scholarly journals Benefits of Integrating Microscopic Land Use and Travel Demand Models: Location Choice, Time Use & Stability of Travel Behavior

2020 ◽  
Vol 48 ◽  
pp. 1956-1967
Author(s):  
Rolf Moeckel ◽  
Michael Heilig ◽  
Tim Hilgert ◽  
Martin Kagerbauer
Keyword(s):  
Land Use ◽  
Time Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Location Choice ◽  
Demand Models

scholarly journals From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior

2017 ◽  
Vol 11 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Rolf Moeckel ◽  
Leta Huntsinger ◽  
Rick Donnelly
Keyword(s):  
Travel Behavior ◽  
Processing Time ◽  
Travel Demand ◽  
Computer Processing ◽  
Professional Judgment ◽  
Trip Generation ◽  
Household Type ◽  
Demand Models ◽  
Generation Mode ◽  
Definition Of

Background: In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective: This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method: Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results: This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

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.

Telecommuting and Its Impact on Activity–Time Use Patterns of Dual-Earner Households

2017 ◽  
Vol 2658 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Rajesh Paleti ◽  
Ivana Vukovic
Keyword(s):  
Time Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Interaction Effects ◽  
Extreme Value ◽  
Activity Time ◽  
Household Level ◽  
Use Patterns ◽  
Dual Earner

Telecommuting choices of workers in multiworker households are likely to be interdependent. These telecommuting choices may also affect the activity–time use choices of all people in the household. From the standpoint of travel behavior and travel demand forecasting, it is important to test these hypotheses and quantify the relationship between telecommuting choices and activity–time use patterns. To do this, the present study developed a generalized extreme value–based joint count model for analyzing the monthly frequency of choosing to telecommute of workers in dual-earner households. A panel multiple discrete continuous extreme value model was also developed to study activity–time use decisions while accounting for household-level interaction effects. The study findings confirm the presence of strong intrahousehold interaction effects in both the telecommuting and activity–time use choices of workers. Telecommuting choices were found to have a significant influence on daily activity–time use decisions for both mandatory and nonmandatory activities.

scholarly journals Using Probability Distributions for Projecting Changes in Travel Behavior

2021 ◽  
Vol 13 (18) ◽  
pp. 10101
Author(s):  
Andreas Radke ◽  
Matthias Heinrichs
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Probability Distributions ◽  
Human Life ◽  
Decision Makers ◽  
The Past ◽  
Demand Models ◽  
Activity Choices ◽  
Aid Decision ◽  
Pattern Simulation

Mobility is a must for human life on this planet, because important activities like working or shopping cannot be done from home for everyone. Present modes of transports contributes significantly to green house gas emissions while the efforts to reduce these emissions can be improved in many countries. Pathways to a more sustainable form of mobility can be modelled using travel demand models to aid decision makers. However, to project human behavior into the future one should analyze the changes in the past to understand the drivers in mobility change. Mobility surveys provide sets of activity diaries, which show changes in travel behavior over time. Those activity diaries are one of the inputs in activity-based demand generation models like travel activity pattern simulation (TAPAS). This paper shows a method of using probability distributions between person and diary groups. It offers an opportunity for an increased heterogeneity in travel behavior without sacrificing too much accuracy. Additionally it will present the use case of temporal back- and forecasting of changes in activity choices of existing mobility survey data. The results show the possibilities within this approach together with its limits and pitfalls.

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.

Modeling Week Activity Schedules for Travel Demand Models

2017 ◽  
Vol 2666 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Tim Hilgert ◽  
Michael Heilig ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Mean Value ◽  
German Population ◽  
Activity Schedules ◽  
Significance Level ◽  
Annual Survey ◽  
Time Period ◽  
Demand Models ◽  
Kolmogorov Smirnov

Activity schedules are an important input for travel demand models. This paper presents a model to generate activity schedules for one week. The approach, called actiTopp, is based on the concept of utility-based regression models and stepwise modeling. In contrast to most of the existing models, actiTopp covers the time period of one week. Few models have covered one week; thus, the activity generation approach of this simulation period is rare. Analysis of weekly activity behavior shows stability between different days (e.g., working durations). Hence, the model explicitly takes these aspects into account, for example, by defining time budgets to spread durations within the week. For model estimation, the study used data from the German Mobility Panel (MOP). This annual survey collects representative data on the travel behavior of the German population. The data from 2004–2013 provide more than 17,500 activity schedules for one week, with more than 450,000 activities. Selected results are shown for the model application to 2014 MOP data, which the study used for validation purposes. The mean value of activities per person and week show a difference of 0.3 activity. To evaluate the model, the study used Kolmogorov-Smirnov tests with a significance level of α = 0.001. For the activity type distribution of the 2014 sample, the analysis could not reject the null hypothesis of equality of the distribution of the model and the survey data at this significance level.

Coast-to-Coast Comparison of Time Use and Activity Patterns

2000 ◽  
Vol 1718 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Sachin Gangrade ◽  
Krishnan Kasturirangan ◽  
Ram M. Pendyala
Keyword(s):  
San Francisco ◽  
Time Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Activity Patterns ◽  
Time Of Day ◽  
Demographic Characteristics ◽  
Use Patterns ◽  
The Individual ◽  
End Times

Activity-based travel analysis has been gaining increasing attention in travel demand research during the past decade. Activity and trip information collected at the person level aids in understanding the underlying behavioral patterns of individuals and the interactions among their activities and trips. Activity and time use patterns across geographical contexts are compared. Such a comparison could shed light on the differences and similarities in travel behavior that exist between areas. To accomplish this objective, activity, travel, and time use information derived from surveys conducted in the San Francisco Bay and Miami areas has been analyzed to identify differences in activity engagement patterns across different sample groups. In general, it was found that activity and time use patterns are comparable across the two areas as long as the commuting status and demographic characteristics of the individuals are controlled for. In addition, the time-of-day distributions of various events such as wake-up time, sleeping time, time of departure and arrival at home, and work start and end times were compared. These events were considered important in defining the temporal constraints under which people exercise activity and travel choices. Once again, it was found that the distributions followed similar trends as long as the commuting status and the demographic characteristics of the individual were controlled for. However, there were noticeable differences that merit further investigation.

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