A Latent Class Pattern Recognition and Data Quality Assessment of Non-Commute Long-Distance Travel in California

Author(s): Goulias, Konstadinos G., PhD; Davis, Adam W.; McBride, Elizabeth C. | Abstract: This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings.

Urban structural and socioeconomic effects on local, national and international travel patterns and greenhouse gas emissions of young adults

Research on women’s mobility has focused mostly on local travel, partly as a result of data availability. Women’s long-distance travel, defined as trips over 161 km (100 mi) one way, is examined, with data from the 1995 American Travel Survey (ATS). Conducted for the Bureau of Transportation Statistics by the Bureau of the Census, the ATS collected information on the origin, destination, volume, and characteristics of long-distance travel from 80,000 households in the United States. Data are presented on women’s long-distance travel disaggregated by, among other things, trip purpose, trip mode, age, race/ethnicity, and household type. Where possible and appropriate, ATS data are compared with data from 1977, the last time such a survey was conducted. The data show women made fewer long-distance trips than men in 1995 and the disparity between the sexes is virtually unchanged since 1977. About 80 percent of the difference in trip making results from men taking more than twice the number of business trips as women, despite women’s business travel having grown faster than men’s over the past 18 years. Most of the rest of the difference results from men’s greater trip making for outdoor recreation. Differences in long-distance travel behavior can be explained, to a degree, by women’s lower income, lower employment rates, and lower driver’s licensing rate, and because women are more likely to be the primary caregivers for children whether they work outside of the home or not.

Long-distance passenger travel has received rather sparse attention for decarbonization. Here we characterize the long-distance travel pattern in England and explore its importance on carbon emissions from and decarbonization of passenger travel. We find that only 2.7% of a person’s trips are for long distance travel (>50 miles one-way), but they account for 61.3% of the miles and 69.3% of the greenhouse gas (CO2 equivalent) emissions from passenger travel, highlighting its importance for decarbonizing passenger transport. Long-distance travel per person has also been increasing over time, trending in the opposite direction to shorter-distance travel. Flying for leisure and social purposes are the largest contributors to long distance miles and emissions, and these miles are also increasing. Overall, per capita travel emissions have started decreasing slowly from 2007, but are still higher than in 1997. We propose a new metric—emissions reduction sensitivity (% emission reduced/% trips altered)—to understand the efficiency of travel demand related initiatives to reduce greenhouse gas emissions. Long-distance travel—especially flying—can offer orders of magnitude larger emissions reduction sensitivity compared with urban travel, which suggests that a proportionate policy approach is necessary.

Transport is a key sector in reducing greenhouse gas (GHG) emissions. A consensus prevails on a causal relationship between distance to the city center and emissions from private transport, which has led to an emphasis on density in urban planning. However, several studies have reported a reverse association between the level of urbanity and emissions from long-distance leisure travel. Studies have also suggested that pro-environmental attitudes and climate change concerns are unrelated or positively related to emissions from long-distance travel. The goals of this case study were to find out the structure, levels, distribution, and predictors of GHG emissions from the local, domestic, and international travel of young adults of the Reykjavik Capital Region. A life cycle assessment (LCA) approach was utilized to calculate emissions, and the materials were collected with a map-based online survey. International leisure travel dominated the overall GHG emissions from personal travel regardless of residential location, modality style, or income level. A highly unequal distribution of emissions was found. A higher climate change awareness was found to predict higher GHG emissions from trips abroad. Emissions from leisure travel abroad were the highest in the city center, which was related to cosmopolitan attitudes among downtown dwellers.

There is an urgent need to devise policy instruments that will reduce GHG emissions to a sufficient extent in order to achieve the climate targets. Approximately 16% of all greenhouse gas emissions are generated by long-distance travel. Our objective is to estimate the impact of a Tradeable Mobility Credit (TMC) scheme − where the total emissions are fixed by design whereas the price is the outcome of travelers’ choices − will have on modal split and trip cancellation for long-distance leisure travel in Europe. To this end, we develop a market equilibrium model which accounts for travel and trading decisions. For our case study consisting of a travel demand of more than 300 million trips performed annually between 3,000 city-pair connections, the credit price that emerges in the market equilibrium state is 272€ per ton of CO2 under an emission reduction target of 30%. Due to the TMC, overall long-distance leisure travel demand is reduced by 20% and the share of air among the remaining trips decreases from 50 to 42% whereas the market share of rail increases from 23 to 26%. Modal shifts and trip cancellation rates vary greatly amongst OD-pair connections, depending on the local value-of-time and the extent of modal competition for the respective connection. Our findings contribute to the on-going debate surrounding instruments for stimulating sustainable (im)mobility, in particular in the context of the long-distance travel market.

Households with children have been suggested to play a key role in ethnic residential segregation. One possible mechanism is that school district boundaries affect their segregation patterns, but direct evidence on this is scarce. This study investigates the role of school catchment areas for ethnic residential segregation among different types of households in the city of Helsinki, Finland, using individual-level register-based data covering the complete population of the city between 2005 and 2014. The analyses consist of three steps: a description of ethnic segregation among different types of households with segregation indices, an analysis of mobility flows between school catchment areas, and a boundary discontinuity analysis of the causal effects of the boundaries of catchment areas on the mobility of different types of Finnish-origin households. The analyses show that ethnic segregation is stronger among households with children than among childless households and the residential mobility of higher-income Finnish-origin households with children is particularly affected by the school catchment area boundaries.

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.

As the nation and various states engage in funding transportation infrastructure improvements to meet future long-distance passenger travel demand, it is imperative to develop effective and practical modeling methods for analysis of long-distance passenger travel. Evaluating national-level infrastructure improvements requires a reliable analysis tool to model the demand for long-distance travel. The national travel demand model presented in this paper implements a person-level tour-based micro-simulation approach for modeling individuals’ long-distance or national activities in the U.S.A. This paper reviews the model framework, explains the model calibration, and presents applications of the model for policy evaluation and demand prediction. The model was estimated using the latest long-distance travel survey in the U.S.A., which is the 1995 American Travel Survey. As the estimation data is old, and no new long-distance travel survey with appropriate sample size is available to re-estimate the model, model calibration is the solution used to update the model and make it capable of capturing up-to-date travel patterns. Calibrating such a large-scale model can be challenging, because each calibration iteration is very costly. This paper describes the calibration effort conducted on the national long-distance micro-simulation model to showcase how a large-scale travel demand model can be calibrated efficiently. A fuel price scenario is analyzed to show how the national travel demand will change under a national fuel price increase scenario in the future year 2040. Another scenario analysis corresponding to construction of high-speed rail (HSR) is conducted to observe the effects of adding a HSR system to the northeast corridor on travel demand from a national perspective.

The Tennessee Department of Transportation replaced the quick-response-based long-distance component in its statewide model by integrating the new national long-distance passenger travel demand model in a new statewide model and calibrating it to long-distance trips observed in cell phone origin–destination data. The national long-distance model is a tour-based simulation model developed from FHWA research on long-distance travel behavior and patterns. The tool allows the evaluation of many policy scenarios, including fare or service changes for various modes, such as commercial air, intercity bus, Amtrak rail, and highway travel. The availability of this tool presents an opportunity for state departments of transportation in developing statewide models. Commercial big data from cell phones for long-distance trips also pre-sents an opportunity and a new data source for long-distance travel patterns, which previously have been the subject of limited data collection, in the form of surveys. This project is the first to seize on both of these opportunities by integrating the national long-distance model with the new Tennessee statewide model and by processing big data for use as a calibration target for long-distance travel in a statewide model. The paper demonstrates the feasibility of integrating the national model with statewide models, the ability of the national model to be calibrated to new data sources, the ability to combine multiple big data sources, and the value of big data on long-distance travel, as well as important lessons on its expansion.

Negative relationships between urban density and greenhouse gas emissions from daily travel are well established in the literature. However, recent research suggests that higher urban density is associated with higher emissions from long-distance leisure travel, such as car weekend trips and international flights. This article presents the first systematic review of empirical evidence on these associations and discusses potential explanations. A two-step article selection process yielded 27 empirical articles, complemented by one article published during the review process. When international travel is included in the analysis, the results suggest that residents of the largest cities, and particularly those from centrally located and densely built areas, travel more to cover long distances than do others, after controlling for demographic and socioeconomic variables. When only domestic travel is included, residents of larger settlements and areas of higher density engage in less long-distance travel for leisure purposes than those living in smaller settlements and sparsely built areas. The results of the review are indicative and warrant more research. Generalization is currently limited because of the wide variety of travel behavior measures used, consideration of different travel modes and trip purposes, and geographic scope. There is a strong need for replication of the results using consistent methodology, using data from longer and more recent time spans, and expanding to more diverse geographical settings, especially outside Europe. The systematic review is followed by a narrative review of theoretical explanations of the associations. The most common explanations include: rebound effects, the compensation hypothesis, access to transport infrastructure, urban lifestyles, sociopsychological characteristics, and social networks. Socioeconomic variables are controlled in a majority of the reviewed studies, and business travel is excluded from the review, so the concentration of wealth and business in cities may explain the findings only to some extent. Nonetheless, there is not enough empirical evidence on the causal character of the associations and therefore further qualitative and multidisciplinary work is needed. Compact city and urban densification policies are not strongly challenged by current evidence, and most common policy recommendations point to including air travel into carbon taxing or quota schemes.

The travel mood perception can significantly affect passengers' mental health and their overall emotional wellbeing when taking transport services, especially in long-distance intercity travels. To explore the key factors influencing intercity travel moods, a field survey was conducted in Xi'an to collect passengers' individual habits, travel characteristics, moods, and weather conditions. Travel mood was defined using the 5-Likert scale, based on degrees of happiness, panic, anxiety, and tiredness. A support vector machine (SVM) and ordered logit model were used in tandem for determinant identification and exploring their respective influences on travel moods. The results showed that gender, age, occupation, personal monthly income, car ownership, external temperature, precipitation, relative humidity, air quality index, visibility, travel purposes, intercity travel mode, and intercity travel time were all salient influential variables. Specifically, intercity travel mode ranked the first in affecting panic and anxiety (38 and 39% importance, respectively); whereas occupation was the most important factor affecting happiness (23% importance). Moreover, temperature appeared as the most important influencing factor of tiredness (22% importance). These findings help better understand the emotional health of passengers in long-distance travel in China.

How does extreme temperature affect shared travel? Evidence from bike-sharing order flow in China

Carsharing exemplifies a growing trend towards service provision displacing ownership of capital goods. We developed a model to quantify the impact of carsharing on greenhouse gas (GHG) emissions. The study took into account different types of households and their trip characteristics. The analysis considers five factors by which carsharing can impact GHG emissions: transportation mode change, fleet vintage, vehicle optimization, more efficient drive trains within each vehicle type, and trip aggregation. Access to carsharing has already been shown to lead some users to relinquish ownership of their personal vehicle. We find that even without a reduction in vehicle-kilometers traveled the change in characteristics of the vehicles used in carsharing fleets can reduce GHGs by more than 30%. Shifting some trips to public transit provides a further 10%–20% reduction in GHGs.

Objective: The cognition of Alzheimer's disease (AD) has a heterogeneous pattern. It is useful to obtain more information about specific subgroups of patients to prevent disease progression. For better identification of the population, we aimed to detect latent groups based on cognitive test scores using latent class (LC) cluster analysis and influencing factors of latent severity groups to assist practitioners in outpatient departments who have restricted time and instrumentation. Materials and Methods: Data for 630 patients with AD in the Mersin University Dementia Outpatient Unit were collected, and cognitive test scores, demographic variables, and other factors such as comorbidities and family history of dementia were obtained. Initially, LC cluster analysis was performed to distinguish subgroups considering clinical dementia scores, age, and sex as covariates. Second, univariate analysis was used to detect the relationship between latent subgroups and influencing factors. Finally, multinomial logistic regression was performed to identify the magnitude of risk for significant factors. Results: Four severity groups were defined as mild, moderate, severe, and very severe cases of AD, and severity was significantly related to educational level, hyperlipidemia, diabetes mellitus, and sarcopenia (P < 0.001, P = 0.001, P = 0.043, and P < 0.001, respectively). Family history also influenced severity (P = 0.024). Disease severity increased with decreased education levels. Family history predicted a 1.555-fold increase in the risk of being in the moderate group versus the mild group. Moreover, diabetes mellitus predicted a 3.690-fold increase of being in the very severe group versus the mild group. Conclusion: LC cluster analysis is effective for determining severity groups for AD, and study results will help prepare a guide for an optimum evaluation tool for the disease.