Transportation Mode Detection Based on Permutation Entropy and Extreme Learning Machine

Lei Zhang,Xiaomei Zou,Suning Bao,Mengting Qiang,Leijun Liu

doi:10.1155/2015/940624

Abstract

With the increasing prevalence of GPS devices and mobile phones, transportation mode detection based on GPS data has been a hot topic in GPS trajectory data analysis. Transportation modes such as walking, driving, bus, and taxi denote an important characteristic of the mobile user. Longitude, latitude, speed, acceleration, and direction are usually used as features in transportation mode detection. In this paper, first, we explore the possibility of using Permutation Entropy (PE) of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection. Second, we employ Extreme Learning Machine (ELM) to distinguish GPS trajectory segments of different transportation. Finally, to evaluate the performance of the proposed method, we make experiments on GeoLife dataset. Experiments results show that we can get more than 50% accuracy when only using PE as a feature to characterize trajectory sequence. PE can indeed be effectively used to detect transportation mode from GPS trajectory. The proposed method has much better accuracy and faster running time than the methods based on the other features and SVM classifier.

Highlights

With the increasing prevalence of positioning technologies, GPS mobile devices, smartphones, and so forth are equipped with multiple sensors [1]
First, we explore the possibility of using Permutation Entropy (PE) of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection
Permutation Entropy estimates the complexity of time series through the comparison of neighboring values

Summary

Introduction

With the increasing prevalence of positioning technologies, GPS mobile devices, smartphones, and so forth are equipped with multiple sensors [1]. Transportation mode detection from GPS data has been studied in the literature. Stenneth et al considered transportation network data which consist of real time locations of buses, rail lines, and bus stops spatial data [9] This approach can achieve over 93.5% accuracy for inferring various transportation modes. Reddy et al combined GPS sensor data with accelerometer data to detect the modes of transportation [10]. They select GPS speed, accelerometer variance, and accelerometer DFT as features. We propose to use PE as a feature for the transportation mode detection.

Transportation Mode Detection with PE and ELM

Extreme Learning Machine

Experiments Evaluation

Experimental Results

Activation function

Conclusions