Incorporating Land Use into Methods of Synthetic Population Generation and of Transfer of Behavioral Data

2017 ◽  
Vol 2668 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Elizabeth C. McBride ◽  
Adam W. Davis ◽  
Jae Hyun Lee ◽  
Konstadinos G. Goulias
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Behavioral Data ◽  
Synthesis Methods ◽  
Population Synthesis ◽  
Household Travel Survey ◽  
Synthetic Populations ◽  
Synthetic Population ◽  
Household Travel

This paper describes a new method of population synthesis that includes land use information. The method is based on an initial identification of suitable land use summaries to build a spatial taxonomy at any spatial scale. This same taxonomy is then used to classify household travel survey records (persons and households) and in parallel geographic subdivisions for the state of California. This land use information is the added dimension in the population synthesis methods for travel demand analysis. Synthetic population generation proceeds by expanding (re-creating) the records of the households responding to the survey and the entire array of travel behavior data reproduced for the synthetic population. The basis for selecting the variables to use in the synthetic population is first testing their significance in simplified specification in models of travel behavior that include land use as an explanatory variable and account for the shape of behavioral data (e.g., observations with no travel). The paper shows differences between synthetic populations with and without land use data to demonstrate the behavioral realism added by this approach.

A Spatial Latent Profile Analysis to Classify Land Uses for Population Synthesis Methods in Travel Demand Forecasting

2018 ◽  
Vol 2672 (49) ◽  
pp. 158-170 ◽  
Author(s):  
Elizabeth C. McBride ◽  
Adam W. Davis ◽  
Konstadinos G. Goulias
Keyword(s):  
Land Use ◽  
Latent Profile Analysis ◽  
Travel Demand ◽  
Profile Analysis ◽  
Demand Forecasting ◽  
Synthesis Methods ◽  
Population Synthesis ◽  
Land Use Classification ◽  
Travel Demand Forecasting ◽  
Latent Profile

In this paper, a new land use classification method is explored for its utility in explaining travel behavior and as a new dimension in population synthesis for travel demand forecasting. This method is based on latent profile analysis applied to 17 business establishment indicators for each of the more than 20,000 block groups in California. The method reproduces the four types of land use environments (urban, suburban, exurban, and rural) identified in a previous paper, and improves our ability to create a finer-grain geographic classification based on land use. It also offers similar indications about the difference between urban dwellers (that make more trips but travel shorter distances) and rural residents (that make fewer trips but with more vehicle miles traveled).

Accessibility and Intraurban Travel

1987 ◽  
Vol 19 (6) ◽  
pp. 735-748 ◽  
Author(s):  
S Hanson ◽  
M Schwab
Keyword(s):  
Travel Behavior ◽  
Activity Spaces ◽  
Household Travel Survey ◽  
Trip Frequency ◽  
Potential Activity ◽  
Using Data ◽  
Household Travel ◽  
Automobile Use ◽  
The Relationship ◽  
Travel Distances

This paper contains an examination of the fundamental assumption underlying the use of accessibility indicators: that an individual's travel behavior is related to his or her location vis-à-vis the distribution of potential activity sites. First, the conceptual and measurement issues surrounding accessibility and its relationship to travel are reviewed; then, an access measure for individuals is formulated. Using data from the Uppsala (Sweden) Household Travel Survey and controlling for sex, automobile availability, and employment status, the authors explore the relationship between both home- and work-based accessibility and five aspects of an individual's travel: mode use, trip frequencies and travel distances for discretionary purposes, trip complexity, travel in conjunction with the journey to work, and size of the activity space. From the results it can be seen that although all of these travel characteristics are related to accessibility to some degree, the travel–accessibility relationship is not as strong as deductive formulations have implied. High accessibility levels are associated with higher proportions of travel by nonmotorized means, lower levels of automobile use, reduced travel distances for certain discretionary trip purposes, and smaller individual activity spaces. Furthermore, the density of activity sites around the workplace affects the distances travelled by employed people for discretionary purposes. Overall, accessibility level has a greater impact on mode use and travel distance than it does on discretionary trip frequency. This result was unexpected in light of the strong trip frequency–accessibility relationship posited frequently in the literature.

Use of a Smartphone GPS Application for Recurrent Travel Behavior Data Collection

2019 ◽  
Vol 2673 (7) ◽  
pp. 89-98 ◽  
Author(s):  
Joann Lynch ◽  
Jeffrey Dumont ◽  
Elizabeth Greene ◽  
Jonathan Ehrlich
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Survey Design ◽  
Demographic Data ◽  
Smartphone Application ◽  
Completion Rate ◽  
Computer Assisted ◽  
Household Travel Survey ◽  
Continuous Survey ◽  
Household Travel

Smartphone-based household travel survey (HTS) studies to date have typically followed the two-part survey process that has historically been used for paper, computer-assisted telephone interviewing, and online HTS. In this two-part survey process, households provide demographic data in a recruit survey (part one) and record trips in a travel diary (part two) often at a later date. The Metropolitan Council, the planning organization serving the Twin Cities metropolitan area in Minnesota, has conducted a pilot study for their cyclical HTS, the Travel Behavior Inventory (TBI), that is one of the first large-scale fields of an all-in-one smartphone HTS design. For the 2018 TBI pilot, the traditional two-part survey was merged into a continuous survey experience within a smartphone app. The TBI pilot used a split sample to test this all-in-one design against a traditional two-part smartphone survey design. For the all-in-one design, households were invited to sign in directly to the smartphone application instead of first recruiting online or by phone. The pilot results provide a direct comparison of the two-part and all-in-one designs at the household-, person-, and trip-levels. The results showed a lower overall recruit and completion rate for the all-in-one design but showed clear promise for increasing representation of younger and lower-income populations—traditionally hard-to-reach groups who completed at a higher rate with all-in-one. The authors discuss several factors which may have contributed to the lower overall completion rate and describe planned updates for future waves of the TBI aimed at improving overall response while maintaining the developments that have improved representation from hard-to-reach groups.

Household Travel, Household Characteristics, and Land Use: An Empirical Study from the 1994 Portland Activity-Based Travel Survey

1998 ◽  
Vol 1617 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Xiaoduan Sun ◽  
Chester G. Wilmot ◽  
Tejonath Kasturi
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Land Development ◽  
Data Set ◽  
Travel Survey ◽  
Forecasting Models ◽  
Demand Models ◽  
Residence Location ◽  
Household Travel ◽  
Use Factors

How a household’s travel behavior is influenced by its socioeconomic and land use factors has been a subject of interest for the development of travel demand forecasting models. This study investigates the relative importance of these factors based on the number of household daily trips and vehicle miles traveled (VMT). The travel data used in the study come from the 1994 Portland Activity-Based Travel Survey. In addition to income, vehicle ownership, and household size, other significant factors in household travel have been identified, such as the presence of car phones, dwelling type, home ownership, and even the length of resident’s time in the current home. Most important, this study has qualitatively revealed that land use makes a big difference in household VMT, whereas its impact on the number of daily trips is rather limited. After controlling for the land use variables, such as density and land development balance, it appears that there is little difference in household income distribution among three different land use areas. The household life stage/lifestyle appears to be more relevant to the residence location. And the land use development of the residence location imposes the greatest impact on the household daily VMT. The results from this study provide some empirical evidence to the development of travel forecasting models. Especially by examining the relationship between land use and household travel, the results shed light on how to incorporate land use factors into comprehensive travel demand models that can be used by policy makers in evaluation of alternative land use policies. This study serves as a step toward more comprehensive studies on transportation and land use. The results presented represent a preliminary analysis of an extensive data set; considerable additional analysis is already in process.

scholarly journals The Role of Destination’s Built Environment on Nonmotorized Travel Behavior: A Case of Long Beach, California

2017 ◽  
Vol 38 (2) ◽  
pp. 152-166 ◽  
Author(s):  
Dohyung Kim ◽  
Jiyoung Park ◽  
Andy Hong
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Mode Choice ◽  
Long Beach ◽  
Environment Factors ◽  
Household Travel Survey ◽  
Future Policy ◽  
Household Travel ◽  
National Household Travel Survey

This study examines how built environment factors at trip destinations influence nonmotorized travel behavior in the City of Long Beach, California. Using 2008–2009 National Household Travel Survey with California Add-Ons, we found that nonmotorized users tend to choose more clustered destinations than motorized users, and that density, diversity, and design at destinations significantly affect mode choice decisions. Transportation networks and nonmotorized facilities at trip destinations are especially important factors for nonmotorized mode choice. Future policy and research need to consider built environment factors at trip destinations to effectively accommodate nonmotorized travel within a city.

scholarly journals Calibration of MATSim in the context of natural hazards in Belgium

2016 ◽  
Author(s):  
Mario Cools ◽  
Ismaïl Saadi ◽  
Ahmed Mustafa ◽  
Jacques Teller
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Travel Demand ◽  
Scale Effects ◽  
Transportation Network ◽  
Transportation Systems ◽  
Generation Process ◽  
Traffic Pattern ◽  
Synthetic Population ◽  
River Floods

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

2021 ◽  
pp. 036119812110525
Author(s):  
Mustapha Harb ◽  
Jai Malik ◽  
Giovanni Circella ◽  
Joan Walker
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Vehicle Miles Traveled ◽  
Household Travel Survey ◽  
Mobility Barriers ◽  
Travel Diaries ◽  
Survey Sample ◽  
Household Travel ◽  
Families With Children ◽  
Made In

To explore potential travel behavior shifts induced by personally owned, fully autonomous vehicles (AVs), we ran an experiment that provided personal chauffeurs to 43 households in the Sacramento region to simulate life with an AV. Like an advanced AV, the chauffeurs took over driving duties. Households were recruited from the 2018 Sacramento household travel survey sample. Sampling was stratified by weekly vehicle miles traveled (VMT), and households were selected to be diverse by demographics, modal preferences, mobility barriers, and residential location. Thirty-four households received 60 h of chauffeur service for 1 week, and nine households received 60 h per week for 2 weeks. Smartphone-based travel diaries were recorded for the chauffeur week(s), 1 week before, and 1 week after. During the chauffeur week, the overall systemwide VMT (summing across all sampled households) increased by 60%, over half of which came from “zero-occupancy vehicle” (ZOV) trips (when the chauffeur was the only occupant). The number of trips made in the system increased by 25%, with ZOV trips accounting for 85% of these additional trips. There was a shift away from transit, ridehailing, biking, and walking trips, which dropped by 70%, 55%, 38%, and 10%, respectively. Households with mobility barriers and those with less auto dependency had the greatest percent increase in VMT, whereas higher VMT households and families with children had the lowest. The results highlight how AVs can enhance mobility, but also caution against the potential detrimental effects on the transportation system and the need to regulate AVs and ZOVs.

Fragmentation in Daily Schedule of Activities using Activity Sequences

2019 ◽  
Vol 2673 (4) ◽  
pp. 844-854 ◽  
Author(s):  
Elizabeth Callahan McBride ◽  
Adam Wilkinson Davis ◽  
Konstadinos G. Goulias
Keyword(s):  
Statistical Analysis ◽  
Travel Behavior ◽  
Daily Activity ◽  
Age Groups ◽  
Activity Participation ◽  
Household Travel Survey ◽  
Daily Schedule ◽  
Household Travel ◽  
Entire Trajectory ◽  
The Life Course

A new method of sequence analysis to measure fragmentation in activity participation is presented in this paper. We applied this method to a sample of residents in the Central Coast of California that participated in the California Household Travel Survey in 2012–2013. This method explores sequences of daily activity and travel employing techniques from the sequencing of events in the life course of individuals. Studying sequences of daily episodes (each activity and each trip) is preferable to other techniques of studying activity-travel behavior because sequences include the entire trajectory of a person’s activity during a day while at the same time considering the number of activities, order of activities in a day, and their durations jointly. We found substantial fragmentation in activity participation among persons with children and in specific age groups (25–65) amplified by the presence of children in the household. We also found poverty plays an important inhibiting role. Examinations of the days of the week showed significant and substantial differences among the days with both Sundays and Saturdays being distinct, but also substantial differences among the weekdays. The paper provides details about this new technique and the statistical analysis of fragmentation. It also provides a discussion about future steps.

Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey

2003 ◽  
Vol 1854 (1) ◽  
pp. 189-198 ◽  
Author(s):  
Jean Wolf ◽  
Marcelo Oliveira ◽  
Miriam Thompson
Keyword(s):  
Travel Time ◽  
Global Positioning System ◽  
Travel Demand ◽  
Positioning System ◽  
Gps Technology ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Time Estimates ◽  
Global Positioning ◽  
Household Travel

Trip underreporting has long been a problem in household travel surveys because of the self-reporting nature of traditional survey methods. Memory decay, failure to understand or to follow survey instructions, unwillingness to report full details of travel, and simple carelessness have all contributed to the incomplete collection of travel data in self-reporting surveys. Because household trip survey data are the primary input into trip generation models, it has a potentially serious impact on transportation model outputs, such as vehicle miles of travel (VMT) and travel time. Global Positioning System (GPS) technology has been used as a supplement in the collection of personal travel data. Previous studies confirmed the feasibility of applying GPS technology to improve both the accuracy and the completeness of travel data. An analysis of the impact of trip underreporting on modeled VMT and travel times is presented. This analysis compared VMT and travel time estimates with GPS-measured data. These VMT and travel time estimates were derived by the trip assignment module of each region's travel demand model by using the trips reported in computer-assisted telephone inter views. This analysis used a subset of data from the California Statewide Household Travel Survey GPS Study and was made possible through the cooperation of the metropolitan planning organizations of the three study areas (Alameda, Sacramento, and San Diego, California).

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