Crossing Solutions at Roundabouts and Channelized Turn Lanes for Pedestrians with Vision Disabilities (2011) / Chapter Skim
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Pages 62-81
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From page 62... ...
This chapter presents the pedestrian delay models and discussion of traffic simulation models consecutively. The reader should be aware that with limited field data, both areas of extension are to be treated with care.
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From page 63... ...
For example, a gap-acceptance–based delay model assumes that pedestrians utilize every crossable gap, which was found not to be the case for blind pedestrians. This section develops mixed-priority pedestrian delay models that capture the mix of yields and crossable gaps encountered and acknowledge the different utilization rates observed for different sites and by different participants.
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From page 64... ...
. The suggested delay model for channelized turn lanes predicts pedestrian delay as a function of the natural logarithm of PCROSS, which is calculated from the four individual probability parameters.
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From page 65... ...
. The suggested delay model for channelized turn lanes predicts pedestrian delay as a function of the natural logarithm of PCROSS, which is calculated from the four individual probability parameters.
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From page 66... ...
. The suggested delay model for channelized turn lanes predicts pedestrian delay as a function of the natural logarithm of PDUAL_CROSS, which is calculated from the availability and encounter probability of dual crossing opportunities.
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The two curves are plotted against the raw average delay data for pretest and posttest conditions. Finally, the figure contains the theoretical average minimum delay for each data point.
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Table 11 shows the final delay models for the three sites for comparison. All three models use natural-log transformed explanatory variables [LN(PCross)
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From page 69... ...
Estimating Probability Parameters The probability parameters that are used as the explanatory variables in the mixed-priority delay models can be estimated directly from field measurements or can be gleaned from the appropriate literature and traffic flow theory concepts. As with any model application, the use of field-measured probabilities is preferable since these give the analyst the greatest confidence.
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From page 70... ...
An analyst can apply the delay models to new sites and conditions by estimating these parameters from field measurements or literature sources. For the field measurement of the explanatory variables, the rate of driver yielding and the availability of crossable gaps can be measured using manual tally and stopwatch methods described in the ITE Manual of Transportation Studies (1994)
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The statistics further show a large range of these values across study participants, making it difficult to generalize for the entire population of blind pedestrians. If a user chooses to apply the average utilization rate, higher delays (due to lower utilization rates)
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Referring to Table 15, the analyst estimates that the utilization rates for yields and crossable gaps are 40% and 30%, respectively. This is about half of the average rates found in this research, and was selected to represent a more conservative and less skilled blind traveler: Since this delay estimate is per crossing leg, the total approach delay is estimated at 52.0 s on average, which falls within HCM LOS = F for unsignalized crossings.
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A dataset containing only sighted pedestrians would not be expected to capture the utilization effect. The resulting delay models are statistically significant and produce good estimates of pedestrian delay that match observed field data.
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The models differ in the specifics of how the interaction between vehicles and pedestrians is modeled and how much flexibility the user has in modifying and calibrating behavioral parameters. However, most models apply some sort of a gap acceptance algorithm to model pedestrians selecting gaps in traffic or to model drivers yielding to pedestrians.
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From page 75... ...
In particular, the four probability parameters would be modeled as follows: • The availability of yielding should be modeled through the use of multiple vehicle classes. The gap acceptance algorithm at the crosswalk that effectively tells drivers to look for gaps in the pedestrian traffic will lead a potential yielder to slow in the presence of a pedestrian.
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The analyst will have to rely on field observations or expert judgment to validate pedestrian results. The following list of model input parameters needs to be collected to set up the initial simulation model: • Geometry: The general geometry of the particular roundabout or CTL is often available in the form of a design drawing or a scaled aerial photograph.
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• Delay times: Vehicle and pedestrian delays in the defined travel time segments are estimated by subtracting the theoretical (undelayed) travel time from the actual travel time through a given segment.
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This sample analysis shows that it is possible to use microsimulation models to extract conflict and delay data for pedestrian–vehicle interaction as a function of run-specific attributes of the two groups. The approach describes the interaction of the two modes in terms of four probability parameters: the likelihood of crossable gap occurrence [P(G)
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From page 79... ...
With these considerations in mind, simulation tools can readily be used to estimate the effect of signals on pedestrian and vehicle delay. Even without fully capturing the behavioral aspects related to the signal, a simulation-based analysis is a great tool for a relative comparison of different signal strategies.
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4. Signalization strategy: The analysis includes a conventional pedestrian signal and a pedestrian hybrid beacon (i.e., HAWK signal)
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From page 81... ...
The first part of this chapter presented empirically derived mixed-priority pedestrian delay models that can be used to estimate pedestrian delay at single-lane roundabouts, two-lane roundabouts, and CTLs. The explanatory variables in these delay models are consistent with the four behavioral probability parameters defined in Chapter 4 and used in the Chapter 5 evaluation of field results.
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