Search results for: traffic monitoring

Authors: Yuxiao Jiang, Lingjun Meng, Mengyu Zhan, Lichunyi Zhang, Yingxia Yun

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On August 8th of 2015, a serious explosion occurred in Binhai New Area of Tianjin. This explosion led to the suspension of Tianjin-Binhai Light Rail Line 9 which was an important transportation mean connecting the old and new urban areas and the suspension causes inconvenience to commuters traveling from Tianjin to Binhai or Binhai to Tianjin and residents living by Line 9. On this regard, this paper intends to give suggestions on how to develop multi-mode transportation system rapidly and effectively after a disaster and tackle with the problems in terms of transportation infrastructure facilities. The paper proposes the idea of traffic resilience which refers to the city’s ability to restore its transportation system and reduce risks when the transportation system is destroyed by a disaster. By doing questionnaire research, on the spot study and collecting data from the internet, a GIS model is established so as to analyze the alternative traffic means used by different types of residents and study the transportation supply and demand. The result shows that along the Line 9, there is a larger demand for alternative traffic means in the place which is nearer to the downtown area. Also, the distribution of bus stations is more reasonable in the place nearer to downtown area, however, the traffic speed in the area is slower. Based on traffic resilience, the paper raises strategies to develop post-disaster multi-mode transportation system such as establishing traffic management mechanism timely and effectively, building multi-mode traffic networks, improving intelligent traffic systems and so on.

Keywords: traffic resilience, multi-mode transportation system, public traffic, transportation demand

Procedia PDF Downloads 316

Authors: Bambang Wispriyono, Satria Pratama, Haryoto Kusnoputranto, Faisal Yunus, Meliana Sari

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Introduction/objective: The impact of the development of motor vehicles is increasing the number of pollutants in the air. One of the substances that cause serious health problems is NO2. The health impacts arising from exposure to NO2 include pulmonary function impairment. The purpose of this study was to determine the relationship of NO2 exposure on the incidence of pulmonary function impairment. Methods: We are using a cross-sectional study design with 110 traffic police who were divided into two groups: exposed (police officers working on the highway) and the unexposed group (police officers working in the office). Election subject convenient sampling carried out in each group to the minimum number of samples met. Results: The results showed that the average NO2 in the exposed group was 18.72 ppb and unexposed group is 4.14 ppb. Pulmonary dysfunction on exposed and unexposed groups showed that FVC (Forced Vital Capacity) value are 88.68 and 90.27. And FEV1 (Forced Expiratory Volume in One) value are 94.9 and 95.16. Some variables like waist circumference, Body Mass Index, Visceral Fat, and Fat has associated with the incidence of Pulmonary Dysfunction (p < 0.05). Conclusion: Health monitoring is needed to decreasing health risk in Policeman.

Keywords: NO2, pulmonary dysfunction, police traffic, Jakarta

Procedia PDF Downloads 226

Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

Procedia PDF Downloads 67

Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

Procedia PDF Downloads 145

Authors: Ramesh Chandra Majhi

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Saturation flow is a very important input variable for the design of signalized intersections. Saturation flow measurement is well established for homogeneous traffic. However, saturation flow measurement and modeling is a challenging task in heterogeneous characterized by multiple vehicle types and non-lane based movement. Present study focuses on proposing a field procedure for Saturation flow measurement and the effect of typical mixed traffic behavior at the signal as far as non-lane based traffic movement is concerned. Data collected during peak and off-peak hour from five intersections with varying approach width is used for validating the saturation flow model. The insights from the study can be used for modeling saturation flow and delay at signalized intersection in heterogeneous traffic conditions.

Keywords: optimization, passenger car unit, saturation flow, signalized intersection

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: N. Ali, S. Nakayama, H. Yamaguchi, M. Nadeem

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Traffic congestion is a matter of prime concern in developing countries. This can be primarily attributed due to poor design practices and biased allocation of resources based on political will neglecting the technical feasibilities in infrastructure design. During the last decade, Lahore has expanded at an unprecedented rate as compared to surrounding cities due to more funding and resource allocation by the previous governments. As a result of this, people from surrounding cities and areas moved to the Lahore city for better opportunities and quality of life. This migration inflow inherited the city with an increased population yielding the inefficiency of the existing infrastructure to accommodate enhanced traffic demand. This leads to traffic congestion on major arterial roads of the city. In this simulation study, a major arterial road was selected to evaluate the performance of the five intersections by changing the geometry of the intersections or signal control type. Simulations were done in two software; Highway Capacity Software (HCS) and Synchro Studio and Sim Traffic Software. Some of the traffic management strategies that were employed include actuated-signal control, semi-actuated signal control, fixed-time signal control, and roundabout. The most feasible solution for each intersection in the above-mentioned traffic management techniques was selected with the least delay time (seconds) and improved Level of Service (LOS). The results showed that Jinnah Hospital Intersection and Akbar Chowk Intersection improved 92.97% and 92.67% in delay time reduction, respectively. These results can be used by traffic planners and policy makers for decision making for the expansion of these intersections keeping in mind the traffic demand in future years.

Keywords: traffic congestion, traffic simulation, traffic management, congestion problems

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Authors: Khalid A. Alsaif, Mosaad A. Foda

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The primary objective of this study is to develop the first round of road traffic noise maps for Riyadh City using Geographical Information Systems (GIS) and software LimA 7810 predictor. The road traffic data were measured or estimated as accurate as possible in order to obtain reliable noise maps. Meanwhile, the attributes of the roads and buildings are automatically exported from GIS. The simulation results at some chosen locations are validated by actual field measurements, which are obtained by a system that consists of a sound level meter, a GPS receiver and a database to manage the measured data. The results show that the average error between the predicted and measured noise levels is below 3.0 dB.

Keywords: noise pollution, road traffic noise, LimA predictor, GIS

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Authors: Sudipta Ghorai, Ossama Salem

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Pavement maintenance, repair, and rehabilitation (MRR) processes may have considerable environmental impacts due to traffic disruptions associated with work zones. The simulation models in use to predict the emission of work zones were mostly static emission factor models (SEFD). SEFD calculates emissions based on average operation conditions e.g. average speed and type of vehicles. Although these models produce accurate results for large-scale planning studies, they are not suitable for analyzing driving conditions at the micro level such as acceleration, deceleration, idling, cruising, and queuing in a work zone. The purpose of this study is to prepare a comprehensive work zone environmental assessment (WEA) framework to calculate the emissions caused due to disrupted traffic; by integrating traffic microsimulation tools with emission models. This will help highway officials to assess the benefits of accelerated construction and opt for the most suitable TMP not only economically but also from an environmental point of view.

Keywords: accelerated construction, pavement MRR, traffic microsimulation, congestion, emissions

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Authors: Gabriele Borg, Alexei De Bono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale.

Keywords: Big Data, vehicular traffic, traffic management, mobile data patterns

Procedia PDF Downloads 74

Authors: Adinarayana Badveeti, Mohammad Shafi Mir

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In Srinagar City, in India, traffic congestion is a condition on transport networks that occurs as use increases and is characterized by slower speeds, longer trip times, and increased vehicular queuing. Traffic congestion is conventionally measured using indicators such as roadway level-of-service, the Travel Time Index and their variants. Several measures have been taken in order to counteract congestion like road pricing, car pooling, improved traffic management, etc. While new road construction can temporarily relieve congestion in the longer term, it simply encourages further growth in car traffic through increased travel and a switch away from public transport. The full paper report, on which this abstract is based, aims to provide policymakers and technical staff with the real-time data, conceptual framework and guidance on some of the engineering tools necessary to manage congestion in such a way as to reduce its overall impact on individuals, families, communities, and societies dynamic, affordable, liveable and attractive urban regions will never be free of congestion. Road transport policies, however, should seek to manage congestion on a cost-effective basis with the aim of reducing the burden that excessive congestion imposes upon travellers and urban dwellers throughout the urban road network.

Keywords: traffic congestion, modeling, traffic management, travel time index

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Authors: Moh’d A. AL-Shboul

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Recently, and due to Arab spring and terrorism around the globe, pushing and driving most governments potentially to harmonize their border measures particularly the regional and an international transit trade within and among Customs Unions. The main purpose of this study is to investigate and provide an insight for monitoring and controlling the trade supply chain within and among different countries by using technological advancement (i.e. an electronic tracking system, etc.); furthermore, facilitate the local and intra-regional trade among countries through reviewing the recent trends and practical implementation of an electronic transit traffic and cargo that related to customs measures by introducing and supporting some case studies of several international and landlocked transit trade countries. The research methodology employed in this study was described as qualitative by conducting few interviews with managers, transit truck drivers, and traders and reviewing the related literature to collect qualitative data from secondary sources such as statistical reports, previous studies, etc. The results in this study show that Jordan and other countries around the globe that used an electronic tracking system for monitoring transit trade has led to a significant reduction in cost, effort and time in physical movement of goods internally and crossing through other countries. Therefore, there is no need to escort transit trucks by customs staff; hence, the rate of escort transit trucks is reduced by more than ninety percent, except the bulky and high duty goods. Electronic transit traffic has been increased; the average transit time journey has been reduced by more than seventy percent and has led to decrease in rates of smuggling up to fifty percent. The researcher recommends considering Jordan as regional and international office for tracking electronically and monitoring the transit trade for many considerations.

Keywords: electronic tracking system, facilitation system, regional and international corridor, supply chain security, transit trade

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Authors: Abdulah Fajar

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Electronic Procurement System has been implemented at government institution in Indonesia. This system has been developed centrally at Institution of National Procurement Policy (LKPP) and implemented autonomously at either local or national government institution. The lack of competency at many institution on Information Technology Management arise several major problems. The main concern of LKPP to local administrator is assured that the system is running normally and always be able to serve the needs of its users. Monitoring system has been identified as the one of solution to prevent the problems appeared. Monitoring system is developed using Simple Network Management Protocol (SNMP) and implemented at LKPP. There are two modules; Main Dashboard and Local Agent. Main Dashboard is intended for LKPP and Local Agent is intended to implement at local autonomous e-procurement system (LPSE). There are several resources that must be monitored such as computation, memory and network traffic. Agile paradigm is applied to this project to assure user and system requirement is met. The length of project is the one of reason why agile paradigm has been chosen. The system has been successfully delivered to LKPP.

Keywords: procurement system, SNMP, LKPP, LPSE

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Authors: Ezil Sam Leni, Shalen S.

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Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes.

Keywords: federated Learning, pothole detection, distributed framework, federated averaging

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

Procedia PDF Downloads 84

Authors: Balaji Ganesh Rajagopal, Subramanian Appavu alias Balamurugan, Ayyalraj Midhun Kumar, Krishnan Nallaperumal

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Intelligent Transportation System is an Emerging area to solve multiple transportation problems. Several forms of inputs are needed in order to solve ITS problems. Advanced Traveler Information System (ATIS) is a core and important ITS area of this modern era. This involves travel time forecasting, efficient road map analysis and cost based path selection, Detection of the vehicle in the dynamic conditions and Traffic congestion state forecasting. This Article designs and provides an algorithm for traffic data generation which can be used for the above said ATIS application. By inputting the real world traffic situation in the form of video sequences, the algorithm determines the Traffic density in terms of congestion, number of vehicles in a given path which can be fed for various ATIS applications. The Algorithm deduces the key frame from the video sequences and follows the Blob detection, Identification and Tracking using connected components algorithm to determine the correlation between the vehicles moving in the real road scene.

Keywords: traffic transportation, traffic density estimation, blob identification and tracking, relative velocity of vehicles, correlation between vehicles

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Authors: Kumar Manoj, Ramesh Kumar, Kumari Arti, Kumar Prashant

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Routing protocols have a central role in any mobile ad hoc network (MANET). There are many routing protocols that exhibit different performance levels in different scenarios. In this paper we compare AODV, DSDV, DSR and ZRP routing protocol in mobile ad hoc networks to determine the best operational conditions for each protocol. We analyses these routing protocols by extensive simulations in OPNET simulator and show that how pause time and the number of nodes affect their performance. In this study, performance is measured in terms of control traffic received, control traffic sent, data traffic received, data traffic sent, throughput, retransmission attempts.

Keywords: MANET, AODV, DSDV, DSR, ZRP

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Authors: Supriya H. S., Chandrakala B. M.

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Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach.

Keywords: MLHN, network traffic prediction

Procedia PDF Downloads 52

Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: data mining, k-means, road traffic accidents, Waze, Weka

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Authors: Ayman M. Mansour

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In this paper, we propose a multi-agent intelligent system that is used for monitoring the health conditions of elderly people. Monitoring the health condition of elderly people is a complex problem that involves different medical units and requires continuous monitoring. Such expert system is highly needed in rural areas because of inadequate number of available specialized physicians or nurses. Such monitoring must have autonomous interactions between these medical units in order to be effective. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goal of elderly monitoring. The agents in the developed system are equipped with intelligent decision maker that arms them with the rule-based reasoning capability that can assist the physicians in making decisions regarding the medical condition of elderly people.

Keywords: fuzzy logic, inference system, monitoring system, multi-agent system

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Authors: Andrey V. Timofeev

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The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems

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Authors: Shiva Kumar G.

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The present study is to know the level of noises emanated from vehicles in intersections located in urban areas using Sound Level Meter and the possibility of reducing noise levels through traffic flow optimization. The main objective is to study traffic noise level of the Intersections located at on-going metro construction activities and which are away from metro construction activities. To compare traffic noise level between stop phase, go phase and drive phase at the Intersections. To study the effect of traffic noise level of directional movement of traffic and variation in noise level during day and night times. The range of Noise level observed at intersections is between 60 to 105 decibel. The noise level of stop and drive phases were minimum and almost same where go phase had maximum noise level. By comparing noise level of directional movement of traffic, it has been noticed that Vijayanagar intersection has no significant difference in their noise level and all other intersection has a significant difference in their noise level. By comparing noise level of stop, go and drive phase it has been noticed that there was a significant difference in noise level during peak hours compared to off-peak hour. By comparing noise level between Metro and Non-Metro construction activity intersections it has been noticed that there was a significant difference in noise level. By comparing noise level during day and night times, significant differences in noise level were observed at all intersections.

Keywords: noise, metro and non-metro intersections, traffic flow optimization, stop-go and drive phase

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Authors: Andrey V. Timofeev

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This paper presents development results of the method of seismoacoustic activity monitoring based on usage vibrosensitive properties of optical fibers. Analysis of Rayleigh backscattering radiation parameters changes, which take place due to microscopic seismoacoustic impacts on the optical fiber, allows to determine seismoacoustic emission sources positions and to identify their types. Results of using this approach are successful for complex monitoring of railways.

Keywords: C-OTDR systems, monitoring of railways, Rayleigh backscattering, eismoacoustic activity

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Authors: Hong Geng, Zaiyu Fan

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With the acceleration of urbanization and motorization in China, the scale of cities and the travel distance of residents are constantly expanding, and the number of cars is continuously increasing, so the urban traffic problem is more and more serious. Traffic congestion, environmental pollution, energy consumption, travel safety and direct interference between traffic and other urban activities are increasingly prominent problems brought about by motorized development. This not only has a serious impact on the lives of the residents but also has a major impact on the healthy development of the city. The paper found that, in order to solve the development of motorization, a number of problems will arise; urban planning and traffic planning and design in residential planning often take into account the development of motorized traffic but neglects the demand for street life. This kind of planning has resulted in the destruction of the traditional communication space of the residential area, the pollution of noise and exhaust gas, and the potential safety risks of the residential area, which has disturbed the previously quiet and comfortable life of the residential area, resulting in the inconvenience of residents' life and the loss of street vitality. Based on these facts, this paper takes a residential area in Wuhan as the research object, through the actual investigation and research, from the perspective of micro-transformation analysis, combined with the concept of traffic micro-reconstruction governance. And research puts forward the residential traffic optimization strategies such as strengthening the interaction and connection between the residential area and the urban street system, street traffic classification and organization.

Keywords: micro-transformation, residential traffic, residents demand, traffic microcirculation

Procedia PDF Downloads 89

Authors: Christos Spandonidis, Stefanos Tsantilas, Elias Sedikos, Nektarios Galiatsatos, Fotios Giannopoulos, Panagiotis Papadopoulos, Nikolaos Demagos, Dimitrios Reppas, Christos Giordamlis

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In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO₂ and O₃ concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensor measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.

Keywords: distributed sensor system, environmental monitoring, Internet of Things, smart cities

Procedia PDF Downloads 112

Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

Procedia PDF Downloads 92

Authors: Ghazwan Al-Haji

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Traffic safety has become a major concern in Southeast Asia due to the increasing number of road accidents resulting in fatalities and injuries. Southeast Asia has one of the highest road traffic fatality rates in the world, in terms of both population and number of cars, nearly six times higher than the EU region. One of the reasons for this concerning trend is the increasing share of motorcycles as a form of transportation throughout Southeast Asia. The purpose of this study is to benchmark traffic safety situations and statistics in six countries in Asia and the EU, which Indonesia, Malaysia, Vietnam, Italy, Portugal and Sweden. The research will assess the priorities and causes of road accidents in the target nations. Further, the study will analyze the existing practices and promote best practices that can be implemented toward safer roads in Asian target countries. In order to achieve this goal, the study categorizes various factors contributing to traffic accidents and best practices into 4 pillars (Safer Behavior, Safer Roads, Safer Vehicles and Road Safety Management). The result of the study consists of a list of recommendations that can be applied by policymakers to promote safer roads in Asia towards 2030. The study is co-financed by the EU project ASIASAFE.

Keywords: traffic safety, ASIASAFE, Southeast Asia, EU project

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Authors: Inese Trusina, Elita Jermolajeva

Abstract:

Global challenges require a transition from the existing linear economic model to a model that will consider nature as a life support system for the developmenton the way to social well-being in the frame of the ecological economics paradigm. The article presentsbasic definitions for the development of formalized description of sustainabledevelopment monitoring. It provides examples of calculating the parameters of monitoring for the Baltic Sea region countries and their primary interpretation.

Keywords: sustainability, development, power, ecological economics, regional economic, monitoring

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