Dynamic demand estimation and prediction for traffic urban networks adopting new data sources. (August 2017)

• Record Type: Journal Article
• Authors: Carrese, Stefano; Cipriani, Ernesto; Mannini, Livia; Nigro, Marialisa
• Is Part Of: Transportation research. Volume 81(2017)
• Page Start: 83
• Page End: 98
• Publication Date: 2017-08
• Subjects: Traffic modelling -- Origin-destination (o-d) estimation/prediction -- Floating Car Data (FCD) -- Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm -- Local Ensemble Transformed Kalman Filter (LETKF); Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.05.013
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Dynamic demand estimation is faced with data from recent technology developments. Paths' information by Floating Car Data are included in the procedure (off-line). An extension of the Kalman filter theory has been experimented (on-line). This extension can englobe new sources of data such as the FCD themselves. Abstract: Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far. The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements. Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF). Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.