A Ramp Metering Method Based on Congestion Status in the Urban Freeway

Abstract

In ramp metering methods, the ALINEA algorithm is a very effective way and has been applied widely. But the critical occupancy in ALINEA algorithm is often difficult to obtain and not particularly accurate. It will greatly affect the performance of ALINEA algorithm. In this paper, an improved ALINEA algorithm, named CS-ALINEA, is proposed. In this method, the traffic flow is used to replace the occupancy as the control parameter and the control rate can be selected according to the congestion status reclassified adaptively. In the existing ramp control methods, to guarantee the traffic efficiency of mainstream, the impact of ramp overflow on ground road traffic is often ignored. In order to resolve this issue, the segmented control method is adopted in this paper. When the ramp queuing length exceeds the critical queue length, the signal timing scheme is adjusted by selecting the control rate to avoid the ramp overflow. The SUMO simulation platform is used to simulate the ramp control and test the CS-ALINEA algorithm. The experimental results show that the proposed method can optimize the ramp queuing length and reduce waiting time of vehicles while the efficiency of urban freeway can be guaranteed.

Edge-Enabled Smart Traffic Management System: An IoT Implementation for Urban Mobility

“…These studies show the ongoing efforts to use IoT and other cutting-edge technology to enhance traffic flow and urban mobility. The first stage in traffic management should be to identify and evaluate congestion, according to research [17]. It places a focus on the utilisation of flow, occupancy, and density measurements, which are frequently obtained via vision-based cameras, to track the state of the roads.…”

Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies

“…According to research [17], the first step in traffic management is to identify and assess congestion. Flow, occupancy, and density are the most regularly used road congestion measures, which are often derived from photographs or videos taken by vision-based cameras.…”

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

“…Many intelligent traffic-responsive algorithms based on advanced machine learning techniques, such as deep learning, have been discussed in the literature, which are reviewed in the background section. While such methods have proven effective in simulated test platforms, issues such as excessive programming complexity, long and difficult training procedures, and high data requirements limit their practicality in real-world scenarios [20].…”

“…Different modifications are proposed to improve ALINEA algorithms to improve its performance in the actual scenarios including UP-ALINEA (Upstream-Occupancy Based ALINEA), FL-ALINEA (Flow-based ALINEA), and X-ALINEA/Q, AD-ALINEA [1,22,23]. More recent extensions to ALINEA include ALINEA with Speed Discovery [24], Data-Driven Iterative Feedback Tuning Approach of ALINEA [25], Proportional Integral ALINEA (PI-ALINEA) [26], Feed Forward (FF-ALINEA) [27], parameterized ALINEA with variable speed limit strategy [28], and CS-ALINEA [20].…”