Document Type : Research Paper
Authors
1
Department of Architecture and Urban Planning, Technical and Vocational University (TVU), Tehran, Iran
2
Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, Iran
10.30488/gps.2026.595618.3926
Abstract
A B S T R A C T
The rhythm of urban activity varies throughout the day and across different types of days, and the mismatch between the temporal presence of the population and the temporal supply of services can affect temporal accessibility and the efficiency of urban spaces. The present study was conducted with the aim of testing a quantitative framework for identifying and comparing diurnal activity rhythms, examining their differences across day types, and prioritizing temporal-spatial interventions in three selected zones of Tehran, namely Tajrish, Enqelab-Valiasr, and Naziabad. To evaluate the framework, a simulated scenario dataset with the status SIMULATED_TRAINING was employed. The data structure comprised 12 nodes, three day types, and six time periods, and in total, 216 primary observations and 108 supplementary observations related to reference stations, equivalent to 324 fifteen-minute observations, were analyzed. Indicators of urban activity intensity, temporal balance, and the share of evening-night activity were utilized, along with Friedman and Wilcoxon tests, Spearman correlation, hierarchical clustering, temporal service gap analysis, and an intervention priority model. The results of the simulated data indicated that the mean activity intensity in the Enqelab-Valiasr zone was higher than in Tajrish and Naziabad. Day type did not have a significant effect on overall activity intensity; however, temporal balance and the share of evening-night activity differed between weekdays and Friday. The three-cluster solution distinguished three types: evening commercial-leisure, residential-night, and bimodal transportation. Sensitivity analysis revealed that some intervention rankings are dependent on policy orientation. This framework enables the integration of activity rhythm, temporal supply of services, and intervention priority; nevertheless, generalizing the results to Tehran requires replacing simulated data with actual observations and reliable spatial sources.
Extended Abstract
Introduction
Cities are chronological systems, and population presence, mobility, and activity within them vary throughout the day-night cycle and across different types of days. Accordingly, the functioning of urban space cannot be explained solely by official land use or static spatial characteristics; it also depends on the timing, intensity, and duration of use. Neglecting this dimension can create a mismatch between the timing of population presence and the accessibility of services. The concept of urban activity rhythm examines these variations across different periods of the day. The theoretical foundations of this approach are linked to time geography and rhythm-analysis. Time geography emphasizes the interaction of space, time, and the constraints of daily activity, while rhythm-analysis considers urban life to be the product of the interplay between cyclical rhythms and linear rhythms arising from employment, transportation, and institutional activity. From this perspective, spaces with similar land uses can also exhibit different temporal signatures. Empirical studies have indicated that functional characteristics, accessibility, and land-use diversity play a role in shaping different chronological patterns. The expansion of spatial and chronological data has enhanced the capacity to examine urban dynamics. Mobile phone data, GPS, smart cards, transit transactions, sensors, and pedestrian counts have been employed to identify mobility patterns and the chronological structure of activity. However, these data face limitations such as difficulty of access, sampling ambiguity, privacy concerns, socio-spatial bias, and weak reproducibility. Therefore, temporally referenced pedestrian observations, combined with spatial characteristics and the chronological supply of services, can provide a complementary approach. Despite the growth of international studies, spatiotemporal research on urban activity in Iran remains inadequate and has focused primarily on mobility and travel demand. The simultaneous analysis of activity intensity, chronological distribution, differences between working and non-working days, and the chronological accessibility of services has received less attention. This gap is particularly significant in Tehran, given its functional diversity and the uneven distribution of opportunities. Accordingly, this study compares daily activity rhythms at selected nodes in Tehran, measures the differences among working days, Thursdays, and Fridays, examines the relationships between spatial-functional characteristics and rhythm indicators, and identifies priority nodes for spatiotemporal intervention based on the mismatch between activity intensity and the temporal supply of services. Activity rhythm is evaluated using indicators of activity intensity, chronological balance, peak time, and the share of evening-night activity. The novelty of the research lies in integrating activity rhythm analysis, measurement of the chronological service gap, and prioritization of interventions within a spatiotemporal planning framework.
Methodology
This applied study adopted a quantitative and comparative approach, considering the urban activity node as the primary unit of analysis. The research focused on three selected zones in Tehran - Tajrish, Enghelab–Valiasr, and Naziabad - which represent parts of northern, central, and southern Tehran, respectively. Four nodes were selected from each zone, resulting in a total of 12 nodes encompassing residential, commercial-administrative-service, transportation, and mixed-public-leisure functions. Node selection was based on dominant function, density and diversity of destinations, access to public transportation, and the node's role in the street network. To extract spatial and service-related contextual variables, a 500-meter radius buffer was defined around each node. Pedestrian flow and stationary presence were examined through standardized 15-minute observations. Each node was evaluated across six daily periods - early morning (06:00–09:00), mid-morning (09:00–12:00), noon (12:00–15:00), afternoon (15:00–18:00), early evening (18:00–21:00), and night (21:00–24:00) - and across three types of days: working days, Thursdays, and Fridays. The dataset comprised 324 observations, of which 216 were allocated to the 12 main nodes and 108 supplementary observations to three reference stations. The reference stations were utilized to construct 24-hour activity profiles.
The urban activity intensity index was calculated from normalized pedestrian flow and normalized stationary presence with equal weighting. Chronological signatures, chronological balance, peak time, and the share of evening-night activity were also extracted. The Friedman test and Wilcoxon pairwise comparisons were employed to examine differences among day types. Spearman correlation examined exploratory and non-causal relationships between activity rhythm indicators and spatial characteristics. Hierarchical clustering with Ward's method and Euclidean distance was used to identify rhythm typologies, and cluster quality was assessed using the Silhouette index and cophenetic correlation. Subsequently, the chronological service gap and a composite intervention priority score were calculated, and sensitivity analysis examined the stability of rankings across different policy weighting scenarios.
Results and Discussion
The results revealed clear spatial and functional differences in the activity rhythms of the selected nodes. Based on the urban activity intensity index, the Enghelab–Valiasr zone had the highest activity intensity with a mean of 0.385, followed by Tajrish with 0.298 and Naziabad with 0.203. At the node level, Valiasr Intersection–City Theater recorded the highest activity intensity with an index of 0.634, followed by Tajrish Bazaar–Imamzadeh Saleh with 0.448 and Enghelab Square–Metro Entrance with 0.427. The high activity intensity in the central zone was associated with the concentration of commercial, educational, cultural, transportation, and service functions, along with greater accessibility and destination diversity. Although chronological balance was relatively high across all three zones, chronological profiles revealed differences in activity timing. The share of evening-night activity was 43.1% in Tajrish and 42.4% in Naziabad, whereas this value reached 39.6% in Enghelab–Valiasr. Thus, despite having the highest overall activity intensity, the central zone exhibited a smaller share of activity in the later periods of the day, while in Tajrish and Naziabad, activity continued more prominently into the evening and night. The 24-hour profiles of the reference stations also indicated that peak activity time varies according to the type of day. In Tajrish, peak activity on working days and Fridays occurred around 19:00. In Enghelab–Valiasr, the working-day peak was around 19:00, but on Fridays it shifted to approximately 17:00. In Naziabad, peak activity was observed around 18:00 on working days and around 19:00 on Fridays. These shifts demonstrate that the type of day can alter the timing of peak activity within a fixed spatial context. Transportation nodes generally exhibited a bimodal pattern, with one peak in the morning and another in the afternoon or early evening. Commercial and mixed-use nodes typically exhibited increasing activity from noon toward the afternoon and early evening, whereas residential nodes displayed lower morning activity and a greater concentration of activity in the later hours of the day.
Hierarchical clustering identified three main activity rhythm types: an evening commercial-leisure cluster comprising six nodes, a residential-night cluster comprising three nodes, and a bimodal transportation cluster comprising three nodes. The three-cluster solution had a Silhouette coefficient of 0.523 and a cophenetic correlation of 0.835, indicating acceptable separation and structural coherence. The placement of transportation and residential nodes from different geographical zones within similar rhythm types demonstrated that functional characteristics can explain chronological similarity more effectively than a simple north-central-south distinction. The comparison among day types indicated that overall activity intensity did not differ significantly, but the chronological organization of activity did. The Friedman test revealed significant differences for chronological balance and the share of evening-night activity, while the mean activity intensity remained statistically similar across day types. Wilcoxon pairwise comparisons indicated that working days exhibited a more balanced chronological distribution than Fridays, whereas the share of evening-night activity was higher on Fridays. Thus, the main difference between working and non-working days pertained more to the timing and concentration of activity than to its overall volume.
Spearman exploratory analysis revealed strong positive relationships between activity intensity and destination diversity, built-up density, access to public transportation, surrounding services, and network centrality. In contrast, the share of evening-night activity had a negative relationship with network centrality. This pattern indicates that central nodes can exhibit higher overall activity intensity without necessarily displaying the greatest activity during the later hours of the day. Given that these relationships were examined across 12 nodes, they should be interpreted as exploratory associations rather than causal effects.
The service gap analysis demonstrated a mean absolute gap of 0.073 across all nodes. The mean gap was 0.098 in Enghelab–Valiasr, followed by Tajrish with 0.071 and Naziabad with 0.050. The largest individual gaps were observed at Valiasr Intersection–City Theater with 0.149, Enghelab Square–Metro Entrance with 0.119, Tajrish Bazaar–Imamzadeh Saleh with 0.099, and Tajrish Square–Metro Entrance with 0.089. Larger gaps were primarily concentrated in central and high-density nodes, whereas several residential and mixed-use nodes exhibited a greater need for evening-night services. Accordingly, central and transportation nodes were more associated with pressure on service capacity, while residential and mixed-use nodes were more sensitive to the chronological accessibility of neighborhood services. The intervention priority model placed Valiasr Intersection–City Theater, Enghelab Square–Metro Entrance, and Tajrish Square–Metro Entrance among the highest-priority nodes in the baseline model. However, sensitivity analysis showed that rankings change under different policy weighting scenarios. When greater emphasis was placed on service equity, the residential node Vesal rose from seventh to first rank, and the residential node Akbar Mashhadi rose from eleventh to third rank. This result indicates that efficiency-based and chronological equity-based approaches do not necessarily identify identical priorities, and the final ranking of nodes depends to some extent on the selected policy objective.
Conclusion
The findings indicated that the selected urban nodes possess distinct spatial and chronological rhythms that cannot be explained solely by static land-use classifications. The three study zones differed in terms of overall activity intensity, evening-night activity, and chronological organization, and functional differences led to the emergence of recognizable rhythm types: evening commercial-leisure, residential-night, and bimodal transportation. The type of day influenced the timing and distribution of activity more than its overall volume. The service gap analysis also exhibited that high activity intensity is not necessarily accompanied by an appropriate chronological supply of services, and that when chronological equity is emphasized, residential nodes may attain higher priority for intervention. These results support a spatiotemporal planning approach in which interventions are differentiated based on node function, chronological demand, and service accessibility. Transportation nodes require greater coordination during peak periods; central commercial nodes may require increased service capacity and alignment of operating hours with demand peaks; and residential areas can benefit from strengthened evening and night neighborhood services, lighting, and local support. This framework does not imply continuous activity across all urban areas; rather, it emphasizes better coordination among activity timing, service supply, transportation, safety, and public space management. This framework introduces time alongside place and function as an operational dimension in urban planning. Given the inadequate number of selected nodes and the study's focus on three zones of Tehran, broader generalization of the results requires the examination of more locations, different seasons, and more diverse chronological conditions.
Funding
This research received no specific financial support from governmental, commercial, or non-profit organizations.
Authors' Contribution
Asma Nourhmohammadi contributed to the research design, the development of the theoretical and methodological framework, data analysis, and the writing of the manuscript. Rezvan Ghorbani Salekhord contributed to the development of the theoretical foundations, interpretation of the results, scientific review, and final revision of the manuscript.
Conflict of Interest
The authors declared that there is no conflict of interest concerning the preparation, writing, or publication of this article.
Acknowledgments
The authors express their sincere gratitude to all individuals who provided scientific or administrative cooperation at various stages of the research and manuscript preparation.
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