Bayesian Inference for Static Traffic Network Flows with Mobile Sensor Data

Vehicle trajectory information are becoming available from mobile sensors such as onboard devices or smart phones. Such data can provide partial information of origin-destination trips and are very helpful in solving the network flow estimation problem which can be very challenging if only link counts are used. Even with this new information, however, there is still structural bias in the maximum likelihood based approach because of uncertainties in the penetration rates. A Bayesian inference approach in which the earlier link-count-based methods are extended is proposed. We incorporate posterior simulation of route-choice probabilities and penetration rates. The results of a numerical example show that our method can infer network flow parameters effectively. Inclusion of mobile sensor data and prior beliefs based on it can yield much better inference results than when non-informative priors and only link counts are used

Optimal Ventilation Control in Complex Urban Tunnels with Multi-Point Pollutant Discharge

We propose an optimal ventilation control model for complex urban vehicular tunnels with distributed pollutant discharge points. The control problem is formulated as a nonlinear integer program that aims to minimize ventilation energy cost while meeting multiple air quality control requirements inside the tunnel and at discharge points. Based on the steady-state solutions to tunnel aerodynamics equations, we propose a reduced form model for air velocities as explicit functions of ventilation decision variables and traffic density. A compact parameterization of this model helps to show that tunnel airflows can be estimated using standard linear regression techniques. The steady-state pollutant dispersion model is then incorporated for the derivation of optimal pollutant discharge control strategies. A case study of a new urban tunnel in Hangzhou, China demonstrates that the scheduling of fan operations based on the proposed optimization model can effectively achieve different air quality control objectives under varying traffic intensity.U.S. Department of Transportation 69A355174711

Decorrelation of Neutral Vector Variables: Theory and Applications

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations