Short-Term Prediction of Signal Cycle in Actuated-Controlled Corridor Using Sparse Time Series Models

TL;DR

This paper develops sparse multivariate time series models to predict short-term cycle lengths of actuated traffic signals along a corridor, accounting for interactions between intersections and traffic demand variations.

Contribution

It introduces a novel sparse multivariate modeling approach that outperforms traditional VAR and univariate ARIMA models in predicting signal cycle lengths in complex corridor settings.

Findings

Sparse models outperform traditional VAR by up to 17%

Proposed methods effectively capture inter-signal dependencies

Models adapt to different traffic demands and spacing configurations

Abstract

Traffic signals as part of intelligent transportation systems can play a significant role toward 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, penalized least squares method are 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 (VAR) model - in some cases up to 17% - as well as being more powerful than univariate models such as ARIMA.