Short-Term Prediction of Signal Cycle on an Arterial With Actuated-Uncoordinated Control Using Sparse Time Series Models
Short-Term Prediction of Signal Cycle on an Arterial With Actuated-Uncoordinated Control Using Sparse Time Series Models
复制标题
使用稀疏时间序列模型通过驱动不协调控制对动脉信号周期进行短期预测
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jiaqi Ma
中科院分区：
文献类型：
--
作者：
Bahman Moghimi;Abolfazl Safikhani;C. Kamga;Wei Hao;Jiaqi Ma
交通信号作为智能交通系统的一部分，可以在城市智能化方面发挥重要作用。传统上，大多数交通信号灯都是定时控制的，这会导致大量的空闲时间(未使用的绿灯时间)。驱动交通灯实时控制交通流量，更能响应交通需求的变化。对于一个孤立的信号，一系列时间序列模型，如自回归积分移动平均(ARIMA)模型，对于预测下一个周期长度是有益的。然而，当多个信号灯布置在不同间距和配置的走廊上时，这些信号灯的周期长度变化不仅与每个信号灯的值有关，而且还受到来自相邻交叉口的车辆排的影响。本文建立了一个多变量时间序列模型，用于分析在完全驱动的情况下沿走廊布置的多个交叉口的信号周期长度的行为。沿着一条走廊模拟了五个信号交叉口，它们之间的间距不同，以及多层次的交通需求。针对该问题的高维特性，在估计过程中采用惩罚最小二乘法来输出稀疏模型。提出的两种稀疏时间序列方法很好地捕捉了信号数据，表现优于传统的向量自回归模型--在某些情况下高达17%--而且比单变量模型(如ARIMA)更强大。
Traffic signals as part of intelligent transportation systems can play a significant role in making cities smart. Conventionally, most traffic lights are designed with fixed-time control, which induces a lot of slack time (unused green time). Actuated traffic lights control traffic flow in real time and are more responsive to the variation of traffic demands. For an isolated signal, a family of time series models, such as autoregressive integrated moving average (ARIMA) models, can be beneficial for predicting the next cycle length. However, when there are multiple signals placed along a corridor with different spacing and configurations, the cycle length variation of such signals is not just related to each signal’s values, but it is also affected by the platoon of vehicles coming from neighboring intersections. In this paper, a multivariate time series model is developed to analyze the behavior of signal cycle lengths of multiple intersections placed along a corridor in a fully actuated setup. Five signalized intersections have been modeled along a corridor, with different spacing among them, together with multiple levels of traffic demand. To tackle the high-dimensional nature of the problem, a penalized least-squares method is utilized in the estimation procedure to output sparse models. Two proposed sparse time series methods captured the signal data reasonably well and outperformed the conventional vector autoregressive model—in some cases up to 17%—as well as being more powerful than univariate models, such as ARIMA.