论文标题
使用Google Maps数据对发展中国家的交通拥堵进行建模
Modeling Traffic Congestion in Developing Countries using Google Maps Data
论文作者
论文摘要
由于城市化，经济增长和工业化，交通拥堵研究正在上升。发达国家投入了大量的研究资金，使用射频识别（RFID），循环探测器，速度传感器，高端交通信号灯和GPS收集交通数据。但是，对于拥有众多非机动车辆，扩散的乘车共享服务以及频繁的行人的发展中国家来说，这些过程是昂贵，不可行的，并且不可降低。本文提出了一种新颖的方法，以最低的成本从Google Map的流量层收集流量数据。我们已经实施了广泛使用的模型，例如历史平均值（HA），支持向量回归（SVR），使用图形（SVR-Graph）支持向量回归，自动回归综合移动平均线（ARIMA）以显示收集到的流量数据在预测未来的交通中的功效。我们表明，即使使用这些简单的模型，我们也可以提前预测交通拥堵。我们还证明，在工作日和周末之间，交通方式有显着不同。
Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end traffic light, and GPS. However, these processes are expensive, infeasible, and non-scalable for developing countries with numerous non-motorized vehicles, proliferated ride-sharing services, and frequent pedestrians. This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We have implemented widely used models such as Historical Averages (HA), Support Vector Regression (SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic data in forecasting future congestion. We show that even with these simple models, we could predict the traffic congestion ahead of time. We also demonstrate that the traffic patterns are significantly different between weekdays and weekends.
