Precise forecasts on individual accessibility in bus system can help make policies to accommodate fluctuating bus travel demand and promoting social equity. In this study, we propose a three-stage method for short-term forecasts on individual accessibility in bus system based on neural network (NN) model. In the first stage, a NN model is designed to tackle the nonlinear mapping between passengers' bus trip appearances in historical periods and those in the predicted period. A rate function, which considers bus trip generation rates of passengers, is then applied using outputs of the designed NN model. In the second stage, probabilities of origin-destinations (ODs) chosen by passengers in the predicted period are calculated. In the third stage, land use information combined with results of previous two stages are used to obtain the individual accessibility in bus system in the predicted period. Compared to individual accessibility calculated by real data, it is found that the average errors of predicted results by the proposed method in weekdays and at weekends are only 8.37% and 10.13%, respectively. The results also demonstrate the capability of combining a NN model, traffic data and land use information to forecast the future spatial distribution of individual accessibility in transport system.

对公交系统中个人可及性的精确预测可以帮助制定政策，以适应不断变化的公交旅行需求并促进社会公平。在这项研究中，我们提出了一种基于神经网络（NN）模型的公交系统中个人可及性短期预测的三阶段方法。在第一阶段，设计了一个NN模型，以解决历史时期和预测时期的乘客公交车出行情况之间的非线性映射。然后，使用设计的NN模型的输出来应用考虑乘客的公交旅行发生率的费率函数。在第二阶段，计算乘客在预测期间内选择的出发地（OD）概率。在第三阶段 土地利用信息与前两个阶段的结果相结合，用于获得预测期内公交系统的个体可及性。与真实数据计算的个人可及性相比，发现该方法在工作日和周末的预测结果的平均误差分别仅为8.37％和10.13％。结果还证明了结合神经网络模型，交通数据和土地使用信息来预测交通系统中个人可及性未来空间分布的能力。