The Contribution of Ramp Demand in the Capacity of Merge Bottleneck Locations

AbstractTransportation engineers rely on the Highway Capacity Manual (HCM) for estimating capacity at freeway segments. According to the HCM 2010, the capacity of the basic freeway segments is a function of the free-flow speed and it ranges from 2,400 passenger cars per hour per lane (pc/h/ln) for FFS 70 or 75 mi/h, to 2,250 pc/h/ln for FFS 55 mi/h. The freeway merge segments methodology in the HCM 2010 Update uses these same capacity values in the analysis procedure, although research has shown that capacities at these bottleneck locations are considerably lower. In addition to that, researchers have also observed that capacity varies significantly from day to day and from one site to the other. Researchers acknowledge that driver behavior and frequent interactions between mainline and ramp vehicles at these junctions are the causal factor of these variations in capacity and the low capacity values; however, this has not been reflected in the updated version of the HCM 2010. Furthermore, the HCM 2010 Update does not account for the conflicting movements and the contribution of the ramp vehicles on the overall merge junction capacity. This paper investigates the relationship between freeway and ramp demand and capacity at merge junctions. For the purposes of this research, historic data at merge bottleneck locations across North America with different geometric and operational characteristics were analyzed. The results of the analysis show that, there is a clear correlation between ramp demand, freeway demand and freeway capacity. More specifically, higher demand on the on-ramps produces lower overall capacity values. In addition, this paper proposes new capacity values for merge junctions as a function of the freeway and ramp demand and number of lanes

Travel time estimation on a freeway using Discrete Time Markov Chains

Travel time is widely recognized as an important performance measure for assessing highway operating conditions. There are two methods for obtaining travel time: direct measurement, or estimation. For the latter, previously developed models tend to underestimate travel times under congested conditions because of the difficulties of calculations of vehicle queue formations and dissipations. The purpose of this study is to develop a model that can estimate travel time on a freeway using Discrete Time Markov Chains (DTMC) where the states correspond to whether or not the link is congested. The expected travel time for a given route can be obtained for time periods during which the demand is relatively constant. Estimates from the model are compared to field-measured travel time. Statistical analyses suggest that the estimated travel times do not differ from the measured travel time at the 99% confidence level.

Probability of breakdown at freeway merges using Markov chains

The objective of this paper is to review our recent research completed on the determination of the probability distribution of the time of breakdown on a freeway. The methodology applied was unique, in that, it applied Markov chains to develop the probability distribution of the time of breakdown. To develop an improved methodology for the prediction of breakdown, the probability distribution of the time of breakdown was determined based on the zonal merging probabilities with respect to the vehicles traveling on the throughway. Freeway flow, available gaps, and drivers' actions as they approach the merge area were taken into consideration in developing the model. The analytical model was developed based on the information obtained from the literature and an initial field data sample. After determining the arrival distributions of the merging vehicles and the probability of transitioning from state to state, the probability distribution of the time of breakdown occurring was determined before any time, t, through a model analysis which applied Markov chains and implemented it in MATLAB® code. Finally, field data were reduced and applied in the model validation. With respect to the application of Markov chains, it was expected that higher arrival rates of the vehicles traveling on the throughway would lead to higher probabilities of breakdown. This is logical since one would expect a greater probability of breakdown occurring when there are a large number of vehicles entering the system. The model did produce results which predicted a higher probability of breakdown for higher arrival rates. The significance of the results is a better understanding of the prediction of breakdown and capacity estimation. With an improved concept of breakdown on freeways, better freeway analysis and simulation software may be developed.