Abstract
In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.
Highlights
Traffic state estimation is of a paramount importance for the implementation of Intelligent Transportation Systems (ITSs) in smart cities of the future [1]
In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only
We propose a new data-driven traffic state estimation approach that relies on infrastructure-less smart city sensing technologies and can be used to support the implementation of traffic management services of smart cities in developing countries
Summary
Traffic state estimation is of a paramount importance for the implementation of Intelligent Transportation Systems (ITSs) in smart cities of the future [1]. A new traffic state estimation algorithm based on the Conditionally Gaussian Observed Markov Fuzzy Switching model (CGOMFSM) and which relies on an explicit representation of the dependence between the traffic flow, speed and state, represented as stochastic processes. To the best of our knowledge, none of the previous research works has explored the use of Conditionally Gaussian Observed Markov Switching Model for traffic state estimation in general, and for traffic flow estimation from aggregated mobile speed measures. P section spanning a time interval T = In, where In are n=0 time periods of equal length, the goal is to determine a model that can be used to (i) explicitly represent the dependence between the stochastic processes F and V using an auxiliary process S and (ii) construct a tractable procedure to estimate the traffic state from the speed data V observed during a.
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