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Spatial-Temporal Demand Forecasting and Competitive Supply via Graph Convolutional Networks
arXiv - CS - Databases Pub Date : 2020-09-24 , DOI: arxiv-2009.12157
Bolong Zheng, Qi Hu, Lingfeng Ming, Jilin Hu, Lu Chen, Kai Zheng, Christian S. Jensen

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 assignment authority is to assign 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 requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem 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) algorithm that predicts the service 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 supplydemand state. We report on extensive experiments with realworld and synthetic data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.

中文翻译:

通过图卷积网络进行时空需求预测和竞争供应

我们考虑一个设置,其中包含在截止日期之前从起点到目的地的一系列不断发展的运输请求,以及一组能够为这些请求提供服务的代理。在此设置中,分配权限将向请求分配代理,从而使代理的平均空闲时间最小化。一个例子是调度出租车(代理)以满足传入的旅行请求,同时确保出租车尽可能少空。在本文中,我们研究了时空需求预测和竞争性供应(SOUP)的问题。我们分两步解决这个问题。首先,我们构建了一个粒度模型,提供请求的时空预测。具体来说,我们提出了一种时空图卷积序列学习 (ST-GCSL) 算法,该算法可以预测跨位置和时隙的服务请求。其次,我们提供路由代理的方法来请求源,同时避免代理之间的竞争。特别是,我们开发了一种需求感知路线规划 (DROP) 算法,该算法同时考虑了时空预测和供需状态。我们报告了对真实世界和合成数据的广泛实验,这些实验提供了对解决方案性能的洞察,并表明它能够胜过最先进的建议。我们开发了一种需求感知路线规划 (DROP) 算法,该算法同时考虑了时空预测和供需状态。我们报告了对真实世界和合成数据的广泛实验,这些实验提供了对解决方案性能的洞察,并表明它能够胜过最先进的建议。我们开发了一种需求感知路线规划 (DROP) 算法,该算法同时考虑了时空预测和供需状态。我们报告了对真实世界和合成数据的广泛实验,这些实验提供了对解决方案性能的洞察,并表明它能够胜过最先进的建议。
更新日期:2020-09-28
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