49 research outputs found
Large-scale Analysis and Simulation of Traffic Flow using Markov Models
Modeling and simulating movement of vehicles in established transportation
infrastructures, especially in large urban road networks is an important task.
It helps with understanding and handling traffic problems, optimizing traffic
regulations and adapting the traffic management in real time for unexpected
disaster events. A mathematically rigorous stochastic model that can be used
for traffic analysis was proposed earlier by other researchers which is based
on an interplay between graph and Markov chain theories. This model provides a
transition probability matrix which describes the traffic's dynamic with its
unique stationary distribution of the vehicles on the road network. In this
paper, a new parametrization is presented for this model by introducing the
concept of two-dimensional stationary distribution which can handle the
traffic's dynamic together with the vehicles' distribution. In addition, the
weighted least squares estimation method is applied for estimating this new
parameter matrix using trajectory data. In a case study, we apply our method on
the Taxi Trajectory Prediction dataset and road network data from the
OpenStreetMap project, both available publicly. To test our approach, we have
implemented the proposed model in software. We have run simulations in medium
and large scales and both the model and estimation procedure, based on
artificial and real datasets, have been proved satisfactory. In a real
application, we have unfolded a stationary distribution on the map graph of
Porto, based on the dataset. The approach described here combines techniques
whose use together to analyze traffic on large road networks has not previously
been reported
Markov modeling of traffic flow in Smart Cities
Modeling and simulating the traffic flow in large urban road networks are important tasks. A mathematically rigorous stochastic model proposed in [8] is based on the synthesis of the graph and Markov chain theories. In this model, the transition probability matrix describes the traffic dynamics and its unique stationary distribution approximates the proportion of the vehicles at the segments of the road network. In this paper various Markov models are studied and a simulation method is presented for generating random traffic trajectories on a road network based on the two-dimensional stationary distribution of the models. In a case study we apply our method to the central region of the city of Debrecen by using the road network data from the OpenStreetMap project which is available publicly
Markov modeling of traffic flow in Smart Cities
Modeling and simulating the traffic flow in large urban road networks are
important tasks. A mathematically rigorous stochastic model proposed in [8]
is based on the synthesis of the graph and Markov chain theories. In this
model, the transition probability matrix describes the traffic dynamics and
its unique stationary distribution approximates the proportion of the vehicles
at the segments of the road network. In this paper various Markov models
are studied and a simulation method is presented for generating random
traffic trajectories on a road network based on the two-dimensional stationary
distribution of the models. In a case study we apply our method to the
central region of the city of Debrecen by using the road network data from
the OpenStreetMap project which is available publicly