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David
Improving alertness , efficiency and safety , your travel companion in Detroit
What's David APK?
David is a app for Android, It's developed by GoRipple Apps author.
First released on google play in 7 years ago and latest version released in 6 years ago.
This app has 10 download times on Google play
This product is an app in Travel & Local category. More infomartion of David on google play
Inspiration
The inspiration behind David is to solve two major challenges in any mode of transport . David , a personal assistant measure the alertness index of a driver ( could be a personal / public vehicle ) based on a few questions that have been compiled after talking to experts and medical advisors from the field . The alertness index advises a driver whether he is alert enough to hit the road or should he find any alternative mode of transport . This is a crucial step , we believe in reducing the number of driver induced errors in accidents . Another core feature of David is that it suggests a time at which a user has to depart to reach his preferred destination on his preferred time schedule , based on his preferred mode of transport . Thus David intends to provide 3 major qualities to it's end users - Alertness , safety and efficiency .
What it does
Alertness Index Alertness index is a simple questionnaire for a driver that calculates the alertness index of the individual thereby letting him know how alert he can be in his job of driving . It asks simple questions that includes info about the amount of time he has slept , the number of hours he has spent driving in the recent past and use of alcohol and / or other alertness depriving substances . The algorithm analyses the answers given and calculates a rating that can have a maximum of up-to 96 . A score of above 76 indicates high alertness and that the user is fit to drive . A score below 60 and 76 indicates normal alertness and the user is advised to be extra vigilant in driving and a score below 60 is considered below par and the user is advised to use alternative modes of transport or find other drivers for the same trip . The algorithm allocates weight to each answer based on the importance of the answers compiled with the help of experts in the field . End users can use this feature to significantly reduce driving errors that can lead to accidents and mishaps .
DepartBy DepartBy is another core feature of the app with which a departure time is predicted to the user after he enters the source and destination addresses , preferred time of arrival expected . Separate times are given for transport by means of a personal vehicle , a cab , a shared cab and public transport . The time predicted calculates the same based on data received from Google Maps , Weather updates , Traffic updates , accident / road work info and historical data . The algorithm allocates weight to each factor considered , consolidates the result obtained and delivers time in 24 hour format for each mode of transport . DepartBy is a feature we believe can help improve the efficiency and punctuality of users using the app .
SmartBus SmartBus feature allows easy access to bus schedules, fares, trip planner, bike rack use, Smart value pass, MyConnector for regular users of the same .
ThePeopleMover ThePeopleMover feature allows easy access to Station Guide, Ride Info, passes, tickets for users of the same .
M-1 Rail M-1 Rail info allows easy access to Station stops, schedule, tickets for users of the same .
How I built it
Alertness Index was built using an algorithm that analyses the user responses and gives weight after compiling usual and normal alertness levels of individuals based on their reaction to scenarios . Depart By feature was built using Google Maps API , open weather API and other traffic APIs .
Challenges I ran into
Developing the algorithm for alertness index was the biggest challenge as it involved extensive research and data from experts in the field and common accident causes analysis . DepartBy feature , thought easier , took longer time to complete because of complexities involved in getting data from various APIs used .
Accomplishments that I'm proud of
The algorithm for alertness index and DepartBy feature excites me the most .
The inspiration behind David is to solve two major challenges in any mode of transport . David , a personal assistant measure the alertness index of a driver ( could be a personal / public vehicle ) based on a few questions that have been compiled after talking to experts and medical advisors from the field . The alertness index advises a driver whether he is alert enough to hit the road or should he find any alternative mode of transport . This is a crucial step , we believe in reducing the number of driver induced errors in accidents . Another core feature of David is that it suggests a time at which a user has to depart to reach his preferred destination on his preferred time schedule , based on his preferred mode of transport . Thus David intends to provide 3 major qualities to it's end users - Alertness , safety and efficiency .
What it does
Alertness Index Alertness index is a simple questionnaire for a driver that calculates the alertness index of the individual thereby letting him know how alert he can be in his job of driving . It asks simple questions that includes info about the amount of time he has slept , the number of hours he has spent driving in the recent past and use of alcohol and / or other alertness depriving substances . The algorithm analyses the answers given and calculates a rating that can have a maximum of up-to 96 . A score of above 76 indicates high alertness and that the user is fit to drive . A score below 60 and 76 indicates normal alertness and the user is advised to be extra vigilant in driving and a score below 60 is considered below par and the user is advised to use alternative modes of transport or find other drivers for the same trip . The algorithm allocates weight to each answer based on the importance of the answers compiled with the help of experts in the field . End users can use this feature to significantly reduce driving errors that can lead to accidents and mishaps .
DepartBy DepartBy is another core feature of the app with which a departure time is predicted to the user after he enters the source and destination addresses , preferred time of arrival expected . Separate times are given for transport by means of a personal vehicle , a cab , a shared cab and public transport . The time predicted calculates the same based on data received from Google Maps , Weather updates , Traffic updates , accident / road work info and historical data . The algorithm allocates weight to each factor considered , consolidates the result obtained and delivers time in 24 hour format for each mode of transport . DepartBy is a feature we believe can help improve the efficiency and punctuality of users using the app .
SmartBus SmartBus feature allows easy access to bus schedules, fares, trip planner, bike rack use, Smart value pass, MyConnector for regular users of the same .
ThePeopleMover ThePeopleMover feature allows easy access to Station Guide, Ride Info, passes, tickets for users of the same .
M-1 Rail M-1 Rail info allows easy access to Station stops, schedule, tickets for users of the same .
How I built it
Alertness Index was built using an algorithm that analyses the user responses and gives weight after compiling usual and normal alertness levels of individuals based on their reaction to scenarios . Depart By feature was built using Google Maps API , open weather API and other traffic APIs .
Challenges I ran into
Developing the algorithm for alertness index was the biggest challenge as it involved extensive research and data from experts in the field and common accident causes analysis . DepartBy feature , thought easier , took longer time to complete because of complexities involved in getting data from various APIs used .
Accomplishments that I'm proud of
The algorithm for alertness index and DepartBy feature excites me the most .
