A hidden Markov model for route and destination prediction

Abstract

We present a simple model and algorithm for predicting driver destinations and routes, based on the input of the latest road links visited as part of an ongoing trip. The algorithm may be used to predict any clusters previously observed in a driver's trip history. It assumes that the driver's historical trips are grouped into clusters sharing similar patterns. Given a new trip, the algorithm attempts to predict the cluster in which the trip belongs. The proposed algorithm has low temporal complexity. In addition, it does not require the transition and emission matrices of the Markov chain to be computed. Rather it relies on the frequencies of co-occurrences of road links and trip clusters. We validate the proposed algorithm against an experimental dataset. We discuss the success and convergence of the algorithm and show that our algorithm has a high prediction success rate.

Bayesian classifier for Route prediction with Markov chains

“…The algorithm resulting if Markov chains are used as the stochastic process models is described in detail in Section 4, and Section 5 provides some experimental validation. We close with some possible extensions and improvements, and the observation that [1] also fits into the presented framework, if the stochastic process model is chosen to be a naive Bayes model instead of Markov chains.…”

“…The present paper falls into that latter category, as it builds on and extends the recent work of [1] and contributes to a novel and flexible approach to the important problem of driver intent prediction. It is structured as follows: after introducing some notation relating to probability, stochastic processes, and Markov chains in the next section, we then show in Section 3 how trips can be modelled as outputs of stochastic processes to obtain an estimate of the posterior probabilities of each known journey pattern.…”

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We present here a general framework and a specific algorithm for predicting the destination, route, or more generally a pattern, of an ongoing journey, building on the recent work of [1]. In the presented framework, known journey patterns are modelled as stochastic processes, emitting the road segments visited during the journey, and the ongoing journey is predicted by updating the posterior probability of each journey pattern given the road segments visited so far.

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Internet of things and virtual sensors for electromobility

“…In particular, a review and classification of the estimation strategies and systems for hybrid and EVs is provided by Kuma and Koroglu, affirming that estimation of any fault, state, or information play an important role in ensuring vehicles stability and reliability and in providing new vehicle services. Estimation and prediction strategies and models are applied to the following macroareas: battery management; vehicle energy management and control ; and route management …”

“…Estimation and prediction strategies and models are applied to the following macroareas: battery management 6 ; vehicle energy management and control 6,9 ; and route management. 10 With respect to the existing literature, the novelty introduced by this paper stands in providing innovative and value-added services to the electromobility domain in order to satisfy specific requirements. In particular, this paper proposes innovative services mainly related to the charge need of EV drivers, based on parameters estimation/prediction provided by the VS.…”