Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies (2012)

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121 C h a p t e r 6 Introduction The research team pursued an empirical approach to studying the determinants of reliability, and specifically, how reliability changes with improvements. Continuous travel time data are required for empirical studies of reliability, and the team was experienced with using these data on past projects. A great deal of continuous travel time data is collected by public agencies (i.e., traffic management centers [TMCs]). Technically, TMC data are almost exclusively speed, volume, and lane occupancy measurements from roadway-based detectors, but if the detec- tors are closely spaced (half a mile or less), travel times can be reasonably estimated from them. Even if the resulting travel time estimates are off the true value, the variability (used to define reliability) would still be internally consistent. Further, relative (percentage) changes are likely to be in line with per- fectly and continuously measured distance-based travel times, a standard that has not yet been achieved in practice. Continu- ous travel time data are an absolute requirement for empirical studies of reliability because reliability is defined by how travel times vary over a considerable time span. Exploratory research revealed that a minimum of 6 months of data is necessary for urban freeways where winter weather is not a problem; more data are needed where winter weather causes problems on a significant number of days. The team strove for a complete year’s worth of data in developing reliability patterns, and achieved this in all but a few cases. Because of the need to obtain traffic data of the highest qual- ity that considered moderately to severely congested locations, the research team did not initially seek locations that were candidates for before-and-after studies. Rather, the team first sought data from locations known from previous experience to satisfy the project requirements and then looked for before- and-after improvements in these areas. Fortunately, 17 before- and-after instances were identified at the study locations. These instances covered only a few types of reliability improve- ments, which the team knew from the beginning would be dif- ficult to cover completely. This known difficulty resulted in the reliance on statistical model development specified in the orig- inal work plan. The types of improvements studied were • Ramp metering (four locations); • Incident management large-truck rapid clearance policies (two locations); • Freeway service patrol implementation (two locations); • High-occupancy toll lane conversion (one location); and • Capacity additions and bottleneck improvements (eight locations). Previous work by members of the research team provided preliminary insight into what could be expected from the before-and-after tests (1). In a hypothetical experiment, travel time data for a complete year on a heavily congested section of I-75 in Atlanta were used. From the travel time distribution, all of the abnormally high travel times (those greater than 7 minutes for the 4.05-mile corridor) were arti- ficially reduced by an across-the-board 25%. This reduction was made to simulate the results of a wide variety of possible improvements on travel times, including capital improvements and operations strategies targeting the events that cause higher- than-normal travel times. As shown in Figure 6.1 and Table 6.1, the effect of this hypothetical before-and-after condition is to reduce delay and improve reliability. Because the analysis reduced all higher-than-normal travel times (not just the travel times on days when disruptions occurred), the experiment is especially relevant for gauging the effects of capital improvements, which will improve travel times on all days, not just the ones with disruptions. The results show that such strategies will improve both the average travel time and reliability. Another previous study by members of the team devel- oped predictive models for recurring and incident delay using a stochastic modeling approach (2). In this approach, a simple test link was used in conjunction with a queuing model to estimate the total delay caused by congestion on the link. Both demand volumes and incident characteristics were Before-and-After Studies of Reliability Improvements

122 Table 6.1. Hypothetical Case of Treating Unreliable Travel Times on Southbound I-75 in Central Atlanta, 4:00 to 7:00 P.M. (1) Travel Time Measure Observed Travel Times Abnormally High Travel Times Reduced by 25% Average travel time (min) 9.0 7.1 95th Percentile (min) 13.1 9.8 Buffer Time Index 46% 39% Table 6.2. Model-Developed Relationship Between AADT/C and Delay (2) AADT/C Recurring Delay Due to Queues (h/vehicle) Incident Delay (h/vehicle mile) 8 0.0000 0.0011 9 0.0086 0.0019 10 0.0271 0.0029 11 0.0551 0.0042 12 0.0924 0.0056 13 0.1389 0.0072 14 0.1942 0.0088 Figure 6.1. Actual and (hypothetical) improved peak period travel times on I-75 southbound in central Atlanta, 2002 (1). reliability, sometimes showing an increase, sometimes a decrease, even when average congestion has decreased. The instability of the Buffer Index is consistent with the results presented in Chapter 4. As a result, the team chose the Planning Time Index (95th percentile travel time divided by free-flow travel time) to be the primary reliabil- ity metric. A summary of the findings appears in Table 6.3, and complete findings are shown in Appendix B. In nearly all cases, the improvements studied proved to be beneficial for both average congestion and reliability. The increases in two cases in Minneapolis–St. Paul may be the result of data problems or major shifts in travel patterns in the after condition. The evaluation of adaptive ramp metering on I-210 is ongoing as the system continues to be refined, but the first results showed that algorithms were not operating as expected. Given the results from all of the sec- tions showing positive effects on both average congestion and reliability, the team does not recommend use of the allowed to vary stochastically; basically, this was a Monte Carlo simulation that for any given run determined whether an incident occurred and if it did, what its lane blocking and duration characteristics were. A series of equations were fit to the results of the Monte Carlo simulation. The results showed that both recurring and incident delay are positively corre- lated with the annual average daily traffic (AADT)-to-capacity (AADT/C) ratio (Table 6.2). Note that the units used to define delay in Table 6.2 differ because recurring delay is a function of the number of vehicles trying to get through a bottleneck, and incident delay is a function of both number of vehicles and section length; longer sections will have more incidents. results A full description of the before-and-after analyses is given in Appendix B. A review of the results in Appendix B shows that the Buffer Index is an unstable indicator of changes in

123 Table 6.3. Summary of Urban Freeway Before and After Studies No. Urban Area Highways Covered Improvement Reliability Impacts (Peak Period) 1 Los Angeles I-210 Ramp metering: design, field implemen- tation, and evaluation of new advanced on-ramp control algorithms on west- bound direction of I-210. Slight increases in average travel time and Planning Time Index (PTI) were observed. However, subsequent to this evaluation, the algorithms have been adjusted. 2 San Francisco Bay Area I-580 Ramp metering. 22% reduction in average travel time. 20% reduction in PTI. 3 Seattle SR 520 Ramp metering. 11% reduction in average travel time. 12% reduction in PTI. 4 Atlanta I-285, Northern Arc Ramp metering. 9% reduction in average travel time. 7% reduction in PTI. 3% increase in sustainable service rate. 5 Atlanta All freeways inside beltway perimeter Incident management: incentive program for reducing large-truck crash incident duration (90 minutes). 13% reduction in large-truck crash incident duration. 9% reduction in lane hours lost per large- truck crash. 6 Los Angeles I-710 Incident management: evaluation of pilot project to deploy towing service for big-rig tractor trailers. 10% reduction in average travel time. 20% reduction in PTI. 7 San Diego I-8 Incident management: expansion of the existing Freeway Service Patrol Beat-7 on I-8. 3% reduction in average travel time. 4% reduction in PTI. 8 San Diego SR 52 Incident management: expansion of the existing Freeway Service Patrol. 20% reduction in average travel time. 10% reduction in PTI. 9 Minneapolis–St. Paul I-94 Capacity expansion: add third lane in each direction. 43% reduction in average travel time. 46% reduction in PTI. 10 Minneapolis–St. Paul I-494 Capacity expansion: add third lane in each direction. 31% reduction in average travel time. 16% reduction in PTI. 11 Minneapolis–St. Paul I-394 Capacity expansion: add auxiliary lanes westbound. 35% reduction in average travel time. 38% reduction in PTI. 12 Minneapolis–St. Paul Highway 169 Capacity expansion: convert signalized intersections to diamond interchanges. 16% increase in average travel time. 11% reduction in PTI. 13 Minneapolis–St. Paula Highway 100 Capacity expansion: add third lane north- bound. Add auxiliary lane southbound. Convert Highway 7 interchange from a clover leaf to a folded diamond. 20% reduction in average travel time. 30% increase in PTI. 14 Seattle I-405 Southbound Capacity expansion: addition of one general-purpose lane. 11% reduction in average travel time. 11% reduction in PTI. 15 Seattle I-405 Northbound Capacity expansion: addition of one general-purpose lane. 42% reduction in average travel time. 35% reduction in PTI. 16 Seattle I-405–SR 167 Interchange Capacity expansion: grade separation ramp connecting southbound I-405 off- ramp with southbound SR 167 on-ramp. 20% reduction in average travel time. 23% reduction in PTI. 17 Minneapolis–St. Paul I-394 High-occupancy toll lane conversion. 8% reduction in average travel time. 30% reduction in PTI. a This long (16-mile) study segment was influenced by a downstream bottleneck. two Minneapolis studies and the I-210 study in user applications. references 1. Cambridge Systematics, Inc., and Texas Transportation Institute. Traffic Congestion and Reliability: Linking Solutions to Problems. Office of Operations, Federal Highway Administration, U.S. Department of Transportation, July 2004. http://ops.fhwa.dot.gov/congestion_ report_04/. Accessed May 17, 2012. 2. Cambridge Systematics, Inc., Science Applications International Corporation, and Federal Highway Administration. Sketch Methods for Estimating Incident-Related Impacts: Final Report. 1998.