IIIT Hyderabad Publications
Social Network driven Impact on a Multi-modal Transport System
Author: Deepika Pathania
Report no: IIIT/TH/2016/19
Preventing traffic congestion by forecasting near time traffic flows is an interesting and important problem as it leads to effective use of transport resources. Many times congestion is caused due to social events like concerts, conferences, etc. happening in the city. Social network provides relevant and useful information about social events and about sentiments of humans towards such events. Thus, with the help of social network, we can estimate which humans might attend a particular event (in near time). This can help to estimate which route is expected to see more traffic. Using these estimates, transport authorities can take appropriate measures to meet increase in ridership and possibly prevent congestion. This opens up a wide area of research and experimentation which poses need to have a framework for traffic management that can capture parameters of real-life behavior and provide an easy way to iterate upon and evaluate new ideas. In our work, we design a framework which provides modules to simulate: • a city with points of interests (house, office, school, shops etc). • human population and their daily activities. • temporally and geographically distributed social events with varied size of target audience. • a social network for humans to share their likes/dislikes with their friends and in turn influence their interest about simulated events. • a transport management system for humans transportation needs. It continuously monitors and plans train schedule to meet the needs of ridership. Our framework has a plug-and-play modular design; it supports easy integration and replacement of components. This makes it favorable for experimentation. We also emphasize on relevance of simulation parameters and design of modules with respect to how closely it mimics real-life. We also define metrics used to evaluate effectiveness of strategies in controlling congestion. To show the utility of the framework, we built a multi-agent simulation system and conducted exper- imental studies of the strategies developed. We simulated city of Singapore and its metro rail as means of public transportation. Singapore’s demographic data is easily available, geographical data available in form of OSM () is up-to-date, it has a robust public transportation system that is heavily used and its population is technologically advanced, hence social networking sites are massively used. As such, Singapore city is a fitting example to study and explain our work. We build our framework on top of DMASF (), which is python-based and SQL-backed multi- agent simulation platform. Humans and transport management actors are modeled as agents. We present experimental studies to demonstrate utility and scalability of our framework, involving 100,000 humans. These experiments involve simulating social events. We found that our framework captures effect of social events which is seen as increased use of public transportation (trains). We also implemented few experimental strategies to estimate traffic flows in case of social events and strategies to control congestion. We found that for up-to medium scale events (i.e. for events involving up-to 1000 humans), our strategy performed well and prevented trains to be congested. But for higher number of attendees (approx 10,000), our strategy did not scale well. We also implemented a strategy in which human alters its trip based on state of ridership in trains and selects route which takes shortest total travel time to reach destination. We noted that there is slight improvement in waiting time and average total trip time for humans with this strategy. Results of these experiments revealed that traffic congestion can be curbed using social network data based near time forecasting. In this thesis, our focus is on design of simulation and component interfaces used in framework. We do not emphasize on any particular solution for congestion avoidance strategy. To show the utility of our system, we focus on trains and walking as means of transport. This work can be further extended to consider full multi-modal transport system having trains, buses and uber like taxis.
Full thesis: pdf
Centre for Data Engineering
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