Abstract
In this era of connected systems that have penetrated everywhere, trans- port units have become a significant source of data, collected from com- muters, vehicles, drivers, or any section being touched by the transport sys- tem. This data, which has both spatial as well as temporal aspects, is utilized for a plethora of services like travel assistant systems, multi-modal transport solutions, real-time travel information, smart parking, autonomous vehicles, to name a few. With the current buzz of sustainable transport, the use of public transport systems have been popularized owing to the economic and environmental savings. The popularity of ride-hailing options like Uber/Lyft have been diminishing the use of public transport; however, this cheaper alternative is still preferred by the majority population. In light of the pre- vailing scenario, in this thesis, we look into the problems linked to public transport units as well as ride-hailing firms. Commuters and drivers being essential parts of the transport system, we emphasize addressing the prob- lems from each of their perspectives — commuters in public transport and drivers in ride-hailing options.
First, we emphasize on collection and storage of a large pool of spatio- temporal commuter data which could be analyzed by different services to improve travel experience for the commuters. We plan to utilize the collected data to generate a rich transit map of the city annotated with all the bus routes as well as different concern-points like bus stops, speed breakers, and turns along with their severity, congested patches, crowding on a bus over a route, etc. Over the developed transit map, we build systems to assist commuters on their journey.
We develop a personalized and focused navigation-cum-alert application which could alert a commuter about the upcoming bus stops, speed breakers, turns, bad road patches, etc. when she is traveling. The system would inform about the severity of approaching speed breakers, turns, or bad roads and the possibility of bus skipping the bus stop depending on the nature of the
1
driving. This would help the commuters to take an informed decision while on-board and also avoid possible dangers.
Next, we develop a personalized route recommendation system designed to suggest the most comfortable route to a commuter based on her personal- ized needs like getting a seat, jerkiness on the road, number of turns or speed breakers on the route, etc.
Finally, we move our focus towards drivers and ride-hailing systems. Sev- eral road accidents have been found out to be a result of poor driving behavior arising from driver stress. Here, we develop a model to compute driver stress from driver’s roster information and establish a quantifiable relationship be- tween driver stress and driving behavior. With this quantifiable relationship, we build two applications. First, an application which based on the historical and upcoming trip information, recommends the driver to accept or reject the trip. The second application extends the stress computation model to predict the possibility of the driver being stressed and, based on this, recommends a break.
Summarizing, we try to develop systems leveraging over the vast pool of spatio-temporal data that could be obtained from different transport units.
The first class of these systems would help to improve the travel experience of commuters in public transport. While the second class of systems would establish a relationship between driver stress and driving behavior of drivers in ride-hailing firms, thus helping reduce the chances of possible accidents.
Keywords: transport; public buses; ride-hailing; spatio-temporal data; crowdsourcing; smartphone; map generation; navigation; alert;
commuter comfort; driver stress; driving behavior
2