Travel Behavior Characterization Using Raw Accelerometer Data Collected from Smartphones

In this paper, we compare different algorithms for the recognition of transportation modes based on features extracted from the accelerometer data. The performance and effectiveness of the transportation mode classifiers presented is evaluated and their accuracy is discussed. The data set used for training and testing algorithms was collected by a group of volunteers in the city of Valencia in 2013; an Android application designed for the recording of trips and transportation modes application was installed on their smartphones. This application collected GPS readings each 10-12seconds and accelerometer data at 1Hz. While GPS data was only used for the validation of trips for the training of the algorithms, accelerometer readings were used entirely for their training. Results show the high performance of Recurrent Neural Networks in recognizing travel modes using accelerometer data.Ferrer López, S.; Ruiz Sánchez, T. (2014). Travel Behavior Characterization Using Raw Accelerometer Data Collected from Smartphones. Procedia Social and Behavioral Sciences. 160:140-149. doi:10.1016/j.sbspro.2014.12.125S14014916

Mobile Systems Location Privacy “MobiPriv” a Robust K-Anonymous System

ABSTRACT Wi th the rapid advancement of positioning and tracking capabilities (mobile phones, on-board navigation systems) location based services are rapidly increasing. Privacy in location based systems is addressed in many papers. Our work is focused on the trusted third party privacy framework that utilizes the concept of k-anonymity with or without I-diversity. In previous anonymization models k may be defined as a personalization parameter of the mobile user or as uniform system parameter for all mobile users . Clearly, k other users may not be available at the time of request in these systems. These requests are discarded because the quality of service (QoS) they require cannot be satisfied. In this paper we introduce a novel suite of algorithms called MobiPriv that guarantees a 100% success rate of processing a mobile request using k anonymity with diversity considerations. We evaluated our suite of algorithms experimentally against previously proposed anonymization algorithms using real world traffic volume data, real world road network and mobile users generated realistically by a mobile object generator

Citywide traffic congestion estimation with social media

Conventional traffic congestion estimation approaches require the deployment of traffic sensors or large-scale probe vehicles. The high cost of deploying and maintaining these equipments largely limits their spatial-temporal coverage. This paper proposes an alternative solution with lower cost and wider spatial coverage by exploring traffic related information from Twitter. By regarding each Twitter user as a traffic monitoring sensor, various real-time traffic information can be collected freely from each corner of the city. However, there are two major challenges for this problem. Firstly, the congestion related information extracted directly from real-time tweets are very sparse due both to the low resolution of geographic location mentioned in the tweets and the inherent sparsity nature of Twitter data. Secondly, the traffic event information coming from Twitter can be multi-typed including congestion, accident, road construction, etc. It is non-trivial to model the potential impacts of diverse traffic events on traffic congestion. We propose to enrich the sparse real-time tweets from two directions: 1) mining the spatial and temporal correlations of the road segments in congestion from historical data, and 2) applying auxiliary information including social events and road features for help. We finally propose a coupled matrix and tensor factorization model to effectively integrate rich information for Citywide Traffic Congestion Eestimation (CTCE). Extensive evaluations on Twitter data and 500 million public passenger buses GPS data on nearly 700 mile roads of Chicago demonstrate the efficiency and effectiveness of the proposed approach

Unlocking the Smartphone's Sensors for Smart City Parking

An early version of this work appeared in the Proc. of IEEE ICC 2016 (Krieg et al., 2016)International audienceStudies have shown that drivers often spend over 20 min cruising for parking in city centers, accounting for as much as 30% of the traffic congestion. In response, cities like San Francisco have deployed systems capable of providing drivers real-time parking availability information. However, such systems rely on specialized infrastructure whose installation and maintenance costs in the tens of millions of dollars, unaffordable for many cities. We present SmartPark, a system for real-time parking information that relaxes the requirement for specialized infrastructure, relying instead on the smartphone’s sensors and the ubiquitous Wi-Fi and cellular infrastructure. To accomplish this, SmartPark addresses two major challenges, under the constraint of minimum impact on battery life: transportation mode detection and location matching. To minimize initial deployment cost and risk, SmartPark introduces an analytical approach for estimating parking availability even when only a small fraction of users adopt the application. We evaluate SmartPark using simulations and in the wild. Simulation results show that SmartPark, benefiting from as little as 20% adoption rate, can estimate parking availability with accuracy above 90%. Experimental results with the help of 12 volunteers show that SmartPark detects unparking events 97% of the time while triggering zero false positives