Density and flow reconstruction in urban traffic networks using heterogeneous data sources

Abstract

In this paper, we consider the problem of joint reconstruction of flow and density in a urban traffic network using heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards model (LWR) conservation equation characterized by a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm.

Dynamic density and flow reconstruction in large-scale urban networks using heterogeneous data sources

“…In Nantes et al (2016), an approach based on the CTM is coupled with a KF using measurements from flow sensors, BT detectors, and GPS traces, to estimate the density in a section of an urban arterial road. In Ladino et al (2018), FCD is used to determine if each road is in free-flow or congested regime, and then uses the flow-density FD and stationary sensor data to provide density estimates to each road in the traffic network. More recently, Rostami Shahrbabaki et al (2020) proposed a data-based density estimation method that does not require the use of an FD.…”

Flow and Density Estimation in Grenoble Using Real Data

“…The extended version of the CTM developed in [11] brings a solution to this problem via a flow maximization formulation under constraints provided by the fundamental diagram. This approach is widely used as can be seen in [12,13]. However, the use of the fundamental diagram, especially in urban networks, is challenging as it requires the calibration of many parameters.…”

Average density estimation for urban traffic networks: Application to the Grenoble network

“…This estimates are then the input to a Luenberger-like observer. In Ladino et al (2018), FCD is used to determine if each road is in free-flow or congested regime, and then uses the flow-density fundamental diagram and stationary sensor data to provided density estimates to each road in the traffic network. More recently, Rostami Shahrbabaki et al (2020) proposes a data-based density estimation method that does not require the use of a fundamental diagram.…”