Average Trip Length Research Articles

Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons

Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.

Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway

Georeferenced messages on social media represent a powerful data source to gain a different perspective for estimating mobility behaviour, which is still mainly based on travel surveys. These data are openly available, yet few studies have explored their potential. This paper assesses the feasibility of large-scale Twitter data as a proxy of human mobility behaviour to complement traditional travel surveys, and for calibration and validation of transport models. Almost 12 million Tweets from more than 90,000 users were further analysed to detect the trip patterns at municipality level in Norway from 2012 to 2022. Results showed that the mobility patterns changed between 2014 and 2019 for the travel survey, as for 2019 most of the reported trips were short and concentrated in the densely populated areas of the country, where most respondents lived, triggering a lack of information for certain areas. In contrast, Twitter data presented a more stable data source along both years with similar population distribution and average trip length. Although Twitter data have limitations in relation to the socio-demographic information of the users, it could complement the travel survey given the broader spatial and temporal distribution of this large-scale data.

Optimal planning of urban air mobility systems accounting for ground access trips

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low.

Whose Miles Are These Anyway?

Problem, research strategy, and findings State, local, and private-sector climate policies increasingly require estimates of site-generated vehicle miles traveled (VMT) for individual properties. No standard, agreed-upon method exists for this purpose. If alternative methods produce reasonably consistent results, then an analyst may select a method based on convenience. The purpose of this study was to determine whether this is the case. We reviewed existing applications and methods for estimating site-generated VMT, which can be conceptualized as the product of site-level trip generation and average length of a site-generated trip. We identified four plausible methods for estimating site-level trip generation and three for estimating average lengths of site-generated trips. Combining these yielded 12 methods for estimating site-generated VMT. We applied these methods to estimate trip generation, average trip length, and total VMT generated by eight existing office sites in the San Francisco Bay Area (CA) and the Wasatch Front Region of Utah. We found that trip generation and trip length estimates varied widely, with greater variation in trip generation estimates than in trip length estimates. Variation was magnified when the estimates were multiplied to produce site-generated VMT estimates, with the minimum estimate at a single site ranging from 84% to 96% lower than the corresponding maximum. Takeaway for practice In the short term, planning policies requiring site developers or occupants to produce site-level estimates of VMT should specify consistent methods to allow for comparison across sites and over time as needed. In the long term, if VMT is to be used as a performance metric, industry-wide standards for estimating it for both existing and prospective projects are needed. Alternatively, policymakers should consider whether the goals of VMT reduction might be better met with a different performance metric.

Regional route guidance with realistic compliance patterns: Application of deep reinforcement learning and MPC

Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused on macroscopic flows and aggregated splitting rates, employing uniformly sampled vehicles as controllable targets. These models, however, overlook the inherent nature of individual drivers’ compliance. It may deteriorate the guidance performance as existing route guidance models cannot effectively generate customized route plans for compliant vehicles. This paper aims to introduce a regional route guidance framework with the utilization of different solution approaches, i.e. model-based and data-driven, considering the compliance pattern. In particular, MPC-based and deep reinforcement learning-based schemes are proposed for the information service provider to send customized route plans to compliant individuals. Within each solution approach, different route guidance strategies are developed for specific purposes and evaluated through numerical experiments under low- and high-congestion scenarios. Besides, the trade-off between total travel time and average trip length is studied, wherein the balance reward in a deep reinforcement learning-based approach enables controllable agents to reduce trip costs while compromising partial system utility. The findings indicate the effectiveness of the proposed strategies in alleviating network congestion. Additionally, multi-agent approaches outperform single-agent ones, highlighting the benefits of cooperative decision-making in traffic management.

A comparative study of bike-sharing and e-scooter sharing users and services in a college town during COVID-19

Shared micro-mobility services have seen a sharp increase during the last decade only to be hindered by the pandemic. Bike-sharing and e-scooter sharing services are the two main services of shared micro-mobility and are often perceived to be similar. However, they may differ because of different mobility trends, associated with each service, stemming from distinct usage habits, particularly in a campus setting, distinct from major cities. In this regard, there is limited understanding of how bike-sharing and e-scooter sharing services differ or are similar in terms of user characteristics within a campus context. Moreover, the effect of the COVID-19 pandemic on shared micro-mobility in campus settings has not been investigated. This study aims to: (a) uncover the users’ perceived barriers and benefits that are associated with each service, (b) determine whether user characteristics and preferences differ between bike-sharing and e-scooter users, (c) evaluate how travel habits and user characteristics can help determine trip purpose of shared micro-mobility services, and (d) detect any change in trip characteristics, namely trip purpose and length, due to COVID-19 for both services. To this end, a survey was conducted between October 2019 and August 2020 in West Lafayette, Indiana, home of Purdue University, gathering 615 responses. Comparative tests were carried out to evaluate the change in trip purpose and length of bike-sharing and e-scooter sharing trips during the pandemic. It was found that commuting trips increased in frequency after COVID-19, while the average trip length during COVID-19 increased for bike-sharing but not for e-scooters. Binary logistic regression results indicate that e-scooter sharing and bike-sharing users hold different characteristics and preferences, which suggests diverse usage patterns. The study findings offer valuable implications for transportation and policy planning in college towns.

Optimal vehicle capacity and dispatching policy considering crowding in public bus transit services

This study investigates the often overlooked impact of on-board crowding on operational and user costs in public transit systems, specifically within a many-to-many bus transit line with varying demand patterns. While previous research has used mathematical programming for similar problems, this paper employs analytical approaches to offer deeper insights and address fundamental questions. First, we propose an approach to determine optimal bus capacities, factoring in in-vehicle crowding costs, assuming a fixed headway. Second, we explore the optimal dispatching policy for buses with fixed capacities, considering crowding costs. Third, we optimize both headway and vehicle capacity simultaneously. Our findings reveal that optimal vehicle capacity correlates with average passenger trip length and the square root of crowding-discomfort costs, especially when crowding increases linearly with load factor. When both headway and capacity are variable, smaller vehicles with shorter headways are favored, particularly in moderate-demand scenarios, especially with cost-effective staff models like autonomous fleets.

An Evaluation of Smartphone Tracking for Travel Behavior Studies

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other.

A general equilibrium model for multi-passenger ridesharing systems with stable matching

This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is modeled as an equilibrium problem in which no ridesharing driver or passenger can reduce his/her ridesharing disutility by unilaterally switching to another matching sequence.This paper is one of the first studies that explicitly integrates the ridesharing platform’s multi-passenger matching problem into the model. By integrating matching sequence with hyper-network, ridesharing-passenger transfers are avoided in a multi-passenger ridesharing system. Moreover, the matching stability between the ridesharing drivers and passengers is extended to address the multi-OD multi-passenger case in terms of matching sequence. The paper provides a proof for the existence of the proposed general equilibrium.A sequence-bush algorithm is developed for solving the multi-passenger ridesharing equilibrium problem. This algorithm is capable to handle complex ridesharing constraints implicitly. Results illustrate that the proposed sequence-bush algorithm outperforms general-purpose solver, and provides insights into the equilibrium of the joint stable matching and route choice problem. Numerical experiments indicate that ridesharing trips are typically longer than average trip lengths. Sensitivity analysis suggests that a properly designed ridesharing unit price is necessary to achieve network benefits, and travelers with relatively lower values of time are more likely to participate in ridesharing.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

Foraging Ecology of Red-Billed Tropicbirds on Saba, Caribbean Netherlands, during Early Chick-Rearing

Prey resources in oligotrophic tropical marine environments are often scattered and unpredictable. Tracking studies of tropical seabirds can provide critical information about ocean habitat affinities, prey choice and the utilisation of surrounding areas, which can be useful for conservation managers. Foraging studies of Red-billed Tropicbird populations in the Caribbean are scarce but increasing. We sought to expand on this by tracking chick-rearing adults using GPS devices and subsequently linking these tracking data to remotely-sensed environmental variables. We related our spatial data to opportunistic sampling of regurgitates in a globally significant nesting colony on Saba, Caribbean Netherlands. Diet samples were dominated by flying fish (Exocoetidae; numerical frequency: 70.73%), but prey items from the squid family (Loliginidae; 9.76%) and the families of flying gurnards (Dactylopteridae; 2.44%) and the ray-finned fish (Carangidae; 2.44%) were also identified, although we were unable to identify 14.63% of samples due to digestion. An additional goal of our study was to compare the foraging ecology of Tropicbirds on Saba with those nesting on St. Eustatius, located circa 25 km south-east. As expected, Tropicbirds nesting on Saba exhibited diurnal foraging patterns, travelling a maximum distance from the colony of 553.7 km, with an average trip length of 117.2 ± 144.6 km (±SD). Adults foraged in shallower, cooler waters with higher chlorophyll a concentrations and higher Exocoetidae species richness compared to travelling points. Despite the proximity of Saba and St. Eustatius, this is contrary to what was found for Tropicbirds nesting on St. Eustatius, where adults foraged in deeper waters with a low Exocoetidae species richness. However, Tropicbirds from Saba and St. Eustatius did exhibit some similarities in their foraging behaviour; specifically, foraging adults traversed multiple exclusive economic zones and marine protected areas, reinforcing our recommendation for nature managers in the Caribbean to create a transboundary network in order to effectively protect and conserve this species.

HIGH SCHOOL ZONING POLICY IMPACT ON TRANSPORTATION IN BANDUNG CITY WEST JAWA PROVINCE

The enactment of the zoning policy through the Minister of Education and Culture Regulation (Number 14/2018) concerning the Admission of New Students states that the admission priority would be given to those who live within the radius of the closest zone from the school. From a transportation perspective, this policy has a potential positive impact in creating a more balanced school trip distribution pattern, and would further reduce the total trip length and improve transportation performance. Taking Bandung City as a case, this study aims to evaluate the impact of the Penerimaan Peserta Didik Baru/PPDB (New Student Admission) based on zoning system policy on the travel distribution patterns of high school students in this city. The method used in this research is spatial distribution analysis and qualitative descriptive analysis. The research performs a before-after analysis of comparison to analyze the impact of the policy on schooling trip distribution. The results show that the zoning policy stimulates the total change of the average schooling trip length of high school students in Bandung by 35%. This research also concludes that the schooling trip distribution pattern is no longer centered in the city center where favorite schools were located.
 Keywords: Government Policy, School Zoning, Bandung City Transportation.

The gender productivity gap in the ride-hailing market

The gender productivity gap in the ride-hailing market has not been well studied due to the difficulty in obtaining driver demographics and trajectory data. To address this gap, we use six-day long trajectory data of around 20,000 ride-hailing drivers from China, infer their gender from their names, and estimate gender disparity in productivity (daily earnings or passenger-carrying distance per unit working time) using the panel fixed effect regression models. The results indicate that the gender gap in productivity of ride-hailing drivers does exist, but it disappears if we compare male and female drivers with the same driving behavior (i.e., same driving speed), average trip lengths, and vehicle fuel type. The Blinder-Oaxaca decomposition shows that most productivity gap is driven by average driving speed and average trip length. These results suggest that although the productivity gap exists in the ride-hailing market, the reasons behind the gap are not worrisome for ride-hailing companies. Thus, these companies can encourage the participation of female drivers without compromising on operational performance, which could help sustain exclusive services like matching female riders with only female drivers and increase demand by making a female-centric safe brand.

Variations in excess commuting by educational and occupational worker subgroups: A case study of Shanghai

The excess commuting framework has been used to explore the potential of reducing commuting. Many papers on excess commuting evaluate commuting efficiency from the perspective of average trip length, while very few studies measure commuting orderliness from another perspective --- spatial organization. Additionally, some previous disaggregate analyses focus on a single socio-economic class, which may fail to uncover the nuanced relationship between observed commuting and urban land use. Based on the National 1% Population Sampling Survey (NPSS) in Shanghai, this study examines variations in excess commuting by three educational levels and five occupational types from the perspectives of average trip length and spatial organization. The results suggest that there are significant excess commuting differences across fifteen worker subgroups, which highlights the importance of considering education and occupation simultaneously. There is a complex relationship between the average trip length and spatial organization of commuting pattern. Manufacturing workers for all education levels commute longer but show less excess commuting than other sectors. Social service workers have a shorter commuting distance but a more disordered commuting organization. The results also show that highly educated workers contribute the most to excess commuting.

Commuting performance in Shanghai: Efficiency and orderliness

The literature on excess commuting has grown significantly over the past three decades. However, there are still some important open questions about the resulting statistics. Despite researchers proposing several commuting benchmarks and excess metrics, there is poor comparability between empirical studies. Many studies use average trip length to evaluate commuting efficiency of the whole region, while very few studies visualize the spatial organization of (excess) commuting patterns and evaluate its commuting orderliness. Based on a finely disaggregated data base --- the National 1% Population Sampling Survey in Shanghai, this study investigates commuting patterns and their relationship to urban land use arrangement by detailed industrial sectors from the perspectives of both average trip length and spatial organization measured by entropy value. It offers insights into social inequalities, spatial mismatch and commuting performance by identifying which groups of workers make more contribution to urban traffic congestion and have more inefficient and disorderly commutes. The results suggest that there are significant commuting differences between the groups, both socially and spatially. Overlooking the heterogeneity of commuter behaviors and socioeconomic characteristics may overestimate or underestimate excess commuting. Skilled workers commute further and experience more excess commuting than less-skilled workers, which is very different from American cities and brings forth new insights into traditional spatial mismatch. This paper also finds that workers in the manufacturing sector commute longer than those in the wholesale and retail sector but the commuting pattern of the former is more organized and orderly than the latter, indicating that long-distance commuting patterns might not necessarily be inefficient and disorderly and vice versa.

Environmental perceptions of global business travel by Swiss companies in the Zurich airport region

Background: This article presents findings from research conducted before the coronavirus disease 2019 (COVID-19) pandemic on companies located in the Zurich airport region of Switzerland, regarding the needs for global business travel and its impacts. Methods: The study involved a mixed methods approach. Five hypotheses were tested using inferential statistics on data obtained from pre-tested closed questions in a web-based survey. Deeper context was explored through an interview-based case-study conducted at a Swiss pharma company. Results: Supporting alternative hypothesis 3 (Ha(3)), a significant positive relationship was found between travel frequency and business growth, F(1, 100) = 11.31, p = 0.0011. Supporting Ha(4), corporate culture had a significant positive relationship with business travel frequency (F(1, 100) = 15.50, p = 0.0002) and average trip length (F(1, 100) = 6.39, p = 0.01). And thirdly supporting Ha(5), corporate social responsibility had a significant relationship with global business travel (91%). Ho(2) and Ho(3) were accepted. The case study found that smart corporate travel policies and regulations should be instantiated to enhance our environment, which would also benefit employee wellbeing. Travel can be reduced significantly despite being demonstrated that physical co-presence is important for building trust. The case study suggests tools to support the monitoring and management of global business travel by organizations. Conclusions: COVID-19 has impacted travel for business significantly, and future research will be necessary to assess its impact. The article explores the ongoing research in this area, and several relevant implications are proposed for future leaders. The case study found willingness to pay both corporate and individual green taxes, and a deficiency in corporate communication around the environment. Business travel is needed to build trust; however, it can be reduced.

Calculation of Transit Performance Measures Using Smartcard Data

Smartcard automated fare collection systems (SCAFC) for transit have been considered primarily for their administrative function of controlling access to the service and for revenue management. However, it is likely that data from these systems also can be used to describe both transport supply and demand. This article illustrates the use of smartcard data to estimate various transit performance measures. Combined with well-established evaluation processes, such measures can help operators monitor their networks in greater detail. The performance of the network supply (vehicle-kilometers, vehicle-hours, commercial speed, etc.) and the statistics on passenger service (passenger-kilometers, passenger-hours, average trip length, etc.) can be calculated from these datasets for any spatial or temporal level of resolution, including route and bus stop levels.

Modeling trucking industry perspective on the adoption of connected and autonomous trucks

This paper predicts the future adoption of Connected Autonomous Truck (CAT) among freight transportation organizations by incorporating the distance decay function-based phenomenon of peer effects into an integrated choice and latent variable model (ICLV). We surveyed 400 freight transportation organizations operating within the United States to capture their preferences and perceptions towards adopting level-3 CATs. By analyzing the responses, we are able to generate a prediction for how quickly freight transportation organizations will choose to test and adopt CATs. Through ICLV, the authors captured the impact of organizations' optimistic attitudes towards their inclination to adopt CATs. The results showed that organizations with an optimistic attitude towards CATs, having members advocating CATs and nationwide coverage, are more likely to adopt CATs. In contrast, organizations with an average trip length of regular trucks of >500 miles are more likely to reject CATs. The peer effect results imply that the future adoption rate of level-3 CATs will be doubled (29%) or quadrupled (57%) from 13% based on the different peer effect coefficient values varying from 0.70 to 0.90. This study will provide key takeaways and policy inputs to planners, policymakers, and researchers to frame and implement plans to bolster the organizational adoption of innovations like autonomous technology.

Understanding the mitigation potential of sustainable urban transport measures across income and gender groups

The global climate is going through a transformation due to increased GHG emissions. Urban transportation is one of the leading contributors to increased GHG emissions due to its dependency on fossil fuels. Mitigation measures are evaluated globally to reduce the emissions from the transport sector. These mitigation strategies have a disproportionate effect on the income-gender groups, and the emissions contribution is non-uniform across income levels and gender. It is imperative to understand the emissions contribution of these groups. In this study, the CO2 and PM2.5 emissions from a combination of eight income-gender groups together are estimated for four urban transport mitigation policy bundles. Further, the emission potential of each mitigation policy bundle for each income-gender group is calculated by comparing emissions from each policy bundle with respective BAU scenario emissions. For a better understanding, policy implication on each income-gender group's specific travel parameters like mode share, average trip length and vehicle kilometres travelled are also assessed. Bundle 4, which is a blend of planning, regulatory, economic and technological policy instruments, showed the highest mitigation potential across all income-gender groups. The study finds that the majority of the female groups showed the highest mitigation potential for both pollutants for 2030 and 2050.

Environmental perceptions of global business travel by Swiss companies in the Zurich airport region.

Background: This article presents findings from research conducted before the coronavirus disease 2019 (COVID-19) pandemic on companies located in the Zurich airport region of Switzerland, regarding the needs for global business travel and its impacts. Methods: The study involved a mixed methods approach. Five hypotheses were tested using inferential statistics on data obtained from pre-tested closed questions in a web-based survey. Deeper context was explored through an interview-based case-study conducted at a Swiss pharma company. Results: Supporting alternative hypothesis 3 (Ha(3)), a significant positive relationship was found between travel frequency and business growth, F(1, 100) = 11.31, p = 0.0011. Supporting Ha(4), corporate culture had a significant positive relationship with business travel frequency (F(1, 100) = 15.50, p = 0.0002) and average trip length (F(1, 100) = 6.39, p = 0.01). And thirdly supporting Ha(5), corporate social responsibility had a significant relationship with global business travel (91%). Ho(2) and Ho(3) were accepted. The case study found that smart corporate travel policies and regulations should be instantiated to enhance our environment, which would also benefit employee wellbeing. Travel can be reduced significantly despite being demonstrated that physical co-presence is important for building trust. The case study suggests tools to support the monitoring and management of global business travel by organizations. Conclusions: COVID-19 has impacted travel for business significantly, and future research will be necessary to assess its impact. The article explores the ongoing research in this area, and several relevant implications are proposed for future leaders. The case study found willingness to pay both corporate and individual green taxes, and a deficiency in corporate communication around the environment. Business travel is needed to build trust; however, it can be reduced.