Signal Phasing and Timing Prediction Using Connected Vehicle Data

Abstract

Signal phasing and timing can be adaptive and actuated in practice. This makes it challenging to understand what the cycle length and phase duration of the next few cycles will be. Many innovative applications can be designed based on the knowledge of future signal timing states such as dilemma zone warning, efficient route planning, and so forth. This work proposes a long short-term memory model capable of predicting both cycle length and phase duration prediction up to six cycles in the future. GPS information of several vehicles are merged with signal timing information of eight intersections. Several key features such as waiting time, approach speed and acceleration, departing speed and acceleration, are calculated based on the geolocation of individual journeys. The results show that cycle length prediction can reach mean absolute error (MAE) of about 7 s while phase prediction MAE is about 9 s.

An Update on Passenger Vehicle Speeds at Roundabouts

“…CV trajectory data has already been utilized to calculate operational performance [17][18][19][20], estimate control parameters [21,22], and manage [23,24] signalized intersections. This data has also been used to estimate delays at roundabouts [25], but there are no published reports on speed characteristics in and adjacent to roundabouts using this emerging data set.…”

Analysis of Connected Vehicle Data to Quantify National Mobility Impacts of Winter Storms for Decision Makers and Media Reports

“…Emerging near real-time CV trajectory-level data provides far greater detail on individual passenger vehicle journey waypoints, and thus alleviates any over or underrepresentation concerns. Researchers have already demonstrated local or state-level examples of the versatility and sufficient representativeness of this CV trajectory data for use in assessing data coverage and filling gaps in traffic counts [29][30][31][32], monitoring mobility and safety through construction work zones [33][34][35][36][37], movement-level detection and performance monitoring at signalized intersections [38][39][40][41], and observing human mobility dynamics [42][43][44]. A pair of recent reports have built upon these methodologies and evaluated the usability of nationally available CV data sets at representative penetration rates towards analyzing the safety and mobility impacts of summer work zone construction as well as winter storm events on interstate travel in the United States [45].…”