Review of transportation mode detection approaches based on smartphone data
Publications associées (39)
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
Urban transportation is currently experiencing major changes, namely because of disruptive innovations driven by the information and communication technologies (ICTs). During the past few years, ride-booking has emerged in many cities around the world as o ...
In our daily lives, our mobile phones sense our movements and interactions via a rich set of embedded sensors such as a GPS, Bluetooth, accelerometers, and microphones. This enables us to use mobile phones as agents for collecting spatio-temporal data. The ...
The location tracking functionality of modern mobile devices provides unprecedented opportunity to the understanding of individual mobility in daily life. Instead of studying raw geographic coordinates, we are interested in understanding human mobility pat ...
Vehicle sharing systems (VSSs) are becoming increasingly popular, primarily due to their financial and environmental advantages. However, VSSs face many operational challenges, including inventory management of vehicles and parking spots, vehicle load bala ...
In everyday life, eating follows patterns and occurs in context. We present an approach to discover daily eating routines of a population following a multidimensional representation of eating episodes, using data collected with the Bites'n'Bits smartphone ...
In recent years there has been a proliferation of privately owned sensing devices such as GPS devices, cameras, home weather stations and, more importantly, smart-phones. Most of these devices are either intrinsically mobile, e.g., smart-phones and GPS dev ...
Rapid urbanization, climate change, sustainable development, resource depletion, the widespread use of the Internet and mobile phones, and the big data phenomenon all pose great challenges to urban planning. By facilitating data exchange, collection, and a ...
This article proposes a probabilistic method that infers the transport modes and the physical paths of trips from smartphone data that were recorded during travels. This method synthesizes multiple kinds of data from smartphone sensors, which provide relev ...
Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...
Modern smartphones are powerful platforms that have become part of the everyday life for most people. Thanks to their sensing and computing capabilities, smartphones can unobtrusively identify simple user states (e.g., location, performed activity, etc.), ...