Showing papers in "Journal of transportation and statistics in 2005"

Vehicle breakdown duration modelling

Abstract: This paper analyzes the characteristics of vehicle breakdown duration and the relationship between the duration and vehicle type, time, location, and reporting mechanisms. Two models, one based on fuzzy logic (FL) and the other on artificial neural networks (ANN) were developed to predict the vehicle breakdown duration. One advantage of these methods is that few inputs are needed in the modeling. Moreover, the distribution of the duration does not affect the results of the prediction. Predictions were compared with the actual breakdown durations demonstrating that the ANN model performs better than the FL model. In addition, the paper advocates for a standard way to collect data to improve the accuracy of duration prediction.

Precision of geocoded locations and network distance estimates

Abstract: This paper addresses the accuracy of the geocoding of travel diaries, the relationships between different network-based distance estimates, and how exact estimates are when distances are self-reported. Three large-scale surveys in Norway and Switzerland demonstrate that very high precision is possible when survey protocol emphasizes the capture of addresses. The study uses the relevant and available databases and networks. Crow-fly, shortest distance path, shortest time path, and mean user equilibrium path distances are systematically related to each other, the pattern of their relationships is matched to theoretical expectations, and the impact of network resolution is reported. In the examples studied, medians of self-reported distances by distance band provide reasonable estimates of crow-fly and shortest distance path distances.

Analysis of work zone gaps and rear-end collision probability

Abstract: This paper studies platooning and headway/gap characteristics of traffic flow in highway short-term and long-term work zones under various car-following patterns. The relationship between traffic volume and the percentage of vehicles in platoons is developed, along with some statistical models for platoon size and headway/gap size distribution. An in-depth analysis of data reveals that vehicles in work zones with higher speed limits maintain shorter car-following time gaps than those in work zones with lower speed limits, even though more time is needed to stop a faster vehicle. This unusual combination of higher speeds and shorter car-following time gaps in work zones may contribute to the high proportion of rear-end collisions among all work zone-related accidents. This paper also presents a new method for evaluating rear-end collision potential, including the probability and the number of vehicles involved in rear-end collisions, by analyzing platoon and gap characteristics for locations without crash records during a construction period.

Spatial and Temporal Transferability of Trip Generation Demand Models in Israel

Abstract: This research investigates the transferability of person-level disaggregate trip generation models (TGMs) in time and space using two model specifications: multinomial linear regression and Tobit. The models are estimated for the Tel Aviv and Haifa metropolitan areas based on data from the 1984 and 1996/97 Israeli National Travel Habits Surveys. The paper emphasizes that Tobit models perform better than regression or discrete choice models in estimating nontravelers. Furthermore, the paper notes that variables and file structures in household surveys need to be consistent. Results of the study show that the estimated regression and Tobit disaggregate person-level TGMs are statistically different in space and in time. In spite of the transferred forecasts, the aggregate forecasts were also similar.

Respondent Behavior in Discrete Choice Modeling with a Focus on the Valuation of Travel Time Savings

Abstract: For models of discrete choice and their parameter estimates the authors examine the impact of assuming that all attributes are deemed relevant to some degree in stated choice experiments, compared with a situation where some attributes are excluded (i.e., not attended to) by some individuals. Using information collected exogenous of the choice experiment on whether respondents either ignored or considered each attribute of the choice task, the authors conditioned the estimation of each parameter associated with each attribute and compare, in the context of tolled vs. free routes for noncommuting car trips, the valuation of travel time savings under the assumption that all attributes are considered and the alternative assumption of relevancy. The authors show empirically that accounting for the relevance of attributes will have a notable influence on the valuation of travel time savings.

A Classification Tree Application to Predict Total Ship Loss

Abstract: Ship accidents frequently result in total ship loss, an outcome with severe economic and human life consequences. Predicting the total loss of a ship when an accident occurs can provide vital information for ship owners, ship managers, classification societies, underwriters, brokers, and national authorities in terms of risk assessment. This paper investigates the use of classification trees to predict this type of loss. It uses a set of predictor variables that correspond to a number of factors identified as the most relevant to the total loss of a ship and sample data generated from a large database of recorded ship accidents worldwide. Through extensive tests of induction algorithms, Exhaustive CHAID was found to be more efficient in classifying the total loss accident cases. The predictive ability of the resulting classification tree structure can be utilized for risk assessment reporting.

The Dynamics of Aircraft Degradation and Mechanical Failure

Abstract: This paper looks at the predictability of system failures of aging aircraft. It presents a stochastic, dynamic model for the trajectory of the operating condition with use. With failure defined as the operating condition below a critical level, the dynamics of the number of failures with accumulated use is developed. The important factors in the prediction of mechanical failures are the number of previous repairs and the time since last repair. Those factors are related to repair procedures, with the time of repair and the extent of repair (fraction of good-as-new) being variables under the control of the operator. The methodology is then applied to data on non-accident mechanical failures affecting safety that result in unscheduled landings. ,

Improved Estimates of Ton-Miles

Abstract: This paper describes recent improvements in measuring ton-miles for the air, truck, rail, water, and pipeline modes. Each modal estimate contains a discussion of the data sources used and methodology employed, presents a comparison with well known existing estimated for reference purposes, and discusses the limitations of the data. The resulting estimates provide more comprehensive coverage of transportation activity than do existing estimates, especially with respect to trucking and natural gas pipelines.

Governors Highway Safety Associations and Transportation Planning: Exploratory Factor Analysis and Structural Equation Modeling

Abstract: The Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 mandated the consideration of safety in the regional transportation planning process. As part of National Cooperative Highway Research Program Project 8-44, "Incorporating Safety into the Transportation Planning Process," we conducted a telephone survey to assess safety-related activities and expertise at Governors Highway Safety Associations (GHSAs), and GHSA relationships with metropolitan planning organizations (MPOs) and state departments of transportation (DOTs). The survey results were combined with statewide crash data to enable exploratory modeling of the relationship between GHSA policies and programs and statewide safety. The modeling objective was to illuminate current hurdles to ISTEA implementation, so that appropriate institutional, analytical, and personnel improvements can be made. The study revealed that coordination of transportation safety across DOTs, MPOs, GHSAs, and departments of public safety is generally beneficial to the implementation of safety. In addition, better coordination is characterized by more positive and constructive attitudes toward incorporating safety into planning.

Sampling and Estimation Techniques for Estimating Bus System Passenger-Miles

Abstract: Most U.S. bus systems conduct on-off counts on a sample of vehicle-trips to estimate annual passenger-miles, which must be submitted to the National Transit Database. The required sample size depends on the techniques used. This paper reviews alternative methods, including simple random sampling, ratio estimation with a variety of possible auxiliary variables, stratified sampling, cluster sampling, and combinations of these approaches. Most of these alternatives take advantage of electronic registering fareboxes to obtain complete counts of boarding passengers. Seven alternative estimation techniques are compared in a case study of Santa Cruz Metro. The most efficient approach combined two techniques, stratified sampling and ratio estimation using the combined ratio technique. The latter technique used on-off data from a sample of trips to estimate the ratio of passenger-miles to potential passenger-miles, a newly proposed auxiliary variable. This approach reduced the sampling burden by over 80% compared with both simple random sampling and a sampling method published by the Federal Transit Administration.

Speed estimation for air quality analysis

Abstract: Average speed is an essential input to the air quality analysis MOBILE6 model for the calculation of emissions factors. Traditionally, speed is obtained from travel demand models; however, such models are not usually calibrated to speeds. Furthermore, for rural areas where such models are not available, no reliable method is available for estimating speed. In this study, the authors developed a procedure based on the model in the Highway Economic Requirement System to estimate average speed using as input various data such as roadway characteristics and traffic conditions. The model was confirmed to be powerful based on the statistical comparisons between the estimated and measured speeds. Various implementation issues including the impact of data quality and potential applications are also discussed.

Estimating Confidence Intervals for Transport Mode Share

Abstract: One of the common statistics used to monitor transport activity is the total travel by a particular method or mode and, for each mode, this share is routinely expressed as a percentage of total personal travel. This article describes a simple model to estimate a confidence interval around this percentage using Monte Carlo simulation. The model takes into account the impact of both measurement errors in counting traffic and daily variations in traffic levels. These confidence intervals can then be used to test reliably for significant changes in mode share. The model can also be used in sensitivity analysis to investigate how sensitive the width of this interval is to changes in the size of the measurement errors and daily fluctuations. A bootstrap technique is then used to validate the Monte Carlo estimated confidence interval.

U.S. transportation models forecasting greenhouse gas emissions: an evaluation from a user’s perspective

Abstract: This paper briefly describes and evaluates some of the more important and frequently used models to estimate greenhouse gas emissions by a number of U.S. government agencies. Among the models covered are: National Energy Modeling System (NEMS); MARKAL-MACRO; MiniCAM; Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model; and Transitional Alternative Fuels and Vehicles (TAFV) Model. These models have been used by the U.S. Congress and federal agencies to assess U.S. strategies to meet the Kyoto Accord, which would require the United States to maintain U.S. carbon emissions at 7% below 1990 levels between 2008 and 2012. In this paper, each model is described and its capabilities and limitations highlighted. Model perspectives are provided from a user's viewpoint, so that potential users will have a full understanding of the capabilities of these models and the resources needed to build, update, and maintain them.

Using Generalized Estimating Equations to Account for Correlation in Route Choice Models

Abstract: This paper presents the use of binary and multinomial generalized estimating equation techniques (BGEE and MGEE) for modeling route choice. The modeling results showed significant effects on route choice for travel time, traffic information, weather, number of roadway links, and driver age and education level, among other factors. Each model was developed with and without a covariance structure of the correlated choices. The effect of correlation was found to be statistically significant in both models, which highlights the importance of accounting for correlation in route choice models that may lead to vastly different travel forecasts and policy decisions.