Search Results (13)

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas. Full article

The influence of street spatial form on thermal comfort from urban morphology and human-centered perspectives has been underexplored. This study, utilizing multi-source data and focusing on urban central districts, establishes a refined index system for street spatial form and a thermal comfort prediction model based on extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). The results reveal the following: (1) Thermal comfort levels display spatial heterogeneity, with areas of thermal discomfort concentrated in commercial zones and plaza spaces. (2) Compared to the human-centered perspective, urban morphology indicators correlate strongly with thermal comfort. (3) The key factors influencing thermal comfort, in descending order of importance, are distance from green and blue infrastructure (GBI), tree visibility factor (TVF), street aspect ratio (H/W), orientation, functional diversity indices, and sky view factor. All but the TVF negatively correlates with thermal comfort. (4) In local analyses, the primary factors affecting thermal comfort vary across streets with different heat-risk levels. In high heat-risk streets, thermal comfort is mainly influenced by distance from GBI, H/W, and orientation, whereas in low heat-risk streets, vegetation-related factors dominate. These findings provide a new methodological approach for optimizing urban thermal environments from both urban and human perspectives, offering theoretical insights for creating more comfortable cities. Full article

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities. Full article

Human impression plays a crucial role in effectively designing infrastructures that support active mobility such as walking and cycling. By involving users early in the design process, valuable insights can be gathered before physical environments are constructed. This proactive approach enhances the attractiveness and safety of designed spaces for users. This study conducts an experiment comparing real street observations with immersive virtual reality (VR) visits to evaluate user perceptions and assess the quality of public spaces. For this experiment, a high-resolution 3D city model of a large-scale neighborhood was created, utilizing Building Information Modeling (BIM) and Geographic Information System (GIS) data. The model incorporated dynamic elements representing various urban environments: a public area with a tramway station, a commercial street with a road, and a residential playground with green spaces. Participants were presented with identical views of existing urban scenes, both in reality and through reconstructed 3D scenes using a Head-Mounted Display (HMD). They were asked questions related to the quality of the streetscape, its walkability, and cyclability. From the questionnaire, algorithms for assessing public spaces were computed, namely Sustainable Mobility Indicators (SUMI) and Pedestrian Level of Service (PLOS). The study quantifies the relevance of these indicators in a VR setup and correlates them with critical factors influencing the experience of using and spending time on a street. This research contributes to understanding the suitability of these algorithms in a VR environment for predicting the quality of future spaces before occupancy. Full article

The visibility and spatial characteristics of commercial space are the key factors that affect the space vitality. However, the coupling visibility graphical analysis of commercial spaces and spatial characteristics to quantitatively analyse consumer behaviour in commercial street spaces and assess the vitality of retail spaces has not been adequately studied. In this paper, the aim is to conduct a visibility graph analysis of Zhuangli Street in Letai Center, Shijiazhuang, using space syntax theory, assessing the spatial vitality of the retail space by investigating the shop visits. First, a methodology for obtaining data on spatial characteristics and consumer behaviour of shopping streets was developed. Secondly, this article constructs a process for a visibility graph analysis of Zhuangli Street based on space syntax theory. Third, two combination variables of the space coefficient and depth coefficient of shop windows in retail spaces of a commercial street are proposed. Finally, the effect of combination variables and business types on spatial vitality was analyzed using correlation and multiple regression methods, and a space vitality prediction model was proposed. The results showed that the shop with the highest shop visits of retail spaces in the shopping street is 13.55 times higher than the smallest shop. The space coefficient of the shop window, depth coefficient of the shop window, and space connectivity of retail spaces in commercial streets have positive effects on space vitality. The workflow proposed in this paper can provide technical support for retail space design in commercial streets as well as evaluating and optimizing commercial street space design solutions. Full article

Urban rail transit offers advantages such as high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou’s Shantang Street station. Shantang Street’s high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case, making it a representative subway station. Wavelet denoising and Long Short Term Memory (LSTM) were combined to predict short-term flows, comparing the results to those of standalone LSTM, Support Vector Regression (SVR), Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average Model (ARIMA). This study illustrates that the algorithms adopted exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance. The research findings can provide fundamental recommendations for implementing appropriate passenger flow control measures at stations and offer effective references for predicting passenger flow and mitigating traffic pressure in various cities. Full article

Recent literature has highlighted the critical issue of urban land value and cost; properly assessing land use costs, particularly for residential and commercial purposes, is crucial in influencing urban development and investments. Therefore, the objective of this research is to create a model for land pricing that considers the urban street networks and hierarchy; by analyzing the spatial plan of the city using space syntax and evaluating the economic impact on land value, the study aims to identify the factors that influence land prices. Furthermore, the study intends to investigate the correlation between urban spatial networks, street hierarchy, and land price to create a predictive model for urban spatial land pricing. Ultimately, the study has successfully built a model for predicting the price of urban land. The case selected is evaluated and compared in three aspects of the analysis, including the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The study found that land prices are influenced by factors such as integration, connectivity, and street width. Commercial zones with good integration and wider roads tend to command higher prices, while narrow local roads generally have lower prices. This result can enhance future urban design regarding urban economy improvements and land costs. Full article

Traffic flow characterizes vitality in commercial clusters, and the accurate prediction of traffic flow based on the street network has significant implications for street planning and vitality regulation in commercial clusters. However, existing studies are limited by certain problems, such as difficulty in obtaining traffic flow data and carrying out technical methods. The purpose of this study is to use urban physical data to study traffic flow so as to quickly and effectively estimate the traffic flow in commercial clusters. This study takes the street networks of 100 commercial clusters in China as the research objects and classifies them into three forms according to the theory of “A city is not a tree”. Taking typical commercial clusters in these three forms as the research unit, space syntax was used to study five metrics of street network connectivity, and integration (Dn) was selected as a proxy variable for street network connectivity. The results show that the traffic flow in the three forms of commercial clusters can be predicted using the multiple regression models established based on the three metrics of integration, the traffic level, and the operation cycle. This study establishes the connection between the street network form and the traffic flow, which enables the possibility of obtaining the traffic flow of commercial clusters quickly and effectively. For areas with poorly structured urban data, the results can help urban planning administrators to predict the potential economic attributes using easily accessible street network data in commercial clusters. Full article

The definition of pedestrian behavior when crossing the street and facing potential collision situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in commercial vehicles. To this end, this article proposes the generation of classification models through the deployment of machine learning techniques that can predict whether there will be a collision depending on the type of reaction, the lane where it occurs, the visual acuity the level of attention, and consider the most relevant factors that determine the cognitive and movement characteristics of pedestrians. Thereby, the inclusion of this type of model in the decision-making algorithm of the AEB system allows for modulating its response. For this purpose, relevant information on pedestrian behavior is obtained through experiments made in an ad-hoc, Virtual Reality (VR) environment, using a portable backpack system in three urban scenarios with different characteristics. Database generation, feature selection, and k-fold cross-validation generate the inputs to the supervised learning models. A subsequent analysis of the accuracy, optimization, error measurement, variable importance, and classification capability is conducted. The tree-based models provide more balanced results for the performance metrics (with higher accuracy for the single decision tree case) and are more easily interpretable and adaptable to the algorithm. From them it is deduced the high importance of the reaction type and the relative position where it occurs, coinciding with the high significance of these factors in the analyzed collisions. Full article

This study conducts a chronological analysis of six mixed-use commercial complexes in the Seoul metropolitan area and examines their planning characteristics and patterns of change. The analysis reveals the following changes. The spatial composition of these complexes is shifting away from large anchor type commercial facilities to small local commercial facilities. Their circulations and arrangement are shifting to consideration for non-consumption tendencies, and circular and three-dimensional connections between each space are emphasized. Central spaces are shifting from a large single center to small multi-centers, and the utilization of central spaces for events and performances is increasing. Concepts that stimulate visitors’ interest and non-daily experiences are being expanded, which include the use of new themes, such as natural motifs, and the reproduction of classical streets in the space, corridors, colors, and material planning. Based on their changing patterns, this study predicted such complexes’ direction of change. First, they will expand their role as the center of the local community. Second, they will bolster their linkage with local streets and expand the street-type circulation plan. Third, small multi-center spaces and themed external spaces will increase. Fourth, non-consumption and non-daily planning elements will increase. Full article

How to create a sustainable urban morphology for the development of cities has been an enduring question in urban research. Therefore, quantitatively measuring the current relationship between urban morphology and urban function distribution is the key step before urban planning practice. However, existing studies only examine the relationship at limited scales or with a single unit. To comprehensively understand the relationship between urban morphology and commercial distribution, this study utilized space syntax and point of interest (POI) data (shopping and food service) and took the city of Xi’an, China as a case study. The evaluation of relationships was performed with two measurement units (500 m × 500 m grids and street blocks) at 16 different scales (from R = 800 m to R = n) by engaging three statistical metrics (mean, maximum, and total). Great variations in the relationships between urban morphology and commercial distribution across scales were observed in the study area at both grid level and block level. However, the change trends of the correlation across scales differ substantially when measured by grids and blocks. Generally, the correlations measured by blocks were stronger than those measured by grids, indicating it is desirable to perform such research at the block level. The correlations were stronger at the small scales (R = 800 m to R = 3600 m) when measured with grids, and the stronger correlations were detected at large scales (R = 5 km to R = 35 km) when measured with blocks. The strongest correlations were found at the scale R = 3600 m with grid unit, and the strongest correlations were detected at the scale R = 10 km with blocks. Among the three space syntax variables, urban morphology measured by integration presents stronger correlation with commercial distribution than choice and complex variable for both shopping and food services. This reveals that the centrality of urban space has a greater impact on the locations of commercial establishments than accessibility and comprehensive potential. As for the three statistical metrics, the total is less useful in measuring the impacts of urban morphology on commercial distribution across scales. However, regardless of measurement by grids or by blocks, urban morphology has a stronger impact on the locations of shopping businesses than on food shops. Based on our findings, it is preferable to predict the potential commerce locations by measuring the centrality of the study area at a scale of 10–20 km. Our method can be easily transferred to other urban regions, and the derived results can serve as a valuable reference for government administrators or urban planners in allocating new commerce establishments. Full article

In this study, an off-grid–type small wind turbine for street lighting was designed and analyzed. Its performance was predicted using a computational fluid dynamics model. The proposed wind turbine has two blades with a radius of 0.29 m and a height of 1.30 m. Ansys Fluent, a commercial computational fluid dynamics solver, was used to predict the performance, and the k-omega SST model was used as the turbulence model. The simulation result revealed a tip-speed ratio of 0.54 with a maximum power coefficient, or an aerodynamic rotor efficiency of 0.17. A wind turbine was installed at a measurement site to validate the simulation, and a performance test was used to measure the power production. To compare the simulation results obtained from the CFD simulation with the measured electrical power performance, the efficiencies of the generator and the controller were measured using a motor-generator testbed. Also, the control strategy of the controller was found from the field test and applied to the simulation results. Comparing the results of the numerical simulation with the experiment, the maximum power-production error at the same wind speed was found to be 4.32%. Full article