Dynamics Of Travel Behavior Research Articles

An Impact Assessment of Cordon Pricing Relaxation on Modal Shift During the COVID-19 Pandemic

ABSTRACT The COVID-19 pandemic brought significant disruptions to transportation patterns worldwide, with a notable increase in Private Vehicle (PV) usage. Many studies have focused on travel restrictions, while neglecting to consider potential changes in transportation-related policies implemented during COVID-19 that may have improved public health. The present study explores the motivations for modal shifts to PVs and tries to analyze which of the risks of exposure and modifications in transportation policies has caused these modifications. Focusing on the case study of Tehran, Iran, where cordon pricing was relaxed during the pandemic, the study collected data through online and paper-based surveys from 1475 respondents. Binary logit models were employed to analyze the data and understand the impact of destination location, residency area, trip purpose, trip frequency, and vehicle characteristics on modal shift behavior. In addition to COVID-19 exposure as a primary reason for the modal shift, respondents also identified the relaxation of cordon pricing restrictions as a factor that increased the utility of PVs. The study's findings contribute to better understanding the dynamics of travel behavior during pandemics, guiding policymakers in devising effective strategies to address both public health concerns and traffic conditions.

Influences of COVID-19 pandemic on travel patterns across past, present, and future

ABSTRACT As societies strive to return to pre-COVID-19 norms, there's growing recognition of the profound changes people have experienced over the past two and a half years. This has prompted discussions on how to shape future transportation strategies to align with evolving lifestyles. Understanding people's travel patterns before, during, and after the pandemic is crucial for shaping post-pandemic actions. This study investigates the relationship between travel patterns across these phases, with a focus on the pandemic's mediating effects. Using structural equation modelling and data from 7,613 U.S. survey responses, we uncover noteworthy shifts in travel behaviour dynamics. During the pandemic, policy measures and sociodemographic factors significantly influenced travel. However, these effects may diminish as measures are lifted and vulnerabilities no longer correlate with sociodemographics. In contrast, past travel behaviour and attitudes, especially favouring alternative modes of transportation, are expected to have a more enduring impact in the post-pandemic era. This shift is likely to emerge ‘strengthened new normal,’ especially among those who reduced travel and embraced alternative modes. Future travel policies should anticipate and accommodate these evolving dynamics. Promoting diverse transportation modes beyond private vehicles will be crucial as travel behaviour undergoes transformation. Thus, policy formulation should consider a range of scenarios to address these changing preferences and behaviours.

Identifying Urban Functional Areas and Their Dynamic Changes in Beijing: Using Multiyear Transit Smart Card Data

Abstract A growing number of megacities have been experiencing changes to their landscape due to rapid urbanization trajectories and travel behavior dynamics. Therefore, it is of great significance...

A Calculation Model of Charge and Discharge Capacity of Electric Vehicle Cluster Based on Trip Chain

The rapid response characteristics and high-speed growth of electric vehicles (EVs) demonstrate its potential to provide auxiliary frequency regulation services for independent system operators through vehicle-to-grid (V2G). However, due to the spatiotemporal random dynamics of travel behavior, it is challenging to evaluate the ability of EV cluster to provide ancillary services under the premise of reaching the expected state of charge (SOC) level. To address this issue, a novel calculation model of charge and discharge capacity of EV cluster based on trip chain with excellent parallel computing performance is presented in this work. Following the introduction of the characteristic variables of the proposed trip chain model, the user’s continuous travel behavior in a time scale of several weeks is simulated. In particular, a bidirectional V2G scheduling strategy based on the five-zone map is designed to guide the charging and discharging behavior of EVs, where the expected SOC levels are guaranteed. The results of a 3-week travel simulation verify the effectiveness of the presented model in coordinating the V2G scheme and calculating the charge and discharge capacity of the EV cluster.

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Dynamic analysis of holiday travel behaviour with integrated multimodal travel information usage: A life-oriented approach

The Integrated Multimodal Travel Information (IMTI) plays an important role in the evolution process of holiday travel behaviour, which is seldom investigated. To fill this gap, this study analyses holiday travel behaviour dynamics with IMTI usage, based on the life-oriented approach. IMTI usage is taken as a separate life domain in this study, and a two-way relationship between holiday travel biography and IMTI usage biography over the life course, is examined after controlling for the effects of residential, household structure, employment/education, and car ownership biographies. Based on the web-based life history survey data, statistical characteristics of mobilities in each life biography are first analysed. Then, different random-effects ordered logistic models are established to investigate the biographical interdependencies from three aspects: intra-domain interdependency, inter-domain interdependency and outer-domain interdependency. The results show that the life biography is not only affected by a personal life course, but also affected by external background of the times. Under the interaction of inner individual factors and outer environment factors, there is an obvious dynamic two-way relationship between holiday travel biography and IMTI usage biography. Meanwhile, residential, household structure, employment/education and car ownership biographies have significant effects on these two life biographies. Especially, the influence of long-term state dependence for different life domains, over the life course, is much more obvious when explaining holiday travel behaviour dynamics and IMTI usage mobilities. Therefore, the life-oriented approach provides a valid method for analysing the dynamics of holiday travel behaviour with IMTI usage.

Modelling activity-travel behaviour dynamics with panel data: The state-of-the-art

The field of travel behaviour dynamics represented a vibrant research area in the 1980s and the 1990s (Golob & Meurs, 1987; Golob, Kitamura, & Long, 1997; Kitamura, 1990), but has since, probably due to lack of mobility panel data, received relatively little attention by transportation researchers. Nevertheless, through the use of panel data (repeated measures of the same individuals) many new insights may be gained with respect to travel behaviour, insights that are fundamentally different from those that can possibly be gained from cross-sectional data.

Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy.

An analysis of the dynamics of activity and travel needs in response to social network evolution and life-cycle events: A structural equation model

Several studies in transportation literature have shown that in the short-term social networks play an important role in discretionary activity and travel decisions of an individual. However, social networks may not remain unchanged in the long term, particularly in response to life-cycle events (for instance, an employment transition). A change in the social network in turn may have a repercussion on activity and travel behaviour, indicating that an investigation of the long term dynamics of social networks are relevant for understanding activity scheduling, or rescheduling behaviour. To this end, the paper advances the concept of social network dynamics in dynamic activity travel behaviour modelling. It explores the dynamics of social networks and life-cycle events, and their influence on activity and travel needs. Dynamics are assumed to be triggered by life-cycle events. For the purpose of the study an event-based retrospective survey was conducted in 2011 in the Netherlands. A structural equation model was developed to elicit activity and travel needs and their dependencies on life-cycle and social network dynamics. The estimated model takes history dependence of activity and travel needs into account. Results suggest that activity and travel dynamics are influenced by life-cycle and social network dynamics. Moreover social network and activity travel dynamics were found to be interdependent (i.e. a change in one leads to change in the other). Furthermore, the study results confirm the general assumption that travel needs are for the most part influenced by activity needs. The paper concludes that the theory and modelling framework of travel behaviour dynamics should take the dynamics of personal networks into account.

Determinants of Attrition Rates in Two-Wave, Two-Day Household Activity Diary: Probit Analysis

Transportation researchers often have used panel data to examine the dynamics of travel behavior. Although panel data are probably the best means by which to better understand choice dynamics, they present various challenges to the analyst, with attrition or dropout one of the most critical. Presented are the results of an analysis of the determinants of attrition in a two-wave panel, conducted in the Netherlands. Panelists reported their activities for a consecutive 2 days with 1 year between. Attrition rates between the two waves were predicted as a function of sociodemographics; number of activities, trips and tours; and the amount of reporting error in the first wave, by using a probit model. The results of the study indicate that none of these variables was significant, suggesting that no systematic attrition occurred between the two waves.