基于GPS的车辆到站实时预测模型的研究

发布时间：2018-12-20 18:09

【摘要】：公共交通系统对于城市交通的缓解有着不可替代的作用,而且智能化和信息化的公交系统能有效提高道路交通系统的运行效率以及对于城市居民的服务能力。作为城市居民,非常关心公共车辆的到站时间,到站时间可以减少乘车在站台的等车时间,提高乘客乘车的满意度,同时也可以优化乘客的出行计划,从而提高公共服务水平。近年来,许多国家都意识到公共交通信息发布的重要性,公交车辆到站时间预测在智能公交技术应用中占有举足轻重的地位,相关的预测和信息发布系统在许多地方得到应用。首先,本文介绍了智能公交系统中公交到站时间的相关研究背景、在应用过程是碰到的一些待解决的问题的基础上展开本的研究,并概括性的介绍了当前智能公交系统中公交到站时间预测的发展情况和本文所做的一些研究工作其次,介绍了GPS系统的结构,然后再分析GPS结构的基础上讲述GPS误差的产生的原因,在后面几小节,针对不同的误差数据,分别给出解决方法予以检出和修正。GPS数据的采集和预处理是公交车到达时间预测模型的基础和前提,也是在现有条件下建立预测模型所必须解决的实际问题,是实现公交车到达时间准确预测的关键技术之一。尽管受各种因素的影响,我们还是可以从公交车辆的行驶历史数据中看出公交车辆的运行时间还是有一定的规律性的,一般情况下,车辆的历史到站时间还是可以作为车辆实时到站时间预测模型的参照值。但是,对于行驶于同一条公交线路上的公交车辆,如果发生突发的交通状况,其行驶时间还是会有一定的偏差的,如果仅仅依靠历史数据的话,还是有可能会产生一定偏差的。但是前车(当前行驶车辆的前一班公交车)的平均瞬时速度还是能够作为该路段交通状况的重要参考因素,在数据采集和预处理的基础上,利用前车的数据对获取的历史经验数据进行评估、修正,提出一种基于前车与历史经验数据的车辆到站时间预测模型。最后,这次实验选择了南昌市高铁2号线路进行预测实验,该条公交线路经过了南昌市市区、西铁站两个重要的区域,路况相对比较复杂路况,非常具有代表性。结果证明模型预测精度和可靠性满足实用要求。
[Abstract]:The public transportation system plays an irreplaceable role in the urban traffic mitigation, and the intelligent and informational public transportation system can effectively improve the efficiency of the road traffic system and the service ability of the urban residents. As city residents, they are very concerned about the arrival time of public vehicles. The arrival time can reduce the waiting time and improve the satisfaction of passengers. At the same time, it can optimize the travel plan of passengers and improve the level of public service. In recent years, many countries have realized the importance of public transportation information release, and bus arrival time prediction plays an important role in the application of intelligent public transport technology, and relevant prediction and information release systems have been applied in many places. First of all, this paper introduces the related research background of bus arrival time in intelligent public transport system, based on some problems to be solved in the application process, The development of bus arrival time prediction in intelligent bus system and some research work done in this paper are introduced. Secondly, the structure of GPS system is introduced. Then, based on the analysis of GPS structure, the causes of GPS error are discussed. In the following sections, different error data are analyzed. The GPS data acquisition and preprocessing are the basis and premise of the bus arrival time prediction model, and are also the practical problems that must be solved in the establishment of the prediction model under the existing conditions. It is one of the key technologies for accurate prediction of bus arrival time. Despite the influence of various factors, we can still see from the historical data of public transport vehicles that the running time of public transport vehicles is still regular. Generally speaking, The historical arrival time of vehicles can still be used as the reference value of the real-time vehicle arrival time prediction model. However, for the public transport vehicles running on the same bus line, if the sudden traffic situation occurs, the travel time will still have a certain deviation, if only depends on the historical data, it is possible to produce a certain deviation. However, the average instantaneous speed of the front car (the previous bus of the current vehicle) can still be used as an important reference factor for the traffic situation in this section, on the basis of data collection and preprocessing, Using the data of the front car to evaluate and modify the obtained historical experience data, a prediction model of the arrival time of the vehicle based on the data of the front car and the historical experience is proposed. Finally, this experiment selected Nanchang City High Speed Line 2 to carry on the forecast experiment, the public transport line passed through the Nanchang city, the west railway station two important areas, the road condition is relatively complex, very representative. The results show that the prediction accuracy and reliability of the model meet the practical requirements.
【学位授予单位】：南昌大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491.17;U495

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