Jeffrey J. LaMondia

Traveler Behavior and Values Analysis in the Context of Vacation Destination and Travel Mode Choices: European Union Case Study

The tourism industry has a dramatic impact on the worlds economy and development. For this reason, it is important to study vacation traveler behavior, including where individuals travel on vacation and what travel mode they use to get there. This study uses the unique Eurobarometer vacation travel survey to model jointly travelers’ choice of holiday destination and travel mode while also considering an extensive array of stated motivation-based preference and value factors. The study further builds on the existing literature by applying the model to a large-scale travel market characterized by multiple origins and destinations within the European Union. The empirical results indicate the important effects of nationality, traveler demographics, travel companionship arrangement, traveler preferences and values, and trip and destination characteristics on holiday destination and travel mode choice. These results have important policy implications not only for each country within the European Union but also for countries and regions around the world.

Development of a Microsimulation Analysis Tool for Paratransit Patron Accessibility in Small and Medium Communities

Paratransit is a critical form of transportation for mobility-impaired, low-income, and small and medium-sized communities. Paratransit systems face many challenges that restrict how well they can serve their communities, including limited funding, aging fleets, limited to no service standard assessments, and few practical modeling and planning practices. This paper discusses a transferable paratransit microsimulation analysis tool for patron accessibility designed to address these challenges. The tool calculates paratransit patron accessibility (defined as paratransit patrons’ perceived ease of access to reach desired activities and destinations) by simulating and measuring daily paratransit patron travel patterns on the basis of service fleet and region information. The tool further allows providers to evaluate patron accessibility for any combination of population groups, travel purposes, and times of day. Transit providers can use the tool to determine how well paratransit patrons are served and the most efficient ways to improve service. The microsimulation framework, including the system of simulation models, the supporting data, and application to Brownsville, Texas, are described in detail.

Shifts in Long-Distance Travel Mode Due to Automated Vehicles: Statewide Mode-Shift Simulation Experiment and Travel Survey Analysis

Although vehicle automation technology has experienced rapid gains in recent years, little research has been conducted on the potential impacts of self-driving vehicles on long-distance personal travel, a major area of travel growth in the United States. Automated vehicles (AVs) offer flexible trip time and origin–destination pairings at travel time costs perceived to be lower; thus, AVs have the potential to dramatically change how travelers pursue long-distance tours. This study analyzed travel surveys and then developed a statewide simulation experiment of long-distance travel to anticipate the impact of AVs on long-distance travel choices. The research explored the Michigan State 2009 Long-Distance Travel Survey and estimated a long-distance trip generation model and a modal-agnostic long-distance mode-choice model. These models were applied in a statewide simulation experiment in which AVs were introduced as a new mode with lower perceived travel time costs (via lowered values of travel time en route) and higher travel costs (to reflect the initially high price of complete vehicle automation). This experiment highlighted the potential shifts in mode choices across different trip distances and purposes. For travel of less than 500 mi, AVs tended to draw from the use of personal vehicles and airlines equally. Airlines were estimated to remain preferred for distances greater than 500 mi (43.6% of trips greater than 500 mi were by air, and 70.9% of trips greater than 1,000 mi were by air). Additionally, at certain AV travel time valuations, travel cost was not a significant factor. The findings showed that as the perceived travel time benefits from hands-free travel rose, monetary costs became less important.

Analysis of factors influencing bicycle-vehicle interactions on urban roadways by ordered probit regression

A study of the impacts of roadway environment, motorist behavior, and bicyclist behavior on bicyclist–motorist interactions was based on video footage of traffic movements during peak commuting hours at four locations in Austin, Texas. The study developed three unique models of ordered probit regression that describe bicyclist lateral location, bicyclist–motorist interaction movement, and bicyclist–motorist lateral interaction distance. This structure of discrete choice model was used for the first time to address bicycle–vehicle interactions and offered more meaningful results because it used a latent measure of bicyclists’ and motorists’ mutual acceptance and comfort level sharing a roadway. It was demonstrated that adding “sharrows” and “Share the Road” signage promoted safer interactions on narrow urban roadways, whereas simply widening travel lanes and adding on-street parking did not necessarily improve bicyclist–motorist interactions.

Effects of Vehicle Volume and Lane Closure Length on Construction Road User Costs in Rural Areas

The objective of this paper is to analyze the effects that vehicle volumes and lane closure lengths have on road user costs (RUCs) in rural freeway work zones. This paper presents a methodology developed to estimate RUCs for a rural four-lane freeway under construction with a single lane closure. Although the scenario presented is limited in its application to rural freeway construction projects, the methodology presented can be applied for any road construction project. Three tools have been developed as a result of this research: (a) a RUC template designed to calculate RUCs for a location on the basis of vehicle volumes, lane closure lengths, and speed values; (b) design graphs that practitioners can quickly reference to estimate RUCs for rural four-lane freeways with a single lane closure; and (c) regression equations derived from this research to quickly estimate RUCs. A practical and repeatable design method for estimating RUCs uses the results of this research. The design method can be used when time-based contractual provisions are developed on the basis of RUCs that are to be included in construction contracts for determining total project costs. When the method is used properly, it provides state highway agencies with a useful, justifiable way to calculate RUCs for single lane closure scenarios that can be used to develop incentive and disincentive provisions in rural roadway construction contracts.

Long-Distance Work and Leisure Travel Frequencies: Ordered Probit Analysis Across Non–Distance-Based Definitions

The objective of this research was to isolate the factors influencing non–distance-based definitions of long-distance travel to help long-distance survey makers know which demographic factors they should query about in their surveys. Instead of the use of a distance-based threshold to define long-distance travel, this study included variations in purpose (e.g., work travel or leisure or personal travel), durations (e.g., overnight trips), modes (e.g., intercity rail or bus), and destinations (e.g., inter national travel) to consider which demographic, employment, commute, household, and geographic factors affected the frequency of longdistance travel. The data were from self-reported retrospective surveys collected from approximately 1,200 participants. Results from ordered probit analysis revealed that education and income generally increased most types of long-distance travel, whereas having a spouse or children decreased some types of long-distance travel. In general, limited factors had the same impact on the work and non–work travel and modes used. Factors also varied by trip type. Commute and employment factors were valuable even for non–work trip frequency estimation. The findings suggest that future data collection for long-distance travel can be tailored to address the specific definition being studied.

Developing Calibration Factors for Crash Prediction Models with Consideration of Crash Recording Threshold Change

The focus of this paper is on presenting a revised method to develop calibration factors (CFs) with consideration of the change in the crash recording threshold (CRT), which is a minimum value to report crashes. The higher the CRT, the fewer the number of recorded property damage only (PDO) crashes. In this paper, a threshold adjustment factor was defined and used to estimate the new CFs. Because the threshold change affects only the total number of crashes and the PDO crashes, the percentage of fatal and injury (F/I) crashes before the threshold change needs to be adjusted to properly estimate the total number of F/I crashes. The revised method was verified with case studies using Illinois data. Five years of crash data were gathered and used to develop CFs for five roadway types, including two-lane undivided (2U), two-lane with a two-way left-turn lane (3T), four-lane undivided (4U), four-lane divided (4D), and four-lane with a two-way left-turn lane (5T). Because of an increase in the CRT in 2009, a method is needed to supplement the standard approach to adjust CFs with consideration of the effect of the new CRT. The CFs for 2U and 3T before–after considering the threshold adjustment factor were 1.44/1.32 and 1.24/1.12, respectively, while the CFs before–after the threshold change for 4U, 4D, and 5T were 0.99/0.85, 0.68/0.55, and 0.77/0.69, respectively. The results proved that the revised method can help state and local agencies predict the number of crashes without redeveloping new CFs resulting from the change in CRT.

Modeling Intertrip Time Intervals Between Individuals’ Overnight Long-Distance Trips

Long-distance and overnight travel are growing in importance in the United States, yet the data and methods needed to model these activity patterns are lacking. One important factor for activity simulation is the interval between overnight trips away from home. This study used a unique 1-year panel data set of overnight trips for the estimation of a negative binomial regression model of intertrip time intervals. Most respondents indicated that they took between two and seven overnight trips per year, but time between trips varied widely regardless of the total number of trips. This time difference suggests some clustering of trips for some but not all people. Model results indicated that both regional and household attributes affected intertrip time intervals, but that both prior and next trip factors were significant. The distance from home on previous trips and income were not factors in intertrip interval times. The results of this study demonstrate the range of factors needed to model the time interval between trips. The model format lends itself well to activity simulation models that can expand beyond 1 day and the local region to include 1 year of travel and, potentially, a global landscape. In this way, long-distance and overnight travel can be integrated more appropriately into transportation system planning.

Matching Household Life-Cycle Characteristics to Clustered Annual Schedules of Long-Distance and Overnight Travel

Research on key aspects of long-distance and overnight travel is limited, and knowledge about the household life cycle is even more limited. This work used data from the 2013 Longitudinal Survey of Overnight Travel (conducted as part of this effort) to (a) identify groups of respondents with statistically significant differences according to their annual long-distance overnight travel schedules and (b) summarize the life-cycle characteristics of the respondents that belonged to each group. The panel was not representative of the general population but, rather, consisted of more frequent travelers of higher income who mostly worked full time; many members of the panel traveled for work. K-means clustering was used to partition the travelers into six distinct groups on the basis of their annual travel. The largest group that was formed put a heavy emphasis on longer-distance travel. One group was dominated by travel with children. The other groups formed placed approximately even levels of emphasis on the number of tours of various distances but placed the very highest level of emphasis on work tours or a high level of emphasis on personal tours. The six groups showed distinct differences in demographic, neighborhood, and regional airport factors. Although additional work to refine these groups will be possible with this data set, to create fully representative groups for long-distance travel, more robust, larger data sets are needed. The findings of this research provide a solid expectation that the use of groups with statistically significant differences is a viable tool within long-distance travel modeling.

Using Bicycle Level of Service for Decision Making Comparison of Common Bicycle Level-of-Service Measures, Roadway Characteristics, and Perceived Bike Route Suitability

This study used infrastructure and cyclist survey data from Auburn, Alabama, to compare the rankings of common bicycle level-of-service (LOS) measures, perceived bicycle route suitability for different types of cyclists, and roadway characteristics. Specifically, four common types of bicycle LOS measures were identified, and a representative measure from each was calculated for all the major roadway segments within the city. Additionally, results from a survey of cyclists on the perceived bicycle route suitability were collected and summarized. The comparisons highlighted a surprising disconnection between LOS measures and suitability. Namely, suitability was perceived to be the same across different types of cyclists, but those segments that were ranked highly suitable did not correspond to those with high levels of service. Additionally, suitability was evaluated in terms of routes, whereas LOS rankings treated segments independently. The distribution of suitability and LOS rankings were significantly different as well: suitability had a few highly suitable routes and an increasing number of less-suitable locations, but the different LOS measures had varying distributions of what was acceptable or not. Significant recommendations are made on how city and regional transportation planners may make better-informed decisions about bicycle facility improvements.