Parallel and Cloud Computing Research, Volume 3 2015 www.seipub.org/pccr doi: 10.14355/pccr.2015.03.002
Research Report on Mass Rule Parallel Processing Technology in Intelligent Traffic System Xu Xiaoping1, Yin Yingyu2, Yan Junhu 3 School of Electronic and Information, Guangdong Polytechnic Normal University, No.293, Zhongshan Highway, Guangzhou, P .R. of China *1
cathy.xu@163.com; 2yinlizheng@21cn.com; 3yanjh8@sina.com
Abstract This article is to introduce the main functions of intelligent traffic information management system based on mass rule parallel processing technology, and the key technologies thereinto, including mass rule description model, mass rule network and optimization, mass rule processing measure and mass rule parallel processing mechanism. Keywords Mass Rule; Parallel Processing; Intelligent Traffic System
Introduction Intelligent traffic information management system based on mass rule parallel processing technology is established on the basis of the perfect intelligent traffic sensor network. Traffic supervisors and participants provide real‐time traffic information for traffic information center by sensors and transmission devices installed on roads, vehicles, transfer stations, parking lots and meteorological centers. After receiving these information, the system analyses, processes, issues and provides real‐time road traffic information, public traffic information, transfer information, traffic meteorological information, parking lot equipment information and other information related to traffic for traffic participants, and creates complete intelligent traffic information management system, mainly including: 1) Traffic Information Management Sub‐System, 2) Traffic Information Service Sub‐System, 3) Public Traffic Information Issuance Sub‐System, 4) Vehicle Information Management Sub‐System, 5) Freight and Equipment Information Management Sub‐System, 6) Road and Electronic Toll Information Sub‐System, 7) Police Affair and Emergence Information Processing Sub‐System, 8) Assistant Decision Making and Compositive Statistics and Inquiry, etc. Mass rule[1] is generally to describe a great amount of rules and rules set with a great amount of computation. The amount of information and data on traffic flow collected by transportation authority are very large. As the collection requirement of these mass information and data is higher and higher, and gets more and more complicated, the existing rule system can hardly meet these demands [2‐3]. It has been very important on how to enhance the semantic of rule system to enable it to process rules with different granularities, how rule system timely processes thousands of, even billions of rules, and how to optimize these mass rules so as to improve rule processing efficiency, etc. Intelligent Traffic Information Management System Intelligent traffic information management system based on mass rule parallel processing technology analyses, processes and issues intelligent traffic network information, and provides real‐time traffic related information for traffic participants and supervisors. Functions mainly include: Traffic Information Management Sub‐System Traffic participants provide real‐time traffic information for traffic information center by sensors and transmission
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devices installed on roads, vehicles, transfer stations, parking lots and meteorological centers. After receiving these information, the system processes and provides real‐time road traffic information, public traffic information, transfer information, traffic meteorological information, parking lot information and other information related to traffic for traffic participants; Travelers can decide their travel methods and select routes as per these information. When automatic positioning and navigating system is installed on vehicles, the system can automatically help drivers to select driving route. Traffic Information Service Sub‐System It is mainly to provide information service for traffic supervisors to check and supervise road traffic, make communication among roads, vehicles and drivers, offer real‐time supervision on traffic condition, traffic accidents, meteorological condition and traffic environment in road system, obtain information related to traffic condition, and supervise traffic as per information collected, such as signal lights, issuing inducement information, traffic control, accident treatment and rescue, etc. Public Traffic Information Issuance Sub‐System The main purpose is to actualize the target of safety, convenience and efficiency of public traffic system, provide consultation on travelling methods, accidents, routes and vehicle selections, etc to the public by personal computers and wired televisions, and provide real‐time bus moving information for bus waiters by displaying on bus stations. Public traffic vehicle management center can make reasonable plans of vehicle dispatching and off‐ running as per real‐time condition of vehicles, etc. to improve working efficiency and service quality. Vehicle Information Management Sub‐System It is to facilitate traffic supervisors to acquire all kinds of information on vehicles in transit and implement vehicle control, so as to acquire safe and efficient driving of vehicles. It includes information on warming and assistance to drivers, avoidance of accidents and obstacle on route, and tracking of illegal vehicles, etc. Freight and Equipment Information Management Sub‐System It makes use of satellite positioning information, geographical information and logistic information, etc. to provide information on equipment of expressway networks and parking lots, as well as goods transportation, etc. to effectively direct vehicle flow. Road and Electronic Toll Information Sub‐System By devices installed on vehicles and special information collecting devices installed on toll gates, it is to master the condition of all roads and toll gates so as to improve the roadway traffic capability. Police Affair and Emergence Information Processing Sub‐System It is to organically integrate information from traffic supervision and control centers and rescue organs to provide service of emergent disposal, tow trucking, rescue and removal of malfunction vehicles on spot for road users. Assistant Decision Making and Compositive Statistics and Inquiry It has functions of statistic and consultation of all kinds of traffic information to offer information and assist in making decisions. Mass Rule Parallel Processing Technology Thousands of users set different kinds of granularity rules in mass rule parallel processing system as per personal demands; Rules set by thousands of users generate a database of thousands of and even billions of mass rules. Upon combinating optimization and displacing optimization by equivalence rule mode, these mass rules will generate multipal independent rule sub‐networks. Finally, it requires cost computation model based on flow to make reasonable partition of these rule sub‐networks, and dispatch these rule sub‐networks to different processing machines for parallel processing.
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Mass Rule Description Model This project designs a rule structurized natural language expressing method, each kind of rule nodes and their graphicrized description models, introduce the concept of flow and flow model of rule nodes, and offer flux of each kind of rule node and the computation methods of costs. Mass Rule Network and Optimization This project designs a set of dynamic maintenance methods of increasing, deleting, updating rule nodes in mass rule network, also designs two optimization methods of mass rule network, including optimization method based on rule combination and optimization method of rule model displacement of nonequivalent cost computation based on function equivalence, so as to enable the mass rule network to be in the condition of computation cost minimization and decrease the computation workload of processing machines. Mass Rule Processing Measure This project puts forward a kind of matching model of mass rule mode. This model is applicable to matching and processing mass rules of different kinds of granularities to largely enhance the semantic of rules, and the parallel processing environment. It consists of four main parts, i.e. data on database relation diagram, mass rule network, mass rule mode matching arithmetic and rule implementation base respectively. 1) Data on Database Relation Diagram Rule initiation is resulted from data change. Once data on database relation diagram changes, it probably leads to fulfillment of conditions of some rules. 2) Mass Rule Network The mass rule network in this article generally refers to the rule network after optimization. It is a gigantic network generated by thousands of rules as per rule increment creating arithmetic set by users. Mass rule network includes two kinds of non‐computation nodes, i.e. rule relation node and rule action node, as well as 6 rule computation nodes, i.e. rule selection node, rule combination node, rule intersection node, rule connection node, rule negative computation node and rule Cartesian product node, etc. 3) Mass Rule Mode Matching Arithmetic Mass rule mode matching [4‐5] arithmetic is the core of this model, which is responsible for adjusting which rules set by users can be processed somewhen and someway. 4) Rule Implementation Base In case conditions of some rules set by users are completely matched successfully after mode matching arithmetic, the rules can be put in rule implementation base for immediate implementation. In case conditions are not matched successfully, it shall keep waiting until the conditions are fulfilled and the mode is matched successfully. According to this model, it can actualize the purpose of improving processing efficiency and decreasing communication costs by a kind of level traversal processing method. Mass Rule Parallel Processing Mechanism This project puts forward a kind of mass rule parallel processing mechanism. It decomposes mass rule processing into five parts, i.e. automatic generation of rule sub‐network, preparative distribution of rule computation cost, partition of rule sub‐network, communication cost processing of rule network and task reflection and distribution of rule computation cost. The basic input is a mass rule network after optimization. Mass rule network probably consists of many rule sub‐networks which do not have any relation. Firstly, we should automatically generate arithmetic by rule sub‐network and generate rule sub‐networks which do not have any relations; Because the quantity of processing machines is limited, and in order to ensure the computation cost of each processing machine is basically equivalent and minimize communication cost, the task of this project to the processing machine firstly requires a preparative distribution process. Due to the need of computation, as to some larger rule sub‐network, it
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still requires further and more suitable partition; Upon partition, because of the computation dependence between computing parts after partition, it requires communication between these computing parts, i.e. when a processing machine is carrying out computing task, it requires interim processing result from another processing machine; After the above‐mentioned step, it is to make a suitable combination of each sub‐task; After combination, it is to reflect each well‐combined task to each processing machine; Finally, it is to distribute the specific task reflected to each processing machine to the corresponding processing machine for processing. We have verified the efficiency of this arithmetic by specific experiments. Conclusions The project has undergone algorithmic research and experimental verification. We are researching and developing an antitype system, and will further develop commercial software later and provide for traffic authority. Relevant technical details will be publicized after this project is completely concluded, checked and accepted, and relevant intellectual property rights are obtained. This project is supported by Science and Technology Planning Project of Guangdong Provincial Science and Technology Bureau and Science and Technology Innovation Project of Guangdong Provincial Education Bureau. We hereby express our sincere gratitude. REFERENCES
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