01590311

Component

With the advance of smartphone technology and prevalence of smartphone ownership, numerous apps are being made available in the market to enhance the quality of our life. In transportation, a plethora of apps have been developed by researchers to collect trip and activity data for travel mode detection, trip experience, and traveler behavior analysis. However, one fundamental concern about the smartphone-based data collection apps is the battery power consumption. When the battery power depletes rapidly from continuous operation of multiple sensors (especially the GPS receiver) on the smartphone, the requirement (and inconvenience) of frequent battery recharge often outweighs the potential benefit of using those apps. In this work, the authors present an approach to conserving smartphone’s battery power using the information from the surrounding Wi-Fi networks, i.e., using the Wi-Fi signal fingerprint technique. In particular, the authros build upon the SmarTrAC app (recently developed by our research group). This smartphone app is designed to incorporate smartphone sensing with advanced statistical and machine learning techniques to automatically detect, identify, and summarize attributes of a traveler’s daily trips and activities. The current approach to battery conservation employed by SmarTrAC was to implement a motion detection procedure using the smartphone’s accelerometer. Specifically, SmarTrAC turns off the GPS sensor to conserve battery power when no motion is detected for a period of time (i.e., no need to track the location if the person is not moving). Similarly, the app restarts the GPS sensor upon detected motion, which is an indicator that a new trip may be starting. However, the authors find that motion detection alone is an insufficient (and less reliable) indicator for handling the GPS sensor, especially for indoor settings, as it often creates false alarms due to non-trip related phone use or movement. In this work, the authors propose to use two indices, Jaccard and normalized weighted signal level change (NWSLC), using the Wi-Fi signal fingerprint technique to detect a location change. Both measures are computed based on the information about the received Wi-Fi signal strength, obtaining which is substantially less demanding on the smartphone battery than obtaining continuous GPS information. The proposed measures were implemented on a test app for verification and validation. Experiments were conducted at 10 different indoor locations (representing potential trip origins) to determine the appropriate numeric thresholds for location change detection. The results suggest that proposed Wi-Fi fingerprint methodology can reliably detect the change of a person’s location prior to resuming the GPS service for trip data collection.

This paper was sponsored by TRB committee ABJ50 Standing Committee on Information Systems and Technology.

01584066

16-2090

English

20p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; References; Tables

Automatic data collection systems; Electric batteries; Location; Methodology; Smartphones; Travel surveys

WiFi services

Data and Information Technology; Highways; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2016 Paper #16-2090

TRIS, TRB, ATRI

Jan 12 2016 4:55PM