Paper Info
Title
Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies
Abstract
The rise in deep learning models in recent years has led to various innovative solutions for intelligent transportation technologies. Use of personal and on-demand mobility services puts a strain on the existing road network in a city. To mitigate this problem, city planners need a simulation framework to evaluate the effect of any incentive policy in nudging commuters towards alternate modes of travel, such as bike and car-share options. In this paper, we leverage MATSim, an agent-based simulation framework, to integrate agent preference models that capture the altruistic behavior of an agent in addition to their disutility proportional to the travel time and cost. These models are learned in a data-driven approach and can be used to evaluate the sensitivity of an agent to system-level disutility and monetary incentives given, e.g., by the transportation authority. This framework provides a standardized environment to evaluate the effectiveness of any particular incentive policy of a city, in nudging its residents towards alternate modes of transportation. We show the effectiveness of the approach and provide analysis using a case study from the Metropolitan Nashville area.
Year
DOI
Venue
2019
10.1109/SMARTCOMP.2019.00064
2019 IEEE International Conference on Smart Computing (SMARTCOMP)
Keywords
Field
DocType
Transportation,Urban Mobility,Simulation
Leverage (finance),Incentive,Altruism,Computer science,Artificial intelligence,Intelligent transportation system,Deep learning,Travel time,Metropolitan area,Environmental economics
Conference
ISBN
Citations 
PageRank 
978-1-7281-1690-7
0
0.34
References 
Authors
5
3
Name
Order
Citations
PageRank
Chinmaya Samal100.34
Abhishek Dubey239357.92
Lillian J. Ratliff38723.32