The on-demand economy has attracted significant attention in recent years, with a rapid growth in on-demand services ranging from ride-hailing to package delivery and grocery pickup. However, real-world spatio-temporal data that can be used for evaluating research on on-demand brokers design and supply-demand regulation are either not publicly available or are very limited in their spatial coverage. Research efforts in generating synthetic spatio-temporal datasets such as traffic generators have only focused on one side of the business model, particularly the demand side, and thus are not convenient for studying market variations such as the problem of supply-demand imbalance. In addition, many of these generators do not accurately reflect real-world data characteristics. In this paper, we propose a supply and demand aware framework for generating synthetic datasets for the purpose of designing on-demand spatial service brokers, while also capturing real-world data characteristics by leveraging multiple publicly available data sources. We also present an evaluation of the quality and performance of our proposed framework.
|Original language||English (US)|
|Title of host publication||Proceedings of IWCTS 2017|
|Subtitle of host publication||10th ACM SIGSPATIAL International Workshop on Computational Transportation Science|
|Publisher||Association for Computing Machinery|
|Number of pages||6|
|ISBN (Electronic)||1595930361, 9781450354912|
|State||Published - Nov 7 2017|
|Event||10th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2017 - Redondo Beach, United States|
Duration: Nov 7 2017 → …
|Name||ACM International Conference Proceeding Series|
|Other||10th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2017|
|Period||11/7/17 → …|
Bibliographical noteFunding Information:
This material is based upon work supported by FORD University Research Program (URP), the National Science Foundation under Grant No. IIS-1320580 and 1737633, the USDOE under Grant No. DE-AR0000795, the USDA under Grant No. 2017-51181-27222, and the Minnesota Supercomputing Institute (MSI) at the University of Minnesota (www.msi.umn.edu). We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.
© 2017 Association for Computing Machinery.
- Demand aware
- On-demand brokers
- On-demand services
- Spatial service broker
- Synthetic data generation
- Synthetic data with real-world characteristics