Urban Commercial Areas Research Articles

Integrated Parking Spaces Arrangement in Commercial Areas (Case Study: Cirebon City)

The effect of on street parking in urban commercial areas can cause a degradation of road performance. Therefore, the development of an integrated parking system is crucial to be carried out in commercial areas so that the performance of road sections does not degrade and does not cause heavy congestion. This study aims to provide recommendations for integrated parking locations based on existing parking conditions in the study area, so that they can be integrated and combined in one location. The method used is a primary survey that refers to the guidelines of the Indonesian Road Capacity Manual (IRCM) 1997 in analyzing traffic performance, and refer to the Guidelines for the Implementation of Parking Facilities 1997 in analyzing parking conditions. Meanwhile, for recommendations in determining alternative parking locations using SWOT analysis. The result of this research is the proposed location for integrated parking at Pasar Balong with the recommendation of a 4-floor building. The SWOT analysis showed that its strengths are its strategic location and high parking demand, its weaknesses are land limitations and lack of integrated parking management, its opportunities are potential for revenue and increased traffic flow, and its threats are resistance from local businesses and significant investment needs. The implication of this study is to provide a solution to the problem of roadside parking in commercial areas that interfere with traffic flow, improve the efficiency and accessibility of parking facilities for the community, and become a reference for further research on integrated parking management in other commercial areas.

Resilience analysis of urban commercial area under the COVID-19 epidemic using night-time light remote sensing data

ABSTRACT The global outbreak of the novel coronavirus has had a great impact on commercial areas, particularly in Wuhan, the first city in China affected by the epidemic. Exploring the changing pattern of these areas during the epidemic period is crucial for effectively restoring the urban economic level and promoting economic development on the basis of epidemic prevention and control in the post-epidemic era. The paper proposes a resilience analysis of urban commercial areas during the COVID-19 epidemic using time-series night-time light remote sensing data. Based on the constructed time-series night-time light remote sensing dataset and considering the urban impervious surface, the urban commercial area was extracted under multi-scale segmentation. Based on night-time light data before and after the epidemic, the resilience of representative commercial areas in Wuhan City, Hubei Province during the COVID-19 epidemic was analysed using a modified improved resilience assessment framework. The results showed that the lighting values of commercial areas and impervious areas decreased by up to 30% following the implementation of city lockdown measures. The change of light brightness in the commercial areas was more prominent, and the range of change is larger than that in the non-commercial areas. The lighting brightness of the commercial areas exhibited a significant downward trend throughout the entire lockdown period, and the downward trend will still remain for a period of time after unlocking. Among all the commercial areas, Wuguang commercial area demonstrated the highest recovery rate during the lockdown period and instantaneous recovery rate after the lockdown, with relatively low closure loss and the highest level of resilience.

An commercial area extraction approach using time series nighttime light remote sensing data——Take Wuhan city as a case

The degree of nighttime illumination in a region is closely related to its economic development, therefore, urban commercial areas can be located based on the intensity of the nighttime light. Aiming at the problem of discontinuous and incomparable brightness of nighttime remote sensing images in different periods, a method of urban commercial area extraction based on temporal nighttime remote sensing data is proposed. Firstly, multi- nighttime remote sensing image correction was carried out based on the invariant target region method, and stable time series NPP/VIIRS data set was generated. Secondly, the impervious water surface was extracted by three index synthesis method to obtain the candidate urban commercial area using Landsat-8 image. Finally, multi-scale segmentation and unsupervised classification are carried out on the temporal nighttime remote sensing data set of the candidate area to realize the extraction of urban commercial area. Taking Wuhan city as an example, the method in this paper is used to extract and verify the urban commercial area. The effectiveness and accuracy of the method are verified according to the commercial planning map of Wuhan city and previous studies.

The Effects of Residents’ Sense of Place on Their Willingness to Support Urban Renewal: A Case Study of Century-Old East Street Renewal Project in Shaoguan, China

With the improvement of urbanization in China, urban renewal has become the main way to meet the increasing demand of residents for urban space and facilities. In this context, this study takes “sense of place” as the theoretical starting point, takes Shaoguan Century-old East Street as the case, and constructs a relationship model between local residents’ sense of place and their willingness to support urban business district reconstruction from a multiscale perspective. By introducing community concern as a mediating variable, this study uses a structural equation model to analyze the influence of residents’ sense of place, including place attachment and place identity, on their intention to support commercial district reconstruction. The results show that residents’ sense of place is significantly different on the scales of block, community, and urban area, and the difference is only in place attachment, not in place identity. Second, the enhancement of residents’ sense of place can not only directly strengthen their willingness to support the renovation of commercial areas, but indirectly improve their willingness to support the renovation of urban commercial areas by enhancing their attention to the community. The conclusion of this study is of great significance to promote the regeneration of historical and cultural blocks and the process of urban renewal.

Distribution of Typical Pollutants from Rainwater Sewer Sediments in Suzhou City

In this study, we separately collected rainwater sewer sediments from typical samples in Suzhou city, such as the urban commercial district, historical and cultural protection area, cultural and educational area, and living area, and analyzed the particle size distribution of the sediments and the characteristics of carbon, nitrogen, phosphorus content, and pollution load distribution under each graded particle size. The median particle size D50 of each sample point was 16.55-327.50 μm, and the particle size trend was as follows:commercial area > living area > historical and cultural protection area > cultural and educational area. D50 was related to the total organic carbon (TOC). The total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH4+-N) were significantly positively correlated, as were the pollutants. The spatial difference of ω(TOC), ω(TN), ω(TP), and ω(NH4+-N) in rainwater sewer sediments from different regions was as follows:commercial area > historical and cultural protection area > living area > cultural and educational area, in which ω(TOC) was 0.84%-6.76%, and ω(TN), ω(TP), and ω(NH4+-N) were 917.5-12707.1, 196.1-2524.8, and 9.3-156.8 mg·kg-1, respectively. TOC, TP, and NH4+-N pollution loads were mainly concentrated on particles ≤ 75 μm and 250-1000 μm. Street dust pollutants highly differed spatially, with a high content of attached pollutants on street dust particles with a particle size of ≤ 75 μm. Various pollutants migrated into the street dust-pipes, and TP and TN showed certain enrichment characteristics in the sewer. Controlling the transportation of street dust and the accumulation of sediments in the sewer can reduce the pollution of sediment into the rivers during the rainy season.

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas

The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R2 of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model.

A spatial-temporal approach for traffic status analysis and prediction based on Bi-LSTM structure

Urban traffic control has become a big issue to help traffic management in recent years. With data explosion, Intelligent Transportation System (ITS) is developing rapidly. ITS is an advanced data-based method for traffic control, which requires timely and effective information supply. This research aims at providing real-time and accurate traffic flow data by intelligent prediction method. Applying multiple road traffic flow data of the Caltrans Performance Measurement System (PeMS) and separating the time series, the mechanism of spatial-temporal differences was taken into consideration. Based on the basic Long Short-Term Memory (LSTM) model, an improved LSTM model with Dropout and Bi-structure (Bi-LSTM) for traffic flow prediction was presented. In the prediction process, we applied three models including the improved Bi-LSTM model, Gated Recurrent Unit (GRU) model and Linear Regression in the experiment, and made a comparison from aspects of model structure complexity, operating efficiency and prediction accuracy. To validate the portability of the prediction model, the features of traffic flow from different datasets were further analyzed. The experimental results show that the improved Bi-LSTM model performs best in traffic flow prediction with comprehensive rationality, which reaches an accuracy of about 92% when considering temporal differences. Particularly, the specific factors of traffic situations and locations which is more applicable to be predicted by the improved Bi-LSTM model are summarized considering spatial differences. This research proposes an advanced and accurate model to provide real-time and short-term traffic flow prediction data, which is of great help to intelligent traffic control. Considering the mechanism between model and road traffic properties, the results suggest that it is more applicable in urban commercial area.

An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery

Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.

Air pollution tolerance, metal accumulation and dust capturing capacity of common tropical trees in commercial and industrial sites

Total nine tree species of common tropical trees were assessed for their air pollution tolerance, dust capturing capacity and possibility to act as metal bio-monitors in commercial, industrial and control sites. Two seasons sampling was done respectively in monsoon and post-monsoon, to study their seasonal variations. According to Air Pollution Tolerance Index (APTI) values Mangifera indica, Azadirachta indica and Ficus religiosa were the most tolerant species while Ficus bengalensis and Alstonia scholaris were intermediately tolerant towards air pollution. Single factor ANOVA of biochemical parameters between the sampling sites, revealed that APTI of plants did not vary significantly in both the seasons. Site-wise variation was significant both at commercial and industrial sites. The same trend of result was also found in Anticipated Performance Index (API) which also includes other social benefits. So, these species can be recommended for the green belt development in urban commercial and industrial areas. Ficus bengalensis, Ficus religiosa and Mangifera indica were also having the best dust capturing capacity as Scanning Electron Microscope image revealed that they have rough and large surface area of leaf with short petiole and large canopy structure. According to the Geoaccumulation index (Igeo) and Enrichment Factor (EF) of soil in the sampling sites were found to be contaminated with copper and cadmium. Psidium guajava, Mangifera indica, Alstonia scholaris and Ficus religiosa were found to be good phytoextractors of copper. They did not accumulate cadmium. So, these plants can be recommended as copper bioindicators. However, the metals accumulation efficiencies were high for Mangifera indica and Ficus religiosa.

Using Space Syntax and Information Visualization for Spatial Behavior Analysis and Simulation

This study used space syntax to discuss user movement dynamics and crowded hot spots in a commercial area. Moreover, it developed personas according to its onsite observations, visualized user movement data, and performed a deep-learning simulation using the generative adversarial network (GAN) to simulate user movement in an urban commercial area as well as the influences such move might engender. From a pedestrian perspective, this study examined the crowd behavior in a commercial area, conducted an onsite observation of people’s spatial behaviors, and simulated user movement through data-science-driven approaches. Through the analysis process, we determined the spatial differences among various roads and districts in the commercial area, and according to the user movement simulation, we identified key factors that influence pedestrian spatial behaviors and pedestrian accessibility. Moreover, we used the deformed wheel theory to investigate the spatial structure of the commercial area and the synergetic relationship between the space and pedestrians; deformed wheel theory presents the user flow differences in various places and the complexity of road distribution, thereby enabling relevant parties to develop design plans that integrate space and service provision in commercial areas. This research contributes to the interdisciplinary study of spatial behavior analysis and simulation with machine learning applications.

Business Improvement Districts as Governance for Urban Regeneration -Policy Lessons from Experiences of the U.S. and the U.K.-

The purpose of this study is to analyze the Business Improvement Districts(BIDs), which have been widely adopted by cities in the U.S. and other developed countries as governance for regeneration of urban commercial districts. Cases of the BIDs in the U.S. cities including the City of New York have inspired researchers, policymakers and urban planning practitioners. From 1990s, the BIDs were accepted as as alternative policy measure for revitalization of urban commercial areas. The literature generally defines BIDs as pubic-private partnerships or governance system which is to manage urban commercial districts by stakeholders including tenants, merchants and local government. The focus of management is the improvement of commercial environment and amenities in the district. The main source of financial resource is the fees from stakeholders. This study is to analyze the Long Island Partnership BID in the City of New York, and the Better Bankside BID in the Metropolitan London under each nation s legal institution. In the recent years, the Korean government and legislature have been in the process of constructing new statutes for BID-like districts. From the case and institutional studies of the U.S. and the U.K., this study suggests the following recommendations: To introduce BIDs in Korea, a new statute is inevitable; the stakeholders like tenants and merchants should bear, at least a part of, the financial burden; legal arrangements should be introduced to ensure accountability of the BID management; appripriate authorities and authonomy should be endowed in managing BIDs; and the government should constitute education and training system for managers of BIDs.

Extraction and monitoring approach of dynamic urban commercial area using check-in data from Weibo

Urban hot spot detection and commercial area analysis for city economic planning and dynamic urban planning is of vital importance. However, it is difficult to obtain a more accurate commercial area boundary in the past. Check-in data obtained by a social networking service (SNS) and/or a location-based service (LBS) is a type of crowd-sourced geographic data that can reveal mass daily life activities, which provides a new big data source for urban hot spot detection and commercial area analysis. In this paper, a dynamic urban commercial area extraction and monitoring approach is proposed using SINA Weibo (a social network) check-in data. First, a check-in data pre-process model is proposed to simplify the amount of check-in data and improve the efficiency of cluster analysis. The spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. Then, an exploratory spatial analysis and hot spot clustering method based on check-in data is proposed to detect urban hot spots and extract commercial areas using a geographic distribution metric with urban commercial hot spots. Second, the hot spot cluster analysis results are taken to determine the center of the commercial area and calculate the distribution of an ellipse, which is adopted to obtain the rough boundary of the commercial area. A planar Delaunay iterative triangulation algorithm is presented to determine the exact boundary of the commercial area. Then, the time sequence extraction result of the commercial area is presented to analyze the evolutionary trend in the city business space. Finally, the Weibo check-in data from 2012 to 2014 of Wuhan city are taken as an example dataset for the commercial area extraction and detection with the proposed approach. The results show that the method can accurately determine the boundary of and changes within the commercial area in Wuhan city. This study provides a new method for the monitoring of hot spots and the geographical situations of city commercial areas.

An investigation of pedestrian traffic flow in a representative commercial area in China

The large number of people that can congregate in urban commercial areas poses a potential risk for pedestrian safety and is an important safety issue for urban administrators. This research describes our investigation of a crowded commercial area to reveal pedestrian traffic flow by counting pedestrians in all transportation nodes over a 14-h period. Some interesting findings were revealed from the on-site investigations: (i) the counting of pedestrian numbers via traffic flow is not exact because the destinations of pedestrians are not fixed, and attention should be paid to the key roads between subway exits and commercial areas; (ii) the proportion of the number of pedestrians between different bus stations is stable because bus numbers at every station are not variable and (iii) the proportion of the number of pedestrians between different subway exits is unstable because subway exits are linked and pedestrians from one station can leave via another exit. These findings serve to assist administrators in on-site crowd control and riot control.HighlightsThe proportion of the number of people between different bus stations is stable.The proportion of the number of people between different subway exits is unstable.Counting pedestrian numbers via transportation modes is not exact.

The Potential of Metering Roundabouts: Influence in Transportation Externalities

Roundabouts are increasingly being used on busy arterial streets for traffic calming purposes. However, if one roundabout leg is near a distribution hub, for example, parking areas of shopping centers, the entry traffic volumes will be particularly high in peak hours. This paper investigated a partial-metering-based strategy to reduce traffic-related costs in a corridor. Specifically, the resulting traffic performance, energy, environmental, and exposure impacts associated with access roundabouts were studied in an urban commercial area, namely: (a) to characterize corridor operations in terms of link-specific travel time, fuel consumption, carbon dioxide and nitrogen oxides emissions, and noise costs; (b) to propose an optimization model to minimize these outputs; and (c) to demonstrate the model applicability under different traffic demand and directional splits combinations. Traffic, noise, and vehicle dynamics data were collected from a corridor with roundabouts and signalized intersections near a commercial area of Guimarães, Portugal. Microscopic traffic and emission modeling platforms were used to model traffic operations and estimate pollutant emissions, respectively. Traffic noise was estimated with a semi-dynamical model. Link-based cost functions were developed based on the integrated modeling structure. Lastly, a sequential quadratic programming-type approach was applied to find optimal timing settings. The benefit of the partial-metering system, in terms of costs, could be up to 13% with observed traffic volumes. The efficiency of the proposed system increased as entering traffic at the metered approaches increased (~7% less costs). The findings enable quantification of metering benefits near shopping areas.

Rainfall-runoff risk characteristics of urban function zones in Beijing using the SCS-CN model

Urbanization significantly increases the risk of urban flooding. Therefore, quantitative study of urban rainfall-runoff processes can provide a scientific basis for urban planning and management. In this paper, the built-up region within the Fifth Ring Road of Beijing was selected as the study area. The details of land cover and urban function zones (UFZs) were identified using GIS and RS methods. On this basis, the SCS-CN model was adopted to analyze the rainfall-runoff risk characteristics of the study area. The results showed that: (1) UFZs within different levels of runoff risk varied under different rainfall conditions. The area ratio of the UFZs with high runoff risk increased from 18.90% (for rainfall return period of 1a) to 54.74% (for period of 100a). Specifically, urban commercial areas tended to have the highest runoff risk, while urban greening spaces had the lowest. (2) The spatial characteristics of the runoff risks showed an obvious circular distribution. Spatial cluster areas with high runoff risk were mainly concentrated in the center of the study area, while those with low runoff risk were mainly distributed between the fourth and fifth ring roads. The results indicated that the spatial clustering characteristic of urban runoff risk and runoff heterogeneity among different UFZs should be fully considered during urban rainwater management.

신호교차로 좌회전 효율적 처리를 위한 유턴차로 설치방법 연구

Until now, u-turn lane installation methods have been studied variously. But, There is no specific standard yet. This study ranges are commercial area in Incheon metropolitan city through field investigation and presents specific design standard for efficient operation of left turn using a field data through calculating relevant permitted u-turn lane length and minimum separation distance from the front intersection to starting point of permitted u-turn lane in urban signalized intersections in commercial area. Relevant permitted u-turn lane length is found to be 32m and minimum separation distances from the front intersection to starting point of permitted u-turn lanes are 72m, 40m, 24m in case of 1 left turn lane, 2 left turn lanes and 3 left turn lanes respectively. By comparing result values and field data, they had a large difference under the similar situations in their lengths. This result is caused of no specific standard about design of u-turn lanes. If results of this study applied to design of u-turn lanes, signalized intersections in urban commercial areas would be operated more safety and efficiently.

지방자치단체의 유형별 보행자사고 특성분석 및 보행환경조성사업 개선방안 연구

본 연구는 비계층적 K-평균 군집분석을 이용하여 보행자사고와 토지이용특성에 따른 지방자치단체의 유형을 분류하고 유형별 보행자사고의 세부특성을 Mann-Whitney U검증과 Kruskal-Wallis 검증을 이용하여 분석한 다음 그 결과로부터 보행환경 조성사업에 대한 개선방안을 제안하였다. 지역은 행정시와 행정구를 제외한 기초지방자치단체를 기준으로 230개 시 군 구로 구분된다. 비계층적 K-평균 군집분석 결과 230개 지방자치단체는 유형 1-A(상업지가 상대적으로 우세한 도시형), 유형 1-B(주거지가 우세한 도시형), 유형 2(농촌형)로 분류되었으며, 지자체 유형별 보행자사고 차이 검증결과 9m 미만 생활형 도로의 보행자사고 발생율은 공통적으로 60% 이상으로 도시형에 비해 농촌형에서 발생할 비율이 더 높았다. 또한 유형별로 유형 1-B는 교차로 내 보행자사고 사상자수가 많고 횡단 중 사고 비율도 높은 반면 길가장자리구역 통행 중 보행자 사상자수 비율은 유형 2에 해당하는 농촌형이 가장 높았다. In this study, nonhierarchical K-mean cluster analysis is used to classify the types of 230 local governments and the Mann-Whitney U test and Kruskal-Wallis analysis are used to analyze the characteristics of pedestrian accidents by region types. With empirical analysis of pedestrian accidents, this study suggests improvements of walking environments reflecting local characteristics. Type 1-A (relatively dominant urban commercial areas), Type 1-B (predominantly urban residence) and Type 2 (rural areas) have been classified using nonhierarchical K-mean cluster analysis. According to the results, pedestrian accident rate on community roads was more than 60% for all types and incidence rate in rural areas was higher than that in urban areas. In addition, pedestrian accidents of Type 1-B have been found to occur more frequently than Type 2 in intersections and crossings, while the number of roadside casualties for Type 2 was highest.

A Study on Installation of Near-side Bus Stops for Efficient Operation of Left Turn at Signal Intersections in Urban Commercial Areas

A Study on Installation of Near-side Bus Stops for Efficient Operation of Left Turn at Signal Intersections in Urban Commercial Areas

An Empirical Research Study on Korean and Japanese Collegians’ Use of Urban Street Tours ‒A Basic Study to Compose Tourist Maps of Urban Central Commercial Areas‒

An Empirical Research Study on Korean and Japanese Collegians’ Use of Urban Street Tours ‒A Basic Study to Compose Tourist Maps of Urban Central Commercial Areas‒

Community Inhabitants’ Attitudes on the Partitioning of Urban Space Derived from South-Eastern Asian Migrant Workers’ Gathering in Urban Commercial Area—A Case Study on Tainan City, Kaohsiung City and Taichung City in Taiwan

This study is to explore south-eastern Asian migrant workers’ impacts and their influential factors of gathering/consumption activities on divided cities in Tainan City, Kaohsiung City and Taichung City. We will take on an empirical study in the cities’ commercial area or shopping street in three cities. Three major research methods are applied, including questionnaire survey, participant observation and regression analysis. Data will be obtained from the questionnaire answers of local inhabitants and analysed via SPSS10.3 statistical methods. The main outcomes of this study are as follows: 1) In Tainan City, Kaohsiung City and Taichung City, we have found that the partitioning of urban space has begun to become. Among this, the outcome of Taichung City is much stronger than that of the two other cities. 2) In the impacts of migrant workers’ gathering on commercial streets, community inhabitants also have more tolerant attitude than shop keepers. The community inhabitants of Taichung City have more hostile than those of the two other cities. 3) In comparison of South-eastern Asian migrant workers’ gathering in urban commercial area and its relations between the partitioning of urban space, from the viewpoint of local inhabitants, we have found different influential factors among Tainan City, Kaohsiung City and Taichung City. We can say that these factors will be valid predictable variables on divided cities resulted from migrant workers’ gathering/consumption activities in the paper. In the meantime, we can obtain these influential factors among these three cities through two variables: 1) community inhabitants’ socio-economic attributes and 2) community inhabitants’ responses to migrant workers’ gathering/consumption activities in commercial area. Basically we can find these influential factors among the paper which are the same as the Netherlands and Singapore, and not the same as Guangzhou, China.