Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and the efficiency of our proposed algorithms.
{"title":"Historical traffic-tolerant paths in road networks","authors":"Pui Hang Li, Man Lung Yiu, K. Mouratidis","doi":"10.1145/2666310.2666483","DOIUrl":"https://doi.org/10.1145/2666310.2666483","url":null,"abstract":"Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and the efficiency of our proposed algorithms.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The problem of finding an electric vehicle route that optimizes both driving time and energy consumption can be modeled as a bicriterion path problem. Unfortunately, the problem of finding optimal bicriterion paths is NP-complete. This paper studies such problems restricted to two-phase paths, which correspond to a common way people drive electric vehicles, where a driver uses one driving style (say, minimizing driving time) at the beginning of a route and another driving style (say, minimizing energy consumption) at the end. We provide efficient polynomial-time algorithms for finding optimal two-phase paths in bicriterion networks, and we empirically verify the effectiveness of these algorithms for finding good electric vehicle driving routes in the road networks of various U.S. states. In addition, we show how to incorporate charging stations into these algorithms.
{"title":"Two-phase bicriterion search for finding fast and efficient electric vehicle routes","authors":"M. Goodrich, Pawel Pszona","doi":"10.1145/2666310.2666382","DOIUrl":"https://doi.org/10.1145/2666310.2666382","url":null,"abstract":"The problem of finding an electric vehicle route that optimizes both driving time and energy consumption can be modeled as a bicriterion path problem. Unfortunately, the problem of finding optimal bicriterion paths is NP-complete. This paper studies such problems restricted to two-phase paths, which correspond to a common way people drive electric vehicles, where a driver uses one driving style (say, minimizing driving time) at the beginning of a route and another driving style (say, minimizing energy consumption) at the end. We provide efficient polynomial-time algorithms for finding optimal two-phase paths in bicriterion networks, and we empirically verify the effectiveness of these algorithms for finding good electric vehicle driving routes in the road networks of various U.S. states. In addition, we show how to incorporate charging stations into these algorithms.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgios Skoumas, Klaus Arthur Schmid, Gregor Jossé, Andreas Züfle, M. Nascimento, M. Renz, D. Pfoser
Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as "nearby" or "next to" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the computed paths yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.
{"title":"Towards knowledge-enriched path computation","authors":"Georgios Skoumas, Klaus Arthur Schmid, Gregor Jossé, Andreas Züfle, M. Nascimento, M. Renz, D. Pfoser","doi":"10.1145/2666310.2666485","DOIUrl":"https://doi.org/10.1145/2666310.2666485","url":null,"abstract":"Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as \"nearby\" or \"next to\" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the computed paths yield competitive solutions in terms of path length while also providing more \"popular\" paths, making routing easier and more informative for the user.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116380488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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