Contemporary cities require excellent walking conditions to support human physical activity, increase humans’ well-being, reduce traffic, and create a healthy urban environment. Various indicators and metrics exist to evaluate walking conditions. To evaluate the spatial pattern of objective-based indicators, two popular indices were selected—the Walkability Index (WAI), representing environmental-based indicators, and Walk Score (WS), which applies an accessibility-based approach. Both indicators were evaluated using adequate spatial units (circle buffers with radii from 400 m to 2414 m) in two Czech cities. A new software tool was developed for the calculation of WS using OSM data and freely available network services. The new variant of WS was specifically designed for the elderly. Differing gait speeds, and variable settings of targets and their weights enabled the adaptation of WS to local conditions and personal needs. WAI and WS demonstrated different spatial pattern where WAI is better used for smaller radii (up to approx. 800 m) and WS for larger radii (starting from 800 m). The assessment of WS for both cities indicates that approx. 40% of inhabitants live in unsatisfactory walking conditions. A sensitivity analysis discovered the major influences of gait speed and the β coefficient on the walkability assessment.

Background: Maintaining mobility is fundamental to active aging, allowing older adults to lead dynamic and independent lives. The perception of mobility among older adults significantly impacts their overall well-being and quality of life. Given the aging population, mobility has become an increasingly pressing issue. Aim: This study focused on the perception of urban neighborhoods, including considerations of urban tissue (crossings and sidewalk maintenance), urban scenes (benches and traffic), and safety (fears and street lighting quality). We investigated the differences in the perception of the surroundings of residences by urban and rural seniors concerning their demographic and social characteristics and environmental determinants. Methods: A quantitative study design utilizing a questionnaire survey was employed. Data were collected mainly through face-to-face interviews in the field (PAPI) and via an online questionnaire (CAWI). The final sample comprised 525 participants. Hypotheses regarding the influence of gender, age, social status, level of physical activity, degree of urbanization, and region on environmental perception were tested using ordinal regression. Results: The hypothesis regarding the dependence of the perception of the surroundings on the level of urbanization was confirmed; that regarding the dependence of the perception of the residence surroundings on seniors’ age was not confirmed. The other hypotheses were partially confirmed. For the seven investigated environmental attributes, gender was significant in two cases, social status and physical activity in three cases, and region in four cases. Conclusion: While most studies have focused on urban settings, this study highlights the situation in rural municipalities. Substantially worse pedestrian conditions in availability of pedestrian crossings, benches, and lighting were recognized in rural municipalities versus cities. Understanding the complexity of mobility and the spatial locations relevant for older persons concerning potential barriers and facilitators for mobility aids in planning and adapting neighborhood environments to promote active and healthy aging in place.

Public transport requires constant feedback to improve and satisfy daily users. Twitter offers monitoring of user messages, discussion and emoticons addressed to official transport provider accounts. This information can be particularly useful in delicate situations such as management of transit operations during the COVID-19 pandemic. The behaviour of Twitter users in Madrid, London and Prague is analysed with the goal of recognising similar patterns and detecting differences in traffic related topics and temporal cycles. Topics in transit tweets were identified using the bag of words approach and pre-processing in R. COVID-19 is a dominant topic for both London and Madrid but a minor one for Prague, where Twitter serves mainly to deliver messages from politicians and stakeholders. COVID-19 interferes with the meaning of other topics, such as overcrowding or staff. Additionally, specific topics were discovered, such as air quality in Victoria Station, London, or racism in Madrid. For all cities, transit-related tweeting activity declines over weekends. However, London shows much less decline than Prague or Madrid. Weekday daily rhythms show major tweeting activity during the morning in all cities but with different start times. The spatial distribution of tweets for the busiest stations shows that the best-balanced tweeting activity is found in Madrid metro stations.

The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R–CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm.

Public transport faced various challenges during the COVID-19 period due to a large ebb in passengers during the pandemic waves, COVID-related restrictions, em-ployee sickness, and economic pressure to balance transport supply and demand. The study assesses the impact of the timetable changes on the accessibility between 2019 and 2021 in the hinterlands of two Czech cities – Hradec Králové and Ostrava. The research question is if there are considerable changes in public transport accessibility during this period which were influenced by the pandemic. Municipal accessibility is determined by the share of inaccessible municipalities, average travel time, population weighted average travel time, and average number of transfers under conditions suitable for seniors. Optimal trips to all municipalities are established with a local OpenTripPlanner server using all public urban and regional timetables including peak and off-peak hours, workdays, and non-working days. Unlike Ostrava, the pattern of accessibility of the Hradec Králové hinterland is influenced by the railway networks and bus transport corridors. Mean accessibility in 2021 slightly improved in the Hradec Králové region and slightly worsened in the Os-trava region. Some municipalities, however, showed grave decline. The most sensitive in-dicator is the share of inaccessible municipalities adapted for seniors' needs. The study confirms the importance of choosing a time of departure/arrival for the results of accessi-bility assessment. The most significant differences in the accessibility of municipalities are visible only for one of the selected departure times. Only a few municipalities show differences for both departure times, indicating variable effects on accessibility depending on the time of day-situation. The results did not confirm the anticipated general deterioration of public transport accessibility in the hinterlands of regional capitals during the pandemic period. URL: https://www.gcass.science.upjs.sk/

While Pavement Management Systems are commonly used for effective roadway inspection and maintenance, similarly efficient solutions for sidewalk assessment remain underdeveloped. This study introduces a semi-automated method for sidewalk evaluation based on a mobile device equipped with a 2D LiDAR scanner, and image processing techniques. The proposed approach includes the design of a cost-effective sensor-equipped vehicle, data acquisition and processing workflows, elevation barrier detection methods, and a comprehensive validation framework. The system integrates data from a 2D LiDAR scanner, wheel encoders, and a localization unit to generate a local digital elevation model (DEM) via constrained spline interpolation. Barrier detection is performed using both a focal range thresholding method and a machine learning-based segmentation approach. Validation is conducted using high spatial resolution reference DEMs (1 mm and 2 cm) derived from Terrestrial Laser Scanning across two pilot sites. Detection quality is evaluated using three key metrics: Coefficient of Area Correspondence, Identification Accuracy, and Point Accuracy. The focal range method achieved overall accuracies of 91% and 92% at the two test sites, while the machine learning approach reached only 36% and 38%. Spatial coincidence indicators revealed reduced agreement, particularly due to shape distortions and segmentation inconsistencies; however, Point Accuracy proved to be the most robust validation metric. The results confirm that the proposed 2D LiDAR-based system and focal range method are effective in identifying significant elevation barriers on sidewalks. The compact design, low operational cost, and ease of use make the solution suitable for widespread deployment in municipal infrastructure assessment.