Modeling travelers’ perception of service quality is an emerging research area in surface transportation. The first national effort to study automobile drivers’ perceptions is NCHRP Project 3-70. The study was developed in response to agencies’ needs to understand which factors enter into users’ perception of quality of service and the interaction between modes on urban streets. This paper describes the research efforts taken to analyze and model automobile level of service (LOS) from the driver's perspective. A data collection approach using video laboratories and a modeling approach suitable for the discrete and ordered nature of the response variable were selected. The approach enabled the estimation of the distribution of LOS responses, given a set of explanatory variables that describe the geometry and operational effectiveness of the urban street facility. Analysis indicated that participants’ responses tended to be highly variable and that models that characterize the distribution would be preferable to point estimates. A model that uses the number of stops per mile experienced on the facility and the presence of exclusive left-turn lanes at intersections gave the best fit to the data and matched the mean video clip LOS rating 71% of the time. Although direct links between various modes to the automobile users’ perceptions of service quality were not identified, the variable “number of stops” incorporates interruptions experienced by automobile drivers when interacting with buses, bicycles, or pedestrians on urban streets and when encountering traffic signal stops. Although the proposed model was found to improve LOS prediction as compared with the current Highway Capacity Manual method, there are implementation issues that have yet to be addressed.
This study focuses on the problem of measuring the queue discharge flow rates for a nonbottleneck freeway section and on developing an approach for estimating the impacts of intelligent transportation system (ITS) measures on the mean and variance of the queue discharge flow rate. The whole-year mean and variance of the queue discharge flow rates for the subject section of freeway are computed on the basis of measured 5-min congested flow rates over the course of a year. The flow data are categorized into congested and uncongested flows on the basis of a speed threshold that separates congested flow conditions from uncongested conditions. The speed threshold is determined by plotting the change in speed between observations and identifying the speed at which speed is most unstable. The mean and variance of queue discharge flow rates for incident conditions are then computed by identifying when incidents were present on the freeway over the course of a year, identifying the corresponding flow rates in the study section for those periods, and segregating the observed flow rates for incidents into congested and uncongested flows by using the same speed threshold as before. Once the means and variances of the queue discharge flow rates have been obtained for the whole year and for incident conditions during the year, the variance decomposition formula is used to compute the mean and variance of queue discharge flow rates for nonincident conditions during the year. A methodology is then proposed and demonstrated for computing the impact of ITS measures (such as faster incident detection or improved congested flow rates during incidents) on the observed portion of the total variance that is attributable to incidents susceptible to amelioration with ITS measures. This same approach can also be used to compute the effectiveness of measures to reduce other causes of nonrecurrent congestion.
Three models were developed for spatially projecting the demand for waste collection service by manufacturing, retail, and service businesses, and were compared with a model commonly used for this purpose. Model I is similar to most earlier work in which unit demand rates (kg employee-1 wk-1) are estimated for a variety of businesses. These rates are then multiplied by the number of employees in the area being modeled. Model II differs from Model I in that unit demand rates are estimated for businesses of different sizes (number of employees), rather than activities. In Model III businesses are cross-classified by both activity and size, and then unit demand rates are estimated within each class. Data for model estimation were obtained by weighing waste deposited for collection by 435 businesses over a 25-wk period in the Portland, Oregon metropolitan region. Business size accounted for significantly more of the variation in unit demand rate than did business activity. This suggests that demand models used in planning solid waste collection and disposal systems can be improved by including indicators of business size.
Integrated transportation planning is an emerging transportation planning concept that is intended to help seek those policies, programs, and projects that meet a given set of transportation goals and objectives for the minimum total social cost. Its elements include analysis of a full range of alternatives, the use of benefit-cost analysis to compare alternatives, public involvement in the development and evaluation of alternatives, analysis of uncertainties in forecasts of future conditions, and continuous monitoring of transportation system performance. The theory and methods of integrated transportation planning for implementation by a metropolitan planning organization developed for the Puget Sound Regional Council are discussed. The discussion includes how integrated transportation planning fits into the strategic planning process, the similarities and differences between integrated transportation planning and the current transportation planning process, and the analytic issues raised by implementing integrated transportation planning.
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