Sanghoon Son

Travel by University Students in Virginia: Is This Travel Different from Travel by the General Population?

To improve regional travel demand models, transportation engineers and planners want to represent subpopulations appropriately. A key segment of the population is university students, and their behavior is neither well understood nor well represented in travel demand models. Furthermore, universities provide a unique context for behavioral research because they are livable, are friendly to alternative travel modes, have a higher density than other contexts, and offer mixed travel modes. This study collected and analyzed data on the travel behavior of university students. With the use of an Internet-based survey instrument, the study collected data on travel behavior, sociodemographics, and context variables at four major universities in Virginia. This paper provides information about the design and implementation of the survey, the instrument structure, and a descriptive analysis of students’ personal and travel characteristics. The results indicated that the sociodemographics and travel behavior of university students were different from those of the general population. Moreover, differences in travel behavior were found between students living on campus and students living off campus and between students attending urban campuses and those attending suburban campuses. The insights gained from this study serve as a basis for further such surveys and help provide an understanding of travel behavior in and around university campuses.

What Can Be Learned from Analyzing University Student Travel Demand

To improve regional travel demand models, transportation engineers and planners desire appropriate representation of subpopulations. University students are a relatively neglected group of the population, often missed in regional behavioral surveys and not well represented in travel demand models. Many students attending a university reside, take classes, work, and perform other activities in the university environment, which is often mixed use, alternative mode friendly, higher density, and livable. The purpose of this paper is to understand the travel behavior of university students and to model associations with their attributes that include personal characteristics, residential location (residing on campus or off campus), and academic status. The data used in this study are from a unique Internet-based survey (N = 1,468) of students at Old Dominion University in Virginia. This effort was conducted in 2010 and was part of the Virginia University Student Travel Survey (USTS) supplement. With USTS data combined with spatial data, rigorous statistical models of automobile and walk–bicycle trip rates are estimated to explore associated factors. Results showed that students living on campus or near campus were significantly more likely to walk and bicycle and less likely to drive automobiles and indicated the value of living in a campus environment with greater accessibility to activities and a walk-and bicycle-friendly network. The behavioral models provide helpful information that can be used to represent better the behavior of university students in regional travel demand models and to improve strategic transportation planning.

Comparative Analysis of University Students' Acquisition and Use of Travel Information

Advanced traveler information systems provide pretrip and en route information, which can improve the travel experience of individuals and increase the efficiency of the transportation system. While research on travelers’ acquisition and use of relevant information on their intended routes and modes has been conducted, behavioral responses of sub-populations that might be particularly sensitive to information are not well understood. A key segment of the population is university students, who are often technologically savvy, are early adopters of new information technology, have widespread access to computers and the Internet, and often use a multitude of travel modes. This study explores student responses to travel information. As part of a larger study, behavioral surveys were conducted to collect and analyze data on university students’ travel behavior. This study focuses on a subset of the collected data that deals with acquisition and use of travel information. It explores how students at four universities in Virginia acquire and respond to travel information, and it identifies important factors associated with these decisions. Statistical models are estimated to test hypotheses. Results show that travel information acquisition is higher when students report longer travel times and on urban campuses. The Internet and variable message signs have the strongest associations with travel decision changes regardless of campus location. Students alter their routes and their modes of travel relatively frequently, especially on suburban campuses, pointing to the importance of delivering multimodal information. Implications of the findings are discussed.

Noncoverage Errors in Travel Surveys Due to Mobile Phone-Only Households

National and regional household travel surveys have conventionally sampled landline telephone households through list-assisted random digit dialing. However, recent increases in “mobile phone-only” households result in either noncoverage or undercoverage of a growing segment of the population. This result could cause a substantial bias in the representativeness of travel behavior toward the target population. To cover mobile phone-only households, an address-based sampling method is of interest. This study explores whether the characteristics and travel behavior of mobile phone-only households differ from those of households with landline telephones. In addition, this study quantifies the extent of noncoverage errors in the surveys in respondents’ travel behavior. Along with census data, the mobile phone-only sample (N = 2,988) was compared with the landline telephone sample (N = 7,774) drawn from the 2008 National Capital Region Household Travel Survey. Results show that the mobile phone-only sample consisted of relatively more single-person households; younger individuals; and Blacks, Asians, and Hispanics, who were generally identified as hard-to-reach groups. Statistical models were estimated to examine differences in travel behavior and suggested that the mobile phone-only households made more transit (41%) and walking (29%) trips. This study shows that the inclusion of the mobile phone-only households can reduce the noncoverage errors, especially for alternative modes. The implications for travel survey methods are discussed.

Quantifying Key Errors in Household Travel Surveys: Comparison of Random-Digit-Dial Survey and Address-Based Survey

Identifying and minimizing potential errors in household travel surveys can facilitate collecting more representative and accurate data. Through a comparison of two recent travel surveys with census data, this paper presents how sampling, noncoverage, nonresponse, and measurement errors work their way into surveys. The 2009 National Household Travel Survey (NHTS) Add-On in Virginia was implemented with a comprehensive survey instrument and random-digit-dial (RDD) sampling. The 2008 National Capital Region Household Travel Survey collected behavioral data with a concise instrument, while adopting address-based sampling (ADD). Focusing on a common area of Northern Virginia, this study examined differences in sociodemographics and travel behavior of the extracted samples (N = 597 and N = 3,581, respectively). Results show that the ADD survey collected data on more single-person households, younger individuals, and Hispanics and Mexicans, which are generally identified as hard-to-reach groups. A comparison of the two samples with the census data shows that the ADD sample was more representative of the population and area, partly because of the inclusion of mobile phone-only households (28%), which were not fully covered in RDD. To quantify a measurement error, this study estimated rigorous statistical models in regard to reported trip frequency. Results show that the NHTS captured 10% more trips, partly as a result of diary instructions and the presence of walking and biking questions in the instrument. Details of other errors and implications for reducing key survey errors are discussed.

Transferring Telephone-Based National Household Travel Survey to the Internet: Application to University Students

Transfer of the National Household Travel Survey (NHTS) and regional travel surveys to the Internet is inevitable, partly because online surveys offer an efficient means of collecting data. Behavioral surveys are increasingly being offered in multiple media and giving respondents the choice of filling out the survey by using the Internet, the telephone, or other means. This study reports experience and lessons learned from transforming the computer-assisted telephone interview methodology used for the NHTS to an Internet-based method for university students, who have ubiquitous access to the Internet. The study reflects innovations in survey research methodology in the context of surveying students at large universities in Virginia. Comparisons of two rounds of behavioral surveys conducted in 2009 and 2010 are provided, and statistical models that quantify trip underreporting are developed. In an attempt to mimic closely the NHTS instrument, the Internet instrument had a relatively high survey response burden. On the basis of analysis of trip frequencies, trip underreporting was suspected in the first round of surveys. Documented here are improvements in survey design that were intended to lower the survey response burden and reduce trip underreporting. Statistical models are estimated to quantify how changes in the instrument captured more trips. Results show that survey design improvements can encourage students to recall and report 15% to 20% more trips. The study also found that, consistent with earlier studies, discretionary trips and shorter trips were more likely to be underreported. The results from this study are valuable for future regional and national survey implementations.

Exploring Bias in Traffic Data Aggregation Resulting from Transition of Traffic States

Transportation engineers and researchers heavily use traffic data, which are generally aggregated by predetermined time intervals (e.g., 5 to 15 min). The aggregation process often discards essential information of traffic state transition (e.g., breakdowns). However, the transition of traffic conditions within an aggregation interval is not well understood. This study explored traffic state transition from uncongested to congested regimes that occurred within a predetermined time interval. From two urban freeway locations in Norfolk, Virginia, traffic data archived at 15-min intervals were obtained. A heuristic method based on a Gaussian mixture model was developed to detect the aggregate traffic data that exhibit the transition of traffic states as well as to partition the data statistically into uncongested and congested traffic states. Results show a substantial difference in travel speed (approximately 20 mph) between the two states. In addition, these results illustrate that aggregating these different traffic conditions can cause substantial traffic data aggregation bias by lowering travel speed and flow rates, especially in high traffic flow situations. Finally, new insights into valid traffic data aggregation and speed–flow–concentration relationship development are discussed.