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40 Conclusions and Recommendations Summary The MPS treatment operates similarly to a coordinated-actuated vehicular traffic control signal at a midblock crossing. In some places, it uses a green arrow rather than a steady green ball. In some locations, the MPS displays a flashing red indication in place of a steady red indication during the pedestrian clearance interval. This project conducted a data-driven analysis to identify the safety performance of this pedestrian treatment. One key to a successful safety analysis is identifying appropriate treated and control sites. The research team identified sites in California, Texas, and Utah. The MPS sites had two legs (i.e., two motorized vehicle approaches) with a traffic control signal that included green (either arrow or ball), yellow, and red phases. In a data-driven safety analysis, ideally, the control sites would also only have two legs with a marked crosswalk that did not have a traffic control signal and with similar vehicle and pedestrian volumes. Due to the inherent challenges with finding such sites, the research team also identified control sites with three or four legs and a range of pedestrian treatments, including traffic control signals. Sites were removed if atypical inter- section geometry was present, such as a large skew or a nearby frontage road that would affect pedestrian movement. Another key consideration for a safety analysis of a pedestrian treatment is vehicle and pedestrian volume. While vehicle counts are frequently available (and for midblock locations neighboring intersection counts can be used), pedestrian counts, especially at midblock crossings, are rare. Recently researchers have used a sample of pedestrian counts to generate direct-demand models that are used to estimate pedestrian counts at other locations. Pedestrian counts were available for several locations in California and Utah using that approach. Pedestrian counts for the Texas sites were from a previous TxDOT project. For the remaining sites in California, the research team developed regression models to predict pedestrian volume using census data. The research team acquired crash data from databases in each state. One of the stateâs data- bases only had FI crashes; the analysis therefore did not use PDO crashes. Because pedestrian crashes are rare, the research team wanted to obtain 7 years of crash data. Crash data for 2014 to 2020 were pulled from each stateâs database. Upon discussions with city staff familiar with the sites, along with ongoing research into the impacts of the pandemic, the research team decided to focus on a prepandemic time frame, so crash data between January 2014 and March 2020 was used. Dates for a site were adjusted if historical aerial or street views identified major changes at the site (e.g., treatment installed or construction). For most of the sites, a 250-ft buffer around the intersection or midblock crosswalk was used to identify crashes; however, for a few of the midblock sites, that buffer was reduced to 150 ft to avoid including crashes associated with a C H A P T E R 6
Conclusions and Recommendations 41 nearby intersection. The crash database was reviewed and cleaned to remove sites where the crashes for a nearby freeway could not be easily filtered (typically when the study site street was either on an overpass or underpass of a freeway) or if other conditions exist that resulted in the site being considered an outlier. The roadway variables considered in the safety analysis, in addition to the presence of the treatment and vehicle and pedestrian volumes, were the number of legs, one-way or two-way traffic operations, posted speed limit, presence of a bike lane or on-street parking, and the siteâs state. Three control groups were tested: ⢠All sites (intersections with two, three, and four legs along with all types of pedestrian traffic control, including intersection traffic control signals) ⢠All two-leg sites with any type of pedestrian traffic control other than the MPS ⢠All two-leg sites with nonactive or not present pedestrian traffic control device, called grey pedestrian traffic control in this study (sites with marked crosswalk and signs, sites with marked crosswalk only, and sites with no pedestrian treatment) The analysis found that more pedestrian FI crashes occur with: ⢠More vehicle volume ⢠More pedestrian volume ⢠More legs at the intersection ⢠More lanes on the main street Fewer pedestrian FI crashes occur when: ⢠The posted speed limit is lower than 30 mph ⢠On-street parking is present ⢠A bike lane is present ⢠One-way traffic is present at two-leg sites The MPS is associated with a reduction in pedestrian crashes and a reduction in all FI crashes when the control group is group 3 (i.e., two-leg sites with grey pedestrian traffic control). The following crash modification factors were identified: ⢠0.554 for pedestrian crashes ⢠0.660 for all crashes With a control group that includes the complete range of pedestrian crossing traffic control from signals to pavement marking to no treatment, the MPS treatment was found to have borderline significance (p-value of 0.0511) for pedestrian FI crashes; therefore, the research team recommends the CMF listed previously that considered a smaller control group. For the all-site control group, the MPS treatment was associated with a reduction in the number of RE FI crashes but not all FI crashes. Discussion This safety study determined that the MPS provides safety benefits for both pedestrians and drivers of motor vehicles. Worth noting are the characteristics of the MPS sites included in this study. All MPS sites had two legs, and 93% of the sites were on roads with 35 mph or lower posted speed limits. The reduction in crashes found in this study is applicable to signalized pedestrian crossings at midblock crossings (not three- or four-leg intersections) located on lower- speed streets.
42 Safety at Midblock Pedestrian Signals Future Research Needs The efforts of this project answered the question of whether the MPS provides safety benefits. During this study, research questions were identified for pedestrian treatments, including: ⢠What is the appropriate illumination (street lighting) level for a pedestrian crossing? This question has been raised in other pedestrian-related research and continues to be an area of research need. ⢠How do we define a pedestrian crosswalk as being midblock or as being at an intersection (i.e., three or four legs)? The MUTCD uses the distance of 30 ft as a part of the definition of an intersection in Section 1A.13. If the decision is based on the distance to a nearby vehicle access point such as a driveway, alley, or minor street, how close can the access point be to a marked crosswalk before the crosswalk is no longer considered midblock? Does the definition change if the access point is gated, has very low activity, or is one-way? When should the nearby access point have a traffic control signal head? ⢠Similar to the previous point is the need to define or characterize a driveway. A driveway that serves a major retail establishment or an active coffee shop would probably have sufficient vehicular activity to affect the operations or safety of the pedestrian crossing. At the other extreme, a driveway that is gated may have minimal traffic activity, resulting in the need to handle that type of access point differently with respect to traffic control. The needed research could identify the driveway characteristics where no control, stop or yield control, or signal control should be considered. Having the vehicle volume for a driveway could simplify the decision, but vehicle volume may not be routinely available. Are there driveway geometric characteristics such as the number of lanes or lane width that could be used as a surrogate for vehicle volume in making the traffic control decision? ⢠The desire to have data-driven results for pedestrian and bicyclist treatments requires efficient methods of obtaining or estimating pedestrian and bicyclist volumes. This project considered a combination of methods, including using census data to estimate volume when no other source was available. Can census data be sufficient for estimating pedestrian volumes for a range of intersection types? ⢠How might anticipated bicyclist volume influence the design of an MPS, especially considering the different speeds of users? Which intersection features associated with bicycles should be included or should be modified to improve safety? ⢠What are the tradeoffsâfor both pedestrians and motoristsâof including a flashing red indication for motorists during the pedestrian walk and clearance intervals, during the pedes- trian clearance interval only, or not at all? Do the tradeoffs change with the countdown timer? How do other users such as runners, cyclists, and micromobility affect the tradeoffs? ⢠Additional information is needed on how and whether to incorporate a bicycle signal with either the MPS or the PHB. ⢠The material on traffic control signals and PHBs in the 2009 MUTCD (2) considers existing pedestrian volumes. Given that pedestrians may not be attempting to cross the street because of concerns for safety, latent demand rather than existing volumes should be considered. How should latent demand be estimated for intersections? How might this information be incorporated into the MUTCD or other documents? ⢠In general, the material on traffic control signals and PHBs in the 2009 MUTCD focuses on criteria for a single location. Should there be different criteria when considering risk on a corridor versus at a single location? How should the criteria differ? This research could also provide insights into how to prioritize different corridors and different sites within a corridor, along with how to select the appropriate treatment for each site. ⢠Portland, OR, and Seattle, WA, have several half signals operating. What is the experience with these treatments by site characteristics? Are there roadway and traffic characteristics where they could be considered, such as the intersection size or number of lanes on the major street?
Conclusions and Recommendations 43 ⢠A few sites had to be eliminated in this study because the method used to identify crashes resulted in capturing crashes on a neighboring freeway or on a freeway that was overpassing or underpassing the street of interest. The research team may have been able to remove those crashes by reviewing the characteristics of each crash or by reviewing the crash narrative, which is a labor-intensive effort. An efficient and effective method for removing crashes occurring on another road is currently not available for the state databases used in this study. A suggestion that would permit the identification of crashes on overpasses or underpasses is for crash databases to include the z-coordinate along with the x- and y-coordinates for a crash.
