Search results for: traffic data processing

Authors: Eric Kumar, Erica Cochran, Zhiang Zhang, Wei Liang, Ronak Mody

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The objective of this article was to create a modelling framework that exposes the marginal costs of shifting workloads across geographically distributed data-centers. Geographical distribution of internet services helps to optimize their performance for localized end users with lowered communications times and increased availability. However, due to the geographical and temporal effects, the physical embodiments of a service's data center infrastructure can vary greatly. In this work, we first identify that the sources of variances in the physical infrastructure primarily stem from local weather conditions, specific user traffic profiles, energy sources, and the types of IT hardware available at the time of deployment. Second, we create a traffic simulator that indicates the IT load at each data-center in the set as an approximator for user traffic profiles. Third, we implement a framework that quantifies the global level energy demands using building energy models and the traffic profiles. The results of the model provide a time series of energy demands that can be used for further life cycle analysis of internet services.

Keywords: data-centers, energy, life cycle, network simulation

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Authors: Ghazwan Al-Haji

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Most accidents occur in urban areas, and the most related casualties are vulnerable road users (pedestrians and cyclists). The traffic calming measures (TCMs) are widely used and considered to be successful in reducing speed and traffic volume. However, TCMs create unwanted effects include: noise, emissions, energy consumption, vehicle delays and emergency response time (ERT). Different vertical and horizontal TCMs have been already applied nationally (Sweden) and internationally with different impacts. It is a big challenge among traffic engineers, planners, and policy-makers to choose and priorities the best TCMs to be implemented. This study will assess the existing guidelines for TCMs in relation to safety and ERT with focus on data from Norrkoping city in Sweden. The expected results will save lives, time, and money on particularly Swedish Roads. The study will also review newly technologies and how they can improve safety and reduce ERT.

Keywords: traffic calming measures, traffic safety, delay time, vulnerable road users

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Authors: Anna Dolinayova, Jozef Danis, Juraj Camaj

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The report deals with comparison of traffic accidents in Slovak republic in road and rail transport since year 2009 until 2014, with evaluation of external costs and consequently with the possibilities of their internalization. The results of road traffic accidents analysis are realized in line with after-effects they have caused; in line with main cause, place of origin (within or out of town) and in accordance to age of people they were killed or hard, eventually easy injured in traffic accidents. Evaluation of individual after-effects is carried in terms of probability of traffic accidents occurrence.

Keywords: external costs, traffic accident, rail transport, road transport

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Authors: Lei Guo, Xu Zhang, Weigang Hou

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Since the traditional network is closed and it has no architecture to create applications, it has been unable to evolve with changing demands under the rapid innovation in services. Additionally, due to the lack of the whole network profile, the quality of service cannot be well guaranteed in the traditional network. The Software Defined Network (SDN) utilizes global resources to support on-demand applications/services via open, standardized and programmable interfaces. In this paper, we implement the traffic grooming application under a real SDN environment, and the corresponding analysis is made. In our SDN: 1) we use OpenFlow protocol to control the entire network by using software applications running on the network operating system; 2) several virtual switches are combined into the data forwarding plane through Open vSwitch; 3) An OpenFlow controller, NOX, is involved as a logically centralized control plane that dynamically configures the data forwarding plane; 4) The traffic grooming based on SDN is demonstrated through dynamically modifying the idle time of flow entries. The experimental results demonstrate that the SDN-based traffic grooming effectively reduces the end-to-end delay, and the improvement ratio arrives to 99%.

Keywords: NOX, OpenFlow, Software Defined Network (SDN), traffic grooming

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Authors: Gregory Kelly, Gary Chai, Sittampalam Manoharan, Deborah Delaney

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A Structural Number (SN) can be calculated for a road pavement from the properties and thicknesses of the surface, base course, sub-base, and subgrade. Historically, the cost of collecting structural data has been very high. Data were initially collected using Benkelman Beams and now by Falling Weight Deflectometer (FWD). The structural strength of pavements weakens over time due to environmental and traffic loading factors, but due to a lack of data, no structural deterioration curve for pavements has been implemented in a Pavement Management System (PMS). International Roughness Index (IRI) is a measure of the road longitudinal profile and has been used as a proxy for a pavement’s structural integrity. This paper offers two conceptual methods to develop Pavement Structural Deterioration Curves (PSDC). Firstly, structural data are grouped in sets by design Equivalent Standard Axles (ESA). An ‘Initial’ SN (ISN), Intermediate SN’s (SNI) and a Terminal SN (TSN), are used to develop the curves. Using FWD data, the ISN is the SN after the pavement is rehabilitated (Financial Accounting ‘Modern Equivalent’). Intermediate SNIs, are SNs other than the ISN and TSN. The TSN was defined as the SN of the pavement when it was approved for pavement rehabilitation. The second method is to use Traffic Speed Deflectometer data (TSD). The road network already divided into road blocks, is grouped by traffic loading. For each traffic loading group, road blocks that have had a recent pavement rehabilitation, are used to calculate the ISN and those planned for pavement rehabilitation to calculate the TSN. The remaining SNs are used to complete the age-based or if available, historical traffic loading-based SNI’s.

Keywords: conceptual, pavement structural number, pavement structural deterioration curve, pavement management system

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Authors: Nicanor Garcia Alvarez, Belarmino Adenso-Diaz, Laura Calzada Infante

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In recent years, researchers have shown an interest in modelling maritime traffic as a complex network. In this paper, we propose a bipartite weighted network to model maritime traffic and detect port maritime communities. The bipartite weighted network considers two different types of nodes. The first one represents Spanish ports, while the second one represents the countries with which there is major import/export activity. The flow among both types of nodes is modeled by weighting the volume of product transported. To illustrate the model, the data is segmented by each type of traffic. This will allow fine tuning and the creation of communities for each type of traffic and therefore finding similar ports for a specific type of traffic, which will provide decision-makers with tools to search for alliances or identify their competitors. The traffic with the greatest impact on the Spanish gross domestic product is selected, and the evolution of the communities formed by the most important ports and their differences between 2019 and 2009 will be analyzed. Finally, the set of communities formed by the ports of the Spanish port system will be inspected to determine global similarities between them, analyzing the sum of the membership of the different ports in communities formed for each type of traffic in particular.

Keywords: bipartite networks, competition, infomap, maritime traffic, port communities

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Authors: Daniel Benson, Yundan Gong, Hannah Kirk

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Advancement in national language processing techniques has led to increased data processing speeds, and reduced the need for cumbersome, manual data processing that is often required when processing data from multilateral organizations for specific purposes. As such, using named entity recognition (NER) modeling and the Organisation of Economically Developed Countries (OECD) Creditor Reporting System database, we present the first geotagged dataset of OECD donor Official Development Assistance (ODA) projects on a global, subnational basis. Our resulting data contains 52,086 ODA projects geocoded to subnational locations across 115 countries, worth a combined $87.9bn. This represents the first global, OECD donor ODA project database with geocoded projects. We use this new data to revisit old questions of how ‘well’ donors allocate ODA to the developing world. This understanding is imperative for policymakers seeking to improve ODA effectiveness.

Keywords: international aid, geocoding, subnational data, natural language processing, machine learning

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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: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

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This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: K. Parandhama Gowd

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The aim of this research paper is to conceptualize, discuss, analyze and propose alternate design methodologies for futuristic Black Box for flight safety. The proposal also includes global networking concepts for real time surveillance and monitoring of flight performance parameters including GPS parameters. It is expected that this proposal will serve as a failsafe real time diagnostic tool for accident investigation and location of debris in real time. In this paper, an attempt is made to improve the existing methods of flight data recording techniques and improve upon design considerations for futuristic FDR to overcome the trauma of not able to locate the block box. Since modern day communications and information technologies with large bandwidth are available coupled with faster computer processing techniques, the attempt made in this paper to develop a failsafe recording technique is feasible. Further data fusion/data warehousing technologies are available for exploitation.

Keywords: flight data recorder (FDR), black box, diagnostic tool, global networking, cockpit voice and data recorder (CVDR), air traffic control (ATC), air traffic, telemetry, tracking and control centers ATTTCC)

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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

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Authors: Hamed Rashid Sarand, Kemal Selçuk Öğüt

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Speed is one of the fundamental variables of road traffic flow that stands as an important evaluation criterion for traffic analyses in several aspects. In particular, varieties of speed variable, such as average speed, free flow speed, optimum speed (capacity speed), acceleration/deceleration speed and so on, have been explicitly considered in the analysis of not only road safety but also road capacity. In the purpose of realizing 'road speed – maximum speed difference across lanes' and 'road flow rate – maximum speed difference across lanes' relations on freeway traffic, this study presents a case study conducted on a basic freeway segment of O-2 in Istanbul. The traffic data employed in this study have been obtained from 5 remote traffic microwave sensors operated by Istanbul Metropolitan Municipality. The study stretch is located between two successive freeway interchanges: Ümraniye and Kavacık. Daily traffic data of 4 years (2011-2014) summer months, July and August are used. The speed data are analyzed into two main flow areas such as uncongested and congested flows. In this study, the regression analyses were carried out in order to examine the relationship between maximum speed difference across lanes and road speed. These investigations were implemented at uncongested and congested flows, separately. Moreover, the relationship between maximum speed difference across lanes and road flow rate were evaluated by applying regression analyses for both uncongested and congested flows separately. It is concluded that there is the moderate relationship between maximum speed difference across lanes and road speed in 50% cases. Additionally, it is indicated that there is the moderate relationship between maximum speed difference across lanes and road flow rate in 30% cases. The maximum speed difference across lanes decreases as the road flow rate increases.

Keywords: maximum speed difference, regression analysis, remote traffic microwave sensor, speed differentiation, traffic flow

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Authors: Bin Wang, Jian Kang

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After years of development, the study of soundscape has been refined to the types of urban space and building. Traffic complex takes traffic function as the core, with obvious design features of architectural space combination and traffic streamline. The acoustic environment is strongly characterized by function, space, material, user and other factors. Traffic complex integrates various functions of business, accommodation, entertainment and so on. It has various forms, complex and varied experiences, and its acoustic environment is turned rich and interesting with distribution and coordination of various functions, division and unification of the mass, separation and organization of different space and the cross and the integration of multiple traffic flow. In this study, it made field recordings of each space of various traffic complex, and extracted and analyzed different acoustic elements, including changes in sound pressure, frequency distribution, steady sound source, sound source information and other aspects, to make cluster analysis of each independent traffic complex buildings. It divided complicated traffic complex building space into several typical sound space from acoustic environment perspective, mainly including stable sound space, high-pressure sound space, rhythm sound space and upheaval sound space. This classification can further deepen the study of subjective evaluation and control of the acoustic environment of traffic complex.

Keywords: soundscape, traffic complex, cluster analysis, classification

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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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

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Authors: Lan Nguyen, Kien Le Tan, Bao Nguyen Pham Gia

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Due to the high cost, specialized dynamic measurement devices for industrial lands are difficult for many colleges to equip for hands-on teaching. This study connects a dynamic measurement sensor and receiver utilizing an inexpensive Raspberry Pi 4 board, some 24-bit ADC circuits, a geophone vibration sensor, and embedded Python open-source programming. Gather and analyze signals for dynamic measuring, ground vibration monitoring, and structure vibration monitoring. The system may wirelessly communicate data to the computer and is set up as a communication node network, enabling real-time monitoring of background vibrations at various locations. The device can be utilized for a variety of dynamic measurement and monitoring tasks, including monitoring earthquake vibrations, ground vibrations from construction operations, traffic, and vibrations of building structures.

Keywords: sensors, FFT, signal processing, real-time data monitoring, ground propagation wave, python, raspberry Pi 4

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Authors: Qiang Zhang, Xiaojian Hu

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Aiming at the problem of the existing real-time evaluation methods for traffic safety status, a video information-based urban road section traffic safety status evaluation method was established, and the rapid detection method of traffic flow parameters based on video information is analyzed. The concept of the speed dispersion of the road section that affects the traffic safety state of the urban road section is proposed, and the method of evaluating the traffic safety state of the urban road section based on the speed dispersion of the road section is established. Experiments show that the proposed method can reasonably evaluate the safety status of urban roads in real-time, and the evaluation results can provide a corresponding basis for the traffic management department to formulate an effective urban road section traffic safety improvement plan.

Keywords: intelligent transportation system, road traffic safety, video information, vehicle speed dispersion

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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: Muhammad Naziru Yahaya, Buhari Samaila, Nasiru Abubakar

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Noise pollution caused by vehicular movement in urban cities has reached alarming proportions due to continuous increases in vehicles and industrialization. Traffic noise causes deafness, annoyance, and other health challenges. According to World Health Organization recommends 60Db daytime sound levels and 40db night time sound levels in hospitals, schools, and other residential areas. Measurements of traffic noise were taken at six different locations of selected healthcare facilities at Birnin Kebbi (Sir Yahaya Memorial Hospital and Federal Medical Centre Birnin Kebbi). The data was collected in the vicinity of hospitals using the slow setting of the device and pointed at noise sources. An integrated multifunctional sound level GM1352, KK2821163 model, was used for measuring the emitted noise and temperatures. The data was measured and recorded at three different periods of the day 8 am – 12 pm, 3 pm – 6 pm, and 6 pm – 8:30 pm, respectively. The results show that a fair traffic flow producing an average sound level in the order of 38db – 64db was recorded at GOPDF, amenityF, and ante-natalF. Similarly, high traffic noise was observed at GOPDS, amenityS, and Fati-LamiS in the order of 52db – 78db unsatisfactory threshold for human hearing.

Keywords: amenities, healthcare, noise, hospital, traffic

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Authors: Xiang Li, Jian-Qiao Sun

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As the crucial component of city traffic network, intersections have significant impacts on urban traffic performance. Despite of the rapid development in transportation systems, increasing traffic volumes result in severe congestions especially at intersections in urban areas. Effective regulation of vehicle flows at intersections has always been an important issue in the traffic control system. This study presents a multi-objective optimization method at intersections with cellular automata to achieve better traffic performance. Vehicle conflicts and pedestrian interference are considered. Three categories of the traffic performance are studied including transportation efficiency, energy consumption and road safety. The left-turn signal type, signal timing and lane assignment are optimized for different traffic flows. The multi-objective optimization problem is solved with the cell mapping method. The optimization results show the conflicting nature of different traffic performance. The influence of different traffic variables on the intersection performance is investigated. It is observed that the proposed optimization method is effective in regulating the traffic at the intersection to meet multiple objectives. Transportation efficiency can be usually improved by the permissive left-turn signal, which sacrifices safety. Right-turn traffic suffers significantly when the right-turn lanes are shared with the through vehicles. The effect of vehicle flow on the intersection performance is significant. The display pattern of the optimization results can be changed remarkably by the traffic volume variation. Pedestrians have strong interference with the traffic system.

Keywords: cellular automata, intersection, multi-objective optimization, traffic system

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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: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Ujjal Chattaraj, M. Raviteja, Chaitanya Aemala

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Over the years vehicular traffic has been given priority over pedestrian traffic. With the increase of population in cities, pedestrian traffic is increasing day by day. Pedestrian safety has become a matter of concern for the Traffic Engineers. Pedestrian comfort is primary important for the Engineers who design different pedestrian facilities. Pedestrian comfort and safety can be measured in terms of different level of service (LOS) of the facilities. In this study video data on pedestrian movement have been collected from different railway foot over bridges (FOB) in India. The level of service of those facilities has been analyzed. A cellular automata based model has been formulated to mimic the route choice behaviour of the pedestrians on the foot over bridges.

Keywords: cellular automata model, foot over bridge, level of service, pedestrian

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Authors: Iraj Mantegh, Charles Vidal

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This paper considers Unmanned Aircraft Systems (UAS) Traffic Management system from a service-oriented architecture point of view and proposes a framework for its performance requirements. The architecture specifically considered is related to the Remotely Piloted Aircraft Systems (RPAS) Traffic Management that is adapted by Transport Canada, in close collaboration with other jurisdictions in the United States and European Union. First, the functional performances for each individual service that comprises the Traffic Management system are defined here, and then quantitative parameters to gauge the performances of individual services are proposed.

Keywords: UAV, drone, UAS, traffic management, UTM

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Authors: A. A. Balkhi, G. M. Mir, Javid A. Sheikh

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To cater the demands of increasing traffic with new applications the cellular mobile networks face new changes in deployment in infrastructure for making cellular networks heterogeneous. To reduce overhead processing the densely deployed cells require smart behavior with self-organizing capabilities with high adaptation to the neighborhood. We propose self-organization of unused resources usually excessive unused channels of neighbouring cells with densely populated cells to reduce handover failure rates. The neighboring cells share unused channels after fulfilling some conditional candidature criterion using threshold values so that they are not suffered themselves for starvation of channels in case of any abrupt change in traffic pattern. The cells are classified as ‘red’, ‘yellow’, or ‘green’, as per the available channels in cell which is governed by traffic pattern and thresholds. To combat the deficiency of channels in red cell, migration of unused channels from under-loaded cells, hierarchically from the qualified candidate neighboring cells is explored. The resources are returned back when the congested cell is capable of self-contained traffic management. In either of the cases conditional sharing of resources is executed for enhanced traffic management so that User Equipment (UE) is provided uninterrupted services with high Quality of Service (QoS). The fuzzy logic-based simulation results show that the proposed algorithm is efficiently in coincidence with improved successful handoffs.

Keywords: candidate cell, channel sharing, fuzzy logic, handover, small cells

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Authors: Tomasz Komornicki

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The primary aim of the presentation is to try to model border traffic and, at the same time to explain on which economic variables the intensity of border traffic depended in the long term. For this purpose, long series of traffic data on the Polish borders were used. Models were estimated for three variants of explanatory variables: a) for total arrivals and departures (total movement of Poles and foreigners), b) for arrivals and departures of Poles, and c) for arrivals and departures of foreigners. Each of the defined explanatory variables in the models appeared as the logarithm of the natural number of persons. Data from 1994-2017 were used for modeling (for internal Schengen borders for the years 1994-2007). Information on the number of people arriving in and leaving Poland was collected for a total of 303 border crossings. On the basis of the analyses carried out, it was found that one of the main factors determining border traffic is generally differences in the level of economic development (GDP) and the condition of the economy (level of unemployment) and the degree of border permeability. Also statistically significant for border traffic are differences in the prices of goods (fuels, tobacco, and alcohol products) and services (mainly basic ones, e.g., hairdressing services). Such a relationship exists mainly on the eastern border (border traffic determined largely by differences in the prices of goods) and on the border with Germany (in the first analysed period, border traffic was determined mainly by the prices of goods, later - after Poland's accession to the EU and the Schengen area - also by the prices of services). The models also confirmed differences in the set of factors shaping the volume and structure of border traffic on the Polish borders resulting from general geopolitical conditions, with the year 2007 being an important caesura, after which the classical population mobility factors became visible. The results obtained were additionally related to changes in traffic that occurred as a result of the CPOVID-19 pandemic and as a result of the Russian aggression against Ukraine.

Keywords: border, modal structure, transport, Ukraine

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Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

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Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

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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: Abhijitsinh Gohil, Kaushal Wadhvaniya, Kuldipsinh Jadeja

Abstract:

Road Safety is a multi-sectoral and multi-dimensional issue. An effective model can assess the risk associated with highway safety. A questionnaire survey is very essential to identify the events or activities which are causing unsafe condition for traffic on an urban highway. A questionnaire of standard questions including vehicular, human and infrastructure characteristics can be made. Responses from the age wise group of road users can be taken on field. Each question or an event holds a specific risk weightage, which contributes in creating an inappropriate and unsafe flow of traffic. The probability of occurrence of an event can be calculated from the data collected from the road users. Finally, the risk score can be calculated by considering the risk factor and the probability of occurrence of individual event and addition of all risk score for the individual event will give the total risk score of a particular road. Standards for risk score can be made and total risk score can be compared with the standards. Thus road can be categorized based on risk associated and traffic safety on it. With this model, one can assess the need for traffic safety improvement on a given road, and qualitative data can be analysed.

Keywords: probability of occurrence, questionnaire, risk factor, risk score

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