Empirical Investigation of the Continuous Logit for Departure Time Choice Using Bayesian Methods

Numerous models of travel timing have been calibrated in the literature. Some treat time as a discrete variable using familiar discrete choice methods, while others have treated time in a continuous fashion. Both approaches offer distinct advantages. Here, a continuous logit model of work tour departure time choice is estimated, which offers the advantage of continuous‐time response using a random utility maximization structure, thus capitalizing on the key advantages of both main approaches to travel timing modeling. Bayesian techniques are used to estimate model parameters, and estimation results suggest a variety of predictive densities for departure times across different individuals. In addition, ordinary least squares (OLS) regression models are used to estimate travel times and their variance across times of day for the auto and transit modes. These network variables are used to inform estimation of the continuous logit model of departure time. The results are meaningful for multiple applications, and the continuous logit can readily be extended to a two‐dimensional choice construct, such that the departure and return times can be modeled simultaneously. In addition, Bayesian estimation techniques allow for the utility function to take any number of forms, which may offer greater predictive ability

Quantifying the External Costs of Vehicle Use: Evidence from the Top Selling Passenger Vehicle Models. Submitted for presentation to the

Vehicle externality costs include emissions of greenhouse and other gases (affecting global warming and human health), crash costs (imposed on crash partners), roadway congestion, and space consumption, among others. These five sources of external costs by vehicle make and model were estimated for the top-selling passenger cars and light-duty trucks in the U.S. Among these external costs, those associated with crashes and congestion are estimated to be the most practically significant. When crash costs are included, the worst offenders (in terms of highest external costs) were found to be pickups. If crash costs are removed from the comparisons, the worst offenders tend to be four pickups and a very large SUV: the Ford F-350 and F-250, Chevrolet Silverado 3500, Dodge Ram 3500, and Hummer H2, respectively. Regardless of how the costs are estimated, they are considerable in magnitude, and nearly on par with vehicle purchase prices

Anticipating new-highway impacts: Opportunities for welfare analysis and credit-based congestion pricing

Pricing of roadways opens doors for infrastructure financing, and congestion pricing seeks to address inefficiencies in roadway operations. This paper emphasizes the revenue-generation opportunities and welfare impacts of flat-tolling schemes, standard congestion pricing, and credit-based congestion pricing policies. While most roadway investment decisions focus on travel time savings for existing trips, this work turns to logsum differences (which quantify changes in consumer surplus) for nested logit specifications across two traveler types, two destinations, three modes and three times of day, in order to arrive at welfare- and revenue-maximizing solutions. This behavioral specification is quite flexible, and facilitates benefit-cost calculations (as well as equity analysis), as demonstrated in this paper. The various cases examined suggest significant opportunities for financing new roadway investment while addressing congestion and equity issues, with net gains for both traveler types. Application results illustrate how, even after roadway construction and maintenance costs are covered, receipts may remain to distribute to eligible travelers so that typical travelers can be made better off than if a new, non-tolled road had been constructed. Moreover, tolling both routes (new and old) results in substantially shorter payback periods (5 versus 20 years) and higher welfare outcomes (in the case of welfare-maximizing tolls with credit distributions to all travelers). The tools and techniques highlighted here illustrate practical methods for identifying welfare-enhancing and cost-recovering investment opportunities, while recognizing multiple user classes and appropriate demand elasticity across times of day, destinations, modes and routes.Highway investment decisions Congestion pricing Traveler welfare analysis Optimal tolls

FROM AGGREGATE METHODS TO MICROSIMULATION: ASSESSING THE BENEFITS OF MICROSCOPIC ACTIVITY-BASED MODELS OF TRAVEL DEMAND

Two competing approaches to travel demand modeling exist today. The more traditional “4step” travel demand models rely on aggregate demographic data at a traffic analysis zone (TAZ) level. Activity-based microsimulation methods employ more robust behavioral theory while focusing on individuals and households. While the vast majority of U.S. metropolitan planning organizations (MPOs) continue to rely on traditional models, many modelers believe that activity-based approaches promise greater predictive capability, more accurate forecasts, and more realistic sensitivity to policy changes. Little work has examined in detail the benefits of activity-based models, relative to more traditional approaches. In order to better understand the tradeoffs between these two methodologies, this paper examines model results produced by both, in an Austin, Texas application. Three scenarios are examined here: a base scenario, a scenario with expanded capacity along two key freeways, and a centralized-employment scenario. Results of the analysis reveal several differences in model performance and accuracy, in terms of replicating travel survey and traffic count data. Such distinctions largely emerge through differing mode