利用灵巧卡数据模拟训练快速交通拥挤动力学
Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data
摘要：
在当今的城市中，调查训练快速交通系统（RTS）中的拥挤是一个挑战，由于有限的数据可用性和模型验证的困难而变得更加复杂。在这里，我们整合信息从旅游灵巧卡数据，数学模型的路线选择，和一个全面的基于代理的模型，新加坡RTS提供更全面的了解，可以通过分析建模单独获得的拥堵动态。我们的模型是经验验证，并允许密切检查的动态，包括车站拥挤，平均旅行时间，错过列车的频率-所有高度相关的因素，在服务质量。根据目前的数据，所有121个监测站的拥挤程度似乎呈对数正态分布。在我们的初步方案中，我们调查人口增长对服务质量的影响。我们发现，目前的人口（200万）低于一个临界点，并增加超过$\sim10\%$的一个因素，导致指数恶化的服务质量。我们还预测，激励通勤者避免最拥挤的时间可以带来适度的改善服务质量的人口仍然低于临界点。最后，我们的模型可以用来生成模拟数据的分析建模时，这些数据是没有经验的，通常是这样的情况。
英文摘要：
Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains---all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of $\sim10\%$ leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for analytical modelling when such data are not empirically available, as is often the case.
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