Smartphone sensor platform to study traffic conditions and assess driving performance

Jm, Gonçalves; Gonçalves, João S. V.; Rossetti, Rosaldo J. F.; Olaverri-Monreal, Cristina

doi:10.1109/itsc.2014.6958106

Cited by 22 publications

(16 citation statements)

References 22 publications

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“…Data collected from smartphone provides a viable alternative to naturalistic driving studies [5,12,24]. Smartphone sensors typically have larger errors compared to dedicated instruments used for NDS.…”

Section: Related Workmentioning

confidence: 99%

Clusters of Driving Behavior From Observational Smartphone Data

Warren

Lipkowitz

Sokolov

2019

IEEE Intell. Transport. Syst. Mag.

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Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. On the other hand, they are very costly and time consuming. Thanks to the ubiquity of smartphones, we have an opportunity to substantially complement more traditional data collection techniques with data extracted from phone sensors, such as GPS, accelerometer gyroscope and camera. We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs. We used novel data sources to support our work. We used cell phone sensor data drawn from five hundred drivers in San Francisco to understand the speed of traffic across the city as well as the maneuvers of drivers in different areas. Specifically, we clustered drivers based on their driving behavior. We looked at driver norms by street and flagged driving behaviors that deviated from the norm.

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Section: Related Workmentioning

confidence: 99%

Clusters of Driving Behavior From Observational Smartphone Data

Warren

Lipkowitz

Sokolov

2019

IEEE Intell. Transport. Syst. Mag.

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“…Goncalves et al [75] designed a platform to acquire data about the traffic condition and to drive performance using a smartphone GPS. Han et al [76] developed the SenSpeed, an accurate vehicle speed estimation system, to address an unavailable GPS signal or inaccurate data in urban environments.…”

Section: A Intra-vehicular Sensormentioning

confidence: 99%

Vehicular Data Space: The Data Point of View

Rettore

Maia

Villas

et al. 2019

IEEE Commun. Surv. Tutorials

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Over the years, governments and automakers launched initiatives to improve road traffic efficiency, safety, and people mobility. They have been working on various aspects of intelligent transportation systems (ITSs), which aim to improve decision-making, availability of information, and communication technologies to provide applications and services to boost the transportation systems. The development of new applications and services for ITS depends on the availability of different data sources, what it is not the current case. Many studies focus on the communication issues of applications and their associated challenges. To reveal the recent vehicular data use, we examined the most remarkable studies of the last few years, which describe services and applications for ITS, however with a focus on the data employed by them. We introduce the concept of vehicular data space (VDS), which is then used to describe the vehicular scenario from the perspective of data. Moreover, we outline a taxonomy, according to the different data sources. We also categorize the applications, highlighting the data each one used in their approach. Finally, we present some challenges and open issues related to the process for data creation, data preparation, data processing, and data use. In a nutshell, this paper constitutes one of the first holistic surveys on services and applications for ITSs, focusing on the data used by them, as well as their future challenges. Index Terms-Vehicular data space (VDS), vehicular sensor data, connected vehicles, intelligent transportation system (ITS), heterogeneous data, vehicular ad-hoc network (VANET). I. INTRODUCTIONM ORE and more cities will have to deal with significant issues related to transportation and traffic because people and goods are in constant need for quicker and safer mobility modes. The number of fatalities and injuries on the road has achieved an alarming scenario. Globally, 1.3 million people die every year, and up to 50 million suffer severe injuries. These facts have a direct impact on the economics of nations, leading to costs in the order of about 2% to 5% of the Gross Domestic Product (GDP) in many countries [1]. It is also reported that traffic congestion results in critical economic and environmental costs. In 2011, 498 U.S. urban areas were

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“…Powerful tools are needed to enhance the user experience and proficiency in manipulating and understanding huge amounts of data collected from many users in various conditions and qualities. One such tool is the Iris geographic information system (GIS) -based a platform using the smartphone Android default API for geolocation [7]. The server side of the Iris platform complements the mobile system to collect data by storing, processing, analyzing, managing and presenting the results.…”

Section: Related Workmentioning

confidence: 99%

“…The server side of the Iris platform complements the mobile system to collect data by storing, processing, analyzing, managing and presenting the results. Additional metrics related to the travel time and vehicle's speeds contribute to the assessment of traffic management issues [7].…”

Section: Related Workmentioning

confidence: 99%

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

Babau¹,

Marcu²,

Tihu³

et al. 2017

Acta Cybern

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Traffic optimization is a subject that has become vital for the world we live in. People these days need to get from a starting point to a destination point as fast and as safe as possible. Traffic congestion plays a key role in the frustration of people and it results in lost time, reduced productivity and wasted resources. In our study we seek to address these issues by proposing a real-time road traffic planning system based on mobile context and crowd sourcing efforts. The first step toward this goal is real-time traffic characterization using data collected from mobile sensors of drivers, pedestrians, cyclists, passengers, etc.. We started developing a data collection and analysis system composed of a mobile application in order to collect user context data and a Web application to view and analyze the data. This new system will eventually give the users an automatically optimized route to the destination and predict the users' traveling route based on live traffic conditions and historical data.

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Smartphone sensor platform to study traffic conditions and assess driving performance

Cited by 22 publications

References 22 publications

Clusters of Driving Behavior From Observational Smartphone Data

Clusters of Driving Behavior From Observational Smartphone Data

Vehicular Data Space: The Data Point of View

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

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