Deep Survival Modelling for Shared Mobility

With an increased focus on minimizing traffic externalities in metropolitan areas, a growing interest in environmentally friendly and shared mobility systems has emerged, such as electric car-sharing systems. However, increasing demand and larger area coverage often make it difficult to keep cars available where and when customers need them. This problem can be alleviated by predicting for how long cars stay vacant at given pick-up/drop-off locations. To maximize their usage, it can be more beneficial to relocate the cars at certain periods to more desired locations. In this paper, we tackle the problem of predicting time-to-pickup for shared cars in a probabilistic way as a function of time by applying time-to-event modelling through survival analysis. Both statistical and deep neural network approaches to survival regression were investigated. The Cox proportional hazards model (CPH) is compared to the deep neural network model DeepSurv. To predict survival times, a two-step approach was formulated, where in the upper level a classification is used to classify cars into two groups based on idle time duration, whereas in the lower level for each given group time-to-event modelling is applied. DeepSurv method demonstrated a stronger fit compared to CPH. The two-step approach resulted in over 15% improvement in performance, comparing to the one-step approach, where no classification is used.