As one of the main development directions in the information field, big data technology can be applied for data mining, data analysis and data sharing in the massive data, and it created huge economic benefits by using the potential value of data. Meanwhile, it can provide decision-making strategies for social and economic development. Big data service architecture is a new service economic model that takes data as a resource, and it loads and extracts the data collected from different data sources. This service architecture provides various customized data processing methods, data analysis and visualization services for service consumers. This paper first briefly introduces the general big data service architecture and the technical processing framework, which covered data collection and storage. Next, we discuss big data processing and analysis according to different service requirements, which can present valuable data for service consumers. Then, we introduce the detailed cloud computing service system based on big data, which provides high performance solutions for large-scale data storage, processing and analysis. Finally, we summarize some big data application scenarios over various fields.

/pdf/big-data-service-architecture-a-survey-4svp7wdqza.pdf

Big Data Service Architecture: A Survey

Vehicles will soon be connected and will be interacting directly with each other and with the road infrastructure, bringing substantial benefits in terms of safety and traffic efficiency. The past decade has seen the development of different wireless access technologies for vehicle-to-everything (V2X) communications and an extensive set of related use cases have been drafted, each with its own requirements. In this paper, focusing on short-range communications, we analyze the technical and economic motivations that are driving the development of new road users' connectivity, discussing the international intentions to mandate on board devices for V2X communication. We also go in depth with the enabling wireless access technologies, from IEEE 802.11p to short-range Cellular-V2X and other complementary technologies, such as visible light communication (VLC) and millimeterWaves, up to hybrid communication and 5G. We conclude our survey with some performance comparison in urban realistic scenarios, underlying that the choice of the future enabling technology is not so easy to predict and mostly depends on mandatory laws at the international level.

A Survey on the Roadmap to Mandate on Board Connectivity and Enable V2V-Based Vehicular Sensor Networks.

In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the k-nearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and low-cost road maintenance.

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor

Investigation of clusters and injuries in pedestrian crashes using GIS in Changsha, China

Parameterized computation theory has developed rapidly over the last two decades. In theoretical computer science, it has attracted considerable attention for its theoretical value and significant guidance in many practical applications. We give an overview on parameterized algorithms for some fundamental NP-hard problems, including MaxSAT, Maximum Internal Spanning Trees, Maximum Internal Out-Branching, Planar (Connected) Dominating Set, Feedback Vertex Set, Hyperplane Cover, Vertex Cover, Packing and Matching problems. All of these problems have been widely applied in various areas, such as Internet of Things, Wireless Sensor Networks, Artificial Intelligence, Bioinformatics, Big Data, and so on. In this paper, we are focused on the algorithms’ main idea and algorithmic techniques, and omit the details of them.

/pdf/parameterized-algorithms-of-fundamental-np-hard-problems-a-oaen27lxfj.pdf

Parameterized algorithms of fundamental NP-hard problems: a survey

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

https://www.mdpi.com/1424-8220/19/9/2059/pdf

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections.

Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

One-way roads have potential for improving vehicle speed and reducing traffic delay. Suffering from dense road network, most of adjacent intersections' distance on one-way roads becomes relatively close, which makes isolated control of intersections inefficient in this scene. Thus, it is significant to develop coordinated control of multiple intersection signals on the one-way roads. This paper proposes a signal coordination control method that is suitable for one-way arterial roads. This method uses the cooperation technology of the vehicle infrastructure to collect intersection traffic information and share information among the intersections. Adaptive signal control system is adopted for each intersection in the coordination system, and the green light time is adjusted in real time based on the number of vehicles in queue. The offset and clearance time can be calculated according to the real-time traffic volume. The proposed method was verified with simulation results by VISSiM traffic simulation software. The results compared with other methods show that the coordinated control method proposed in this paper can effectively reduce the average delay of vehicles on the arterial roads and improve the traffic efficiency.

Coordinated control method of intersection traffic light in one-way road based on V2X

The invention discloses a traffic signal lamp green light duration determination device, method and system, and relates to the field of traffic information perception and control. The method comprisesthe following steps: calculating to obtain the maximum number of detained vehicles of a lane group by utilizing the obtained number of driving-in vehicles and the obtained number of driving-out vehicles; calculating a saturated headway by utilizing the obtained time interval; acquiring speed and position information of the vehicle of the vehicle-mounted unit; comparing to obtain a key traffic flow; and calculating the green light duration by using the key traffic flow, the preset starting loss time and the saturated headway. The state information of all vehicles in each lane of the intersection is obtained in real time by using the geomagnetic vehicle detectors arranged in each lane of the intersection and the vehicle-mounted units arranged on the vehicles; and the key traffic flow of thelane is determined according to the state information, and the green light duration is calculated by using the key traffic flow, the preset starting loss time and the saturated headway so that the problem of low vehicle passing efficiency caused by unreasonable intersection signal lamp timing can be effectively alleviated.

Traffic signal lamp green light duration determination device, method and system

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Papers

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections.

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

Coordinated control method of intersection traffic light in one-way road based on V2X

Traffic signal lamp green light duration determination device, method and system