com.graphhopper.matching.util.HmmProbabilities Maven / Gradle / Ivy
/**
* Copyright (C) 2015-2016, BMW Car IT GmbH and BMW AG
* Author: Stefan Holder ([email protected])
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.graphhopper.matching.util;
/**
* Based on Newson, Paul, and John Krumm. "Hidden Markov map matching through
* noise and sparseness." Proceedings of the 17th ACM SIGSPATIAL International
* Conference on Advances in Geographic Information Systems. ACM, 2009.
*/
public class HmmProbabilities {
private final double sigma;
private final double beta;
/**
* @param sigma standard deviation of the normal distribution [m] used for
* modeling the GPS error
* @param beta beta parameter of the exponential distribution used for modeling
* transition probabilities
*/
public HmmProbabilities(double sigma, double beta) {
this.sigma = sigma;
this.beta = beta;
}
/**
* Returns the logarithmic emission probability density.
*
* @param distance Absolute distance [m] between GPS measurement and map
* matching candidate.
*/
public double emissionLogProbability(double distance) {
return Distributions.logNormalDistribution(sigma, distance);
}
/**
* Returns the logarithmic transition probability density for the given
* transition parameters.
*
* @param routeLength Length of the shortest route [m] between two
* consecutive map matching candidates.
* @param linearDistance Linear distance [m] between two consecutive GPS
* measurements.
*/
public double transitionLogProbability(double routeLength, double linearDistance) {
// Transition metric taken from Newson & Krumm.
Double transitionMetric = Math.abs(linearDistance - routeLength);
return Distributions.logExponentialDistribution(beta, transitionMetric);
}
}