Type
ArticleKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2015-12-17Online Publication Date
2015-12-17Print Publication Date
2016-05-01Permanent link to this record
http://hdl.handle.net/10754/592626
Metadata
Show full item recordAbstract
Travel planning and recommendation are important aspects of transportation.We propose and investigate a novel Collective Travel Planning (CTP) query that finds the lowest-cost route connecting multiple sources and a destination, via at most k meeting points. When multiple travelers target the same destination (e.g., a stadium or a theater), they may want to assemble at meeting points and then go together to the destination by public transport to reduce their global travel cost (e.g., energy, money, or greenhouse-gas emissions). This type of functionality holds the potential to bring significant benefits to society and the environment, such as reducing energy consumption and greenhouse-gas emissions, enabling smarter and greener transportation, and reducing traffic congestions. The CTP query is Max SNP-hard. To compute the query efficiently, we develop two algorithms, including an exact algorithm and an approximation algorithm. The exact algorithm is capable finding the optimal result for small values of k (e.g., k = 2) in interactive time, while the approximation algorithm, which has a 5-approximation ratio, is suitable for other situations. The performance of the CTP query is studied experimentally with real and synthetic spatial data.Citation
Collective Travel Planning in Spatial Networks 2015:1 IEEE Transactions on Knowledge and Data Engineeringae974a485f413a2113503eed53cd6c53
10.1109/TKDE.2015.2509998