Agent-based modelling has been suggested as a highly suitable approach for the tackling of future mobility challenges. However, the application of disaggregate models is often hindered by the high granularity of the required input. Recent research has suggested a combinatorial optimization-based framework to enable the conversion of typical origin–destination matrices (ODs) to suitable input for agent-based modelling (e.g., trip-chains, tours, or activity-schedules). Nonetheless, the combinatorial nature of the approach requires very efficient and scalable optimization processes to handle large-scale ODs. This study suggests an advanced optimization technique, coined as the adaptive sampling simulated annealing (ASSA) algorithm, able to exploit high-level calibration information (in the form of a joint distribution) for the efficient addressing of large-scale combinatorial problems. The proposed optimization algorithm was evaluated using high-level information about the departure profile, the types of activities, and the travel time of the expected output and a set of large-scale trip-purpose- and time-period-segmented OD matrices of 253,000 trips. The obtained results showcase the ability of the methodology to accurately and efficiently convert large-scale ODs into disaggregate mobility traces since the inputted ODs were converted into thousands of travel-demand equivalent, disaggregate mobility traces with an accuracy exceeding 90%. The implications are significant since the abundance of travel-demand information in ODs can be now exploited for the preparation of disaggregate mobility traces, suitable for sophisticated agent-based transport modelling.

已经提出基于代理的建模是解决未来移动性挑战的一种非常合适的方法。但是，通常由于所需输入的高粒度而阻碍了分类模型的应用。最近的研究提出了一种基于组合优化的框架，该框架可以将典型的原点-目的地矩阵（OD）转换为适合基于代理的建模的输入（例如，旅行链，旅行或活动时间表）。尽管如此，该方法的组合性质要求非常高效且可扩展的优化过程来处理大规模OD。这项研究提出了一种先进的优化技术，称为自适应采样模拟退火（ASSA）算法，能够利用高级校准信息（以联合分布的形式）来有效解决大规模组合问题。使用有关出发情景，活动类型和预期输出的行进时间的高级信息以及一组253,000的大规模旅行目的和时间分段的OD矩阵，对提出的优化算法进行了评估。旅行。由于输入的OD被转换为成千上万的行车需求当量，分解后的移动迹线，其准确性超过90％，因此获得的结果证明了该方法能够将大规模OD准确有效地转换为分解后的移动迹线的能力。