Algorithm Engineering for integrated multimodal route planning

本子项目的目标是设计多式联运网络中有效的路线规划算法，并将其应用于综合公共交通规划。第一个重点是扩展多模式路线规划的时间表信息算法。重点研究了两种时刻表信息算法：连接扫描算法（Connection Scan Algorithm， CSA）和基于轮询的公共交通路由算法（Round-Based Public Transit Routing， RAPTOR）。根据第一阶段的研究结果，我们将把这些算法扩展到无限的行人路，下一步将会整合更多的交通方式。这将需要CSA或RAPTOR算法的加速，以及将之前考虑的优化标准旅行时间和传输次数扩展到4或5个优化标准。这个子项目的第二个重点是使用路线规划算法来改善公共交通的综合规划。在第一个资助阶段已经开始研究使用路线规划算法进行公共交通分配，并将继续估计总体乘客。第一步，纯公共交通网络将扩展到不受限制的步行。对于路线选择的评估模型，需要考虑额外的标准，特别是车辆容量，以实现更现实的乘客路线选择。

The objective of this sub-project is the design of efficient algorithms for route planning in multimodal transport networks and their use in integrated public transport planning. The first focus is on the expansion of timetable information algorithms for multimodal route planning. We focus mainly on the two timetable information algorithms Connection Scan Algorithm (CSA) and Round-Based Public Transit Routing (RAPTOR). Following the findings of the first funding phase for the extension of these algorithms to unlimited footpaths, further modes of transport will be integrated in the next step. This will require an acceleration of the CSA or RAPTOR algorithms, as well as an extension of the previously considered optimization criteria travel time and number of transfers to four or five optimization criteria.The second focus of this sub-project is on the use of route planning algorithms to improve integrated planning in public transport. Research started already in the first funding phase on using route planning algorithms for public transit assignment and estimation of the overall passenger will be continued. In a first step, the pure public transport network is to be extended as well to unrestricetd walking. For the assessment model for the route selection, additional criteria are to be taken into account, in particular vehicle capacities, in order to achieve a more realistic route selection of the passengers.