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44
Crowdsourcing computing resources for shortest-path computation
- Proceedings of the 20th International Conference on Advances in Geographic Information Systems
, 2012
"... ABSTRACT 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 ..."
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ABSTRACT 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.
On the Complexity of Time-Dependent Shortest Paths
"... We investigate the complexity of shortest paths in timedependent graphs, in which the costs of edges vary as a function of time, and as a result the shortest path between two nodes s and d can change over time. Our main result is that when the edge cost functions are (polynomial-size) piecewise line ..."
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We investigate the complexity of shortest paths in timedependent graphs, in which the costs of edges vary as a function of time, and as a result the shortest path between two nodes s and d can change over time. Our main result is that when the edge cost functions are (polynomial-size) piecewise linear, the shortest path from s to d can change Θ(log n) n times, settling a several-year-old conjecture of Dean [Technical Reports, 1999, 2004]. We also show that the complexity is polynomial if the slopes of the linear function come from a restricted class, present an outputsensitive algorithm for the general case, and describe a scheme for a (1 + ɛ)-approximation of the travel time function in near-quadratic space. Finally, despite the fact that the arrival time function may have superpolynomial complexity, we show that a minimum delay path for any departure time interval can be computed in polynomial time. 1
Space-Efficient SHARC-Routing
, 2009
"... Accelerating the computation of quickest paths in road networks has been undergoing a rapid development during the last years. The breakthrough idea for handling road networks with tens of millions of nodes was the concept of shortcuts, i.e., additional arcs that represent long paths in the input. V ..."
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Accelerating the computation of quickest paths in road networks has been undergoing a rapid development during the last years. The breakthrough idea for handling road networks with tens of millions of nodes was the concept of shortcuts, i.e., additional arcs that represent long paths in the input. Very recently, this concept has been transferred to time-dependent road networks where travel times on arcs are given by functions. Unfortunately, the concept of shortcuts yields a high increase in space consumption for time-dependent road networks since the travel time functions assigned to the shortcuts may become quite complex. In this work, we present how the space overhead induced by time-dependent SHARC, a technique relying on shortcuts as well, can be reduced significantely. As a result, we are able to reduce the overhead stemming from SHARC by a factor of up to 11.5 for the price of a loss in query performance of a factor of 4. The methods we present allow a flexible trade-off between space consumption and query performance.
Optimizing landmark-based routing and preprocessing
- In Proceedings of the 6th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS ’13
, 2013
"... 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 ∗ + L ..."
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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 unidi-rectional and bidirectional versions of the ALT algorithm, provid-ing fast enough query times (including full-path unpacking) suit-able for real-time services and continental road networks. Categories and Subject Descriptors
Energy-optimal routes for electric vehicles
- In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’13
, 2013
"... Abstract. We study the problem of electric vehicle route planning, where an important aspect is computing paths that minimize energy consumption. Thereby, any method must cope with specific properties, such as recuperation, battery constraints (over- and under-charging), and frequently changing cost ..."
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Abstract. We study the problem of electric vehicle route planning, where an important aspect is computing paths that minimize energy consumption. Thereby, any method must cope with specific properties, such as recuperation, battery constraints (over- and under-charging), and frequently changing cost functions (e. g., due to weather conditions). This work presents a practical algorithm that quickly computes energy-optimal routes for networks of continental scale. Exploiting multi-level overlay graphs [26, 31], we extend the Customizable Route Planning approach [8] to our scenario in a sound manner. This includes the efficient computation of profile queries and the adaption of bidirectional search to battery constraints. Our experimental study uses detailed consumption data measured from a production vehicle (Peugeot iOn). It reveals for the network of Europe that a new cost function can be incorporated in about five seconds, after which we answer random queries within 0.3ms on average. Additional evaluation on an artificial but realistic [22, 36] vehicle model with unlimited range demonstrates the excellent scalability of our algorithm: Even for long-range queries across Europe it achieves query times below 5ms on average—fast enough for interactive applications. Altogether, our algorithm exhibits faster query times than previous approaches, while improving (metric-dependent) preprocessing time by three orders of magnitude. 1
Evolution and evaluation of the penalty method for alternative routes
- In ATMOS
, 2013
"... Computing meaningful alternative routes in a road network is a complex problem – already giving a clear definition of a best alternative seems to be impossible. Still, multiple methods [1, 2, 4, 17, 18] describe how to compute reasonable alternative routes, each according to their own quality criter ..."
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Computing meaningful alternative routes in a road network is a complex problem – already giving a clear definition of a best alternative seems to be impossible. Still, multiple methods [1, 2, 4, 17, 18] describe how to compute reasonable alternative routes, each according to their own quality criteria. Among these methods, the penalty method has received much less attention than the via-node or plateaux based approaches. A mayor cause for the lack of interest might be the unavailability of an efficient implementation. In this paper, we take a closer look at the penalty method and extend upon its ideas. We provide the first viable implementation –suitable for interactive use – using dynamic runtime adjustments to perform up to multiple orders of magnitude faster queries than previous implementations. Using our new implementation, we thoroughly evaluate the penalty method for its flaws and benefits.
Hub Label Compression
- IN PROCEEDINGS OF THE 12TH INTERNATIONAL SYMPOSIUM ON EXPERIMENTAL ALGORITHMS (SEA’13), VOLUME 7933 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2013
"... The hub labels (HL) algorithm is the fastest known technique for computing driving times on road networks, but its practical appli-cability can be limited by high space requirements relative to the best competing methods. We develop compression techniques that substantially reduce HL space requirem ..."
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The hub labels (HL) algorithm is the fastest known technique for computing driving times on road networks, but its practical appli-cability can be limited by high space requirements relative to the best competing methods. We develop compression techniques that substantially reduce HL space requirements with a small performance penalty.
Customizable Point-of-Interest Queries in Road Networks
"... We present a unified framework for dealing with exact point-of-interest (POI) queries in dynamic continental road networks within interactive applications. We show that partition-based algorithms developed for point-to-point shortest path computations can be naturally extended to handle augmented q ..."
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We present a unified framework for dealing with exact point-of-interest (POI) queries in dynamic continental road networks within interactive applications. We show that partition-based algorithms developed for point-to-point shortest path computations can be naturally extended to handle augmented queries such as finding the closest restaurant or the best post office to stop on the way home, always ranking POIs according to a user-defined cost function. Our solution allows different trade-offs between indexing effort (time and space) and query time. Our most flexible variant allows the road network to change frequently (to account for traffic information or personalized cost functions) and the set of POIs to be specified at query time. Even in this fully dynamic scenario, our solution is fast enough for interactive applications on continental road networks.
Computing Multimodal Journeys in Practice?
"... Abstract. We study the problem of finding multimodal journeys in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. A natural solution to this problem is to use multicriteria search, but it tends to be slow and to produce too many jou ..."
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Abstract. We study the problem of finding multimodal journeys in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. A natural solution to this problem is to use multicriteria search, but it tends to be slow and to produce too many journeys, several of which are of little value. We pro-pose algorithms to compute a full Pareto set and then score the solu-tions in a postprocessing step using techniques from fuzzy logic, quickly identifying the most significant journeys. We also propose several (still multicriteria) heuristics to find similar journeys much faster, making the approach practical even for large metropolitan areas. 1
