Dynamic origin-destination (OD) demand estimation and prediction (DODE) and drivers’ route diversion behavior (DRDB) are two fundamental determinants in the development of effective group route guidance (GRG) strategies for balancing traffic loads and enhancing system performance. This work presents a unified approach to incorporate DRDB into a DODE model. This model accounts for the influence of route diversion on DODE and predicts the OD demand in information provision environment. This study also develops a dynamic aggregate DRDB model based on the traffic data obtained through online detection and integrates this model with the DODE model. The test results of the case study show that the maximum deviation between the real OD volumes and the proposed DODE model under information provision, mean absolute percentage error, and normalized root mean square error is approximately 11.46%, 4.53%, and 5.29%, respectively. The integrated demand-diversion prediction model can accurately estimate and predict possible DRDB and the effect of traffic information on OD demand prediction using real-time traffic detected data. The model can also enhance the accuracy of OD demand and traffic state prediction under information provision, consequently increasing the effectiveness of the proposed GRG strategies.

动态原点目的地（OD）需求估计和预测（DODE）以及驾驶员的路线转移行为（DRDB）是开发有效的团体路线引导（GRG）策略以平衡交通负荷和增强系统性能的两个基本决定因素。这项工作提出了将DRDB合并到DODE模型中的统一方法。该模型考虑了路线转移对DODE的影响，并预测了信息提供环境中的OD需求。该研究还基于通过在线检测获得的交通数据开发了动态聚合DRDB模型，并将此模型与DODE模型集成在一起。案例研究的测试结果表明，在提供信息的情况下，实际OD量与拟议的DODE模型之间的最大偏差，平均绝对百分比误差，和均方根误差分别约为11.46％，4.53％和5.29％。集成的需求转移预测模型可以使用实时流量检测数据准确估算和预测可能的DRDB以及流量信息对OD需求预测的影响。该模型还可以在信息提供下提高OD需求和交通状态预测的准确性，因此提高了所提出的GRG策略的有效性。