In considering the effect of TNC PUDO on congestion, it is useful to consider other scenarios in which a vehicle movement has an effect on congestion beyond simply driving on the roadway. Several examples where this occurs include taxis (16), delivery trucks (41), and movements into or out of on-street parking spaces (42, 43). Wijayaratna (44) provides a useful method for considering the congestion effect of on-street parking that follows the capacity adjustment approach used frequently in the Highway Capacity Manual (45). The approach scales the capacity of the road lane adjacent to the on-street parking based on the share of time that the lane is blocked. To model the effect of TNC PUDO, we took a similar approach, but defined the PUDO effect in PCEs so that it was in the same units as our dependent variable, and expressed the effect asβAvgDur*PUDO*PHF3600*CL(3)where PUDO is the number of PUDO in the period, PHF is the peak hour factor to convert the PUDO to an hourly value, C is the capacity of the link, L is the number of lanes, and βAvgDur is an estimated model parameter. For simplicity, we expressed this term, excluding the estimated coefficient, as VAvgDur. βAvgDur can be interpreted as the average duration that each PUDO blocks or disturbs traffic in the curb lane. In congested conditions, this can be longer than the duration of the stop itself, because it can take some time for a queue to dissipate if it builds up behind a stopped vehicle and for traffic to recover to its pre-PUDO condition. βAvgDur can also be shorter than the actual duration of a stop if there is some probability that the stopping vehicle can pull out of traffic or if volumes are low enough that the probability of a vehicle arriving behind the stopped vehicle is low.

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