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Many acceleration techniques exist for the single-pair shortest path problem on road networks. Most of them have been significantly improved over the years to achieve faster preprocessing times and superior performance. In this spirit, our current work significantly improves the classic ALT (A * + Landmarks + Triangle equality) algorithm. By carefully optimizing both preprocessing and query phases, we managed to effectively minimize preprocessing time to a few seconds, making the ALT algorithm also suitable for dynamic scenarios, i.e., road networks with changing edge weights due to traffic updates. We also accelerated the query phase for both unidirectional and bidirectional versions of the ALT algorithm, providing fast enough query times (including full-path unpacking) suitable for real-time services and continental road networks.

Crowdsourcing road network data, i.e., involving users to collect data including the detection and assessment of changes to the road network graph, poses a challenge to shortest-path algorithms that rely on preprocessing. Hence, current research challenges lie with improving performance by adequately balancing preprocessing with respect to fast-changing road networks. In this work, we take the crowdsourcing approach further in that we solicit the help of users not only for data collection, but also to provide us their computing resources. A promising approach is parallelization, which splits the graph into chunks of data that may be processed separately. This work extends this approach in that small-enough chunks allow us to use browser-based computing to solve the pre-computation problem. Essentially, we aim for a Web-based navigation service that whenever users request a route, the service uses their browsers for partially preprocessing a large, but changing road network. The paper gives performance studies that highlight the potential of the browser as a computing platform and showcases a scalable approach, which almost eliminates the computing load on the server.

Although recent scientific output focuses on multiple shortest-path problem definitions for road networks, none of the existing solutions does efficiently answer all different types of SP queries. This work proposes SALT, a novel framework that not only efficiently answers SP related queries but also k-nearest neighbor queries not handled by previous approaches. Our solution offers all the benefits needed for practical use-cases, including excellent query performance and very short preprocessing times, thus making it also a viable option for dynamic road networks, i.e., edge weights changing frequently due to traffic updates. The proposed SALT framework is a deployable software solution capturing a range of network-related query problems under one "algorithmic hood".