CAUSAL PROBABILISTIC SPATIO-TEMPORAL FUSION IN TWO-SIDED RIDE-HAILING MARKETS

Abstract

Achieving accurate spatio-temporal predictions in large-scale systems is extremely valuable in many real-world applications, such as weather forecasts, retail forecasting, and urban traffic forecasting. So far, most existing methods for multi-horizon, multi-task and multitarget predictions select important predicting variables via their correlations with responses of interest, and thus it is highly possible that many forecasting models generated from those methods are not causal, leading to poor interpretability. The aim of this paper is to develop a collaborative causal spatio-temporal fusion transformer, named CausalTrans, to establish the collaborative causal effects of predictors on multiple forecasting targets, such as supply and demand in ride-sharing platforms. Specifically, we integrate the causal attention with the Conditional Average Treatment Effect (CATE) estimation method in causal inference. Moreover, we propose a novel and fast multi-head attention evolved from Taylor's expansion instead of softmax, reducing time complexity from O(V 2 ) to O(V), where V is the number of nodes in a graph. We further design a spatial graph fusion mechanism to significantly reduce the parameters' scale. We conduct a wide range of experiments to demonstrate the interpretability of causal attention, the effectiveness of various model components, and the time efficiency of our CausalTrans. As shown in these experiments, our CausalTrans framework can achieve up to 15% error reduction compared with various baseline methods.

1. INTRODUCTION

This paper is motivated by solving a collaborative probabilistic forecasting problem of both supply and demand in two-sided ride-hailing platforms, such as Uber and DiDi. Collaborative supply and demand relationships are common in various two-sided markets, such as Amazon, Airbnb, and eBay. We consider two-sided ride-hailing platforms as an example. In this case, we denote supply and demand as online driver number and call orders, respectively, on the platform at a specific time in a city. Some major factors for demand include rush hours, weekdays, weather conditions, transportation network, points of interest, and holidays. For instance, if it rains during peak hours in weekdays, demand will dramatically increase and last for a certain time period. In contrast, some major factors for supply include weather, holidays, traffic condition, weekdays, and platform's dispatching and repositioning policies. Moreover, supply tends to gradually cover the area with many unsatisfied orders, that is, the distribution of supply tends to match with that of demand. We are interested in establishing collaborative causal forecasting models for demand and supply by using various predictors (or covariates). Although many learning methods have been developed to address various collaborative prediction tasks, such as spatio-temporal traffic flow prediction (Zhu & Laptev, 2017; Du et al., 2018; Zhang et al., 2019b; Ermagun & Levinson, 2018; Luo et al., 2019) , multivariate prediction (Bahadori et al., 2014; Liang et al., 2018 ), multi-task prediction (Tang et al., 2018; Chen et al., 2018; Chandra et al., 1 

