行为模式在智能公交系统的应用研究与设计
发布时间：2018-07-14 21:08
【摘要】：随着智慧城市的发展,对城市公交技术提出了更高的要求,城市公交系统的相关信息处理问题是如今研究的热点问题。如今研究者无论是在硬件还是软件上都做了许多相关技术的研究,也取得了一定的成果,其主要体现于GPS北斗导航定位,线路调度等,以方便了乘客。但就智能城市而言,却无法准确获得车上具体的信息,智能性不强。城市公交系统与调度中心不能做出实时的调整,调度的决策仅限于对线路的调度,对客流量的预测即是通过以往数据的猜测和估算,并没有很好的将智能性和实时性体现出来。本文将行为模式的智能性引入城市公交系统中,以行为模式分析理论为基础,通过分析车辆在运行过程中的行为,建立了车辆的行为模型,通过传感器模块提取车辆当前行为特征数据,如车辆速度、车门的开和关、车辆当前GPS位置数据、实时乘客人数等。利用这些行为特征数据确定车辆的行为模式,根据行为模式来制定相应的控制和管理方案,提高了城市公交的智能性和灵活性,更加有效和方便的管理车辆。行为模式包括乘客行为模式和车辆行为模式等。乘客行为模式识别是通过实时乘客行为模式来确定车的载荷即人数计量。然而现有的车的载荷计量方法不准确,并没有很好的实时性,不能很好的满足行为模式识别的需求。本文通过行为模式分析乘客在上下车时的行为,建立乘客的行为模型,通过多种传感器提取了乘客上下车时的行为特征,如压力变化、乘客与车辆之间距离、人脸朝向等行为特征数据。并对特征数据进行分类,定义了乘客的行为,进而完成人数的实时计量。为了提高了城市公交的智能性和实时性,以及准确性,本文基于IPv6组播的管理技术,设计了城市公交的线路和车辆的分组管理方案,使城市公交系统的管理更方便。同时本文针对车辆数据的传输给出了基于车辆行为的传输控制策略和机制。该机制通过判定车辆行为模式,动态控制车辆数据传输。实验证明,本文所设计的乘客人数计量准确度达到90%以上,能很好的向城市公交的智能调度提供准确而且实时的数据支持,方便了公交系统的管理,为城市公交向智能化、科学化发展奠定了坚实的基础。
[Abstract]:With the development of intelligent city, higher demands have been put forward on the technology of urban public transport, and the information processing of urban public transport system is a hot issue. Nowadays, researchers have done a lot of research on hardware and software, and have made some achievements, which are mainly reflected in GPS Beidou navigation, line scheduling and so on, in order to facilitate passengers. But in the case of intelligent city, it can not accurately obtain the specific information on the vehicle, so the intelligence is not strong. Urban public transport system and dispatching center can not make real-time adjustment, the scheduling decision is limited to the dispatch of the line, and the prediction of passenger flow is based on the guesses and estimates of the previous data, and it does not reflect the intelligence and real-time performance very well. In this paper, the intelligent behavior pattern is introduced into the urban public transport system. Based on the theory of behavior pattern analysis, the behavior model of the vehicle is established by analyzing the behavior of the vehicle in the course of operation. The sensor module is used to extract the characteristic data of the vehicle's current behavior, such as the vehicle speed, the opening and closing of the door, the current GPS position data of the vehicle, the real-time number of passengers and so on. Using these behavioral characteristics data to determine the behavior pattern of the vehicle and to formulate the corresponding control and management scheme according to the behavior model, the intelligence and flexibility of the urban public transport are improved, and the vehicle management is more effective and convenient. The behavior pattern includes passenger behavior pattern and vehicle behavior pattern. Passenger behavior pattern recognition is based on real-time passenger behavior pattern to determine the load of the vehicle, that is, the number of measurement. However, the existing load measurement methods are not accurate, and can not meet the requirements of behavior pattern recognition. In this paper, the behavior of passengers during boarding and disembarking is analyzed, and the behavior model of passengers is established. The behavior characteristics of passengers are extracted by a variety of sensors, such as pressure changes, distance between passengers and vehicles. Face orientation isobaric feature data. The characteristic data are classified, and the behavior of passengers is defined, and the real-time measurement of the number of passengers is accomplished. In order to improve the intelligence, real-time and accuracy of urban public transport, this paper designs a group management scheme of urban public transport lines and vehicles based on IPv6 multicast management technology, which makes the management of urban public transport system more convenient. At the same time, this paper gives the transmission control strategy and mechanism based on vehicle behavior for vehicle data transmission. The mechanism dynamically controls vehicle data transmission by determining vehicle behavior patterns. The experiment proves that the measurement accuracy of the passenger number designed in this paper is more than 90%, which can provide accurate and real-time data support to the intelligent dispatch of urban public transport, facilitate the management of the public transport system, and provide intelligence for the urban public transport. Scientific development laid a solid foundation.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;U491.17
本文编号：2122951
[Abstract]:With the development of intelligent city, higher demands have been put forward on the technology of urban public transport, and the information processing of urban public transport system is a hot issue. Nowadays, researchers have done a lot of research on hardware and software, and have made some achievements, which are mainly reflected in GPS Beidou navigation, line scheduling and so on, in order to facilitate passengers. But in the case of intelligent city, it can not accurately obtain the specific information on the vehicle, so the intelligence is not strong. Urban public transport system and dispatching center can not make real-time adjustment, the scheduling decision is limited to the dispatch of the line, and the prediction of passenger flow is based on the guesses and estimates of the previous data, and it does not reflect the intelligence and real-time performance very well. In this paper, the intelligent behavior pattern is introduced into the urban public transport system. Based on the theory of behavior pattern analysis, the behavior model of the vehicle is established by analyzing the behavior of the vehicle in the course of operation. The sensor module is used to extract the characteristic data of the vehicle's current behavior, such as the vehicle speed, the opening and closing of the door, the current GPS position data of the vehicle, the real-time number of passengers and so on. Using these behavioral characteristics data to determine the behavior pattern of the vehicle and to formulate the corresponding control and management scheme according to the behavior model, the intelligence and flexibility of the urban public transport are improved, and the vehicle management is more effective and convenient. The behavior pattern includes passenger behavior pattern and vehicle behavior pattern. Passenger behavior pattern recognition is based on real-time passenger behavior pattern to determine the load of the vehicle, that is, the number of measurement. However, the existing load measurement methods are not accurate, and can not meet the requirements of behavior pattern recognition. In this paper, the behavior of passengers during boarding and disembarking is analyzed, and the behavior model of passengers is established. The behavior characteristics of passengers are extracted by a variety of sensors, such as pressure changes, distance between passengers and vehicles. Face orientation isobaric feature data. The characteristic data are classified, and the behavior of passengers is defined, and the real-time measurement of the number of passengers is accomplished. In order to improve the intelligence, real-time and accuracy of urban public transport, this paper designs a group management scheme of urban public transport lines and vehicles based on IPv6 multicast management technology, which makes the management of urban public transport system more convenient. At the same time, this paper gives the transmission control strategy and mechanism based on vehicle behavior for vehicle data transmission. The mechanism dynamically controls vehicle data transmission by determining vehicle behavior patterns. The experiment proves that the measurement accuracy of the passenger number designed in this paper is more than 90%, which can provide accurate and real-time data support to the intelligent dispatch of urban public transport, facilitate the management of the public transport system, and provide intelligence for the urban public transport. Scientific development laid a solid foundation.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;U491.17
【参考文献】
相关期刊论文 前1条
1 伍佑明;杨国良;丁圣勇;;IPv6技术及其在移动互联网中的应用[J];电信科学;2009年06期
，本文编号：2122951
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