Addressing errors in automated sensor data for real‐time traffic state estimation using dynamical systems approach

Fulari, Shrikant; Vanajakshi, Lelitha; Subramanian, Shankar C.

doi:10.1049/iet-its.2016.0041

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…A data fusion approach using both location-based and spatial data sources was explained by Anand et al in [24] and a nonlinear model, based on the conservation principle and the fundamental traffic flow was presented in [25] by Dhivyabharathi et al In [26], Bekiaris-liberis et al developed linear parameter-varying models derived from the conservation equation with mixed connected and conventional vehicles. Fulari et al [27] developed a data fusion-based estimation scheme by using the dynamical systems approach. Nantes et al developed a model-based methodology to build a real-time traffic prediction model for arterial corridors using data from multiple sources in [28].…”

Section: Introductionmentioning

confidence: 99%

Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic

2020

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Managing congestion in mixed traffic conditions, characterized by heterogeneous and lane-less traffic, is a challenging task. Traditionally density, defined as the number of vehicles in a road stretch, is used to quantify congestion. However, direct measurement of density is difficult and hence is usually estimated from other variables. In this paper, a relationship is derived between traffic density and area occupancy, a variable that can incorporate heterogeneity and lane-less movement. Using the derived density-area occupancy relation, a non-continuum macroscopic single state linear time varying model was developed. Estimation of density was done by using the Kalman filtering technique and corroborated with simulated density. The need for dynamic estimation is motivated by evaluating the performance of two static estimation schemes in the presence of uncertainties. Performance was tested for different traffic scenarios such as congestion and non-recurrent traffic incidents. Further, to improve the estimation accuracy in scenarios involving transitions in traffic conditions, an adaptive estimator was developed. It was found that the adaptive estimator provided the best estimation accuracy. INDEX TERMS Adaptive Kalman filter, area occupancy, heterogeneous traffic, lane-less traffic, traffic density estimation.

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