01661314

Component

https://doi.org/10.1177/0361198118797509

Continued growth in travel demand and the corresponding congestion occurrence accentuates the need for active transportation and demand management (ATDM) for predictive rather than reactive congestion mitigation strategies. These strategies (applications) reduce demand and thus improve the performance of different surface transportation facilities. Earlier research work mainly utilized simulations and field experiments to suggest improvements to ATDM applications associated with pricing and/or provision of information to travelers; however, few such studies considered the dynamics of travelers’ motivations as an essential component in the performance of a given ATDM application. In other words, how travelers’ motivations change as a function of the ATDM applications’ characteristics remains largely unexplored. This study thus examines the theory of planned behavior (TPB) to analyze travelers’ motivations associated with different mode choices and captures and measures the corresponding dynamics of motivations when facing different surrounding circumstances and ATDM regimes. Utilizing a survey and an online data collection platform to estimate motivational patterns behind travelers’ mode choices, following and extending the TPB paradigm, the study concludes that: (a) travelers’ mode choices are primarily determined by intentions (motivations); (b) income and age are two additional characteristics that influence mode choices; (c) travelers’ reasoned choices are mainly attitude-oriented; and (d) different attitudinal aspects are accentuated or compromised along with the changes of travel conditions and ATDM applications.

18-05179

English

pp 171-181

2018-12

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures (4) ; Maps; References (19) ; Tables (4)

Behavior; Highway traffic control; Mode choice; Traffic congestion; Travel demand

Active Transportation and Demand Management

Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Jan 8 2018 11:18AM

• A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China
• A Stated Preference Survey Approach to Understanding Mobility Choices in Light of Shared Mobility Services and Automated Vehicle Technologies in the U.S.
• Alternative Activity Pattern Generation for Stated Preference Surveys
• Are Activity Patterns Stable or Variable? Analysis of Three-Year Panel Data
• Coordinated Daily Activity Pattern Type in an Applied Activity-Based Travel Model System: Specifics of Modeling Large Households and Modality Preferences
• Effects of Traffic Congestion on Vehicle Miles Traveled
• Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services
• Exploring the Self-Reported Long-Distance Travel Frequency of Millennials and Generation X in California
• How Should Travel Demand and Supply Models Be Jointly Calibrated?
• Incorporating Multiple Uncertainties into Projections of Chinese Private Car Sales and Stock
• Investigating Behavioral Differences in Heterogeneous Decision Rule Segments: An Empirical Analysis
• Learning Daily Activity Sequences of Population Groups using Random Forest Theory
• Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model
• Modeling the Structural Relationships of Activity-Travel Participation of Working Women
• Relationships between Consecutive Long-Term and Mid-Term Mobility Decisions over the Life Course: A Bayesian Network Approach
• The Times They Are A-Changin’: What Do the Expanding Uses of Travel Time Portend for Policy, Planning, and Life?
• Time-Dependent Intermodal A* Algorithm: Methodology and Implementation on a Large-Scale Network
• Tolerance and Indifference Bands in Regret–Rejoice Choice Models: Extension to Market Segmentation in the Context of Mode Choice Behavior
• Travel Behavior Classification: An Approach with Social Network and Deep Learning
• Trip Characteristics Analysis of the Effects of a Travel Behavior Change Program