Trip Duration 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.

Comparative analysis of particle exposure in commuters: Evaluating different modes of transportation in Tehran

Daily commuting significantly contributes to the overall exposure to particulate matter (PM) in urban areas, underscoring the need to understand the factors influencing PM exposure. This study assessed personal exposure levels and inhalation doses of PM2.5, and PM10 across various transportation modes in Tehran, Iran. The study included buses, open-window taxis, subways, walking, and cycling for over 125 trips. Data were collected during the morning and evening peak hours using low-cost sensors, with an average trip distance of 7.50 km. On average, buses exhibited the highest mean PM2.5 concentration (25.0 μgm3), followed by subways (17.0 μgm3), taxis (14.4 μgm3), cycling (13.5 μgm3), and walking (8.80 μgm3). Pearson correlation analysis showed a strong relationship between traffic volume and PM concentrations in taxi and bus modes (r = 0.78 and 0.56, respectively). Subway commuters experienced the lowest PM2.5 inhalation dose (2.50 μg), whereas bus commuters experienced the highest (6.20 μg). Pedestrians had the highest average inhalation dose of PM2.5 with 20.7 μg due to longer trip durations. The daily average PM2.5 levels in all transportation modes exceeded the WHO thresholds by 1.60 times. These findings offer crucial insights into personal exposure concentrations among commuters and serve as a basis for effective air quality management plans and broader global investigations of air pollution.

Beyond half-mile circle: Measuring the impact of subway expansion on home-based travels in Beijing, China

Subways play an important role in supporting travel needs, yet whether the benefits of subway expansion can justify the substantial investments remains a much-debated question. Existing studies have arbitrarily defined the catchment area of a subway station using walking distance and have disproportionately focused on commuting trips, potentially underestimating broader subway effects. To address these limitations, this study employed the distance-band approach to accurately define the treatment area of subway stations while incorporating both local and network effects. Based on longitudinal mobile signaling data, we used the difference-in-differences approach to estimate the average treatment effects of 32 new subway stations on different types of home-based travels in Beijing, China. We found that the opening of new subway stations has led to significant increases in subway usage, trip duration, and trip distance within a 2000-metre radius, and that commuting trips have been more affected than home-based non-commuting trips, except during the COVID-19 pandemic. Moreover, the new subway stations also enhanced the connectivity of the city-wide network, influencing the travel behavior of residents in the vicinity of existing subway stations. We further found that subway expansion is particularly beneficial for women, younger residents, and residents of low-rent neighborhoods. According to these findings, planning new subway stations in Beijing and other densely populated megacities requires an integrated approach that considers the relationships between the existing transport network and diverse travel demand.

Handheld Cellphone Use and Risky Driving in Adolescents

Handheld cellphone use while driving is associated with increased motor vehicle crash risk among adolescents. To examine the association of handheld cellphone use while driving with kinematic risky driving (KRD) events-hard braking and rapid acceleration-in adolescent drivers. Adolescents aged 16.50 to 17.99 years licensed 365 days or less in Pennsylvania were eligible to participate in this cross-sectional study. Enrollment occurred from July 29, 2021, to June 6, 2022. Participants downloaded a smartphone telematics cellphone app to record driving data for 60 days. Trip characteristics, including frequency, length, and duration; presence of speeding; frequency and duration of handheld cellphone use; time of day; and presence of precipitation. Kinematic risky driving events per 100 miles driven. Zero-inflated Poisson regression models examined whether individual characteristics and trip characteristics were associated with KRD. Incidence rate ratios were computed. Of 405 adolescents who responded to recruitment, 151 enrolled, 140 completed study procedures, and 119 with 12 360 trips were included in the analytic sample (60 female participants [50.4%]; mean [SD] age, 17.2 [0.4] years). Adolescents drove a mean (SD) of 103.8 (65.7) trips, 565.0 (487.3) miles, and 25.1 (19.3) hours. Adolescents had minimal night trips (1.5% [192]), and few trips with precipitation present (9.0% [1097]). Speeding occurred in 43.9% (5428) of the trips and handheld cellphone use occurred in 34.1% (4214) of the trips. Kinematic risky driving events occurred in 10.9% (1358) of the trips at a rate of 2.65 per 100 miles. In adjusted models, increased KRD events were associated with handheld cellphone use (incidence rate ratio [IRR], 2.62; 95% CI, 1.53-4.48), speeding (IRR, 2.12; 95% CI, 1.06-4.26), and minutes driving (IRR, 1.02; 95% CI, 1.01-1.02). Trips at night, precipitation presence, licensure for less than 6 months, and sex were not associated with increased KRD events. In this cross-sectional study of adolescent drivers, trips with handheld cellphone use and speeding were associated with higher rates of KRD, while individual characteristics were not. The findings suggest that smartphone telematics apps provide an opportunity to observe behaviors as well as surveil changes due to intervention efforts.

Foraging areas and trip duration vary with the main prey captured, in a day-foraging/night-provisioning seabird

Foraging areas and trip duration vary with the main prey captured, in a day-foraging/night-provisioning seabird

Application of Historical Comprehensive Multimodal Transportation Data for Testing the Commuting Time Paradox: Evidence from the Portland, OR Region

There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.

Northern Gannet foraging trip length increases with colony size and decreases with latitude

Density-dependent competition for food influences the foraging behaviour and demography of colonial animals, but how this influence varies across a species’ latitudinal range is poorly understood. Here we used satellite tracking from 21 Northern Gannet Morus bassanus colonies (39% of colonies worldwide, supporting 73% of the global population) during chick-rearing to test how foraging trip characteristics (distance and duration) covary with colony size (138–60 953 breeding pairs) and latitude across 89% of their latitudinal range (46.81–71.23° N). Tracking data for 1118 individuals showed that foraging trip duration and maximum distance both increased with square-root colony size. Foraging effort also varied between years for the same colony, consistent with a link to environmental variability. Trip duration and maximum distance also decreased with latitude, after controlling for colony size. Our results are consistent with density-dependent reduction in prey availability influencing colony size and reveal reduced competition at the poleward range margin. This provides a mechanism for rapid population growth at northern colonies and, therefore, a poleward shift in response to environmental change. Further work is required to understand when and how colonial animals deplete nearby prey, along with the positive and negative effects of social foraging behaviour.

A new model for fuel consumption and route time computations – a case study in the Quebec forest industry

ABSTRACT In forest transportation in Quebec, transportation rates are typically based on an estimated trip or travel time duration during which a truck travels loaded from an origin to a destination and then returns empty to the origin. These transportation rates implicitly consider fuel consumption for fuel surcharge costs in negotiation, but for most shippers and carriers, fuel consumption remains a rough estimate, leading to underestimation or overestimation of actual consumption. In this paper, we propose a new fuel consumption model and a more detailed trip time computation to support accurate estimations. The model takes into account road network characteristics that affect fuel consumption, such as road elevation profiles, including slopes that significantly affect fuel consumption compared to flat roads, and curves where trucks change speed, as well as intersections where trucks need to stop or slow down. The road network of the province of Quebec (Canada) is represented in a route network that integrates all the characteristics considered by the fuel consumption model. The model is validated in a case study using a timber truck equipped with GPS and information about overall fuel consumption between a set of refueling points. Utilizing the fuel consumption model and a route planner enables accurate estimation of fuel consumption and, consequently, associated greenhouse gas (GHG) emissions and travel times. A case study involving three companies is then conducted to analyze how more detailed information can inform new transportation rates.

Web-Based Decision Support System for Budget Prediction Using the SAW Method for Tourism Recommendations

Tourism plays a crucial role in regional and national economies, with local tourism in Indonesia, particularly in Jombang Regency, East Java, offering substantial development potential. However, tourists often face difficulties in planning trips that align with their budgets due to inadequate integration of information on costs, trip duration, and participant numbers. This study develops a web-based Decision Support System (DSS) utilizing the Simple Additive Weighting (SAW) method to enhance trip planning for tourism in Jombang. The DSS integrates data on budget, trip duration, and the number of participants to provide optimal recommendations based on user preferences and constraints. The SAW method facilitates a systematic and objective evaluation process for recommending tourist destinations. The research follows the ADDIE development model, encompassing Analysis, Design, Development, Implementation, and Evaluation stages. Key objectives include creating a system that consolidates cost, duration, and participant data, applying SAW for accurate recommendations, and ensuring an objective evaluation process. The system aims to improve trip planning efficiency, provide accurate recommendations, and enhance user satisfaction. The scope of this study is limited to applying the SAW method for budget prediction in tourism recommendations for Jombang. The developed system is a prototype that may require further refinement for comprehensive implementation.

Commuting Behaviors of People with Disabilities and the Implications for Transport Planning and Policy: A Preliminary Study

This research aimed to obtain an initial understanding of the commuting behaviors of people with disabilities, which has not been widely discussed in the transportation planning literature in Indonesia. Facets studied included frequency of weekly commuting; trip duration to the workplace; trip chaining behavior; usage of public transportation; and utilization of private vehicles. Commuting trips were limited to the home-work-home context, and the research was conducted on adult disabled commuters (aged 17 to 60) in the Jakarta Metropolitan Area (Jabodetabek). A total of 51 commuters with motor and sensory (visual and auditory) disabilities participated in the survey. The results indicate that people with disabilities tend to spend more hours commuting; use public transportation, with the TransJakarta Bus Rapid Transit and paratransit in the form of online motorcycle taxis as the primary modes of transport; and engage in pre-planned trip chaining activities, primarily for visiting family, friends, and clients as well as doing grocery shopping and getting medical treatment. The research findings can serve as valuable resources for developing inclusive transportation policies and planning, such as improving and enhancing access to public transportation for people with disabilities, supporting inclusive training policy in the public transportation field, implementing TOD strategies, and advancing transportation technology to enhance convenience for people with disabilities.

Shared e-scooters: A last-minute mode?

In this paper we assess whether shared e-scooters are being used in hurried situations to avoid arriving late, making trips we define as last-minute trips. We identify trip characteristics for last-minute trips using data from shared e-scooters in Stockholm. Trip arrival time patterns, distances, durations, and speeds are analyzed in relation to desired arrival times. We observe a peak in the number of trips ending before the full hour and a smaller peak of trips ending before the half hour of weekday mornings. These trips have lower average trip durations and higher average speeds, indicating a larger share of last-minute trips. Arrival patterns shift when the assumed preferred arrival time changes, and when the risk associated with arriving late increases. Furthermore, there is an increased share of trips that started close to public transportation around the full hour, indicating that a portion of last-minute trips may also be last-mile trips. Our results show that while last-minute trips are infrequent per user, they do make up a relatively large share of weekday morning trips. These results highlight an important market segment for shared e-scooter services and may also explain why some research reports e-scooter usage replacing walking.

Logistics 4.0 – digital transformation with smart connected tracking and tracing devices

Tracking and tracing devices can provide real-time information from the supply chain and enable digital transformation in the logistics and supply chain industry. In this connection, Logistics 4.0 refers to the potential for information technology and smart connected assets to be used in logistics in the same way as the Industry 4.0 concept which has been applied in operations and manufacturing. This paper analyses current tracking and tracing-focused applications that can provide value for logistics operations through a case study approach.This study employs an exploratory multiple-case study approach, which is based on interviews with development project stakeholders. The paper analyses three industrial case studies and how tracking/tracing applications are connected to value processes.The findings of this study show that the value of Logistics 4.0, through the deployment of tracking/tracing applications, is delivered in terms of operational efficiency, visibility, transparency, and safety/security. The payback depends on the volume of transactions, the possibility of reusing the tracking tags, the duration of trips, and the supply chain structure. The paper provides insight into how Logistics 4.0 technology can enhance logistics performance value. Based on the analysis, the study proposes the following potential application domains: (1) intermodal tracking of the shipments for operations control, (2) asset management of containers, and (3) certification of the process steps and authenticity.

A survey of general road transportation: How and why horses are transported in the U.S.

Horses are regularly transported in the United States (U.S.); however, how, and why horses travel by road has not been explored. Consequently, an online nationwide survey was conducted to understand 1) the most common reasons for travel; 2) the types of journeys undertaken when being transported by road in the U.S. and 3) the general management practices when transporting for 3 h or less. Responses were collected from 1294 participants with at least one response from every state in the continental U.S. The most common survey taker was a female (93.9 %), adult amateur (81.2 %), horse owner (64.6 %) who rode recreationally (33.1 %) and transported their own horse (79.4 %). The most common reasons for travel were for trail or leisure riding (34.2 %) followed by showing and competition (25.3 %); however, this varied by discipline. The most common trip duration was less than one hour (46.8 %), with only 12.4 % of the most common trip durations being 4 h or more. The most common specific horse transported by road for 3 h or less was an adult (age 5-15; 59.0 %), Quarter Horse (21.2 %), used for pleasure or trail riding (44.3 %). The biggest concern when transporting was injury to the horse (26.7 %), whilst the biggest factor when planning to travel was the weather (24.1 %). These results provide insight into why horses are being transported by road in the U.S. and that it is more common to transport horses for shorter durations.

Recreation specialization: Resource selection functions as a predictive tool for protected area recreation management

Widespread and ongoing increases in park and protected area (PPA) visitation presents managers with the imminent challenge of preserving ecological conditions while also maintaining accessibility in the open spaces that are fundamental to the health and wellness needs of society. In the field of recreation resource management, recreation specialization, the selective channeling of interests and abilities into a specific recreational activity, has contributed to a more comprehensive understanding of recreation behavior, site preference, management perceptions and conservation support. Contributing to historical understandings of specialization can inform recreation planning on the diversity of uses occurring in urban-proximate PPAs. In this study, survey and GPS data collected in urban and peri-urban parks in the Nature Reserve of Orange County, CA, USA, were used to classify recreationists into three specialization types based on empirically derived dimensions of involvement, commitment and skill. For each survey participant, thirteen spatio-temporal metrics (STM) were calculated. A principle component analysis (PCA) reduced STMs into 3 factors representing expressions of spatial behavior and a one-way analysis of variance indicated unique patterns between specialization types and time spent recreating, elevation gained, speed traveled and stopping behavior. Additionally, GPS point data were analyzed with an analytical approach adopted from the field of wildlife movement ecology; a resource selection function (RSF). The RSF assisted in quantifying spatial distribution patterns specific to each specialization type across activity types at four park locations and demonstrated a landscape-based statistical analysis of probability of use in relation to change in elevation, distance to starting points and park amenities. Committed hikers and mountain bikers largely demonstrated longer trip durations, more elevation gain, less overall stopping, faster overall speeds and farther total distances, while e-bikers in this study demonstrated unintuitive specialization dynamics; casual specialization types travelling farther from starting points and dispersing more across trail systems. Further spatial results revealed spatial behavior to be inherently complex and influenced by numerous confounding factors (i.e. activity type/bike type, starting points, park topography and trail design). This novel spatial examination of recreation specialization according to activity type and park location, is useful in understanding recreation behavior and park use in a spatial context. This ability is helpful in a predictive managerial context when reviewed in correspondence with historical evidence identifying behaviors with increased potential for ecological impact.

The utility of telematics data for estimating the prevalence of driver handheld cellphone use, 2019–2022

Introduction: Driver distraction from handheld cellphone use contributes to fatal crashes every year but is underreported in terms of the proportion of crashes attributed to any distraction or cellphone use specifically. Existing methods to estimate the prevalence of cellphone distractions are also limited (e.g., observing drivers stopped at intersections, when crash risk is low). Our study used data from Cambridge Mobile Telematics to estimate the prevalence of drivers’ handheld calls and cellphone manipulation while driving, with “cellphone motion” based on movement recorded by the phones’ gyroscopes used as a surrogate for manipulation. Method: We compared the telematics measures with the National Highway Traffic Safety Administration’s roadside observations of driver electronic device use, and logistic regression tested relationships between regional, legislative, and temporal factors and the odds of cellphone behaviors occurring on a trip or at a given point in time. Results: Results showed 3.5% of trips included at least one handheld phone call and 33.3% included at least an instance of cellphone motion, with handheld calls occurring during 0.78% of overall trip duration and cellphone motion during 2.4% of trip duration. Conclusions: Correspondence between trends in cellphone distractions across regional, legislative, and temporal factors suggest telematics data have considerable utility and appear to complement existing datasets.

Carotenoid skin ornaments as flexible indicators of male foraging behavior in a marine predator: Variation among Mexican colonies of brown booby (Sula leucogaster)

Carotenoid-dependent ornaments can reflect animals’ diet and foraging behaviors. However, this association should be spatially flexible and variable among populations to account for geographic variation in optimal foraging behaviors. We tested this hypothesis using populations of a marine predator (the brown booby, Sula leucogaster) that forage across a gradient in ocean depth in and near the Gulf of California. Specifically, we quantified green chroma for two skin traits (foot and gular color) and their relationship to foraging location and diet of males, as measured via global positioning system tracking and stable carbon isotope analysis of blood plasma. Our three focal colonies varied in which foraging attributes were linked to carotenoid-rich ornaments. For gular skin, our data showed a shift from a benthic prey-green skin association in the shallow waters in the north to a pelagic prey-green skin association in the deepest waters to the south. Mean foraging trip duration and distance of foraging site from coast also predicted skin coloration in some colonies. Finally, brown booby colonies varied in which trait (foot versus gular skin color) was associated with foraging metrics. Overall, our results indicate that male ornaments reflect quality of diet and foraging–information that may help females select mates who are adapted to local foraging conditions and therefore, are likely to provide better parental care. More broadly, our results stress that diet-dependent ornaments are closely linked to animals’ environments and that we cannot assume ornaments or ornament signal content are ubiquitous within species, even when ornaments appear similar among populations.

Women’s Mobility and Activity Trends in Urban Areas of Ogun State, Nigeria

In the dynamic landscape of modern life, the movement of people and goods fuels economic progress. However, transportation disparities between genders persist globally. Recognizing distinct mobility needs and behaviours between women and men is crucial. This study delves into the travel and activity patterns specifically among women in urban centers of Ogun State, Nigeria. Employing a cross-sectional community survey design, the study sampled women from four cities in Ogun State. A total of 1,597 respondents participated, with analysis focusing on descriptive statistics. Patterns in women's travel, including trip duration, distance, purpose, and mode of transportation, were examined. Results highlighted purpose-driven travel, predominantly for work, household tasks, and religious engagements within town. Motorcycles, tricycles, and walking were common modes of transportation, influenced by factors like trip speed and purpose. Additionally, leisure and visiting friends and relatives constituted frequent out-of-town travel motives. Three distinct daily activity patterns emerged. Addressing challenges like limited access to private vehicles, inadequate infrastructure, and security concerns is vital to enhancing women's travel experiences in the region. Keywords: Women’s mobility, Activity patterns, Urban centres, Ogun State.

Modelling the determinants of electrical vehicles adoption in Riyadh, Saudi Arabia

This study investigates consumer acceptance of electric vehicles (EVs) in Riyadh, focusing on their response to adoption incentives. Utilising a stated preference survey, a mixed logit model was used in conjunction with survey insights to derive the final findings. Those include finding a higher sensitivity to changes in the monetary values of EVs than conventional vehicles. Also, the presence of accessible charging infrastructure and non-financial incentives (e.g., designated public parking spaces) were found to significantly increase the likelihood of EV adoption. While Saudi participants were less likely than expats to purchase EVs, females and consumers in their 40s showed a likelihood of purchasing an EV. Furthermore, shorter trip durations and personal acquaintance with EV owners in Riyadh positively influenced adoption rates. It is worth noting that the study shows that almost 60% of the targeted population indicated an interest in purchasing a vehicle in the upcoming three years, where the great majority stated that a new vehicle would replace an existing one. This creates a window of opportunity to implement optimal incentivising policies, as once a conventional vehicle is purchased, investment becomes ‘locked’ for years. The study then concludes with policy insights that aim to contribute to the policies of large-scale EV adoption in Riyadh.

Machine learning approach to identify malaria risk in travelers using real-world evidence

BackgroundPre-travel consultation and chemoprophylaxis measures for malaria are a key component in the prevention of imported malaria in travelers. In this study we report a predictive tool for assessing personalized malaria risk in travelers based on the analysis of electronic medical records from travel consultations. The tool aims to guide physicians in the recommendation of appropriate prophylaxis prior to their trip. We also provide best-practice recommendations for pre-processing noisy and highly sparse real world evidence data. MethodsWe leveraged a large EMR dataset, containing demographic information about travelers and their destination. The data has been previously preprocessed using various strategies to handle missing and unbalanced data. We compared multiple machine learning approaches to assess the risk of malaria acquisition in travelers during their travels. Additionally, a feature importance analysis was performed using SHAP (SHapley Additive Explanations) values to identify patterns associated with malaria risk. ResultsOur study revealed that our XGB models achieved high predictive capacity (AUC >0.80). The most significant features predicting malaria infection during travel included travel destinations with low malaria risk, vaccination history, number of countries visited, age, and trip duration. Remarkably, we were able to obtain a reduced model with only five features. When comparing this model with a population of travelers recommended for malaria chemoprophylaxis, we observed that it was deemed necessary in only 40% of these travelers. This suggests that 60% received chemoprophylaxis despite having a low personalized risk of malaria. ConclusionWe have developed an algorithmic tool that utilizes a concise survey to generate a personalized travel risk assessment, effectively minimizing the prescription of unnecessary malaria chemoprophylaxis. Through the identification of patterns linked to predictions, our model significantly enhances the efficacy of pre-travel consultations.

Changes to the Transport Behaviour of Inhabitants of a Large City Due the Pandemic

On 11 March 2020, the World Health Organisation (WHO) classified the COVID-19 outbreak as a global pandemic and, in consequence, many countries took steps to prevent the importation and subsequent local transmission of the SARS-CoV-2 virus, resulting in restrictions on economic activity, transport, travel, and daily mobility. Although the COVID-19 pandemic and its impact on daily mobility have been widely addressed in the literature, there is a limited number of studies that indicate to what extent these changes have become permanent. The purpose of this study was to determine the nature and scale of the impact of the COVID-19 pandemic on the local transport system of a large city in Poland (Łódź) and, above all, to identify the permanence of this impact. To accomplish these objectives, a questionnaire survey was conducted using the computer-assisted telephone interviewing (CATI) technique on a sample of 500 residents, which included questions on daily mobility in the period before (autumn 2019) and after (autumn 2022) the pandemic. In addition, the results of the questionnaire survey were juxtaposed with data from intelligent transport systems (ITS) (data from 20 induction loops, and data on the number of tickets validated in public transport vehicles). Not only did the pandemic change the frequency of spatial motivations, but it also affected trip durations and preferred modes of transport. The most unfavourable changes concern the modal division and the increase in the use of private transport at the expense of public transport. Understanding the durability of the impact of the pandemic on changes in the spatial mobility of the population may help to develop transport policies and increase the resilience of transport systems to possible future pandemics.