Advanced Gap Seeking Logic for Actuated Signal Control Using Vehicle Trajectory Data: Proof of Concept

Abstract

As detection systems improve, opportunities are emerging for using vehicle position and speed to drive signal control. This study explored how basic actuation processes might be improved by using vehicle position and speed. The position and speed of arriving vehicles were used to calculate their estimated time of arrival. Two variations on vehicle extension logic were developed that extended a green until there was a headway in the arriving traffic above a size that corresponded to a minimum flow rate, at which it was desired to terminate the phase. In the first method, the headway was measured from the point where the leading vehicle passed the stop bar, whereas the second method measured the headway from where the leading vehicle was unlikely to stop at the onset of yellow. This was compared against a control with a conventional stop bar and upstream detectors. A simulation study was carried out for an eight-phase intersection. The results suggest that at higher levels of demand, trajectory-based actuation could yield substantial reductions in delay. The trajectory-based methods were able to terminate green more efficiently, leading to reductions in delay in some cases, and reductions in emissions and fuel consumption, although there was a tradeoff in the number of split failures. These early results show promise for the development and fine-tuning of the methods.

How PTV Vissim Has Been Calibrated for the Simulation of Automated Vehicles in Literature?

“…e first stream (e.g., see [7,12,25,32]) uses the internal modelling, and modifies the default Wiedemann driving logic (car-following) and lane-changing parameters to simulate the desired driving behaviours. e second stream, which uses the external Vissim interfaces and the external modelling methodology, sends user-defined algorithms to a dynamic link library to imitate the driving logic of AVs (e.g., see [33,34]). e present study follows the first stream of the literature.…”

Methodology for the Identification of Shock Wave Type and Speed in a Traffic Stream Using Connected Vehicle Data

“…The recent wide-scale availability of high-resolution connected vehicle traffic data presents a unique opportunity to measure shock wave characteristics which previously have only been represented by models. More recently, trajectory-based granular vehicle data have been utilized for a wide variety of applications including operational performance gains [15], intersection safety, systemwide identification of signal retiming opportunities, work zone safety and management [16], winter weather maintenance, asset management, etc., aiding transportation agencies.…”

Impacts of Different Types of Automated Vehicles on Traffic Flow Characteristics and Emissions: A Microscopic Traffic Simulation of Different Freeway Segments

“…The second stream, following the external modeling approach, simulates driving behavior of AVs/CAVs through external VISSIM interfaces (e.g., Component Object Model Application Programming Interface (COM API), and External Driver Model (EDM) [36]) and user defined algorithm and code development. The COM API enables changes in vehicle movements and driving behaviors and can be developed in several programming languages (e.g., C# [37], Python [38]). The EDM is compiled in C++ and replaces the default driving behavior model in VISSIM (e.g., see [39,40]) such that the information on all vehicles in the network will be collected and sent to a dynamic link library to determine the behavior of the vehicles based on specifications of the custom/user-defined algorithm.…”