基于多源数据的公交乘客出行模式挖掘关键技术及应用研究
Study on Key Technologies of Transit Passengers Travel Pattern Mining and Applications based on Multiple Sources of Data
摘要：
在本研究中，我们提出了一系列的方法来挖掘公交乘客的出行模式和行为偏好，然后我们使用这些知识来调整和优化公交系统。贡献包括：1）为了提高数据的有效性：a）提出了一种新的AFC（Automated Fare Collection）系统和AVL（Automated Vehicle Location）系统之间数据的时间差异校正方法，该方法将数据事件转换为信号，并应用时域相关技术检测和校正它们之间的相对差异。b）通过结合历史数据和乘客售票时间戳，我们引入和补偿AVL数据集中的缺失信息。2)为了推断乘客的下车点，我们引入了一个最大概率模型，结合乘客的家乡，以恢复其完整的过境轨迹从半完整的登机记录，然后我们提出了一个增强的活动识别算法，能够指定乘客的短期活动，从普通的换乘。最后，分析了公交客流的时空特征。3)发现旅客出行需求。我们整合每个乘客在多天的轨迹数据，构建一个混合出行图（HTG）。然后，我们使用深度搜索算法来获得空间封闭的过境出行链;最后，我们使用封闭的过境出行链的乘客，从不同的角度来研究他们的出行模式。最后，通过对乘客关键出行链的聚合，对城市公交走廊进行分析。4）推导出8个影响因素，构建了不同情景下乘客出行选择模型。接下来，我们验证我们的模型使用乘客重新分配模拟。最后，我们对乘客的时间选择偏好进行了综合分析，并利用这些信息来优化城市交通系统。
英文摘要：
In this research, we propose a series of methodologies to mine transit riders travel pattern and behavioral preferences, and then we use these knowledges to adjust and optimize the transit systems. Contributions are: 1) To increase the data validity: a) we propose a novel approach to rectify the time discrepancy of data between the AFC (Automated Fare Collection) systems and AVL (Automated Vehicle Location) system, our approach transforms data events into signals and applies time domain correlation the detect and rectify their relative discrepancies. b) By combining historical data and passengers ticketing time stamps, we induct and compensate missing information in AVL datasets. 2) To infer passengers alighting point, we introduce a maximum probabilistic model incorporating passengers home place to recover their complete transit trajectory from semi-complete boarding records.Then we propose an enhance activity identification algorithm which is capable of specifying passengers short-term activity from ordinary transfers. Finally, we analyze the temporal-spatial characteristic of transit ridership. 3) To discover passengers travel demands. We integrate each passengers trajectory data in multiple days and construct a Hybrid Trip Graph (HTG). We then use a depth search algorithm to derive the spatially closed transit trip chains; Finally, we use closed transit trip chains of passengers to study their travel pattern from various perspectives. Finally, we analyze urban transit corridors by aggregating the passengers critical transit chains.4) We derive eight influential factors, and then construct passengers choice models under various scenarios. Next, we validate our model using ridership re-distribute simulations. Finally, we conduct a comprehensive analysis on passengers temporal choice preference and use this information to optimize urban transit systems.
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