Institute of Transportation, MOTC

Jen-Chieh Chung, 2008.06
Institute of Traffic and Transportation National Chiao Tung University

　　Ramp metering is one of the most popular and effective strategy for freeway traffic control. It aims to control on-ramp traffic so as to enhance mainline level of service, reduce on-ramp queue and prevent accidents. 　Numerous related researches have been conducted and even been field tested for over thirty years. The ramp metering algorithms can be divided into three main categories: pre-timed ramp metering, traffic responsive metering (isolated and integrated), and gap-acceptance merge control. Since traffic responsive metering can adaptively respond to real time traffic conditions, it has received intensive attentions from researchers. Many traffic responsive metering algorithms have been developed, such as ALINEA, SWARM, METALINE, linked-ramp algorithm, linear programming. Most of them employ mathematic models to determine the optimal metering rates by considering real-time traffic information.

 　　However, due to the rapid and remarkable fluctuation of traffic conditions, it might be rather risky to control the on-ramp traffic based upon a clear-cut (crisp) judgment and control. Fuzzy logic controller (FLC), an expert system based on if-then fuzzy rules, has the advantages of treating ambiguous or vague aspects of human perception and judgment, with which a non-fuzzy expert system normally cannot deal. Thus, this study first develops a traffic phase determination model to indicate the traffic condition from four phases: free-flow, light synchronized, heavy synchronized, and wide moving jam.　Fuzzy logic ramp metering models by considering mainline traffic phase and on-ramp queue length are then developed under two metering strategies: isolated and integrated. The former strategy is to determine the metering rate based the local traffic information alone, while the latter strategy further considers the upstream metering rate as an extra state variable. In order to further investigate and compare the performances and traffic phase transitions of various ramp metering strategies, a microscopic traffic simulation model, cellular automata (CA), is then developed.

　　To demonstrate the performances of the proposed ramp metering models: isolated and integrated, case studies on an exemplified example and a field example of are conducted, respectively. Comparisons with non-metering, pre-time metering and ANCONA metering models under various geometric networks and traffic scenarios are also made. The results on both exemplified example and field example consistently show that the integrated fuzzy logic ramp metering model performs best, which can curtail 2.16~6.66% and 7.96% of total travel time of non-metering model under exemplified and field examples, respectively, followed by the isolated fuzzy logic ramp metering model. In addition, from the in-depth investigation of the temporal and spatial variations of vehicular speed, it indicates that the average speed can be largely increased while speed variations can be reduced under the proposed metering models. Thus, the applicability and performance of the proposed models have been validated.