Interactive+Computing+Lab
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Improving Traffic Safety with a Crowdsourced Traffic Violation Reporting App
KAIST researchers revealed that crowdsourced traffic violation reporting with smartphone-based continuous video capturing can dramatically change the current practice of policing activities on the road and will significantly improve traffic safety.
Professor Uichin Lee of the Department of Industrial and Systems Engineering and the Graduate School of Knowledge Service Engineering at KAIST and his research team designed and evaluated Mobile Roadwatch, a mobile app that helps citizen record traffic violation with their smartphones and report the recorded videos to the police.
This app supports continuous video recording just like onboard vehicle dashboard cameras. Mobile Roadwatch allows drivers to safely capture traffic violations by simply touching a smartphone screen while driving. The captured videos are automatically tagged with contextual information such as location and time. This information will be used as important evidence for the police to ticket the violators. All of the captured videos can be conveniently reviewed, allowing users to decide which events to report to the police.
The team conducted a two-week field study to understand how drivers use Mobile Roadwatch. They found that the drivers tended to capture all traffic risks regardless of the level of their involvement and the seriousness of the traffic risks. However, when it came to actual reporting, they tended to report only serious traffic violations, which could have led to car accidents, such as traffic signal violations and illegal U-turns. After receiving feedback about their reports from the police, drivers typically felt very good about their contributions to traffic safety.
At the same time, some drivers felt pleased to know that the offenders received tickets since they thought these offenders deserved to be ticketed. While participating in the Mobile Roadwatch campaign, drivers reported that they tried to drive as safely as possible and abide by traffic laws. This was because they wanted to be as fair as possible so that they could capture others’ violations without feeling guilty. They were also afraid that other drivers might capture their violations.
Professor Lee said, “Our study participants answered that Mobile Roadwatch served as a very useful tool for reporting traffic violations, and they were highly satisfied with its features. Beyond simple reporting, our tool can be extended to support online communities, which help people actively discuss various local safety issues and work with the police and local authorities to solve these safety issues.”
Korea and India were the early adaptors supporting video-based reporting of traffic violations to the police. In recent years, the number of reports has dramatically increased. For example, Korea’s ‘Looking for a Witness’ (released in April 2015) received more than half million reported violations as of November 2016. In the US, authorities started tapping into smartphone recordings by releasing video-based reporting apps such as ICE Blackbox and Mobile Justice. Professor Lee said that the existing services cannot be used while driving, because none of the existing services support continuous video recording and safe event capturing behind the wheel.
Professor Lee’s team has been incorporating advanced computer vision techniques into Mobile Roadwatch for automatically capturing traffic violations and safety risks, including potholes and obstacles. The researchers will present their results in May at the ACM CHI Conference on Human Factors in Computing Systems (CHI 2017) in Denver, CO, USA. Their research was supported by the KAIST-KUSTAR fund.
(Caption: A driver is trying to capture an event by touching a screen. The Mobile Radwatch supports continuous video recording and safe event captureing behind the wheel.)
2017.04.10 View 9809 -
An Exploratory Study on Smartphone Abuse among College Students
Professor Uichin Lee
Professor Uichin Lee of the Department of Knowledge Service Engineering, KAIST, and his research team developed a system that automatically diagnoses the levels of smartphone addiction based on an analysis of smartphone use records.
Professor Lee investigated the usage patterns of 95 smartphone users (college students) by conducting surveys and interviews and collecting logged data. The research team divided participants into “risk” and “non-risk” groups based on a self-reported rating scale to evaluate their abuse of smartphones. As a result, 36 students were categorized as “high risk” and 59 were categorized as “low risk.”
The researchers collected over 50,000 hours of smartphone use encompassing power levels, screen, battery status, application use, internet use, calling, and texting.
The results showed that the “high risk” group used only 1~2 applications, focusing on mobile messengers (Kakotalk, etc.) and SNS (Facebook, etc.).
In addition, a relationship was found between alarm function and addiction levels. Users who set alarms for Kakaotalk messages and SNS comments used smartphones for an additional 38 minutes per day on average.
Results also showed that “high risk” students were on their smartphones for 4 hours and 13 minutes per day, 46 minutes longer than “low risk” students who used smartphones for 3 hours and 27 minutes. The difference was prevalent during 6 am and noon, and 6pm and midnight. In addition, “high risk” students accessed their smartphones 11.4 times more than “low risk” students.
Based on the collected data, Professor Lee developed an automatic system that distinguished users into “high risk” or “low risk” categories with 80% accuracy. The new system is expected to give an early diagnosis of addiction to smartphone users, thereby allowing for early treatment and intervention before the user becomes addicted.
Professor Lee commented that, "the conventional addiction analysis based on self-analysis surveys did not provide real-time data and were largely inaccurate. The new system overcomes these limitations through data science and personal big data analysis" and that he is "developing an application that monitors smartphone abuse."
Figure 1. Usage amount: overall and application-specific results
Figure 2. Usage frequency: overall and application-specific results
Figure 3. Overall diurnal usage time and frequency
2014.06.05 View 7139