Travel Intensity Research Articles

Understanding the benefits and mechanisms of working from home as a sustainable urban strategy on air pollution: Empirical study in 19 cities in China

Working from home (WFH) arrangements are already acknowledged as a sustainable measure for alleviating urban congestion and air quality issues, but fewer large-scale empirical studies investigating the benefits and mechanisms of WFH on air pollution (AP). The exogenous shock of the epidemic provides unique conditions that enable us to conduct empirical studies on the positive impacts of working from home. Building upon this background, this paper designs a series of empirical models to investigate the benefits of WFH on AP and the mediating effect of intra-city travel intensity (ITI) concerning the WFH-AP relationship to depict the mechanism of the impact of WFH on AP. This paper collected panel data on ITI and AP from January 1 to March 15, 2020, in 19 cities in China for regression analyses. The findings indicate that WFH reduces ITI by 65.0 %, while an increase in ITI leads to higher AP emissions, particularly of NO2. WFH positively impacts urban air quality, resulting in a 26.3 % decrease in the Air Quality Index (AQI) and reductions of 17.2 %, 4.1 %, 32.7 %, 18.8 %, and 13.6 % for the five air pollution categories (CO, NO2, PM2.5, PM10, SO2) respectively. Intermediate analyses demonstrated that ITI partially mediated the decrease in AQI, PM2.5, and CO, while PM10 and NO2 showed complete mediation. However, ITI had a weak mediating effect on SO2 emissions. The contribution of this study is that it provides supporting evidence that working from home positively impacts urban air quality and commuting. Additionally, it offers urban administrators a fact-based approach to traffic management and environmental protection, pointing to important directions for future urban sustainability strategy development.

Who are on the road? A study on vehicle usage characteristics based on one-week vehicle trajectory data

ABSTRACT Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management. However, it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use. Thanks to the advances in data collection and processing techniques, multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns. In this paper, based on a week's operation data of 6600 passenger cars in Shanghai, we develop a systematic approach for identifying trips and travel purposes, and classify vehicles into four categories using a Gaussian-Mixed-Model. A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles. We find that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles. The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction, but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.

Feasibility, economic and carbon reduction benefits of ride-hailing vehicle electrification by coupling travel trajectory and charging infrastructure data

Ride-hailing has become a common travel choice in global megacities. However, ride-hailing fleets are facing severe challenges toward electrification owing to high travel intensity, high price sensitivity, inconvenience related to charging, and range anxiety. This study established a comprehensive feasibility–economic–environment assessment system to explore the applicability, life-cycle economic as well as carbon reduction benefits of ride-hailing electrification. For full-time ride-hailing vehicle drivers who face the greatest electrification challenges, it is found that charging station service radius of 1 km, average charging power of 37.5 kW, and state of charge of 30–70% as charging threshold are optimal for balancing the charging feasibility and driving experience. Ride-hailing electrification for full-time drivers can achieve synergetic economic and environmental benefits by saving 14000–24500 $ and reducing 47–66 t of CO2 emissions in a whole lifetime. This research suggests that developing a smart order dispatch system for ride-hailing to guide more efficient charging activities would help relieve range anxiety and further promote ride-hailing electrification.

Do footbridge and underpass improve pedestrian safety? A Hong Kong case study using three-dimensional digital map of pedestrian network

Hong Kong is a compact city with high activity and travel intensity. In the past decades, many footbridges and underpasses were installed to reduce the pedestrian-vehicle conflicts on urban roads. However, it is rare that the effects of configuration of pedestrian network on pedestrian crashes are investigated. In Hong Kong, many footbridges and underpasses are connected to major transport hubs and commercial building development and become parts of giant elevated and underground walkway systems. It is challenging to characterize such a complicated pedestrian network. In this study, a three-dimensional digital map is applied to estimate the connectivity and accessibility of pedestrian network, and measure the relationship between pedestrian network characteristics and pedestrian safety at the macroscopic level. Hence, the effects of footbridge and underpass on pedestrian safety are examined. For example, comprehensive built environment, pedestrian network, traffic, and crash data are aggregated to 379 grids (0.5 km × 0.5 km). Then, multivariate Poisson lognormal regression approach is applied to model fatal and severe injury (FSI) and slight injury pedestrian crashes, with which the effects of unobserved heterogeneity, spatial correlation, and correlation between crash counts are accounted. Results indicate that population density, traffic volume, walking trip, footpath density, node density, number of vertices per footpath segment, bus stop, metro exit, residential area, commercial area, and government and utility area are positively associated with pedestrian crashes. In contrast, average gradient, accessibility of footbridge, accessibility of underpass, and number of crossings per road segment are negatively associated with pedestrian crashes. In other word, pedestrian safety would be improved when footbridge and underpass are more accessible. Findings have implications for the design and planning of pedestrian network to promote walkability and improve pedestrian safety.

Exploring Heterogeneous Structural Relationships Between E-Shopping, Local Accessibility, and Car-Based Travel: An Application of Enriched National Household Travel Survey Add-on Data

This study analyzes structural relationships between information and communication technology (ICT), e-shopping, local accessibility, and travel intensity in the U.S. state of Georgia. Beginning with the 2017 National Household Travel Survey (NHTS), we enriched the data by exploiting the add-on program. After obtaining contact information for the Georgia NHTS participants who expressed willingness to take a follow-up survey, we conducted another survey with those participants. In addition, we appended land use characteristics associated with respondents’ home locations. To test the hypothesis of possible heterogeneity in structural relationships, we applied a mixed structural equation model. We corroborated our hypotheses that the use of ICT has a positive impact on e-shopping frequency, whereas local accessibility reduces e-shopping frequency. In addition, we identified two meaningful segments exhibiting different structural relationships. The smaller segment (12.7%) showed complementary effects of e-shopping on car-based travel intensity, but local accessibility did not have a significant impact on car-based travel intensity. The dominant segment (87.3%) presented a negative impact of local accessibility but no meaningful effect of e-shopping frequency on car-based travel intensity. A post-hoc analysis revealed meaningful differences in demographics between the two classes. The study suggests that an assumption of homogeneous structural relationships between e-shopping and car-based travel intensity and between local accessibility and car-based travel intensity may not hold. The paper discusses benefits and practical issues concerning fusion of the NHTS data with other data sources. In addition, it stresses the potential of the NHTS add-on program and notes some recommendations.

A comparison of tourism-related stressors experienced by residents of three island destinations

Resident stress has emerged as a problem in many tourism communities. However, little is known about differences in resident stress across diverse destinations. This research gap is addressed by comparing stressors experienced by residents of three global island destinations of O’ahu, Hawaii, U.S., Falmouth, Jamaica, and São Miguel, Azores, Portugal. These three island tourism destinations have diverse tourism types and differing levels of tourism development along the tourism area life cycle (TALC). The ‘Travel Intensity Indices’ (TII) were used to evaluate tourism development inflection points and determine stage of the TALC for each destination. Thematic analysis of open-ended responses about stressors experienced by residents revealed that in the early stages of tourism development, residents experience stress related to physical changes to their community. In later stages of development, residents experience stress related to the presence of tourists in their community. The type of tourism present in the community also played a role in resident stress, with cruise tourism creating a unique set of stressors. The implications of above findings have two sections. Theoretically, examining resident stress in diverse destinations expands the application of stress theory in the tourism context and begins to establish destination level characteristics that influence resident stress. These findings also extend the application of TALC model to the health and wellness of residents’ vis-a-vis the stress they experience. Practically, exploring similarities and differences across these destinations can inform destination developers and managers of the potential stressors that residents may be experiencing that are unique to their level and type of development.

The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries

Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April–October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a ‘travel as spark’ principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.

The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants

We investigate the impact of air travel mobility and global connectivity on viral transmission by tracing the announced arrival time of COVID-19 and its major variants in countries around the world. We find that air travel intensity to a country, “effective distance” as measured by international air traffic, is generally a significant predictor for the announced viral arrival time. The level of healthcare infrastructure in a country is less important at predicting the initial transmission and detection time of a virus. A policy variable, notably the percentage reduction of total inbound seats in response to a viral outbreak, is largely ineffective at delaying viral transmission and discovery time. These findings suggest that air network connectivity is a major contributor to the speed of viral transmission. However, government attempts to delay viral transmission by reducing air network connectivity after the virus is first discovered are largely ineffective.

Evaluation and prevention and control measures of urban public transport exposure risk under the influence of COVID-19-Taking Wuhan as an example.

BackgroundSince December 2019, COVID-19 began to spread throughout the world for nearly two years. During the epidemic, the travel intensity of most urban residents has dropped significantly, and they can only complete inflexible travel such as "home to designated hospital" and "home to supermarket" and some special commuting trips. While ensuring basic travel of residents under major public health emergency, there is also a problem of high risk of infection caused by exposure of the population to the public transport network. For the discipline of urban transport, how to use planning methods to promote public health and reduce the potential spread of diseases has become a common problem faced by the government, academia and industry.MethodBased on the mobility perspective of travel agents, the spatial analysis methods such as topological model of bus network structure, centrality model of public transport network and nuclear density analysis are used to obtain the exposure risk and spatial distribution characteristics of public transport from two aspects of bus stops and epidemic sites.ResultsThe overall spatial exposure risk of Wuhan city presents an obvious "multi center circle" structure at the level of bus stops. The high and relatively high risk stops are mainly transport hubs, shopping malls and other sites, accounting for 35.63%. The medium and low-risk stops are mainly the villages and communities outside the core areas of each administrative region, accounting for 64.37%. On the other hand, at the scale of epidemic sites, the coverage covers 4018 bus stops in Wuhan, accounting for 36.5% of all bus stops, and 169 bus lines, accounting for 39.9% of all routes. High risk epidemic sites are mainly concentrated in the core areas within the jurisdiction of Wuhan City, and in the direction of urban outer circle diffusion, they are mainly distributed in the low and medium risk epidemic sites. According to the difference of the risk level of public transport exposure, the hierarchical public transport control measures are formulated.DiscussionThis paper proposes differentiated prevention and control countermeasures according to the difference of risk levels, and provides theoretical basis and decision-making reference for urban traffic management departments in emergency management and formulation of prevention and control countermeasures.

Is the effect of human activity on air pollution linear or nonlinear? Evidence from Wuhan, China, under the COVID-19 lockdown

This study explores the fundamental question of how and how much human activity affects air quality. It focuses in particular on whether this relationship is linear or nonlinear and provides suggestive results to the intriguing mechanism. To determine that answer, we make two important assumptions about the impact of human activity on air quality that we then test using a new analytical framework. In early 2020, the period of lockdown in Wuhan, China, due to the COVID-19 pandemic offers “extreme” observations with which we can test our assumptions from the perspective of either differences or ratios. In addition to offering a theory with a novel design, we also introduce statistical methods that are suitable for empirically estimating and testing our assumptions and obtaining suggestive results. Moreover, we construct a precise equation in quadratic form for examining the marginal effects of several types of air quality indicators/metrics concerning human activity as well as some important control variables for both natural and social factors. The level of human activity, measured as the index of travel intensity, that has the largest effect on air quality is around 1.657 for the most common urban air pollutants. This study implies that urban air quality can be improved more effectively by avoiding regions where human activity has the maximum marginal effect on increases in urban air pollution. This implication for urban policy and planning is valuable both domestically and globally.

Understanding China's resumption of work and production during the critical period of COVlD‐19 based on multi‐source data

AbstractMonitoring the resumption of work and production (RWP) is of great significance to the restoration of social order and the economy after the coronavirus disease 2019 (COVID‐19) epidemic. In this study, the most critical period of epidemic prevention and control and economic recovery was divided into four phases according to the changes in emergency response levels during the epidemic in China, and based on VIIRS I‐Band 375‐m active fire data (VNP14IMG), NO2 tropospheric vertical column density (TVCD) data, and intracity travel intensity data, the density‐based spatial clustering of applications with the noise logistic regression industrial heat source identification model, NO2 difference model, and urban relative recovery index were constructed to analyze work resumption at different phases. The results indicate that: (1) the production intensity of China's heat source enterprises decreased first and then exceeded the epidemic period compared with the same period in history. From phases 1 to 3, the heat source enterprise production intensity was lower than the same period in 2019 (the production intensities were 19,562.79, 22,462.57, and 38,466.89, respectively), and the reduction rates of production intensity were 27.5, 26.1, and 22.7%, respectively. In phase 4, enterprise production intensity achieved an increase of 7%. (2) The process of the RWP in China is closely related to NO2 TVCD. In the first three phases, significantly decreased emission sources of NO2 reduced the total value of NO2 by varying degrees in most provinces of the country. Correspondingly, in phase 4, the NO2 TVCD in some provinces recovered to the value of the same period in 2019, including typical industrial bases (Shandong, Hebei, Shanxi, etc.) and economically developed coastal areas (Jiangsu, Guangdong, Zhejiang, etc.). (3) In phase 3, the intracity travel intensity in all cities improved, and the city relative recovery index (RRI) rose across the board. In particular, the western cities were better than the eastern cities. Among them, the RRIs of most cities in southwest China (Sichuan, Yunnan) and northwest China (Gansu) were more than 90%. Notably, the recovery situation exhibited an obvious “siege phenomenon,” and the recovery of core cities required a longer period. Overall, the sudden COVID‐19 epidemic seriously affected the production of Chinese enterprises. However, benefitting from rapid and effective epidemic prevention and control measures, the resumption of work and production has been a success. Our study provides guidance for economic recovery strategies during post‐pandemic stages.

Estimating the Economic Effects of the Early Covid-19 Emergency Response in Cities Using Intracity Travel Intensity Data

In the early days of the Covid-19 pandemic, China implemented the most stringent and serious emergency response. To understand the effect of such an emergency response strategy on the economic system, this study proposed a simultaneous overall estimation method using intracity travel intensity data. The overall effect is represented by the difference between intracity travel intensity with and without the emergency response. Using historical data and time series analysis, we compared intracity travel intensity post China’s implementation of the emergency response with predicted intracity travel intensity without such a response. The loss rates, defined by the proportion of intracity travel intensity loss, were calculated for 360 cities within 33 provincial-level regions in China based on data availability. We found that 30 days after the emergency response, 21% of the cities saw over 80% recovery and 10% of the cities showed more than 90% recovery; 45 days after the emergency response, more than 83% of the 360 cities witnessed 80% recovery. The correlation between gross domestic production loss rate and travel intensity loss rate was studied quantitatively to demonstrate the representativeness of the intracity travel intensity loss rate. This indicator was also used to analyze the spatial and temporal patterns of the effects on the economy. The results of this study can help us understand the economic effects caused by the early Covid-19 emergency response and the method can be a reference for fast and real-time economic loss estimation to support emergency response decision making under pandemic conditions.

Can parkland mitigate mental health burden imposed by the COVID-19? A national study in China

The worldwide coronavirus disease 2019 (COVID-19) pandemic has seriously affected not only physical health but also mental wellbeing (i.e mental stress and suicide intention) of numerous urban inhabitants across the globe. While many studies have elucidated urban parkland enhances and mental wellbeing of urban residents, the potential for parkland to mitigate mental health burden imposed by the COVID-19 has received no attention. This nationwide study systematically explored the association between parkland, the COVID-19 pandemic situation and mental wellbeing from 296 cities in China. The study innovatively used big data from Baidu Search Engine to assess city-level mental wellbeing, thereby enabling comparisons among cities. The results show that the provision of parkland is positively associated with mental wellbeing during the COVID-19 epidemic. For COVID-19-related indicators, the geographical distance to Wuhan city, work resumption rate, and travel intensity within the city are also positively associated with mental wellbeing, while the number of COVID-19 infections and the proportion of migrants from Hubei Province for each city are negatively associated with mental wellbeing. Last, the most important finding is that parkland reduces the negative effect of COVID-19 on mental wellbeing during the COVID-19 epidemic. To achieve the goal of promoting mental wellbeing through urban planning and design during the future pandemics, policymakers and planners are advised to provide more well-maintained and accessible parkland and encourage residents to use them with proper precautions.

Viability assessment of electric cars for personal use in India

The study assesses the viability of electric cars for personal use in India based on the driving range and travel pattern, vehicle ownership and income levels and energy demand. The study estimates the expected personal electric car stock in India using a scenario-based approach. This is used to further estimate the electricity demand. Growing income levels present a potential for converting personal cars to electric, however, the limited percentage of multicar households in India limits its viability. The analysis shows that the low powertrain models can meet most of the daily travel demand in a single charge. However, the increasing ownership of large-size vehicles and growing travel distances impose a burden on electricity demand. There is a need to adopt a package of policies to restrain the growing vehicle ownership, retain the existing travel intensity and manage the energy demand.

Deep habits and travel resilience after COVID-19

This research examines how deep travel habits in the form of habit formation and reduced substitutability between tourism travel and other leisure goods impact travel resilience after COVID-19. Using microdata for almost 3000 tourists in Andalusia (Spain), we relate post-pandemic outbreak tourism participation to pre-pandemic travelling intensity and whether taking a vacation is considered a priority good. In doing so, we control for standard sociodemographic characteristics and province fixed effects. Our results clearly show that the probability of continuing travelling during the summer of 2020 is positively associated with pre-pandemic travel intensity and tourism being considered a priority leisure activity. Travel resilience is found to be strongly associated with income, education level and the tenure of a second residence.

Impacts of COVID-19 Traffic Control Policy on Population Flows in Changsha

To investigate the impact of COVID-19 traffic control policies on population flow in Changsha, this paper divided the prevention and traffic control policies into different stages corresponding to the real-time epidemic situation in Changsha. Based on Baidu migration big data, the difference- in- difference model was used to identify different stages of traffic prevention and control policies and quantify the effect of prevention. With the traffic control policy implemented during COVID-19, the average inflow intensity of Changsha City decreased by 83.68%, the average outflow intensity decreased by 69.24% and the internal travel intensity respectively, decreased by 59.74%. After the end of the traffic control policies, the population flow intensity of Changsha City gradually rebounded, and the urban internal travel intensity basically recovered to the same level as in 2019. The results indicated the effectiveness of the traffic control policies on the limitation of population flow and epidemic spread. The results also provide reference for making effective prevention and control policies for the normalized COVID-19 epidemic situations. Copyright © 2021 by Science Press.

An evaluation of COVID-19 transmission control in Wenzhou using a modified SEIR model.

In December 2019, the first confirmed case of pneumonia caused by a novel coronavirus was reported. Coronavirus disease 2019 (COVID-19) is currently spreading around the world. The relationships among the pandemic and its associated travel restrictions, social distancing measures, contact tracing, mask-wearing habits and medical consultation efficiency have not yet been extensively assessed. Based on the epidemic data reported by the Health Commission of Wenzhou, we analysed the developmental characteristics of the epidemic and modified the Susceptible-Exposed-Infectious-Removed (SEIR) model in three discrete ways. (1) According to the implemented preventive measures, the epidemic was divided into three stages: initial, outbreak and controlled. (2) We added many factors, such as health protections, travel restrictions and social distancing, close-contact tracing and the time from symptom onset to hospitalisation (TSOH), to the model. (3) Exposed and infected people were subdivided into isolated and free-moving populations. For the parameter estimation of the model, the average TSOH and daily cured cases, deaths and imported cases can be obtained through individual data from epidemiological investigations. The changes in daily contacts are simulated using the intracity travel intensity (ICTI) from the Baidu Migration Big Data platform. The optimal values of the remaining parameters are calculated by the grid search method. With this model, we calculated the sensitivity of the control measures with regard to the prevention of the spread of the epidemic by simulating the number of infected people in various hypothetical situations. Simultaneously, through a simulation of a second epidemic, the challenges from the rebound of the epidemic were analysed, and prevention and control recommendations were made. The results show that the modified SEIR model can effectively simulate the spread of COVID-19 in Wenzhou. The policy of the lockdown of Wuhan, the launch of the first-level Public Health Emergency Preparedness measures on 23 January 2020 and the implementation of resident travel control measures on 31 January 2020 were crucial to COVID-19 control.

Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data.

The COVID-19 pandemic caused drastic changes in human activities and nighttime light (NTL) at various scales, providing a unique opportunity for exploring the pattern of the extreme responses of human community. This study used daily NTL data to examine the spatial variations and temporal dynamics of human activities under the influence of COVID-19, taking Chinese mainland as the study area. The results suggest that the change in the intensity of NTL is not correlated to the number of confirmed cases, but reflects the changes in human activities and the intensity of epidemic prevention and control measures within a region. During the outbreak period, the major provincial capitals and urban agglomerations were affected by COVID-19 more than smaller cities. During the recovery, different regions showed different recovery processes. The cities in West and Northeast China recovered steadily while the recovery in coastal cities showed relatively greater fluctuations due to an increase in imported cases. Wuhan, the most seriously affected city in China, did not recover until the end of March. Nevertheless, as of 31 March, the overall NTL across China had recovered to an 89.5% level of the same period in the previous year. The high consistency between the big data of travel intensity and NTL further proved the validity of the results of this study. These findings imply that daily NTL data are effective for rapidly monitoring the dynamic changes in human activities, and can help evaluate the effects of control measures on human activities during major public health events.

Urban travel behavior adaptation of temporary transnational residents

Temporary transnational relocation is a growing type of migration. However, travel behavior adaptation of highly skilled temporary residents and its urban impacts have largely been ignored. This study extends the knowledge of mobility biographies, mobility cultures, and mobility of millennials by examining how temporary residents adapt their intra-urban travel behavior in response to a transnational relocation. The data used here comes from semi-structured interviews with students and researchers of nine different nationalities, aged between 19 and 31 years, temporarily living in Portugal (Lisbon or Porto). We found supporting evidence for the occurrence of residential self-selection, although prior information on study/workplace combined with low knowledge on neighborhood-level make it somewhat specific. Given their shortterm perspective, temporary residents are more prone to rely on public transport and non-motorized modes, having a low likelihood of purchasing vehicles. Thus, measures aimed at improving and facilitating the use of active modes can have an immediate effect on this group's travel behavior and contribute to reaching critical mass for these sustainable alternatives. Temporary residents are also a potentially interesting market segment for public transportation operators for increases in revenues, as they tend to display a relatively higher travel intensity and a wider diversity of activities and destinations. Finally, technology usage was found to reduce the stress-related to traveling to unfamiliar places by increasing the perceived spatial orientation, having the downside of generating a feeling of confidence that decreases the internalization of information. Providing timely and persuasive information at the very beginning of temporary residents' stay can help induce their travel behavior decisions.

Polarized Politics of Pandemic Response and the COVID-19 Connectedness Across the U.S. States

This paper investigates the state-level differences in government and community responses to the COVID-19 pandemic, leading to different growth trajectories of COVID-19 cases and their connectedness across the U.S. states. Our regression analysis shows that higher growth trajectories are observed in the states that implemented the lax government and community response to the pandemic. Moving to the analysis of spillovers/connectedness of COVID-19 cases across the states, we apply the Diebold-Yilmaz connectedness methodology to the growth rates of COVID-19 cases. Using the total directional connectedness measures, we find that the states with lax government and community response generated connectedness of COVID-19 cases to others. These findings are also supported by the secondary regression analysis of pairwise connectedness measures over time. Finally, the travel intensity between the pairs of states, indirectly measured by the data on smartphone location exposure, contributes significantly to the pairwise directional connectedness of COVID-19 across the states.