Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies
Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies
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DOI：
10.1109/smartcomp.2019.00064
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发表时间：
2019-06
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影响因子：
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通讯作者：
Chinmaya Samal;A. Dubey;L. Ratliff
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文献类型：
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作者：
Chinmaya Samal;A. Dubey;L. Ratliff
近年来，深度学习模型的增长导致了智能运输技术的各种创新解决方案。使用个人和按需移动服务会给城市的现有道路网络带来压力。为了减轻这个问题，城市规划人员需要一个模拟框架来评估任何激励政策在将通勤者推向替代旅行模式（例如自行车和汽车共享选择）中的效果。在本文中，我们利用基于代理的仿真框架Matsim集成了代理偏好模型，这些模型除了与旅行时间和成本成正比的分离性外，捕获了代理人的利他行为。这些模型是以数据驱动的方法来学习的，可以用来评估代理对系统级别的分离性和货币激励措施（例如，运输机构）的敏感性。该框架提供了一个标准化的环境，以评估城市任何特定激励政策的有效性，以将其居民推向替代运输方式。我们展示了该方法的有效性，并使用大都会纳什维尔地区的案例研究提供了分析。
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.