Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Efficient Algorithms for Fully Multimodal Journey Planning

We study the journey planning problem for fully multimodal networks consisting of public transit and an arbitrary number of non-schedule-based transfer modes (e.g., walking, e-scooter, bicycle). Obtaining reasonable results in this setting requires multicriteria optimization, making the problem highly complex. Previous approaches were either limited to a single transfer mode or suffered from prohibitively slow running times. We establish a fully multimodal journey planning model that excludes undesirable solutions and can be solved efficiently. We extend existing efficient bimodal algorithms to our model and propose a new algorithm, HydRA, which enables even faster queries. On metropolitan and mid-sized country networks with walking and e-scooter as transfer modes, HydRA achieves query times of around 30 ms, which is fast enough for interactive applications

Efficient Algorithms for Fully Multimodal Journey Planning

We study the journey planning problem for fully multimodal networks consisting of public transit and an arbitrary number of non-schedule-based transfer modes (e.g., walking, e-scooter, bicycle). Obtaining reasonable results in this setting requires multicriteria optimization, making the problem highly complex. Previous approaches were either limited to a single transfer mode or suffered from prohibitively slow running times. We establish a fully multimodal journey planning model that excludes undesirable solutions and can be solved efficiently. We extend existing efficient bimodal algorithms to our model and propose a new algorithm, HydRA, which enables even faster queries. On metropolitan and mid-sized country networks with walking and e-scooter as transfer modes, HydRA achieves query times of around 30 ms, which is fast enough for interactive applications

An efficient solution for one-to-many multi-modal journey planning

We study the one-to-many journey planning problem in multi-modal transportation networks consisting of a public transit network and an additional, non-schedule-based mode of transport. Given a departure time and a single source vertex, we aim to compute optimal journeys to all vertices in a set of targets, optimizing both travel time and the number of transfers used. Solving this problem yields a crucial component in many other problems, such as efficient point-of-interest queries, computation of isochrones, or multi-modal traffic assignments. While many algorithms for multi-modal journey planning exist, none of them are applicable to one-to-many scenarios. Our solution is based on the combination of two state-of-the-art approaches: ULTRA, which enables efficient journey planning in multi-modal networks, but only for one-to-one queries, and (R)PHAST, which enables efficient one-to-many queries, but only in time-independent networks. Similarly to ULTRA, our new approach can be combined with any existing public transit algorithm that allows a search to all stops, which we demonstrate for CSA and RAPTOR. For small to moderately sized target sets, the resulting algorithms are nearly as fast as the pure public transit algorithms they are based on. For large target sets, we achieve a speedup of up to 7 compared to a naive one-to-many extension of a state-of-the-art multi-modal approach

An Efficient Solution for One-To-Many Multi-Modal Journey Planning

We study the one-to-many journey planning problem in multi-modal transportation networks consisting of a public transit network and an additional, non-schedule-based mode of transport. Given a departure time and a single source vertex, we aim to compute optimal journeys to all vertices in a set of targets, optimizing both travel time and the number of transfers used. Solving this problem yields a crucial component in many other problems, such as efficient point-of-interest queries, computation of isochrones, or multi-modal traffic assignments. While many algorithms for multi-modal journey planning exist, none of them are applicable to one-to-many scenarios. Our solution is based on the combination of two state-of-the-art approaches: ULTRA, which enables efficient journey planning in multi-modal networks, but only for one-to-one queries, and (R)PHAST, which enables efficient one-to-many queries, but only in time-independent networks. Similarly to ULTRA, our new approach can be combined with any existing public transit algorithm that allows a search to all stops, which we demonstrate for CSA and RAPTOR. For small to moderately sized target sets, the resulting algorithms are nearly as fast as the pure public transit algorithms they are based on. For large target sets, we achieve a speedup of up to 7 compared to a naive one-to-many extension of a state-of-the-art multi-modal approach

Algorithm Engineering for Realistic Journey Planning in Transportation Networks

Diese Dissertation beschäftigt sich mit der Routenplanung in Transportnetzen. Es werden neue, effiziente algorithmische Ansätze zur Berechnung optimaler Verbindungen in öffentlichen Verkehrsnetzen, Straßennetzen und multimodalen Netzen, die verschiedene Transportmodi miteinander verknüpfen, eingeführt. Im Fokus der Arbeit steht dabei die Praktikabilität der Ansätze, was durch eine ausführliche experimentelle Evaluation belegt wird

Faster Preprocessing for the Trip-Based Public Transit Routing Algorithm

We propose an additional preprocessing step for the Trip-Based Public Transit Routing algorithm, an exact state-of-the art algorithm for bi-criteria min cost path problems in public transit networks. This additional step reduces significantly the preprocessing time, while preserving the correctness and the computation times of the queries. We test our approach on three large scale networks and show that the improved preprocessing is compatible with frequent real-time updates, even on the larger data set. The experiments also indicate that it is possible, if preprocessing time is an issue, to use the proposed preprocessing step on its own to obtain already a significant reduction of the query times compared to the no pruning scenario