  Individual mobility prediction is an essential task for transportation demand
management and traffic system operation. There exist a large body of works on
modeling location sequence and predicting the next location of users; however,
little attention is paid to the prediction of the next trip, which is governed
by the strong spatiotemporal dependencies between diverse attributes, including
trip start time $t$, origin $o$, and destination $d$. To fill this gap, in this
paper we propose a novel point process-based model -- Attentive Marked temporal
point processes (AMTPP) -- to model human mobility and predict the whole trip
$(t,o,d)$ in a joint manner. To encode the influence of history trips, AMTPP
employs the self-attention mechanism with a carefully designed positional
embedding to capture the daily/weekly periodicity and regularity in individual
travel behavior. Given the unique peaked nature of inter-event time in human
behavior, we use an asymmetric log-Laplace mixture distribution to precisely
model the distribution of trip start time $t$. Furthermore, an
origin-destination (OD) matrix learning block is developed to model the
relationship between every origin and destination pair. Experimental results on
two large metro trip datasets demonstrate the superior performance of AMTPP.
