Modelling of Accelerometer Data for Travel Mode Detection by Hierarchical Application of Binomial Logistic Regression

AbstractHousehold trip data collection is essential for design and construction of transportation infrastructure. Conventionally, this information is collected by travel surveys, which require the respondents to answer a list of questions targeting their daily travelling. As the responses depend on the memory of the respondents, inaccuracies usually occur during the reporting process. To improve the accuracy of the collected data, a lot of research is currently being focused on inferring the important information from data collected automatically with the help of devices like smartphones. The current study proposes a new method for identifying the travel mode, by applying the binomial logistic regression in a hierarchical manner, using the data collected by the accelerometer of the smartphone. Three methods of application are discussed, namely ranking, one against rest and one against all. Apart from train, all the other modes are successfully modelled with goodness of fit approaching to 1. Low goodness of fit in case of train is due to the wide range of accelerations recorded. Although, all the three methods exhibit good outcomes, one against all method provides relatively better results

Vehicle point of interest detection using in-car data

Intelligent transportation systems often identify and make use of locations extracted from GPS trajectories to make informed decisions. However, many of the locations identified by existing systems are false positives, such as those in heavy traffic. Signals from the vehicle, such as speed and seatbelt status, can be used to identify these false positives. In this paper, we (i) demonstrate the utility of the Gradient-based Visit Extractor (GVE) in the automotive domain, (ii) propose a classification stage for removing false positives from the location extraction process, and (iii) evaluate the effectiveness of these techniques in a high resolution vehicular dataset

Review of transportation mode detection approaches based on smartphone data

The usage of smartphones has rapidly increased during the last years. In addition to communication capabilities, they are also equipped with several sensors, and are usually carried by people throughout the day. The data collected by the means of modern smartphones (e.g. location based, GSM, and other contextual data) are thus valuable source of information for transportation analysis. In this paper we focus on smartphone data used for transportation mode detection. This is important for many applications including urban planning, context related advertisements or supply planning by public transportation entities. We present a review of the existing approaches for transportation mode detection, and compare them in terms of (i) the type and the number of used input data, (ii) the considered transportation mode categories and (iii) the algorithm used for the classification task. We consider these aspects as the most relevant when evaluating the performance of the analyzed approaches. Finally, the paper identifies the gaps in the field and determines future research directions

Determining trip and travel mode from GPS and accelerometer data

Indiana University-Purdue University Indianapolis (IUPUI)The use of Global Positioning Systems (GPS) and/or accelerometers to identify trips and transportation modes such as walking, running, bicycling or motorized transportation has been an active goal in multiple disciplines such as Transportation Engineering, Computer Science, Informatics and Public Health. The purpose of this study was to review existing methods that determined trip and travel mode from raw Global Positioning System (GPS) and accelerometer data, and test a select group of these methods. The study had three specific aims: (1) Create a systematic review of existing literature that explored various methods for determining trip and travel mode from GPS and/or accelerometer data, (2) Collect a convenience sample of subjects who were assigned a GPS and accelerometer unit to wear while performing and logging travel bouts consisting of walking, running, bicycling and driving, (3) Replicate selected method designs extracted from the systematic review (aim 1) and use subject data (aim 2) to compare the methods. The results were be used to examine which methods are effective for various modes of travel

User activity type and transportation mode detection using embedded mobile device sensors

Este proyecto tiene como objetivo mejorar la aplicación para Android llamada MobilitApp (http://mobilitat.upc.edu) que consiste en el análisis de la movilidad de la ciudadanía de Barcelona. Hemos diseñado nuestro propio sistema al cual le hemos eliminado el uso abusivo del GPS para ahorrar energía del dispositivo y las APIs de detección del modo de transporte que tienen un uso limitado para el usuario. Hemos estudiado dos métodos de detección y utilizamos únicamente los sensores de los smartphones. Detectamos diversos modos de actividad y transporte del usuario tales como estático, a pie, corriendo, en bicicleta, en coche, en autobús, en tranvía y en tren. Nuestra mejora ha consistido en grabar con una cámara de vídeo todas las actividades para tener registrado todo lo ocurrido en la actividad. En el primer método, construimos vectores característicos a través de la captura de los datos de los sensores del dispositivo y utilizamos estos datos en el aprendizaje automático. En el segundo método, experimentamos con redes neuronales para ver si el sistema es capaz de reconocer las características por si mismo si solo le proporcionamos datos de los sensores. También hemos analizado la posibilidad de hacer estos estudios sin la necesidad de capturar vídeos de la cámara y observar solo los datos proporcionados por los sensores de los dispositivos.Aquest projecte té com a objectiu millorar l'aplicació per a Android anomenada MobilitApp (http://mobilitat.upc.edu) que consisteix en l'anàlisi de la mobilitat de la ciutadania de Barcelona. Hem dissenyat el nostre propi sistema, hem eliminat l'ús abusiu del GPS per estalviar bateria del dispositiu i les APIs de detecció del mode de transport que tenen un ús limitat per l'usuari. Hem estudiat dos mètodes de detecció i utilitzem només els sensors dels telèfons intel·ligents. Detectem diversos modes d'activitat i transport de l'usuari tal com estàtic, a peu, corrents, amb bicicleta, amb cotxe, amb autobús, amb tramvia i amb tren. La nostra millora ha consistit en enregistrar amb una càmera de vídeo totes les activitats per tenir registrat tot el que ha ocorregut a l'activitat. Al primer mètode, vàrem construir vectors característics a través de la captura de les dades dels sensors dels dispositius i utilitzem aquestes dades en l'aprenentatge automàtic. Al segon mètode, vàrem experimentar amb xarxes neuronals per observar si el sistema és capaç de reconèixer les característiques per si mateix si només li proporcionem les dades dels sensors. També hem analitzat la possibilitat de fer aquests estudis sense la necessitat d'enregistrar amb la càmera i observar només les dades proporcionades pels sensors dels dispositius.This study aims to improve an existing mobile application MobilitApp (http://mobilitat.upc.edu) for citizen mobility analytics. By eliminating the use of APIs with limited user activity and transportation mode detection, and energy wasting GPS, we developed our own system, using two approaches and only embedded mobile device sensors. We captured various user activity and transportation modes such as stationary, walking, running, riding a bicycle, motorcycle, driving a car, taking a bus, tram and train. We recorded all activities with video camera to be aware when activity actually happened. At first approach, we build feature vectors through experimentation and then use this data in machine learning. In the second approach, we experimented with neural networks if they are capable recognizing features by them self, if we provide them only raw data from embedded mobile device sensors. We also do studies if controlled capturing data with camera is required or can be done without supervision on larger scale

Sensing Human Activity for Smart Cities’ Mobility Management

Knowledge about human mobility patterns is the key element towards efficient mobility management. Traditionally, these data are collected by paper/phone household surveys or travel diaries and serve as input for transportation planning models. In this chapter, we report on current state-of-the-art techniques for sensing human activity and report on their applicability for smart city mobility management purposes. We particularly focus on the use of location-enabled devices and their potential towards replacing traditional data collection approaches. Furthermore, to illustrate applicability of smartphones as ubiquitous sensing devices we report on the use of Routecoach application that was used for mobility data collection in the city of Leuven, Belgium. We provide insights into lessons learned, ways in which collected data were used by different stakeholders, and identify existing gaps and future research needs in this field

Review of current study methods for VRU safety : Appendix 4 –Systematic literature review: Naturalistic driving studies

With the aim of assessing the extent and nature of naturalistic studies involving vulnerable road users, a systematic literature review was carried out. The purpose of this review was to identify studies based on naturalistic data from VRUs (pedestrians, cyclists, moped riders and motorcyclists) to provide an overview of how data was collected and how data has been used. In the literature review, special attention is given to the use of naturalistic studies as a tool for road safety evaluations to gain knowledge on methodological issues for the design of a naturalistic study involving VRUs within the InDeV project. The review covered the following types of studies: •Studies collecting naturalistic data from vulnerable road users (pedestrians, cyclists, moped riders, motorcyclists). •Studies collecting accidents or safety-critical situations via smartphones from vulnerable road users and motorized vehicles. •Studies collecting falls that have not occurred on roads via smartphones. Four databases were used in the search for publications: ScienceDirect, Transport Research International Documentation (TRID), IEEE Xplore and PubMed. In addition to these four databases, six databases were screened to check if they contained references to publications not already included in the review. These databases were: Web of Science, Scopus, Google Scholar, Springerlink, Taylor & Francis and Engineering Village.The findings revealed that naturalistic studies of vulnerable road users have mainly been carried out by collecting data from cyclists and pedestrians and to a smaller degree of motorcyclists. To collect data, most studies used the built-in sensors of smartphones, although equipped bicycles or motorcycles were used in some studies. Other types of portable equipment was used to a lesser degree, particularly for cycling studies. The naturalistic studies were carried out with various purposes: mode classification, travel surveys, measuring the distance and number of trips travelled and conducting traffic counts. Naturalistic data was also used for assessment of the safety based on accidents, safety-critical events or other safety-related aspect such as speed behaviour, head turning and obstacle detection. Only few studies detect incidents automatically based on indicators collected via special equipment such as accelerometers, gyroscopes, GPS receivers, switches, etc. for assessing the safety by identifying accidents or safety-critical events. Instead, they rely on self-reporting or manual review of video footage. Despite this, the review indicates that there is a large potential of detecting accidents from naturalistic data. A large number of studies focused on the detection of falls among elderly people. Using smartphone sensors, the movements of the participants were monitored continuously. Most studies used acceleration as indicator of falls. In some cases, the acceleration was supplemented by rotation measurements to indicate that a fall had occurred. Most studies of using kinematic triggers for detection of falls, accidents and safety-critical events were primarily used for demonstration of prototypes of detection algorithms. Few studies have been tested on real accidents or falls. Instead, simulated falls were used both in studies of vulnerable road users and for studies of falls among elderly people

Transportation mode detection by using smartphone sensors and machine learning

<span>The aim of this study is to detect transportation modes of the users by using smartphone sensors. Therefore, GPS (Global Positioning System), accelerometer and gyroscope sensor data have been collected while walking, running, cycling and travelling by bus or by car from the smartphone of the user. Sensor data were tagged with 12 second interval and 2500 pattern were obtained. 14 features were acquired from the dataset. Machine learning methods were tested on the dataset. Best result was obtained from GPS, accelerometer and gyroscope sensor combination and Random Forest method with 99.4% accuracy rate.</span