Highway Capacity Manual Methodologies for Corridors Involving Freeways and Surface Streets (2020)

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- 4 - 2. Literature Review A review of the literature regarding operational effects of freeway-urban street interactions yielded a limited number of publications. Although much of the published research on this topic does not directly address performance measures in a manner consistent with HCM procedures, the concepts and framework presented in the research are useful in developing the necessary procedural adjustments. For example, previous research has quantified the delay incurred to freeway mainline vehicles as a result of off-ramp queue spillback, but the HCM Freeway Facilities procedure does not estimate delay explicitly. Rather, average travel speed is estimated, and the difference between that and the freeway’s free-flow speed can be used to estimate delay. This section first presents an overview of the literature found related to performance measures for networks, followed by an overview related to the effects of oversaturation on freeway performance. The last subsection discusses literature related to the effects of oversaturation on surface street performance. Performance Measurement A literature review on recent developments regarding performance measures yielded a range of different approaches for the subject. Dong et al. (2016) propose a travel-time reliability prediction framework for evaluating service quality on urban streets. According to the authors, “from a driver’s perspective, travel time and its reliability are considered more intuitive measures of service quality than the levels of service defined in the Transportation Research Board’s 2010 Highway Capacity Manual”. The proposed framework, which takes into consideration the weather conditions on travel-time classification, was tested on a freeway network in Iowa using probe vehicle data (it was also compared to VISSIM simulations with similar results) with good results in representing field conditions for small networks. The work did not consider lane-changing behavior and the percent of heavy vehicles in the analysis. Friedrich (2016) presented an approach for evaluating entire multimodal journeys based on O-D points, based on the German Guideline for Integrated Network Planning (RIN, 2008). In this framework typical performance indicators include direct speed (direct distance over time, where direct distance is defined as the straight-line distance) trip time ratio of public transport/car, and number of transfers. For each one of the performance indicators, the evaluation outputs are presented as LOS ranges from A to F, similar to the HCM, as the author states that results in this format are very easy to understand for decision makers. Arun et al. (2016) present an operational analysis approach focused on the variability of driver characteristics. The authors indicate that they had difficulties in implementing HCM methods in India due to the diverse driver behavior and characteristics. According to the authors, the simple use of adjustment factors is not adequate for representing the heterogeneity in Indian traffic conditions. Therefore, they explored the possibility of adopting different sets of performance measures to better suit different locations, and highlighted the strengths and limitations of each one. In summary, the research team did not find any research directly pertaining to the HCM and systems analysis. Most research on highway system performance prior to the start of this project had addressed multimodal trips (for which performance measures are needed to address specific questions such as transit to private automobile trip times), and performance measurement under varying driver populations. Spillback Effects from Urban Streets to Freeways A review of the literature regarding operational effects of freeway-urban street interactions yielded a limited number of publications. Although much of the research published prior to this project on this topic does not directly address performance measures in a manner consistent with HCM procedures, the concepts and framework presented were useful in developing the necessary procedural adjustments. For example, previous research quantified the delay incurred to freeway mainline vehicles as a result of off-ramp queue

- 5 - spillback, but the HCM Freeway Facilities procedure does not account for this delay caused by queue spillback into the mainline. Rather, average travel speed is estimated and the difference between that and the freeway’s free-flow speed can be used to estimate delay. In a study performed to compare field observations against this theory, Daganzo and Munoz (2000) confirmed that off-ramp queues spilling over onto the freeway mainline indeed cause a significant reduction in discharge flows downstream of the exit. During the queued steady state, an average discharge rate of 4,520 vehicles per hour on a three-lane freeway mainline (a 37.2% reduction from the HCM “base” capacity of 7,200 vehicles per hour, according to the Basic Freeway Segments procedure) was observed. Note that this rate should be compared to the “discharge capacity” of a mainline section, and the current version of the HCM uses a generic capacity value, which does not consider the two-capacity phenomenon. The proportion of exiting vehicles on the freeway also proved to have a significant effect on capacity: on average, discharge rates increased from 4,520 to 5,720 vehicles per hour (26.5% increase) when the proportion of exiting vehicles decreased from 29% to 24%, even though the actual number of exiting vehicles remained constant. In a separate observation, vehicles were found to transition from free-flow speeds to “queueing speeds” approximately 1 kilometer, or 3,280 feet, upstream of the queue. It was also observed that drivers tend to adopt larger headway spacings over time “en masse”, and the authors indicated that this happened likely because once the length and severity of the queue was collectively realized, driver aggression as a whole subsided. In a separate paper based on the data obtained in the previously described study, Munoz & Daganzo (2002) focused instead on the “behavior” of the queue. It was found that the variation in speeds across mainline lanes is greatest closest to the diverge point, whereas occupancy detectors positioned further upstream indicated less variation. Specifically, lanes closer to the off-ramp queue were more affected in terms of speed reduction, whereas the leftmost lane(s) showed very little difference between the presence of a queue and free-flow conditions. Additionally, non-exiting vehicles in the vicinity of the off-ramp queue “traveled more cautiously, with slightly wider but predictable spacings … and [that] more lane changes can be expected.” In terms of capacity reduction, an average discharge flow of 1,500 veh/h/ln was recorded immediately beyond the diverge point – 25% lower than that which this particular freeway’s geometric conditions (as estimated by the authors) could potentially accommodate. A similar study by Cassidy et al (2002) found that, in general, longer exit queues from the over-saturated off-ramp were accompanied by lower discharge rates for the non-exiting vehicles, although no exact measure of correlation between the two was established. The authors also note that exiting drivers sometimes obstructed non-exiting vehicles by attempting to force their way into the queue rather than wait for their turn. The authors do not discuss the possible correlation between queue length and forced queue entries. The presence of a queue also affected the non-exiting vehicles’ average speed: upon the onset of queueing at an off-ramp, non-exiting vehicles reduced their speed across all lanes, reaching speeds as low as 25 kilometers per hour (15.5 miles per hour) before returning to free-flow speed downstream of the diverge point. Traffic operations during an incident may be similar to operations when a queue is present and thus we briefly review here studies related to the capacity and traffic operational quality during incidents. In a comprehensive comparison between various incident-related studies in the literature, Lu & Elefteriadou (2013) present sets of capacity “adjustment factors” based on various conditions. In a regression analysis developed based on past studies, capacity additions (denoted by a “plus” sign) and reductions (denoted by a “minus” sign) were found to be dependent on congestion occurrence (+320 veh/h), number of lanes (+1,213 veh/h/ln) and the number of lanes/shoulder affected (–1,948 veh/h, –1,116 veh/h and –182 veh/h for shoulder, 1 lane and 2 lanes blocked, respectively). A similar regression structure could conceivably be developed for queueing-related capacity reduction, albeit one that incorporates the probability of further lane blockage in consideration of the queue length variability as well as driver variability (and particularly the probability of forced lane changes into the queue.)

- 6 - Based on the review of the literature, we can conclude that estimating operational measures in the case of spillback from an off-ramp analytically is challenging, as it is very difficult to anticipate the wide variety of driver actions. The following trends and observations documented in previous research are used in this research to develop the framework of the proposed methodologies: • Discharge flows along the mainline are affected by the presence of an off-ramp queue, with one study observing 4,520 vehicles per hour on a three-lane freeway mainline (≈ 1,500 veh/h/ln.) • Discharge rates along the mainline increase with decreasing off-ramp flows; they were also found to increase with decreasing queue lengths, which are correlated with off-ramp demands. • Rightmost lanes are more affected in terms of speed reduction, whereas the leftmost lane(s) show very little difference between the presence of a queue and free-flow conditions. • Exiting drivers sometimes obstruct through lanes by attempting to force their way into the queue. However, research has not established any quantitative measures for the probability of such blockage and its potential association with the off-ramp queue length. • The presence of a queue at the off-ramp reduces the mainline vehicles’ speed with values observed as low as 25 kilometers per hour (15.5 mi/h) If the off-ramp queue blocks the right-most lane and is relatively short, non-exiting mainline drivers may be willing to remain in the queued lane – albeit at a significantly reduced travel speed – and accept a small amount of delay. An FHWA-sponsored study (Saxton DTFH61-12-D-00020, Task Order 15: Highway Capacity Manual (HCM) Systems Analysis Methodology) conducted by UF (PI: L. Elefteriadou) developed preliminary procedures for conducting network analysis. However, the scope of that project did not include data collection. The report proposed a series of modifications to the HCM in order to address spillback conditions, and the research team used some of the findings and recommendations from that report in crafting the data collection effort for this project. Spillback Effects from Freeways to Urban Streets Limited research has been reported to address freeway spillback onto signalized intersections. The HCM Merge/Diverge Segments methodology determines whether volume exceeds capacity at any critical points along the segment, and estimates the maximum expected queue along each on-ramp. However, the method does not consider the effects the resultant queue may have on the upstream surface street. The HCM Ramp Terminals and Alternative Intersections procedure includes an adjustment to consider spillback from the downstream intersection to the upstream in the form of additional lost time. This lost time is estimated for each upstream movement as a function of the downstream queue length and storage availability. A similar logic can be applied in the case of spillback from the on-ramp to a local signalized intersection, as it results in additional lost time for some/all the signal phases that serve traffic movements destined for the on-ramp. Tian et al (2004) analyzed the effects of ramp metering spillback onto a diamond interchange using the simulator DRIVE. Capacity reduction and delay increase were found upstream from the ramp meters due to discharging flow reductions resulting from queue spillback and intersection blockage. The authors estimated the delay incurred by the affected movements with a theoretical plot of demand over time. For freeways without ramp metering, the queue discharge rate depends on freeway merge operations. While arrival rates at the back of the on-ramp queue are an input to HCM procedures, departure rates into the mainline during congested conditions are currently not available, and no guidance was found in the literature to provide such estimates. This is a critical aspect of evaluating spillback conditions at a merge ramp, as the discharge rate of the on-ramp traffic onto the freeway is a key parameter to calculate the queue length along the ramp over time.

- 7 - In conclusion, there are tools available to estimate the lost time incurred at the upstream intersections as a function of a downstream queue length and storage availability. Still, extensive field observations are necessary to document discharge rates at congested on-ramps and based on these estimate the resultant queue length along the on-ramp.