The prediction of bus arrival time using automatic vehicle location systems data
The prediction of bus arrival time using automatic vehicle location systems data
复制标题
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
R. Jeong
中科院分区：
文献类型：
--
作者：
R. Jeong
利用自动车辆定位系统数据预测公交车到达时间。（2004年12月）Ran Hee Jeong，B.S.，Hong-ik University; M.S.，弘益大学咨询委员会联合主席：劳伦斯·R·先进的出行者信息系统（ATIS）是智能交通系统（ITS）的一个组成部分，而ATIS的一个主要组成部分是行程时间信息。提供及时和准确的过境旅行时间信息很重要，因为这可以吸引更多的乘客，提高过境用户的满意度。ITS的电子产品和零部件的成本已经降低，ITS的部署正在全国范围内扩大。自动车辆定位系统（AVL）作为ITS的一部分，已被许多公交机构采用。这使他们能够实时跟踪他们的运输车辆。使用AVL数据预测公交出行时间的模型或技术的需求正在增加。虽然已经对这一主题进行了一些研究，但研究表明，需要对这一主题进行更多的研究。本研究的目的是：1）开发和应用一个模型来预测公交车到达时间使用AVL数据，2）确定公交车到达时间的预测区间和公交车准时的概率。在这项研究中，出行时间预测模型明确包括停留时间，时间段的时间表遵守，交通拥堵，这是关键的准确预测巴士到达时间。测试平台是德克萨斯州休斯顿市中心的一条公交线路。基于历史的模型，回归模型和人工神经网络（ANN）模型被开发来预测公交车到达时间。结果发现，人工神经网络模型的表现大大优于基于历史数据的模型或多元线性回归模型。假设ANN能够识别复杂的非线性
The Prediction of Bus Arrival Time Using Automatic Vehicle Location Systems Data. (December 2004) Ran Hee Jeong, B.S., Hong-ik University; M.S., Hong-ik University Co-Chairs of Advisory Committee: Dr. Laurence R. Rilett Dr. Amy Epps Martin Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear