Bottlenecks, Shockwave, and Off-Ramp Blockage on Freeways

Freeway congestion may spill back for several kilometers, blocking a number of on/off-ramps upstream. As a consequence, flows at the off-ramps may be substantially reduced, and vehicles bound for the off-ramps are trapped in the mainstream congestion, causing intensified spillback of congestion that blocks even more off-ramps further upstream. Such off-ramp blockage is readily understood and its impact is empirically recognized, but there is a lack of analysis to provide more insights. In this paper, some flow conditions for the activation of bottlenecks and congestion propagation are first established, and the mechanism of the off-ramp blockage is theoretically explored. Macroscopic and microscopic simulations are conducted to demonstrate the analytical results, and some general relations between the total demand, total inflow, total off-ramp outflow, and the number of vehicles within a freeway system are examined.

Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application.

Development of a Methodology for Determining Statewide Recurring and Nonrecurring Freeway Congestion: Virginia Case Study

State transportation agencies working to alleviate congestion need to design appropriate and effective congestion mitigation strategies for each sub-region and corridor, but solutions to solve recurring and nonrecurring congestions problems differ. Past research shows large variations in the proportion of delay attributable to recurring and nonrecurring sources and cannot be readily used by other states to develop practical solutions. An affordable, automated, and sound delay estimation methodology that breaks down congestion cause components could bring some insights to this problem. This paper seeks to use traffic and event data elements that are commonly available to Departments of Transportation to develop a methodology that can be broadly adopted by states to estimate the magnitude of recurring and nonrecurring congestion. The methodology uses data commonly available to public agencies, and does not require additional data-collection efforts or periodic re-calibration processes. A case study with the Virginia Interstate network, covering over 2,200 directional miles, is summarized in this paper. The results show that nonrecurring congestion contributed to around 24% of total delays. The result also clearly shows that recurring congestion contributed to most of the delays in urban districts, whereas nonrecurring congestion contributed to most of the delays in more rural districts. It can, therefore, be concluded that the use of a static statewide congestion profile is not suitable for individual district needs.