Trip Generation Rates Research Articles

The propagation of uncertainty through travel demand models: An exploratory analysis

The future operations of transportation systems involve a lot of uncertainty – in both inputs and model parameters. This work investigates the stability of contemporary transport demand model outputs by quantifying the variability in model inputs, such as zonal socioeconomic data and trip generation rates, and simulating the propagation of their variation through a series of common demand models over a 25-zone network. The results suggest that uncertainty is likely to compound itself – rather than attenuate – over a series of models. Mispredictions at early stages (e.g., trip generation) in multi-stage models appear to amplify across later stages. While this effect may be counteracted by equilibrium assignment of traffic flows across a network, predicted traffic flows are highly and positively correlated.

Truck-Trip Generation at Container Terminals: Results from a Nationwide Survey

Research conducted on truck-trip generation at marine container terminals based on a nationwide survey gathered data from 21 container terminals in the United States. The main features of the data are analyzed to provide a characterization of the problem. The data were used to generate trip-generation rates as a function of the area of the container terminal, and through a set of regression models of one-way truck traffic, as a function of a set of independent variables—most notably, the number of 20-ft-equivalent units and the number of container boxes handled by the terminal in a given year. The analyses of both trip-generation rates and the regression models indicate that there are statistically significant differences in the regional patterns of trip generation.

Trip generation rates and land use-transport planning in urban environment

In order to assess the land use policy implications, trip generation rates are essential knowledge for both planners and engineers working in the field. Within the framework of this paper trip generation rates are presented for the Thessaloniki Metropolitan Area (T.M.A.). The structure of the land use system in T.M.A. produces numerous problems the transportation system. The results of research activities of the Laboratory of Transportation Engineering of A.U.Th concerning trip generation rates regarding different land uses (education, administration, banks, cultural, hotel industry, hospitals) in T.M.A. are presented. These results are in the form of mathematical equations that have been derived with regression analysis and take into account data resulting from properly designed questionnaire surveys.

Trip Generation Rates of Correctional Facilities

In the planning of a system of new regional jails in West Virginia, a general lack of trip generation data for correctional facilities was noted. Consequently, five regional jails and one federal correctional facility in West Virginia were studied. First, trip rates were calculated exclusively for the regional jails. Since the regional jails were a homogenous grouping of correctional facilities with respect to facility type and size, the trip rates calculated for the regional jails are believed to be reliable. Second, the regional jail data were synthesized with data from the Institute of Transportation Engineers and the federal correctional facility in West Virginia. These facilities were not homogenous with respect to size or facility type. It was determined that both the facility type (i.e., prisons versus jails) and size have a significant impact on the trip rate. Trip rates were calculated for the more generalized grouping of “correctional facilities” based on the synthesized database. However, users are cautioned to adjust the trip rates to reflect actual conditions with respect to facility type, size, and visitation policy.

Traffic Forecasting as if Intersection Control Matters: The Sequel

For the second time within 5 years, the traffic forecasting model application developed by the planning staff for the Cedar Rapids, Iowa, area metropolitan planning organization (MPO) has achieved not only a base-year accuracy that exceeds what other, larger MPOs have accomplished, but also a travel-volume and travel-time accuracy for the relatively high-volume streets that, in most cases, is not significantly different from the sampling error of field observations. Comparable success has also been achieved in modeling peak-hour traffic patterns, where applications have also been developed to model peak-hour factoring and design hour volumes for transportation projects. The standard of accuracy that has been achieved for this metro area has been made possible by two major differences from typical MPO practice. First, travel estimates (trip generation rates) are based on field-measured vehicle-trip rates rather than the common practice of a stated-response travel survey of the public (i.e., on what people do rather than what they say they do). Second, and far more important, the modeling is based on the dynamic integration of the Highway Capacity Manual (HCM) methods of intersection traffic control analysis with traditional traffic forecasting processes. Although there are many planning agencies that claim to be using HCM procedures, the process described is shown to be the one that much more faithfully replicates the flexible approach capacities and volume/delay relationships that are the direct result of HCM-level analysis.

Evaluating Effects of Transportation Demand Management Strategies on Trip Generation by Using Poisson and Negative Binomial Regression

Most trip generation models are insensitive to the effects of transportation demand management (TDM) strategies. To evaluate the potential effectiveness of TDM solutions, transportation professionals must rely largely on the results of case studies, which cannot be generalized for all urban areas. To evaluate TDM strategies in a context that is sensitive to the unique characteristics of each urban area, TDM strategies should be incorporated into regional travel demand models. Five TDM strategies affecting trip generation rates are examined: telecommunications, alternative work schedules, on-site amenities, pricing strategies, and land use strategies. To analyze these strategies, data from the Puget Sound Transportation Panel (PSTP) were used. Variables derived from the PSTP data that may help explain the impacts of these strategies were evaluated for significance in trip generation models for several home-based and non-home-based trip purposes. The trip generation models were specified using Poisson and negative binomial regression techniques. After the models were estimated, the significance of the variables representing the impacts of TDM strategies was analyzed. Many of the TDM variables were indeed significant in the trip generation models; however, in some cases, the significance of the variables can be attributed to factors such as trip chaining, which does not describe the effects of TDM strategies. Additional research is needed to fully determine the effects of trip chaining on the variables examined in this study, and additional data could enable the development of variables that more accurately describe the effects of TDM strategies.

Trip Generation and Parking Requirements in Traditional Shopping Districts

The New Urbanists assume that if you build a village center or Main Street-style retail businesses in the middle of a residential neighborhood, it will, among other things, reduce the level of automobile usage. Based on the claim of reduced automobile travel, advocates suggest that parking requirements and transportation impact fees should similarly be reduced. Although it would be ideal to test these claims using New Urbanist development, current developments lack well-established retail businesses. Thus, this study considers these claims of the New Urbanists using six prototypical traditional shopping districts in the Oakland-Berkeley subarea of the San Francisco Bay Area. Each of these districts is surrounded by residential areas of moderately high density [between 5.3 and 8.5 persons per hectare (13–21 persons per gross acre)] and middle-class residents. These shopping areas vary in scale and mix of businesses covering the range of sizes and types espoused by the New Urbanists. The trip generation rates and parking needs for each of these prototypical shopping areas are calculated and compared with ITE-based rates for both an average hour and a daily rate. Based on these comparisons, a conclusion is reached that the claims of the New Urbanists for reduced parking and transportation fees cannot be wholeheartedly supported if the needs of the neighborhood are to be considered. Although many customers walk to these shopping areas, the trips by modes other than automobile are offset by a higher overall level of activity in the shopping area.

Trip Generation Modeling Using CHAID, a Criterion-Based Segmentation Modeling Tool

Conventional trip generation models are identified, as are the difficulties of model application typical of segmentation problems: identification and categorization of explanatory variables and of the interactions among them. The use of CHAID (Chi-Squared Automatic Interaction Detection), a criterion-based segmentation modeling tool, is explored to analyze household trip generation rates. CHAID models are presented in the form of a tree, each final node representing a group of homogenous households concerning daily trip making. An application to data from an origin-destination survey for São Paulo produced interesting results, in agreement with theoretical expectations and amenable to interpretation based on the likely activity-travel patterns of each group of households generated by the technique. CHAID can be used as an exploratory technique for aiding model development or as a model by itself. The use of CHAID results as a trip generation model was verified through an evaluation of its predictive capability in a cross comparison of two subsamples and through a comparison of observed versus predicted trips at a zone level; the segmentation of households produced by the technique provided good estimates of trip rates and zone totals. The application of a modeling approach requiring a highly disaggregate projection of the population may become possible considering the advances in methods for the generation of synthetic populations. The use of these methods in conjunction with a segmentation model represents an alternative to conventional trip generation models and an opportunity to introduce new population forecasting techniques into transportation planning practice.

Traditional neighborhoods and commuting in the San Francisco Bay area

Neo-traditional designs, proponents argue, reduce dependency on the automobile and provide attractive environments for walking, bicycling, and transit riding. This paper explores the extent to which this proposition holds for seven traditional neighborhoods in the San Francisco Bay Area that evolved around early streetcar services. Matched-pair comparisons of modal shares and trip generation rates for work trips are made between these neighborhoods and newer auto-oriented suburbs, controlling for the effects of income and, to a lesser extent, existing bus service levels. Pedestrian/bicycle modal shares and trip rates tended to be considerably higher, in some cases five time as high, in transit-oriented than in the paired auto-oriented neighborhood. Transit neighborhoods also averaged around 70 more daily transit work trips per 1,000 households than auto-oriented neighborhoods, though trip rates varied considerably among neighborhood pairs. Higher residential densities were also found to have a proportionately greater impact on transit commuting in transit-oriented than in auto-oriented neighborhoods. The paper concludes that in order to yield significant transportation benefits, neo-traditional development must be coordinated with larger regional planning efforts and public policy initiatives to reduce automobile dependency.

Beyond Polycentricity: The Dispersed Metropolis, Los Angeles, 1970-1990

This paper examines the distribution of employment among subcenters in the Los Angeles metropolitan region in 1970, 1980, and 1990, defined in terms of trip generation rates rather than employment ...

Commuting in Transit Versus Automobile Neighborhoods

Abstract In recent years, there has been a chorus of calls to redesign America's suburbs so that they are less dependent on automobile access and more conducive to transit riding, walking, and bicycling. This article compares commuting characteristics of transit-oriented and auto-oriented suburban neighborhoods, in the San Francisco Bay Area and in Southern California. Transit neighborhoods averaged higher densities and had more gridded street patterns compared to their nearby counterparts with auto-oriented physical designs. Neighborhoods were matched in terms of median incomes and, to the extent possible, transit service levels, to control for these effects. For both metropolitan areas, pedestrian modal shares and trip generation rates tended to be considerably higher in transit than in auto-oriented neighborhoods. Transit neighborhoods had decidedly higher rates of bus commuting only in the Bay Area. Islands of transit-oriented neighborhoods in a sea of freeway-oriented suburbs seem to have negligible effects on transit commuting.

Measurement of the socio-economic effects of upgrading road access to a rural community

A preliminary quantitative measure of the ongoing socio-economic benefits of upgrading access roads to rural communities can be gained from the trip generation rates of such communities.

Bayesian updating of trip generation data: Combining national trip generation rates with local data

With traffic impact analyses and impact fee assessment becoming more popular, the need for accurately estimating the trip generation rate of a proposed development is becoming more important. An overwhelming percentage of state transportation agencies depend either partly or entirely on the ITETrip Generation Report to predict the traffic that will be attracted to and/or produced from a proposed development. However, the rates obtained from the ITE publication have been derived from data collected throughout the United States. They represent a national average and fail to take into account the local trip generation characteristics that the site under consideration might have. This paper establishes a methodology for obtaining more reliable local trip generation rates using Bayesian statistics. In this method, the ITE rates are assumed to be the prior information, which are updated using limited local trip generation data that are available. The method also allows for temporal updating, incorporating subjective judgment and using “borrowed” data in the updating procedure. Sample calculations in this paper illustrate the developed methodology.

Transportation Demand Management Planning, Development, and Implementation

Abstract Transportation demand management (TDM) is the art of modifying travel behavior, usually to avoid more costly expansion of the transportation system. TDM is not a panacea, but it can help ease some transportation problems. TDM requires the cooperation of many actors, who may include developers; landowners; employers; business associations; and municipal, county, regional, and state levels of government. This article reviews new TDM organizational forms, including transportation management associations, trip reduction ordinances, and negotiated public-private agreements. More flexible approaches appear to work best. TDM evaluation is difficult, because reductions in trip generation rates, i.e., relative changes in travel demand, are not easy to measure. Evidence suggests that TDM can be applied in a wide variety of situations, with equally variable, and sometimes quite good, overall results. TDM strategies that often have proven to be effective include on-site employee transportation coordination, parking management provisions, and alternative work schedules.

The Simplified Travel Demand Estimation Method Based on Small Sample Surveys

We propose the Simplified Travel Demand Estimation Model. This model, which is basically extention of the conventional 4 step model, is based on small sample surveys and full use of rich existing PT survey results. Adaptation of this model to Miyazaki City and Utsunomiya City shows that 1) the Simpli-fied Travel Demand Estimation Model is adaptable to these cities with good estimation results, and that 2) three hund-red households are enough to estimate trip generation rates, trip length and disaggregate mode choice models which are the key parts of whole estimation process.

A note on the stabilities of work trip travel demands in Toronto

The 1971 and 1981 census journey-to-work data are used to examine the temporal and spatial stabilities of home-based work trip travel demands in the Toronto census metropolitan area (CMA). Regression analysis is used to establish consistent trip generation equations at the census tract level using population, household, and dwelling unit data; the stabilities of alternative equations over time are examined. All of the partial regression coefficients shifted over time, reflecting the substantial changes that have occurred in household structure, female labour force participation, and the characteristics of the housing market. The spatial distributions of the residuals are examined in terms of the spatial differentiation that exists in the household sector in the Toronto CMA in terms of variables such as household size, population age, and occupation status. The use of traditional trip generation techniques is difficult to sustain given the temporal and spatial variations in the trip generation rate. It is concluded that travel demands can only be estimated from a careful consideration of the residential dynamics of the major subareas in a region.

Baysian Updating of Trip Generation Parameters

The use of borrowed models from similar urban areas is one way of reducing the costs associated with conducting full-scale travel surveys. However, in order to tune borrowed models to reflect the local conditions of the study area, these models can be updated using low-cost information (objective or subjective) from the study area. A method based on Bayesian statistics for updating cross-classified household trip generation rates is presented. Some updating techniques are briefly reviewed. Previous applications of Bayesian statistics in the transportation field are also discussed; a methodology for their use to update cross-classified trip rates is presented along with sample application.

Estimating the demand for urban bus travel

The disadvantages of conventional transportation study models, in particular their large data requirements and their weaknesses in dealing with changes in trip generation rates have led to a need for a simple model that can quickly and at low cost examine alternative public transport strategies.