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.

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.

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.

scholarly journals Analysis of Time of Day Fare Discounts on Urban Mass Transit Travel Behavior, Crowding, and Waiting Time

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xiao Guo ◽  
Huijun Sun
Keyword(s):  
Objective Function ◽  
Waiting Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
Mass Transit ◽  
Time Of Day ◽  
Pressure Equalization ◽  
Schedule Delay ◽  
Urban Mass Transit ◽  
Artificial Network

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters’ behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models.

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.

The Demand for Travel

2001 ◽  
Author(s):  
Marlon Boarnet ◽  
Randall C. Crane
Keyword(s):  
Travel Behavior ◽  
Urban Form ◽  
Travel Demand ◽  
Resource Constraints ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Trade Offs ◽  
Relative Attractiveness ◽  
Conventional Manner

As described in chapter 1, the new urban designs are part philosophy, part art, part economics, and part social optimism. Still, a key to their popularity is the open embrace of conventional and even conservative standards of neighborhood form, scale, and style. Many new urban designs self-consciously recall small town settings where neighbors walk to get a haircut and stop on the way to chat with neighbors sitting on the front porch watching the kids play. The attraction of these ideas is subjective, personal, yet pervasive. After all, in principle, what is not to like about pretty homes in quiet, friendly, and functional neighborhoods? But will they improve the traffic? Chapter 3 concluded that existing evidence is unsatisfactory in several respects. Among the problems identified in the literature was the common absence of a conceptual framework for hypothesizing how urban form might be expected to influence travel behavior. In particular, only a small share of the studies in this area even attempt to model travel behavior in the conventional manner, that is, as travel demand. In this chapter, we develop a framework for consistently evaluating the net travel impacts of changing land-use patterns, such as many new urban designs propose. The idea is to adapt a simple model of travel demand to measurable urban form elements. This permits us to derive specific conclusions that follow directly from the assumptions of the model as well as specific hypotheses that can be tested only with data on observed behavior. These assumptions are summarized in figure 4.2. The last part of the chapter develops an empirical implementation of the model and these hypotheses, which is applied to data in chapter 5. The theory of demand provides perhaps the most straightforward way to analyze travel behavior, by emphasizing how overall resource constraints force trade-offs among available alternatives, such as travel modes and trip distances, and how the relative attractiveness of those alternatives in turn depends on relative costs, such as trip times. This framework assumes that individuals make choices, either alone or as part of a family or other group, based on their preferences over the goods in question, the relative costs of those goods, and available resources (e.g., Kreps, 1990).

Travel Behavior Classification: An Approach with Social Network and Deep Learning

2018 ◽  
Vol 2672 (47) ◽  
pp. 68-80 ◽  
Author(s):  
Yu Cui ◽  
Qing He ◽  
Alireza Khani
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Travel Behavior ◽  
Travel Demand ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Time Of Day ◽  
Working People ◽  
Matrix Similarity ◽  
Activity Matrix

Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.

Exploratory Analysis of Weekend Activity Patterns in the San Francisco Bay Area, California

2005 ◽  
Vol 1926 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Allison M. Lockwood ◽  
Sivaramakrishnan Srinivasan ◽  
Chandra R. Bhat
Keyword(s):  
San Francisco ◽  
Travel Behavior ◽  
Transportation Planning ◽  
Travel Demand ◽  
San Francisco Bay ◽  
San Francisco Bay Area ◽  
Exploratory Analysis ◽  
Travel Patterns ◽  
Peak Period ◽  
Bay Area

Research on travel demand modeling has predominantly focused on weekday activity–travel patterns, with studying the effects of commute travel on peak period traffic congestion as a major objective. Few studies have examined the weekend activity–travel behavior of individuals. However, weekend travel volume has been increasing over time and is comparable to weekday travel volumes. Hence, weekend activity–travel patterns warrant careful attention in transportation planning. This paper focuses on presenting a comprehensive exploratory analysis of weekend activity–travel patterns and contrasting weekday and weekend activity participation characteristics. Data from the 2000 San Francisco Bay Area Travel Survey, California, are used in the analysis. A comparative analysis of several aggregate activity–travel characteristics indicates that, although weekday and weekend travel volumes are comparable, there are several key differences in activity–travel characteristics. Specifically, weekend activity–travel is predominantly leisure oriented and undertaken during the midday period. Average trip distances are longer on weekends. Transit shares are lower but occupancy levels in personal automobiles are higher on weekends. The weekend activity sequencing and trip-chaining characteristics explored in this study provide further insights into individuals’ activity organization patterns on weekend days. This paper highlights the importance of studying weekend activity–travel behavior for transportation planning and air-quality modeling. Insights from this exploratory analysis can form the basis for comprehensive weekend activity–travel modeling efforts.

Sign in / Sign up

Export Citation Format

Share Document