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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

Unmanned aircraft systems (UASs) are a promising new mode of transportation for cargo delivery. Although control, navigation, and communication technologies are becoming available on individual flight units, system-level motion management for dense UAS traffic remains an open question. This paper presents a motion-planning model based on nonlinear optimization techniques to centrally coordinate paths for all vehicles traversing a shared two-dimensional (2D) space. An exact bound is derived to characterize the discrete-time separation constraints, and a set of new metrics is proposed to measure 2D traffic flow efficiency. The grand goal is to make all vehicles that come under dispatch reach their respective destinations quickly and efficiently while maintaining a safe intervehicle separation at all times. This infinite-horizon operational problem is formulated as a nonlinear nonconvex optimization model that must be solved in a progressive, receding-horizon fashion. To ensure feasibility and overcome path deadlocks attributed to local optima, a series of heuristic measures are developed. By pivoting on artfully coined intermediate feasibility, the algorithm is able to circumvent imminent deadlocks in a predictive manner and progressively construct the solution with guaranteed feasibility. Simulation experiments are performed at various traffic density levels to generate useful insights for airspace regulators and traffic managers.

Predicting traffic flow is one of the fundamental issue to smart cities. However it is still challenging in vehicular cyber-physical systems because of ever-increasing urban traffic data. Most previous models for traffic flow prediction suffers the problem with variance error depends on time and location. In this paper, a novel graph convolution network (GCN) approach is proposed for predicting citywide traffic flow. Our key idea is to introduce spatial and temporal attention to the GCN model to lessen the impact of urban data complexity. A framework of flow-based graph convolutional network is established to improve traffic flow prediction while investigating the spatial and temporal correlation of traffic flow. We further propose practical strategies that efficiently learn parameters of the model. Experimental results demonstrate that the proposed approach to traffic flow prediction outperforms state-of-the-art approaches.

Among traffic flow analysis, there are many methods presented for microscopic and mesoscopic traffic flow in past decades. However, the methods for macroscopic traffic flow analysis are relatively few. That is because the macroscopic traffic flow analysis needs more data and computational resources. In this paper, we focus on macroscopic traffic flow analysis using massive traffic data. A new method, traffic speed cloud maps, is proposed. It can present the traffic states in a large scale, accurately. Also, it can describe the variations of the congestion area. More importantly, with traffic speed cloud maps, people can capture the forming and degrading processes of the congestion and investigate the operation rules of macroscopic traffic flow. In order to evaluate the effectiveness of traffic speed cloud maps, 14 786 073 traffic samples are taken to perform experiments. Experimental results show that traffic speed cloud maps can present the traffic state of large areas accurately, and help us to investigate the variation of macroscopic traffic flow states intuitively and efficiently.

Appearance and motion, which are complementary, account for a dominant proportion of visual information. We propose to synergize them using a low-rank representation framework for the estimation and analysis of traffic flow. Taking advantage of the downward-looking camera configuration, we do the processing only on the measure line, called virtual gantry, instead of dealing with the whole frame, resulting in much improved efficiency. Enforcing the low-rank constraint on the spatiotemporal image which is generated via stacking pixels on virtual gantry over time, we introduce the block-sparse robust principal component analysis algorithm, in which the motion cue is leveraged to highlight the foreground and realize vehicle detection with high accuracy. The motion flow is further exploited for size normalization to classify vehicles into lite, small, medium, and large categories. Benefiting from the low-rank representation, our method is parameter insensitive, robust to illumination changes, and requires no training. We perform extensive experiments on the 24/7 videos collected over the highways in China and Singapore, obtaining nearly 100% accuracy. Meanwhile, insightful observations on the obtained traffic information are given, which could be very valuable to the users, especially to the traffic management sectors.

In this paper, we propose a traffic flow estimation system for intelligent highway surveillance applications under rainy conditions. Major contributions of the proposed system include flexible feature extraction, robust estimation with adaptive clustering, and effective graph-based traffic flow mapping model. To detect rain-drop tampered scenes, features are extracted via salient region detection and block segmentation. For traffic flow estimation, lane directions are automatically detected for daytime scenes. Foreground moving edges accumulated along the traffic flow direction are used as features. We utilize an adaptive clustering algorithm to estimate vehicle count for each frame. For nighttime scenes, statistical features are extracted from the segmented blocks, and regression models are applied to generate per-frame vehicle count. Finally, an effective graph-based mapping method is incorporated to map the vehicle count sequences to per-minute traffic flow. The accuracy of the traffic flow analysis is satisfying even when the cameras are seriously affected by rain. The experiments demonstrate that the proposed system can effectively analyze traffic flow under rainy conditions for highway surveillance cameras.

Traffic flow analysis in complex areas (e.g., intersections and roundabouts) plays an important part in the development of intelligent transportation systems. Among several methods for analyzing traffic flow, image and video processing has emerged as a potential approach to extract the movements of vehicles in urban areas. In this regard, this study develops a traffic flow analysis method, which focuses on extracting traffic information based on Video Surveillance (CCTV) for turning volume estimation at complex intersections, using advanced computer vision technologies. Specifically, state-of-the-art techniques such as Yolo and DeepSORT for the detection, tracking, and counting of vehicles have enveloped to estimate the road traffic density. Regarding the experiment, we collected data from CCTV in an urban area during one day to evaluate our method. The evaluation shows the proposing results in terms of detecting, tracking and counting vehicles with monocular videos.

By 2050, almost 70% of the population will live in cities. As the population grows, travel demand increases and this might affect air quality in urban areas. Traffic is among the main sources of pollution within cities. Therefore monitoring urban traffic means not only identifying congestion and managing accidents but also preventing the impact on air pollution. Urban traffic modeling and analysis is part of the advanced traffic intelligent management technologies that has become a crucial sector for smart cities. Its main purpose is to predict congestion states of a specific urban transport network and propose improvements in the traffic network that might result into a decrease of the travel times, air pollution and fuel consumption.This paper describes the implementation of an urban traffic flow model in the city of Modena based on real traffic sensor data. This is part of a wide European project that aims at studying the correlation among traffic and air pollution, therefore at combining traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness about air quality where necessary.

Highway bridge networks (HBN) are one of the critical components of a transportation system that supports the operation of modern society. The traffic-carrying capacity of a transportation system is essential for emergency responses in a catastrophic seismic hazard. In this article, a methodology is proposed to assess the regional seismic performance of HBN considering the post-disaster traffic demand of a transportation system in emergency conditions. First, the framework of the methodology is introduced. Then, topological properties and vulnerability analyses are conducted to understand the connectivity of the HBN and the traffic-carrying capacity of the damaged bridge networks. In addition, traffic flow analysis is employed to consider the traffic situation in an emergency condition. Then, the entropy-right method is adopted to integrate these factors and to assess the importance of each bridge link in an emergency condition. Last, the seismic performance of HBN is also evaluated considering th...

We discuss coupling conditions for the p-system in case of a transition from supersonic states to subsonic states. A single junction with adjacent pipes is considered where on each pipe the gas flow is governed by a general p-system. By extending the notion of demand and supply known from traffic flow analysis we obtain a constructive existence result of solutions compatible with the introduced conditions.

This paper proposes a host behavior based darknet traffic decomposition approach to identifying groups of malicious events from massive historical darknet traffic. In this approach, we segment traffic flows from captured darknet data, distinguish scan from non-scan flows, and categorize scans according to scan width spreads. Consequently, event groups are appraised by applying the criterion that malicious events generated by the same attacker or malicious software should have similar average packet delay, AvgDly. We have applied the proposed approach to 12 months darknet traffic data for malicious events grouping. As a result, several large scale event groups are discovered on host behavior in the category of port scan, IP scan and hybrid scan, respectively.

The expansion of road infrastructure, in spite of increasing congestion levels, faces severe restrictions from all sorts: economical, environmental, social, or technical. An efficient and, usually, less expensive alternative to improve mobility and the use of available infrastructure is the adoption of traffic management. A particular case of interest occurs when inbound and outbound traffic on a given facility is unbalanced throughout the day. This scenario may benefit of a lane management strategy called tidal flow (or reversible) lane control, in which case the direction of one or more contraflow buffer lanes is reversed according to the needs of each direction. This paper proposes a simple and practical real-time strategy for efficient motorway tidal flow lane control. A state-feedback switching policy based on the triangular fundamental diagram, that requires only aggregated measurements of density, is adopted. A theoretical analysis based on the kinematic wave theory shows that the strategy provides a Pareto-optimal solution. Microsimulations using empirical data from the A38(M) Aston Expressway in Birmingham, UK, are used to demonstrate the operation of the proposed strategy. The robustness of the switching policy to parameter variations is demonstrated by parametric sensitivity analysis. Simulation results confirm an increase of motorway throughput and a smooth operation for the simulated scenarios.

Cost effective, smooth multimedia streaming to the remote customer through the distributed “video on demand” architecture is the most challenging research issue over the decade. The hierarchical system design is used for distributed network to satisfy more requesting users. The distributed hierarchical network system contains all the local and remote storage multimedia servers. The hierarchical network system is used to provide continuous availability of the data stream to the requesting customer. In this work, we propose a novel data stream that handles the methodology for reducing the connection failure and smooth multimedia stream delivery to the remote customer. The proposed session based single-user bandwidth requirement model presents the bandwidth requirement for any interactive session like pause, move slowly, rewind, skip some of the frame, and move fast with some constant number of frames. The proposed session based optimum storage finding algorithm reduces the search hop count towards the remote storage-data server. The modeling and simulation result shows the better impact over the distributed system architecture. This work presents the novel bandwidth requirement model at the interactive session and gives the trade-off in communication and storage costs for different system resource configurations.

This paper focuses on quantifying the effect of rainfall and temperature intensities on urban traffic characteristics at peak and off-peak periods respectively using traffic data from Greater Manchester, UK, as case study. Three broader issues are addressed: (1) the impact of rainfall on urban traffic; (2) the impact of rainfall intensity on traffic flow parameters at peak and off-peak periods respectively; (3) the impact of atmospheric temperature level on peak and off-peak urban traffic. Our contribution arises both from the combination of factors included in the study as well as distinct analyses of peak and off-peak traffic data. This is the first study undertaken in a real urban environment with reduced operating speed (30 mph), and can provide urban traffic policymakers with crucial information that can be used to modify or develop traffic planning decisions in order to maximize traffic network utilization.

In the face of imbalance between supply and demand of parking facilities in urban central business district (CBD) areas and the increasingly prominent parking problems, it has become necessary to develop and construct underground parking systems so as to alleviate ground traffic pressure and reduce traffic congestion. Underground parking system planning includes the following main components: system level division, parking scale determination, entrance and exit settings, and internal flow organization of the system. By analyzing the current parking characteristics of the CBD in Linping New City of Hangzhou, and based on indicators such as base conditions of the underground parking system, the total number of parking berths and the number of parking units, a scoring standard is used to determine a grading method for the underground parking system. According to different levels of underground parking system, and based on the number of system parking units and the number of vehicles entering and leaving the system during peak hours, the principle of setting the number of entrances and exits of underground parking system is clearly defined. According to the layout pattern of underground parking system and the internal traffic flow analysis of the vehicles entering and leaving the system, the setting of underground vehicle lanes and underground parking units in the system is taken into account to determine the internal traffic organization of underground parking system. This research provides a reference for the planning and design of urban underground parking systems.

The aim of the article is the introduction of the video system GEM CDU 2605 Zeus and the traffic flow analysis of the selected section, and specifically to show the extra value of the device. The respective video system is located close to the Zilina city centre (northern Slovakia). Based on the evaluated statistical data obtained from the video system it is possible to design the traffic precautions in the specific section, or to install the required technical equipment for traffic moderation. The use of these systems can increase the traffic safety and fluency including the traffic calming, traffic accidents number decrease, engine exhaust emissions decrease and an increase of the living standard in the towns and villages. Results of analysis and its evaluation and comparison are presented in the final part of this paper.

This demo presents the MONROE distributed platform and how it can be used to implement measurement and assessment experiments with operational mobile broadband networks (MBBs). MONROE provides registered experimenters with open access to hundreds of nodes, distributed over several European countries and equipped with multiple MBB connections, and a backend system that collects the measurement results. Experiments are scheduled through a user-friendly web client, with no need to directly access the nodes. The platform further embeds tools for real-time traffic flow analysis and a powerful visualization tool.

In this paper, we present a system to detect and count the number of vehicles in traffic surveillance videos based on Fast Region-based Convolutional Network (Fast R-CNN). Fast R-CNN is a state-of-the-art object detection network, which takes an entire image and a set of object proposals as input, produces bounding-box positions with probability estimates over object classes as output. First, we fine-tune a pre-trained Fast R-CNN net with images captured from traffic videos for accuracy improvement. Second, we define a series of rules of bounding boxes screening for vehicle counting. The proposed system takes around 3 seconds per image to count vehicles on a GTX970 GPU, and then records the corresponding number of vehicles into a database for traffic flow analysis. Experimental results demonstrated that the proposed system can provide significant improvements on the detection accuracy. In addition, experiments on challenging videos with occlusions or full of vehicles show that the proposed system works effectively.

Best Demo Award. Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool. TRUE pub

In recent time, traffic congestion problem has become one of the most costly, both monetary and environmentally, problems in many cities all over the world. It also causes valuable time loss of citizens. Many experts suggested that the foremost step to mitigate the congestion problem is to identify traffic congestion characteristics. This study has been conducted to measure the intensity of traffic congestion of Pabna town. Traffic volume survey and spot speed study were the main methodological approaches to conduct the study. The data collected has been analyzed through the use of different statistical computer packages. Different traffic flow theories and models have also been used to calculate traffic flow parameters that expose traffic characteristics. The study has explored that auto-rickshaw, cycle rickshaw, bicycle, and motorcycle are the popular traffic mode and traffic flow becomes so intensive during the morning, noon and evening hours. It has also revealed that Ataikula road is the most congestion-prone route than A.H road and the level of services of all intersections is F except traffic morr which provided a comparatively better level of services to the travelers of the town. The study has also identified the major problems associated with different road sections having poor level of services.

Traffic congestion on the roads is mainly the result of overcrowding and this phenomenon happens when a great number of vehicles storm the road, resulting in the disruption of the smooth traffic flow. This greatly affects the daily routines of the people. Not to mention the time that is wasted while a person feels stranded in such situation and it results in the loss of productivity, also deteriorates the societal behavior to a certain extent and have adverse effects on the economy. The natural calamities add to the miseries. It becomes very difficult to manage the traffic flow in situations when there are flash floods or other accidents. Therefore the trend of the traffic seems very unpredictable. The real-time information and the past data are deemed as the significant inputs for the predictive analysis. Modern day researchers perform the predictive analysis using the simulations as it does not seems to have any accurate and exact predictive model, mainly because of the higher complexity and the perplexing situation the researchers face while performing the analysis. Open Traffic seems to be a viable option, as it is an open source and can be linked with the Open Street. This research targets to study and understand the Open Traffic platform. In this regard the real-time traffic flow pattern in Kuala Lumpur area was successfully been extracted and the analysis was performed using Open Traffic. It was observed and deduced from the results that Kuala Lumpur faces congestion on every major avenue, junction or intersection it mostly owes to the offices and the economic and commercial centers during the peak hours. Some avenues experience the congestion problem due to the tourism.