding traffic congestion issues in Karachi for the appropriate response by decision makers of urban transport planning and urban traffic management institutions in city.

An IoT based Ambulance Guidance System Under Emergency Condition

In our country almost all the states are facing traffic congestion severely, mainly in urban areas. The road accidents are increasing because of the traffic blockage which ruin the human lives. Due to population increase, the numbers of the regions are widening and the number of vehicles is also rising. Traffic is the main factor contributing to accident and it also causes some delay in getting to the destination. Thus traffic managing and regulating is the main traffic management problem. The ambulance and the traffic signals are integrated with some embedded systems to find out the accident spot through GPS. The accidents are determined by fixing the sensors in it. This paper provides an idea in order to control the accidents by implementing a solution where Traffic signals are dynamically operated in its way and traffic congestion rates are lowered.

Analytical and Numerical Stability Analysis of Road Cut Slopes in Garhwal Himalaya, India

The rocks of the Himalayan terrain are highly deformed and distorted due to complex geological and tectonics setup. Failures of slopes are always reported along National Highway (NH)-7, in the Uttarakhand Himalayan region, which causes loss of lives, traffic blockage, and destruction of property and also deterioration of the environment gradually. The road cut slope stability analysis of five locations were carried out along NH-7, between Shivpuri to Kaudiyala in Uttarakhand, India. For that a rigorous field investigation was done to collect the geotechnical parameters of slopes and also the potential instability condition of cut slopes were monitored with real time. To know the characteristic of rock mass, the geotechnical data's were studied based on rock mass rating (RMR) and geological strength index (GSI). The kinematics of the blocky (good and fair) rock-mass shows in general wedge, toppling and planar type of failures for different rock slopes. The petrography of representative rock samples was also carried out to see the mineralogical variation in quartzite and phyllitic quartzite. The comparative analysis of different empirical methods for slope stability as slope mass rating (SMR), continuous slope mass rating (Co-SMR), and Chinese slope mass rating (CSMR) shows a decent correlation and revealed that slopes are mostly partially stable. Qslope stability has also been applied to reveal the stability problems and to find out stable slope angle for different slopes. Further to clarify the stability of these slopes, numerical models (LEM and FEM) were applied. The numerical result, (FoS) of different slopes revealed that slopes are stable, critically stable and unstable, with a good agreement between LEM and FEM models. The collective effort of slope stability analysis through analytical and numerical methods will give the better perception to find out the potential remedial measures and optimum slope design.

Advanced Traffic Management System using a Fuzzy Logic Controller to Differentiate Parking Vehicle and Moving Vehicle

In smart cities, traffic congestion is one of the significant problems for citizens. Traffic management is an essential one for the quick development of populace and urban movement in metropolitan areas, and traffic blockage is often seeming on streets. To handle different issues for managing traffic on the streets and to help experts in inappropriate arrangement, a smart traffic management system with the IoT (Internet of Things) is proposed in this paper. Mechanisms to utilize IR sensors to distinguish traffic density isn't easy as smooth a solo vehicle recognized at the last sensor so that it can suggest traffic density in high in any event, even if there is free space before it. A technique to be proposed to solve the previously mentioned issues efficiently is by utilizing the Internet of things for traffic management systems. This paper aims to propose a Fuzzy controller to deal with traffics in smart cities. Fuzzy induction used to compute exact traffic, which separates the parking vehicle and moving vehicle. There is an issue of separating parking and un-parking vehicles in the existing systems. So, we planned to solve this using fuzzy logic.

A simplified mathematical model for estimating gas temperature and velocity under natural smoke exhaust in sloping city tunnel fires

In the previous study, the prediction of gas temperature and velocity in the naturally-ventilated inclined tunnels under traffic blockage has been well concerned (Y. Zhou et al., Sustain. Cities Soc. 45, 2019, 258–270). In this paper, we continue to pay attention to the estimation of internal gas temperature and velocity under natural smoke extraction in both single-slope and gable-slope tunnel fires, a more hazardous scenario. A series of simplified mathematical models based on the equations of continuity, energy and pressure balance was proposed to estimate the gas temperature and velocity in sloping tunnel fires, in which the influences of fire source location, tunnel slope and tunnel geometry were taken into account. In addition, corresponding numerical simulations based on full-size tilted urban tunnel fires were performed using Computational Fluid Dynamics (CFD) method. The results show that the variation trends between CFD and analytical results are basically the same, and the iterative values of gas temperature and velocity coincide well with the simulations. A fast and convenient method for the estimation of gas temperature and velocity in sloping tunnel fires has been proposed, and the results of this work can provide a reference for the optimal design and management of natural smoke extraction systems, and help to ensure the fire safety of single-slope and gable-slope city tunnels.

Vanet heterogeneous networks with wireless technology variation according to the capacity of users in Urban Areas

As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, and government offices have begun to invest a ton of energy together towards the acknowledgment of the idea of huge scope vehicular interchanges. One of the primary methodologies in this kind of system is the advancement of remote advances and their assorted organizations, concentrating on the association with the Internet through WiFi systems, cell systems, or specially appointed vehicular systems. VANETs are essentially intended to give data trade through Vehicle to Vehicle (V2V) and Vehicle to foundation (V2I) interchanges, permitting ceaseless network and being exceptionally utilized for short-range correspondence, with high transmission speed through which it is proposed that clients keep up an association and distinguish occasions about clog or street conditions. This exploration presents a vehicular situation that tries to acquire a sufficient presentation while executing a heterogeneous network in a few segments of the city of Bogotá, Colombia.

Traffic Flow Calculation using Big Data

Traffic is one of the primary issues in world. It makes numerous medical issues people on foot and bikers. It is additionally one of the practical setting of a nation. U.S.A. alone squandered almost $160 billion of fuel in year 2014 alone. Mumbai remains at no.1 position in the rundown of most exceedingly awful traffic stream while Delhi taking no.4 position. In this task we use BIGDATA for guaranteeing that the explorers doesn't get struck in the rush hour gridlock. BIGDATA can enable clients to settle on better travel choices, lighten traffic blockage, diminish carbon outflows, and improve traffic activity proficiency. Our goal of traffic stream forecast is to give a superior traffic stream data. Traffic stream forecast has picked up its significance because of fast development in urban areas and increment in rush hour gridlock blockage. Traffic stream forecast intensely relies upon authentic and ongoing traffic information gathered from different sensor sources, including inductive circles, radars, cameras, portable Global Positioning System, publicly supporting, internet based life, and so on. In this paper, we propose a profound learning-based traffic stream forecast technique.

Método de construcción de dígrafos a partir de redes viales reales en mapas digitales con aplicaciones en la búsqueda de rutas óptimas

Actualmente las redes viales en zonas urbanas sufren obstrucciones, ya sea por manifestaciones, embotellamientos, u otro tipo de bloqueo, causando el cierre momentáneo o permanente de vías o zonas de tránsito que obligan a conductores a establecer nuevas rutas. Una solución es la creación de rutas alternativas en mapas digitales a partir de dígrafos relacionados con las características de una red vial real, y la aplicación de algoritmos de optimización de rutas. En este trabajo se propone un método para construir dígrafos con una aplicación en la API de Google Maps en la extracción visual de elementos como vértices (intersecciones), aristas (calles) y flechas de sentido (dirección vial), lo que permite la aplicación del algoritmo de Dijkstra en busca de rutas alternativas.