The diversified route planning finds multiple paths that are sufficiently different from each other while as short as possible. It is of great significance to traffic alleviation through provided alternative routes during navigation. However, it is NP-H to find the optimal result and the existing solutions have either high quality (exact path enumeration-based) or high efficiency (alternative path), which is highly affected by the network properties and query parameters but has never been investigated before. Therefore, this paper proposes a hybrid diversified routing system that can handle any query efficiently with quality as high as possible. Specifically, we first analyze the path enumeration problem from the ground up and unify all the existing algorithms theoretically to identify the factors that affect algorithm performance. After that, we review and select the alternative path methods to identify the suitable ones for our system. Finally, we propose a query classification module to estimate the hardness of a query and determine how it should be processed. Extensive experiments on real-life networks validate the effectiveness and efficiency of our hybrid system compared with state-of-the-art solutions.

In this paper, we revisit the problem of the current routing system in terms of prediction scalability and routing result optimality. Specifically, the current traffic prediction models are not suitable for large urban networks due to the incomplete information of traffic conditions. Besides, existing routing systems can only plan the routes based on the past traffic conditions and struggle to update the optimal route for vehicles in real-time. As a result, the actual route taken by vehicles is different from the ground-truth optimal path. Therefore, we propose a Just-In-Time Predictive Route Planning framework to tackle these two problems. Firstly, we propose a Travel Time Constrained Top-$k^{n}$Shortest Path algorithm which pre-computes a set of candidate paths with several switch points. This empowers vehicles to continuously have the opportunity to switch to better paths taking into account real-time traffic condition changes. Moreover, we present a query-driven prediction paradigm with ellipse-based searching space estimation, along with an efficient multi-queries handling mechanism. This not only allows for targeted traffic prediction by prioritizing regions with valuable yet outdated traffic information, but also provides optimal results for multiple queries based on real-time traffic evolution. Evaluations on two real-life road networks demonstrate the effectiveness and efficiency of our framework and methods.

Route planning is ubiquitous and has a profound impact on our daily life. However, the existing path algorithms tend to produce similar paths between similar OD (Origin-Destination) pairs because they optimize query results without considering their influence on the whole network, which further introduces congestions. Therefore, we investigate the problem of diversifying the top-k paths between an OD pair such that their similarities are under a threshold while their total length is minimal. However, the current solutions all depend on the expensive graph traversal which is too slow to apply in practice. Therefore, we first propose an edge deviation and concatenation-based method to avoid the expensive graph search in path enumeration. After that, we dive into the path relations and propose a path similarity computation method with constant complexity, and propose a pruning technique to improve efficiency. Finally, we provide the completeness and efficiency-oriented solutions to further accelerate the query answering. Evaluations on the real-life road networks demonstrate the effectiveness and efficiency of our algorithm over the state-of-the-art.