Points Of Interest Research Articles

Urbanization and Plant Diversity in Urban Fringes: Differential Responses Across Life Forms

Urban fringe areas, as critical transition zones between urban expansion and natural environments, provide essential ecosystem services that enhance the overall health and sustainability of urban environments. However, previous studies have focused on urban built–up areas while neglecting the unique conditions of urban fringe zones. This study investigated the spatial distribution patterns of plant diversity across different life forms in Jiangdong New District and their responses to urbanization levels. The results indicate that hot and cold spots of diversity for trees and shrubs show similarity, while diversity in herbaceous plants differs significantly. Nighttime light intensity, POI density, and road density are primary influencing factors, with nighttime light intensity and POI (Point–of–Interest) density having the greatest combined impact, and herbs showing the highest sensitivity to these factors. Further analysis reveals that in the northwestern forest, grassland, and farmland mosaic areas, urbanization levels are positively correlated with tree and shrub diversity but negatively correlated with herb diversity. In the southeastern nursery planting area, urbanization levels consistently relate to the diversity of these three plant types. These findings underscore the necessity of adopting diverse conservation and management strategies in urban fringe areas to address the ecological needs and functional contributions of different plant types. This provides new perspectives on urban planning and ecological conservation that can help improve the overall health and sustainability of urban ecosystems.

Algorithm for partial sweep coverage on a line

Sweep cover is a basic issue in wireless sensor networks, which uses mobile sensors to inspect points of interest (PoIs) periodically. There are many studies on the problem assuming that all mobile sensors have a uniform velocity, while some literature shows that using heterogeneous velocities may greatly increase the difficulty of research. In this paper, we study a minimum energy partial sweep cover (MinEPSC) problem on a line, the goal of which is to minimize energy consumption to sweep-cover at least K PoIs, where the energy consumption of a mobile sensor is related with its velocity in somewhat arbitrary but monotone manner. We design algorithms with theoretically guaranteed approximation ratios under different assumptions on the candidate velocity sets.

Urban travel carbon emission mitigation approach using deep reinforcement learning.

The urbanization process has led to a significant increase in energy consumption and carbon emissions, which can be mitigated through scientific urban planning and management. This research proposes a bottom-up urban carbon emission mitigation strategy based on deep reinforcement learning (DRL). Using Ningbo City as a case study, multi-source urban data, including points of interest (POI) data and urban transportation system data, are utilized, along with varying carbon emission coefficients for different travel modes, to construct a comprehensive carbon emission environment for urban areas. The proposed DRL model adopts an Actor-Critic framework, which iteratively optimizes the land use configuration and building type proportions within the urban matrix to achieve the goal of mitigating travel carbon emissions. Experimental results demonstrate that this approach exhibits significant carbon reduction effects in urban scenario. By adjusting the discount rate of the reward function, various optimization strategies can be obtained, such as short-term and long-term strategies, achieving reductions of 0.47% and 0.61%, respectively, which are notably higher than the 0.39% reduction expected if travel emissions were uniformly distributed across the matrix. The findings highlight the potential of DRL-based approaches in urban planning to achieve adaptive and data-driven strategies for carbon emission mitigation.

An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies.

Exploring the Complex Association Between Urban Built Environment, Sociodemographic Characteristics and Crime: Evidence from Washington, D.C.

The urban built environment and sociodemographic characteristics have complex relationships with urban crime. However, previous studies have had limitations such as generalizing urban green space types, urban functionality, and sociodemographic characteristics. Given these, this study aimed to explore the relationship between them using more detailed indicators. The study utilized Google Street View and points of interest to depict the built environment. Building on previous work that segmented natural and artificial elements in streetscape images, this study further distinguished trees, bush, and grass. Additionally, it incorporated data from the Data Analysis and Visualization Unit of the DC Office of Planning to reflect a broader range of individual characteristics. Weighted least squares regression and Pearson correlation analysis were used to test the relationship between the built environment, sociodemographic, and crime, respectively. Some of the key findings are as follows. (1) Trees, bushes, and grass all reduce crime. (2) Urban functionality is hard to curb crime by enhancing informal public surveillance. (3) Among the sociodemographic variables, the walking commute rate is the variable most strongly positively correlated with crime. (4) Family relationships play an important role in suppressing crime. This study examined a more comprehensive range of indicators affecting urban crime in favor of safer cities.

Multifunctionality and the Tradition-Innovation Relationship in Rural Areas – The Case Study of the Municipality of Cinfães

Rural areas are recognized by type of settlement and the surrounding areas, where agricultural and forestry practice was performed. This settlement model lasted until the advent of industrialization, implemented at different rhythms for developed countries and that is also reflected in a long and severe process of rural depopulation for city cores. Currently, the urban population continues to increase, while rural decreases, reaching with frequency ratios that lead to concentration problems, given the scarce opportunities for development and innovation discoveries in rural areas, instituting a repulsive character to the fixing population in these spaces. Nevertheless, in the last 30 years or so, there have been models that support the maintenance and preservation of rural areas, especially multifunctionality models, in other words, the concentration of several different functions in these areas in an attempt to energize and attract young and skilled population, with city roots phenomenon titled naturbanização. As it relates to the Cinfães municipality, in this territory since the middle of last century that the demographic decline is significant. However, Cinfães presents full potential, which are divided between the scenic beauty of the Montemuro mountains and unspoilt nature that surrounds the Bestança river. It is a town with numerous points of interest among which stands out the historical and cultural heritage, traditions still very marked by a full population of stories and memories, in addition to the tranquility and safety of small towns, but where there are still a number of specialized services. If we associate all these advantages to promote new opportunities, Cinfães municipality will gain new momentum.

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A* algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. Furthermore, the DETIL algorithm’s obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.

INTERNATIONAL STRATEGIC INTERESTS AT THE BLACK SEA – ENERGY SECURITY ANALYSIS

The birth, development and propagation of regional and world conflict, as well as its escalation towards military war, global economic interests that converge towards the monopolization of strategic hydrocarbon energy resources, it transforms the Black Sea into a geostrategic space of geopolitical and geo-economic importance, but also with huge potential for confrontation and war. Positioning Romania in an inflection point of interests, in an area of strategic importance, being the buffer between NATO and the Russian Federation (the most important regional actor), it gives us the status of a state provider of regional security and welfare. The Black Sea region is important in that it is rich in hydrocarbons (oil and natural gas), it is an energy transit center and becomes a hot spot for the extended world security because it delimits the border between NATO and the Russian Federation, but also the influences from China.

A Scene–Object–Economy Framework for Identifying and Validating Urban–Rural Fringe Using Multisource Geospatial Big Data

Rapid urbanization has led to the emergence of urban–rural fringes, complex transitional zones that challenge traditional urban–rural dichotomies. While these areas play a crucial role in urban development, their precise identification remains a significant challenge. Existing methods often rely on single-dimensional metrics or administrative boundaries, failing to capture the multi-faceted nature of these zones. This study introduces a novel “Scene–Object–Economy” (SOE) framework to address these limitations and enhance the precision of urban–rural fringe identification. Our approach integrates multisource geospatial big data, including remote sensing imagery, nightlight data, buildings, and Points of Interest (POI), leveraging machine learning techniques. The SOE framework constructs feature from three dimensions: scene (image features), object (buildings), and economy (POIs). This multidimensional methodology allows for a more comprehensive and nuanced mapping of urban–rural fringes, overcoming the constraints of traditional methods. The study demonstrates the effectiveness of the SOE framework in accurately delineating urban–rural fringes through multidimensional validation. Our results reveal distinct spatial patterns and characteristics of these transitional zones, providing valuable insights for urban planners and policymakers. Furthermore, the integration of dynamic population data as a separate layer of analysis offers a unique perspective on population distribution patterns within the identified fringes. This research contributes to the field by offering a more robust and objective approach to urban–rural fringe identification, laying the groundwork for improved urban management and sustainable development strategies. The SOE framework presents a promising tool for future studies in urban spatial analysis and planning.

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Exploring the built environment impacts on Online Car-hailing waiting time: An empirical study in Beijing

Waiting time is a critical metric for evaluating the quality of online car-hailing services, with studies indicating a close association between Online Car-hailing waiting time (CWT) and the urban built environment (BE). Points of interest (POI) data is widely utilized to characterize the built environment. However, quantifying the proper combination or spatial relationships between different types of POIs to capture the functional features of the built environment within a region poses a challenge. This paper proposes a framework for analyzing built environment characteristics based on a semantic probabilistic topic model utilizing Latent Dirichlet Allocation, demonstrating that integrating the building area of POIs along with the travel activity intensity at their respective locations enables a more precise identification of regional functions. Moreover, while some studies have explored the correlation between the built environment and waiting time, few have evaluated the nonlinear interactions between them. On this foundation, employing the machine learning technique XGBoost model in conjunction with online car-hailing order data, we probe the relationship between the built environment and waiting time. The research indicates that CWT is comprehensively affected by multiple factors. Taking weekday evening peak period as an example, the CWT of Central-urban area, where has dense commercial and office land use, positively correlated with commercial and office topics, while negatively correlated with educational, residential, leisure and healthcare topics, leading to a longer CWT in the Central-urban. In addition, the interaction between BEs can weakens their individual effects on CWT. A higher degree of job-residence balance can mitigate the negative impact of residential and office topics on increasing CWT, particularly in Intermediate-urban areas. Additionally, BE topics may also suppress the positive effect of road density on reducing CWT. The relationship between the BE and CWT exhibits threshold effects and a V-shaped relationship, indicating that the BE is significantly associated with CWT only within specific ranges. This correlation also exhibits a gradient pattern from the Central-urban to the Sub-urban, especially concerning office and residential topics. These outcomes elucidate the salient ranges of the built environment that exert substantial impacts on waiting time, informing strategic planning for online car-hailing dispatch and urban development to augment passenger travel satisfaction.

LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities

The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of 5.167 between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics.

Bayesian analysis of urban theft crime in 674 Chinese cities

Current academic research on fitting the volume of urban theft crimes at a macro size is limited, especially from the urban functionality perspective. Given this gap, this study utilizes a Bayesian model to conduct a fitting analysis of theft crime data from 674 cities in China from 2018 to 2020. This research aims to explore novel pathways for theft crime fitting. Results indicate that the size of urban functionality, particularly points of interest (POIs), exhibits excellent performance in fitting theft crimes, with POIs related to public services and commercial activities demonstrating the most significant fitting effects. This research successfully identifies effective indicators for crime fitting, thereby offering a new perspective and supplement to theft crime research. This study holds significant value for gaining a profound understanding of criminal phenomena and explaining the causes and mechanisms underlying the differences in theft crimes among various cities in China.

Spatial Coordination Analysis and Development Methods of the Catering Sector in Yongkang City

The catering sector is recognized as a crucial driver for stimulating domestic consumption and fostering sustained economic growth. A scientifically informed spatial planning of the catering sector in county-level cities can help mitigate the overconcentration or excessive dispersal of resources, thereby enhancing the efficiency of land and commercial resource utilization, reducing waste, and promoting sustainable development within a county’s economy. This study focuses on Yongkang City, located in central Zhejiang Province, as a case study. Using Python 3.1 software to extract point-of-interest (POI) data, spatial analysis techniques such as standard deviation ellipses and kernel density estimation are employed to elucidate the spatial distribution characteristics of the catering sector, comprehensive transportation, and public services in Yongkang City. Furthermore, a coupling coordination degree model is utilized to quantify the coupling coordination degrees between the catering sector and comprehensive transportation (D1), as well as between the catering sector and public services (D2), ultimately assessing the overall coupling coordination degree among the three sectors (D3). Based on the analysis results, the coupling coordination degrees are categorized into five levels, followed by a detailed evaluation of the catering sector’s development across different subdistricts. The findings indicate a certain degree of spatial imbalance in the distribution of the catering sector in Yongkang City, with the western region exhibiting a more favorable development trajectory. Notably, Dongcheng Subdistrict achieved the highest coupling coordination degree (0.993), while the northern region, hindered by underdeveloped economic conditions and limited transportation infrastructure, exhibited the lowest coordination degree (0.098). Considering the unique developmental characteristics and current conditions of each subdistrict, this study advocates for the formulation of a long-term sustainable development plan for the catering sector in Yongkang City. The findings of this research may offer valuable insights for other regions and countries, thereby promoting the sustainable advancement of the catering sector.

Identifying Points of Interest (POIs) as sentinels for infectious disease surveillance: a COVID-19 study

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.

Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts

Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.

Oblique or short incisions reduce the risk of saphenous nerve damage during hamstrings harvesting. A model for mapping nerve pathways at the harvest site.

INTRODUCTIONHamstring autografts are frequently used for ligament reconstruction surgery. Twelve to 84% of patients describe hypo or dysaesthesia secondary to injury of the saphenous nerve or one of its branches after hamstring harvesting. Type of incision (orientation and length) is subject of much debate to limit the risk of nerve damage. A cadaveric study was performed to determine which type of incision limits the risk of injury to the saphenous nerve or one of its branches, based on an anatomic model for mapping nerve pathways at the harvest site. METHODSAn anatomical study was performed on 20 knees from 12 embalmed bodies. Distance between saphenous nerve branches and 4 points of interest along the tibial crest was measured. Based on these measurements, a digital model of the saphenous nerve and its branches was created. A model of three common types of incision (vertical, horizontal and oblique) was overlaid. Each incision was modelled for three lengths (2, 3 and 4cm). Percentage of collision between nerve course model and incision model was then calculated to determine the risk of nerve damage for each type of incision. Based on the nerve course model, a “low collision risk” safe zone was identified. RESULTSNerve damage risk after an oblique incision was significantly lower than for a horizontal or vertical incision, for incision lengths of 3 and 4cm (p<0.05). For a specific incision orientation, the length of the incision did not affect the risk of nerve damage. A trapezoidal space close to the tibial crest and distal to the anterior tibial tuberosity appears to reduce risk of nerve damage. CONCLUSIONThis cadaveric study suggests that during hamstring harvesting, incisions shorter than 2cm reduce the risk of saphenous nerve’s branches injuries. For incisions longer than 2cm, using an oblique incision may reduce the risk compared to vertical or horizontal incisions. LEVEL OF EVIDENCELevel of evidence not applicable: Laboratory experiments

Motion tracking with automated pose estimator can enhance ankle-foot-orthoses alignment

BackgroundAnkle-foot orthoses are commonly prescribed to address motor impairments in individuals with neurological disorders. Proper alignment of Ankle-foot orthoses, commonly assessed by the ground reaction force in relation to the knee joint center and the shank-to-vertical angle, is crucial for their effectiveness but is time-consuming. This study validates automated pose estimator DeepLabCut on measuring these metrics using 2D videos with force vector overlay in individuals with neurological disorders wearing ankle-foot orthoses. MethodsThirty subjects with neurological disorders wearing ankle-foot orthoses participated. DeepLabCut's performance was compared to 3D gait analysis (Vicon). Subjects were randomly divided into three groups to train (20 subjects) and evaluate (10 subjects) DeepLabCut models. The number of subjects with untrackable points of interests in their videos was determined. The mean difference, root mean square error, intraclass correlation and repeatability coefficient between DeepLabCut and Vicon were computed for videos with trackable points of interest. FindingsOnly two subjects for ground reaction force distance to the knee and four subjects for the shank-to-vertical angle had untrackable points of interest. Excellent agreement between DeepLabCut and Vicon was found for the ground reaction force distance to the knee (mean difference 0.1 mm, root mean square error 6.7 mm, intraclass correlation 0.99, repeatability coefficient 6.7 mm) and shank-to-vertical angle (mean difference 0.7°, root mean square error 1.8°, intraclass correlation 0.99, repeatability coefficient 1.6°). InterpretationThis study demonstrates that DeepLabCut can accurately and efficiently assess ankle-foot orthosis alignment parameters, paving the way for a simple and straightforward 2D video setup with a force-vector overlay for automated ankle-foot orthosis alignment.

Analysis of pedestrian crossing violations at an overpass location by using a logit model

Jaywalking significantly increases accident risks compared to marked crosswalks. Overpasses are often installed to address this, but violations continue. This study examines pedestrian behavior at overpass locations, focusing on 438 individuals recorded on video. Factors like parked vehicles, gender, age, group size, mobile phone distraction, carrying items, the number of waiting pedestrians upon arrival, waiting times, and traffic volume were analyzed. Since jaywalking is more frequent during off-peak hours when traffic is not congested, observations were made at random intervals during these times. A binary logit model was used to identify the factors influencing jaywalking. The study found that points of interest near overpasses significantly affect pedestrian behavior. Additionally, parked vehicles near the overpass reduce crossing distance, encouraging jaywalking. Policy recommendations include implementing parking restrictions and strategically locating overpasses to align with pedestrian movement patterns and nearby points of interest.