A system for estimating a future traffic density in an environment, wherein the environment includes multiple ingress points and multiple egress points, the system comprising: at least one processor; and a memory having instructions stored thereon that cause the at least one processor of the system to: receive, for at least one object, at least one partial trajectory and a sequence of observation vectors, wherein the at least one object is moving from an ingress point of the multiple ingress points to an egress point of the multiple egress points, and wherein the at least one object is represented by a plurality of particles; process: the at least one partial trajectory with a trajectory prediction model trained to predict a probabilistic distribution of a location of the at least one object over different locations in the environment at a future time instant; and predict, based on the predicted probabilistic distribution, a location of each particle of the plurality of particles at the future time instant; and the sequence of observation vectors with an entering particle prediction model trained to predict a probability of observing an entering particle at each ingress point at the future time instant; and estimate the future traffic density for the future time instant with an iterative sampling model based on the predicted location of each particle and the predicted probability of observing the entering particle at each ingress point at the future time instant, wherein at least one of the following a) -b) is provided: a) wherein the environment is an indoor space including multiple cabins, and wherein the at least one processor is further configured to deploy at least one service robot (519a-d) to at least one cabin of the multiple cabins, based on the estimated future traffic density in the environment; or wherein the environment is an outdoor space including multiple roads and a traffic light at a junction of the multiple roads, and wherein the at least one processor is further configured to control the traffic light based on the estimated future traffic density in the environment; b) wherein the sequence of observation vectors is obtained from cameras installed in the environment. receive, for at least one object, at least one partial trajectory and a sequence of observation vectors, wherein the at least one object is moving from an ingress point of the multiple ingress points to an egress point of the multiple egress points, and wherein the at least one object is represented by a plurality of particles; process: the at least one partial trajectory with a trajectory prediction model trained to predict a probabilistic distribution of a location of the at least one object over different locations in the environment at a future time instant; and predict, based on the predicted probabilistic distribution, a location of each particle of the plurality of particles at the future time instant; and the sequence of observation vectors with an entering particle prediction model trained to predict a probability of observing an entering particle at each ingress point at the future time instant; and the at least one partial trajectory with a trajectory prediction model trained to predict a probabilistic distribution of a location of the at least one object over different locations in the environment at a future time instant; and predict, based on the predicted probabilistic distribution, a location of each particle of the plurality of particles at the future time instant; and the sequence of observation vectors with an entering particle prediction model trained to predict a probability of observing an entering particle at each ingress point at the future time instant; and estimate the future traffic density for the future time instant with an iterative sampling model based on the predicted location of each particle and the predicted probability of observing the entering particle at each ingress point at the future time instant, a) wherein the environment is an indoor space including multiple cabins, and wherein the at least one processor is further configured to deploy at least one service robot (519a-d) to at least one cabin of the multiple cabins, based on the estimated future traffic density in the environment; or wherein the environment is an outdoor space including multiple roads and a traffic light at a junction of the multiple roads, and wherein the at least one processor is further configured to control the traffic light based on the estimated future traffic density in the environment; b) wherein the sequence of observation vectors is obtained from cameras installed in the environment.