Development of Guidelines for Quantifying Benefits of Traffic Incident Management Strategies​ (2022)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

137 COMMON TIM RULES OF THUMB ASSESSMENT This section provides information on several TIM rules of thumb. It is acknowledged that rules of thumb provide fast and easy analysis at a high level. These information sheets provide knowledge on the origin and research supporting several common TIM rules of thumb, and recommendations on caveats and special considerations, strengths, limitations, and an overall assessment of the validity. These tables should be useful to TIM practitioners in providing context for the application and use of the rules of thumb selected for investigation. 7.1. Traffic Delay Due to Incidents TIM Rule of Thumb: Every minute a freeway lane is blocked due to a traffic incident results in 4 minutes of added travel delay. [Variations: 4-6 minutes delay] Research and Validation Discussion  Good discussion of this topic is contained in a memorandum prepared by Oregon DOT.  VISSIM simulation study explored this very issue at different levels of demand and incident severity (Saka, Estimation of Traffic Recovery Time for Different Flow Regimes on Freeways, Morgan State University, 2008).  Recovery time is uniform with incident duration until traffic volumes approach capacity (v/c ratio of 0.7 or higher).  Recovery time increases faster as traffic volumes approach capacity (e.g., v/c ratio range from 0.75-0.9). Considerations for wording/caveats Add one of these qualifiers to the rule of thumb:  During peak periods or congested periods  Under high volume/capacity ratios (>0.8 or higher) “Every minute a freeway lane is blocked due to a traffic incident during congested periods results in 4 minutes of added travel delay.” Strengths  Relatively easy concept to understand and intuitive  Stresses the importance of reducing the time it takes to clear incidents  Long-standing rule of thumb that has been widely used  Has been validated in general terms by modeling studies Limitations  Very imprecise and rough estimate  Every freeway network situation is dynamic and different, it is difficult to generalize with a nice and neat formula (due to traffic demand patterns, number of lanes, availability of exit points/alternate routes, incident severity and type, etc.)  Rule of thumb for delay has not been systematically or rigorously validated with field data  Only valid at high levels of congestion and in situations with limited exit points upstream of the incident Overall Assessment  Use with caution, adding caveats/context! Source(s)  Traffic Delay Recovery Time, Robert Maestre and Richard Munford, Planning and Economic Analysis Unit, Oregon Department of Transportation memo (http://www.oregon.gov/odot/td/tp/reports/trafficdelayrecoverytime.pdf)

138 7.2. Reduction in Traffic Flow Due to Incidents TIM Rules of Thumb: One blocked lane out of three will reduce traffic flow by about 50 percent; Two blocked lanes out of three will reduce traffic flow by about 80 percent. Research and Validation Discussion  Rules of Thumb values based on Goolsby (TTI) o Based on 2-year detailed study of incidents in Houston (1969-71) o Over 1150 Crashes were analyzed  For one (1) lane out of 3 blocked: o Task 4 findings showed a reduction in effective capacity of 45%, since the average available capacity ratio (ACR) is 55% o 2010 HCM estimates a 51% reduction o 50% appears to be a slight overestimate of the impact of one lane of 3 being blocked. (50.5% was actual value fr/ Goolsby)  For two (2) lanes out of 3 blocked: o Task 4 findings showed a reduction in effective capacity of 76%, since the average available capacity ratio (ACR) is 24% o 2010 HCM estimates a 83% reduction o 80% may slightly overestimate the capacity loss, but is in between task 4 and HCM estimates (79.3% was actual value fr/ Goolsby)  See task 4 discussion and results for further information. Considerations for Wording/Caveats  Only valid during peak periods or congested periods; (will not see as dramatic a reduction in traffic flow if traffic is not heavy)  Can be referred to as a reduction (or temporary drop) in (effective) capacity  Capacity during incidents has been measured or estimated under bottleneck conditions  May wish to use 45%-50% as a range for one of 3 lanes being blocked; 75%-80% as a range for 2 of 3 lanes being blocked Strengths  50% and 80% are nice, round figures to use  Facilitates an understanding that incidents have a bigger impact than the geographic real estate (proportion of lanes) that they occupy  Estimates taken from previous work (based on well-known study) Limitations  Estimates of the impact of incidents appear to be only slightly exaggerated with these rules of thumb  Only applicable to freeway facilities  Possibility exists that urban drivers are more aggressive now versus the time period of the TTI study (late 60s/early 70s), which would be consistent with the incident situations studied in task 4 having less of an impact on capacity than less than those associated with the development of the rule of thumb  Task 4 estimates for these specific cases are based on detailed observation of 19 incidents in urban areas of Dallas and Northern Virginia (small sample size) Overall Assessment  Good estimate based on sound study and data analysis! These rule-of-thumb estimates appear to have stood the test of time. Source(s)  Goolsby, M., Influence of Incidents on Freeway Quality of Service, Highway Research Board, Highway Research Record, No. 349, Washington, D.C., pp. 41- 46, 1971.

139 7.3. Contribution of Traffic Incidents to Total Congestion from All Sources TIM Rule of Thumb: Highway incidents cause about 25 percent of the total congestion (delay). Research and Validation Discussion  Rule of Thumb value established from source, Traffic Congestion and Reliability: Linking Solutions to Problems (FHWA)  Value based on merging data sets from several independent studies and is an approximation Considerations for wording/caveats  Based on national-level data; local conditions can vary widely  Generally, areas with less recurrent congestion can expect that the percentage of delay caused by highway incidents may be higher than those areas with high recurrent congestion  May wish to say “highway incidents cause about 25% of the total congestion in congested urban areas” Previous work has shown that congestion is the result of seven root causes, often interacting with one another.  Physical Bottlenecks (Insufficient "Capacity") [causes estimated 40% of the total congestion nationally] Capacity is the maximum amount of traffic capable of being handled by a given highway section. Capacity is determined by a number of factors: the number and width of lanes and shoulders; merge areas at interchanges; and roadway alignment (grades and curves).  Traffic Incidents [25%] Events that disrupt the normal flow of traffic, usually by physical impedance in the travel lanes. Events such as vehicular crashes, breakdowns, and debris in travel lanes are the most common form of incidents.  Work Zones [10%] Are construction activities on the roadway that result in physical changes to the highway environment. These changes may include a reduction in the number or width of travel lanes, lane "shifts," lane diversions, reduction, or elimination of shoulders, and even temporary roadway closures.  Weather [15%] Environmental conditions can lead to changes in driver behavior that affect traffic flow.  Traffic Control Devices [5%] Intermittent disruption of traffic flow by control devices such as railroad grade crossings and poorly timed signals also contribute to congestion and travel time variability.  Special Events [5%] a special case of demand fluctuations whereby traffic flow in the vicinity of the event will be radically different from "typical" patterns. Special events occasionally cause "surges" in traffic demand that overwhelm the system.  Fluctuations in Normal Traffic – Day-to-day variability in demand leads to some days with higher traffic volumes than others. Varying demand volumes superimposed on a system with fixed capacity also results in variable (i.e., unreliable) travel times. Strengths  Based on analysis of national-level data  25% can be considered on the conservative side Limitations  Value applies better to congested urban areas than it does for small cities and rural areas.  In rural areas, most delay will be event-related rather than caused by bottlenecks (insufficient capacity). In rural areas, estimates suggest that traffic incidents and work zones alone cause 80 to 90 percent of what delay that does occur. Overall Assessment  Use with caution! Use local data on sources of congestion wherever possible. Source(s)  Cambridge Systematics and the Texas Transportation Institute. 2004. Traffic Congestion and Reliability: Linking Solutions to Problems. Federal Highway Administration, Washington, D.C.

140 7.4. Secondary Crashes as a Percentage of All Crashes TIM Rule of Thumb: Approximately 20 percent of all incidents are secondary incidents. Research and Validation Discussion  Rule of thumb from FHWA  Recent studies, using advanced research methodologies have found that secondary crashes occur with frequency lower than 20%  Study by Chung estimated the frequency of secondary crashes in the same and opposite directions were 7.4% and 3.8% of total primary crashes, or 6.7% and 3.4% of all incidents, respectively. This means secondary crashes comprise about 10.1% of all crashes.  Study of accident records for a 27-mile stretch of the New Jersey Turnpike by Yang et al. found number of secondary crashes about 8% of total incidents.  Task 3 analysis to compute the reduction in secondary crashes revealed the average computed estimate (for methods other than Raub, which assumes 15%) for secondary crashes is about 16% in Maryland and 9% in Dallas.  See task 3 discussion and results for further information. Considerations for wording/caveats  Estimates from literature do not always use the FHWA definition of secondary accidents, which is “number of unplanned incidents beginning with the time of detection of the primary incident where an incident occurs as a result of the original incident either within the incident scene or within the queue in either direction.”  Frequently, only the secondary crashes occurring in the same direction of travel as the primary incident are quantified. It is possible that this under-reports the frequency of secondary incidents.  Based on recent results from this study and others, it appears that 20% may be too high; perhaps a range should be provided for the user.  From 8% to 20%; or from 10% to 15% would be a good range  Point estimate of 10% would be fairly conservative  Point estimate of 15% would be more aggressive Strengths  20% is a nice round number, and comes from FHWA (and most likely was based on actual data analysis), though the origin could not be identified. Limitations  Based on recent findings, including this study, 20% appears to be high.  The relative proportion of secondary crashes compared to all incidents does not really reveal the benefits of TIM; however, if you have good before-and-after data on the total number of incidents, you can estimate the number of secondary crashes reduced by the TIM strategies being evaluated.  A better indication of TIM benefits would concentrate on the reduction in frequency and duration of incidents; the risk of secondary crashes goes down as the incident duration is reduced; in turn, this should reduce the total number of incidents. Data on these parameters are difficult to obtain. (The Chou method was the only method in this study to incorporate incident duration directly in the estimation process). Overall Assessment  Recommend using a range instead of a point value. Source(s)  Traffic Incident Management, Federal Highway Administration. http://ops.fhwa.dot.gov/aboutus/one_pagers/tim.htm.  Chung, Y. (2013). Identifying Primary and Secondary Crashes from Spatiotemporal Crash Impact Analysis. Transportation Research Record: Journal of the Transportation Research Board, No. 2386, pp. 62-71.  Zhan, C., A. Gan, and M. Hadi. (2009). Identifying Secondary Crashes and their Contributing Factors. Transportation Research Record, Vol. 2102, pp. 68-75.  Yang, H., B. Bartin, and K. Ozbay. (2013). “Investigating the Characteristics of Secondary Crashes on Freeways.” Presented at the 92nd Annual Meeting of the Transportation Research Board, Washington, D.C.