TY - JOUR
T1 - SOUP
T2 - Spatial-Temporal Demand Forecasting and Competitive Supply in Transportation
AU - Zheng, Bolong
AU - Hu, Qi
AU - Ming, Lingfeng
AU - Hu, Jilin
AU - Chen, Lu
AU - Zheng, Kai
AU - Jensen, Christian S.
N1 - Publisher Copyright:
IEEE
PY - 2023/2
Y1 - 2023/2
N2 - We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an authority assigns agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. We address the problem of spatial-Temporal demand forecasting and competitive supply (SOUP) in two steps. First, we build a granular model that provides spatial-Temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) model that predicts requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-Aware route planning (DROP) algorithm that considers both the spatial-Temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-The-Art proposals.
AB - We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an authority assigns agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. We address the problem of spatial-Temporal demand forecasting and competitive supply (SOUP) in two steps. First, we build a granular model that provides spatial-Temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) model that predicts requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-Aware route planning (DROP) algorithm that considers both the spatial-Temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-The-Art proposals.
KW - Data models
KW - Demand forecasting
KW - graph convolutional networks
KW - Planning
KW - Predictive models
KW - Public transportation
KW - Roads
KW - route planning
KW - Spatial-temporal request forecasting
KW - Vehicles
KW - Spatial-Temporal request forecasting
UR - http://www.scopus.com/inward/record.url?scp=85115135608&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2021.3110778
DO - 10.1109/TKDE.2021.3110778
M3 - Journal article
AN - SCOPUS:85115135608
SN - 1041-4347
VL - 35
SP - 2034
EP - 2047
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 2
ER -