scholarly journals Interpersonal and Intrapersonal Variabilities in Daily Activity-Travel Patterns: A Networked Spatiotemporal Analysis

2021 ◽  
Vol 10 (3) ◽  
pp. 148
Author(s):  
Wenjia Zhang ◽  
Chunhan Ji ◽  
Hao Yu ◽  
Yi Zhao ◽  
Yanwei Chai
Keyword(s):  
Daily Activity ◽  
Pattern Mining ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Spatiotemporal Analysis ◽  
Travel Patterns ◽  
Travel Behaviors ◽  
Typical Day ◽  
Intrapersonal Variability ◽  
Selection Of

Interpersonal and intrapersonal variabilities are two important perspectives to understand daily travel behaviors, while only a small number of studies incorporate them for understanding human dynamics. This paper employed a network analysis approach to detecting daily activity-travel patterns of 680 Beijing’s residents within a week and then used a multilevel multinomial logit model to analyze the intrapersonal variability in patterns and the socioeconomic linkages behind them. Results suggest that most activity-travel patterns have significant day-to-day intrapersonal and interpersonal variabilities. This suggests that the application of a typical day of activity-travel behaviors to measure and represent a week’s or even longer-term behaviors may be biased, due to the existence of day-to-day intrapersonal variability. This study also provides a hint for the selection of days of a week to conduct a diary survey for activity pattern mining or travel demand modeling.

Generation of Synthetic Daily Activity-Travel Patterns

1997 ◽  
Vol 1607 (1) ◽  
pp. 154-162 ◽  
Author(s):  
Ryuichi Kitamura ◽  
Cynthia Chen ◽  
Ram M. Pendyala
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Activity Type ◽  
Individual Activity ◽  
Sequential Approach ◽  
Data Set

Microsimulation approaches to travel demand forecasting are gaining increased attention because of their ability to replicate the multitude of factors underlying individual travel behavior. The implementation of microsimulation approaches usually entails the generation of synthetic households and their associated activity-travel patterns to achieve forecasts with desired levels of accuracy. A sequential approach to generating synthetic daily individual activity-travel patterns was developed. The sequential approach decomposes the entire daily activity-travel pattern into various components, namely, activity type, activity duration, activity location, work location, and mode choice and transition. The sequential modeling approach offers practicality, provides a sound behavioral basis, and accurately represents an individual’s activity-travel patterns. In the proposed system each component may be estimated as a multinomial logit model. Models are specified to reflect potential associations between individual activity-travel choices and such factors as time of day, socioeconomic characteristics, and history dependence. As an example results for activity type choice models estimated and validated with the 1990 Southern California Association of Governments travel diary data set are provided. The validation results indicate that the predicted pattern of activity choices conforms with observed choices by time of day. Thus, realistic daily activity-travel patterns, which are requisites for microsimulation approaches, can be generated for synthetic households in a practical manner.

scholarly journals Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bhawat Chaichannawatik ◽  
Kunnawee Kanitpong ◽  
Thirayoot Limanond
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Peak Period ◽  
Intercity Travel ◽  
Departure Time ◽  
Departure Time Choice

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

Simulation of Daily Activity Patterns Incorporating Interactions within Households

2005 ◽  
Vol 1926 (1) ◽  
pp. 135-141 ◽  
Author(s):  
Ondřej Přibyl ◽  
Konstadinos G. Goulias
Keyword(s):  
Decision Trees ◽  
Daily Activity ◽  
Travel Demand ◽  
Activity Patterns ◽  
Demand Analysis ◽  
New Method ◽  
Travel Patterns ◽  
The Past ◽  
Microsimulation Models ◽  
Daily Activity Patterns

Activity-based approaches to travel demand analysis have gained attention in the past few years and rapidly created the need to develop alternative microsimulation models for comparisons. In this paper, one such example simulates an individual's daily activity–travel patterns and incorporates the interactions among members of households. This model uses several tools to simulate the activity patterns, including a new method to extract activity patterns from data and decision trees to take into account personal and household characteristics. The model outputs are the individuals’ daily activity patterns on a detailed temporal scale. These patterns respect individuals’ constraints, which are implicitly embedded in the simulated activity and travel schedules via the intrahousehold interactions. This model was evaluated with data from 1,500 persons in Centre County, Pennsylvania, collected during fall 2002 and spring 2003.

En Route to a Sustainable Campus–An Analysis of University Students’ Travel Patterns Via 7 day Travel Diary

2014 ◽  
Vol 70 (4) ◽  
Author(s):  
Nur Sabahiah Abdul Sukor ◽  
Sitti Asmah Hassan
Keyword(s):  
University Students ◽  
Travel Demand ◽  
Trip Generation ◽  
Travel Patterns ◽  
Vehicle Ownership ◽  
Travel Pattern ◽  
Travel Diary ◽  
Planning Strategies ◽  
Distance Travel ◽  
Demand Behaviour

The objective of this study is to analyse the travel pattern of students in the Engineering Campus of Universiti Sains Malaysia, by using a 7-day travel diary survey. After screening the data obtained, 98 of the 100 responses received were processed and analysed. The results show that there were major differences in travel patterns between weekdays and weekends in terms of activities, trip generation, modal split, travel distance, travel time and cost. These differences were found to be contributed by the factors such as gender and motorized vehicle ownership. In conclusion, the travel demand behaviour of the students was better understood through the study of travel patterns, as well as the intra and interpersonal variability of the students. This information is particularly important for the establishment of better infrastructures, transport planning strategies, and policies for the sustainability of the campus.

A Model of Students' Daily Activity Patterns

1973 ◽  
Vol 5 (2) ◽  
pp. 231-266 ◽  
Author(s):  
Janet Tomlinson ◽  
N Bullock ◽  
P Dickens ◽  
P Steadman ◽  
E Taylor
Keyword(s):  
Spatial Distribution ◽  
Daily Activity ◽  
Time Budget ◽  
Activity Patterns ◽  
Social Constraints ◽  
Diary Methods ◽  
Typical Day ◽  
Daily Activity Patterns ◽  
Made In

A model is described whose purpose is to predict the distribution of students in different activities and locations during the course of a typical day, depending on the effective restrictions imposed by the spatial distribution of buildings and sites, and by administrative and social constraints on the timing of activities. The model is of an entropy-maximising type; the data against which it is tested are drawn from time budget surveys made in two universities, using diary methods. A series of exploratory experiments made with the model are reported; these are designed to test the effects of alternative planning and administrative policies on activity patterns and the use of facilities.

An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region

2018 ◽  
Vol 2672 (51) ◽  
pp. 45-55 ◽  
Author(s):  
Lei Zhang ◽  
Di Yang ◽  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Integrated Model ◽  
Time Of Day ◽  
Practical Implementation ◽  
Baseline Scenario ◽  
Demand Model ◽  
Dynamic Traffic

The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.

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