Urban Crowd Research Articles

The logistical governance of vertical commuting in the central business district

The centre of major cities is a focus of commuting patterns, and this article sets out how major cities use calculative practices to guide commuters through a complex, multiplanar, volumetric city. It examines how public transport officials, consultants, city planners and property developers interact to move commuters through inter-locking public and private spaces on a journey between underground, surface and high-rise commercial structures. Using a case study of Sydney’s central business district, it presents three areas where the governance of this movement can be observed. First, it considers how underground rail planning has adopted new modes of organising capacity, especially in terms of the use of behavioural psychology in organising platform and escalator crowd behaviour. Second, the article discusses navigation at ground level, where rail commuters emerge onto the pavement to continue their journey. Urban planners, along with specialists in wayfinding and people movement, calculate capacity and make behavioural interventions to influence movement up, down and across surfaces. Third, it explores the relationship between elevator technology, vertical people flow analysis and the floorplate design of offices. The article’s contribution is in its conceptual and empirical illustration of how the rhythms of urban crowds are tracked, calculated and structured by a range of experts; in turn, we can see how these experts have emerged as significant agents in maximising the ability to extract value from the built volume of cities.

Crowdsourced data as a strategic approach to include the human dimension in outdoor environmental quality assessments

Outdoor environments extend living spaces as venues for various activities. Comfortable open public spaces can positively impact citizens' health and well-being, thereby improving the livability and resilience of cities. Considering the visitors' perception of these environments in comfort studies is crucial for ensuring their well-being and promoting the use of these spaces. However, traditional survey methods may be time- and resource-consuming to gather significant sample sizes, usually focusing on selected homogeneous samples. Crowdsourced data, then, has emerged as an alternative for assessing human perception, as it eases the collection of subjective feedback and potentially amplifies impact and inclusivity. This study presents a strategic approach for analyzing publicly available and willingly reported crowdsourced data from a digital mapping platform in outdoor comfort evaluations, aiming to verify whether these data are informative regarding environmental quality perception and to identify the environmental factors that people are most sensitive to. Urban parks located in New York City served as a case study. A multi-source, interdisciplinary information framework combined crowdsourced reviews with environmental data used to determine prevailing thermal conditions. Overall perception of parks was well-rated, revealing that their attractions and activities are probably the most appealing characteristics for park attendance. Regarding environmental perception, acoustic and thermal factors are clearly the most influential. Acoustics were well-rated, while the main aspect regarding the thermal domain is the recognition of shading as a mitigator for hot conditions. Environmental data provided complementary insights, particularly concerning the range of thermal sensations experienced in urban parks. The findings confirm that willingly reported crowdsourced data can provide valuable insights into urban crowd environmental perception, presenting a potentially suitable and effective method to include the human perspective in environmental quality assessments, as well as to evaluate and predict environmental-related risks.

ARFGCN: Adaptive Receptive Field Graph Convolutional Network for Urban Crowd Flow Prediction

Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fixed receptive fields, failing to account for urban region heterogeneity where different functional zones interact distinctly with their surroundings. Secondly, they lack mechanisms to adaptively adjust spatial receptive fields based on temporal dynamics, which limits prediction performance. To address these limitations, we propose an Adaptive Receptive Field Graph Convolutional Network (ARFGCN) for urban crowd flow prediction. ARFGCN allows each region to independently determine its receptive field size, adaptively adjusted and learned in an end-to-end manner during training, enhancing model prediction performance. It comprises a time-aware adaptive receptive field (TARF) gating mechanism, a stacked 3DGCN, and a prediction layer. The TARF aims to leverage gating in neural networks to adapt receptive fields based on temporal dynamics, enabling the predictive network to adapt to urban regional heterogeneity. The TARF can be easily integrated into the stacked 3DGCN, enhancing the prediction. Experimental results demonstrate ARFGCN’s effectiveness compared to other methods.

SHIP: A State-Aware Hybrid Incentive Program for Urban Crowd Sensing With for-Hire Vehicles

SHIP: A State-Aware Hybrid Incentive Program for Urban Crowd Sensing With for-Hire Vehicles

Research on spatiotemporal characteristics of urban crowd gathering based on Baidu heatmap

With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic dynamics congestion. In order to solve the problem that it is difficult to determine the dynamics of traffic congestion areas caused by uneven distribution of vitality in different regions of mountainous cities, a crowded mega mountainous city is selected as research object and it proposes a model to calculate the change characteristics of regional crowd gathering. Baidu Heatmap is used as it could distinguish crowd gathering in certain urban core area. The heat map pictures in dozens of consecutive days is extracted and researchers conducted pixel statistical classification on thermal map images. Based on the pixel data of different levels of the pictures, the calculation model is established and an algorithm based on particle swarm optimization is proposed. The calibration of the relative active population equivalent density is conducted, and the distribution characteristics of crowd gathering in time and space are analyzed. The results show that there are obvious spatiotemporal characteristics for this selected city. In time, holidays have an important impact on crowd gathering. The peak time of crowd gathering on weekdays is different from that on rest days. The research in this paper has a direct practical value for the identification of traffic congestion areas and the corresponding governance measures. The dynamic identification of population gathering areas in mountainous mega cities, demand prediction for various transportation regions, and future population OD(Origin—Destination) planning are of great significance.

Escaping the Crowds: The Harmonious Environments of Sociability in Eighteenth-Century Writings

This article looks at the eighteenth-century ways of thinking about nature in connection with sociability and retirement by juxtaposing some of the poetry of the period with the discourses concerned with gardens and the aesthetics of garden design. In the writings of Anthony Ashley Cooper, 3rd Earl of Shaftesbury, Joseph Addison, William Gilpin, Elizabeth Montagu, and William Shenstone, the interest in the value of retirement as a much-needed respite from polite society is accompanied with some concern about the dangers of inactivity and idleness. Nature is thus supposed to offer an appropriately shaded, harmonious environment that provides spiritual reinvigoration and cultivation of taste, away from the tumult of urban crowds. In the poetry of Thomas Gray and William Blake, in turn, one may notice the evocation of nature as a more disturbing force, which, rather than merely confirming to the taste for harmony, seems to challenge some of the premises of polite culture. Paying attention to aesthetic thought, cultural history and poetic discourse, the article provides some insights into the complexity of the eighteenth-century aesthetics of nature.

MG-ASTN: Multigraph Framework With Attentive Spatial–Temporal Networks for Crowd Mobility Prediction

Predicting urban crowd patterns/flows is a challenging task due to complex spatial-temporal (ST) dependencies. In this paper, we aim to examine and report the capability and effectiveness of the current most widely used Graph Convolutional Networks (GCNs) on mobile data analysis in ST networks for crowd flow forecasting. Specifically, we propose a novel dual-stream framework leveraging Multi-Graph with Attentive Spatial-Temporal Networks (MG-ASTN) to simultaneously predict crowd in-out flow and OD flow based on the trajectory data collected by on-board devices (e.g., GPS). MG-ASTN utilizes Multi-Graph Convolutional Networks encoding non-Euclidean correlations to explore pair-wise relationships among regions. In addition, we further apply a cross-channel attention mechanism with 3D Temporal Convolutional Network to address the heterogeneity of ST features and capture more meaningful data representations for multi-task learning. In the evaluation, we conduct experiments based on 2 real-world datasets and verify most well-known state-of-the-art methods for crowd flow prediction. The results demonstrate that MG-ASTN could outperform other solutions -in-out flow prediction with lowest RMSE and MAE, and OD flow prediction beyond others in most cases, thus it has great potential in modeling the complex correlations among regions in ST networks and enabling accurate prediction in urban computing.

Human Resource Dynamics in Urban Crowd Logistics: A Comprehensive Analysis

Human Resource Dynamics in Urban Crowd Logistics: A Comprehensive Analysis

Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation

Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity.

An urban crowd flow model integrating geographic characteristics

Predicting urban crowd flow spatial distributions plays a critical role in optimizing urban public safety and traffic congestion management. The spatial dependency between regions and the temporal dynamics of the local crowd flow are two important features in urban crowd flow prediction. However, few studies considered geographic characteristic in terms of spatial features. To fill this gap, we propose an urban crowd flow prediction model integrating geographic characteristics (FPM-geo). First, three geographic characteristics, proximity, functional similarity, and road network connectivity, are fused by a residual multigraph convolution network to model the spatial dependency relationship. Then, a long short-term memory network is applied as a framework to integrate both the temporal dynamic patterns of local crowd flow and the spatial dependency between regions. A 4-day mobile phone dataset validates the effectiveness of the proposed method by comparing it with several widely used approaches. The result shows that the root mean square error decreases by 15.37% compared with those of the typical models with the prediction interval at the 15-min level. The prediction error increases with the crowd flow size in a local area. Moreover, the error reaches the top of the morning peak and the evening peak and slopes down to the bottom at night.

Review of Virtual Simulation of Crowd Motion for Urban Emergency Management

With increasing urbanization in China, there are more and more urban crowds, bringing new challenges to public safety management. The analysis and modeling of crowd behavior are important aspects of emergency management in smart cities. This paper reviews and summarizes existing research into crowd behavior simulation and analyzes existing simulation data collection methods. Crowd simulation technology is discussed from three aspects: crowd modeling, motion navigation, and emotion-driven crowd animation. The indicators of the simulation models are analyzed from the perspective of model evaluation. Finally, combined with the status quo, the existing research limitations are analyzed, and the direction of further research into crowd simulation behavior is discussed.

Online-Offline Interactive Urban Crowd Flow Prediction Toward IoT-Based Smart City

Urban crowd flow prediction is very challenging for public management and planning in smart city applications. IoT based technologies make urban-scale flow detection and prediction possible. Existing work mostly focuses on spatial and temporal dependence based flow prediction by learning patterns from historical crowd flow data with prior knowledge such as weather, events and location attributes, etc. However, these approaches are not well suited for predictions of instantaneous flow change usually due to social emergency incidents and accidents, which are not with obvious patterns but vital for urban safety. In this article we propose an Online to Offline Interaction based Dilated Casual Convolutional Neural Network framework (O2O-DCNN) to make predictions on urban crowd flow. Both online attention behavior and offline crowd shift factors are considered in our framework, in case to capture the dependence between them and make more accurate predictions especially for instantaneous flow variations. The online and offline features are processed by dilated casual convolutions and then put into CBOW model based full connected network to make interactions. Our framework combines the causality of tempo-spatial related flow time series and semantic-based online attention behavior time series without too deep layers of neural network. The performance evaluations are based on realistic User Detail Record (UDR) dataset of a southern city in China provided by China Unicom. O2O-DCNN is compared with the other related baselines in terms of MASE and MAE. The results show that our framework is with much better accuracy, especially for instantaneous flow variation scenarios.

Multisize Patched Spatial-Temporal Transformer Network for Short- and Long-Term Crowd Flow Prediction

The prediction of urban crowds is crucial not only to traffic management but also to studies on the city-level social phenomena, such as energy consumption, urban growth, city planning, and epidemic prevention. The challenges of accurately predicting crowd flow come from the non-linear spatial-temporal dependence of crowd flow data, periodic laws, such as daily and weekly periodicity, and external factors, such as weather and holidays. It is even more challenging for most existing short-term prediction models to make an accurate long-term prediction. In this paper, we propose a novel patched Transformer-based sequence-to-sequence model, called MultiSize Patched Spatial-Temporal Transformer Network (MSP-STTN), to incorporate rich and unified context modeling via a self-attention mechanism and global memory learning via a cross-attention mechanism for short- and long-term grid-based crowd flow prediction. In particular, a multisize patched spatial-temporal self-attention Transformer is designed to capture cross-space-time and cross-size contextual dependence of crowd data. The same structured cross-attention Transformer is developed to adaptively learn a global memory for long-term prediction in a responding-to-a-query style without error accumulation. In addition, a categorized space-time expectation is proposed as a unified regional encoding with temporal and external factors and is used as a base prediction for stable training. Furthermore, auxiliary tasks are introduced for promoting feature encoding and leveraging feature consistency to assist in the main prediction task. The experimental results reveal that MSP-STTN is competitive with the state of the art for one-step and multi-step short-term prediction within several hours and achieves practical long-term crowd flow prediction within one day on real-world grid-based crowd data sets TaxiBJ, BikeNYC, and CrowdDensityBJ. Our code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xieyulai/MSP-STTN</uri> .

Spatial–Temporal Convolutional Model for Urban Crowd Density Prediction Based on Mobile-Phone Signaling Data

Urban crowd density prediction is essential for transport demand management and public safety monitoring. Existing studies for crowd density prediction only focus on a few transport modes which do not cover all urban crowds. Moreover, most existing models rely on grid-based divisions, which cannot accommodate unevenly distributed populations. This study proposes a novel model for predicting crowd density in urban areas with mobile-phone signaling data (MPSD). MPSD contain diversified travel and activity information that is not limited to specific transport modes and are practical for activity-travel research. We introduce methods for preprocessing MPSD and extracting information on crowd density and activity choice behaviors (such as activity location, activity type, and activity duration). A novel spatial-temporal convolutional model is proposed for urban crowd density prediction. We design an attention-based feature fusion block, enabling the model to comprehensively consider historical crowd density, activity choices, and external factors (such as weather conditions and holidays). With tailored spatial-temporal convolution blocks, our proposed model successfully captures spatial-temporal information related to crowd movement in different scenes. Unlike existing grid-based models, the proposed model can predict crowd density in irregular-shaped regions. Experimental results show that our model outperforms baseline models that do not apply activity information when selecting different prediction offsets, especially for long-term predictions. The correlation between prediction error and the regularity of crowd activities is also analyzed. The subsequent parameter analysis shows that the influence of external factors on urban crowd movement varies according to activity choices and other travel-related factors like commuting distance.

Study on Subjective Evaluation of Acoustic Environment in Urban Open Space Based on "Effective Characteristics".

With the continuous expansion of urban scale with dense population and traffic and the gradual improvement of residents’ requirements for environmental quality, the traditional evaluation method relying on acoustic energy is not enough to reflect the feelings of urban crowds about acoustic environment quality. The acoustic environment quality evaluation method based on human subjective perception has gradually become one of the research focuses in the field of environmental noise control. In recent years, various subjective and objective acoustic characteristic parameters have been introduced into the study of acoustic environment assessment in the global literature. However, the extraction of “effective characteristics” from a large number of physical and psychoacoustic characteristics contained in acoustic signals and the creation of a scientific and efficient subjective evaluation model have always been key technical problems in the field of acoustic environment evaluation. Based on subjective human perceptions, the overall acoustic environment quality evaluation of urban open spaces is studied in this paper. Based on the “effective characteristic” parameters and the subjective characteristic proposed in the previous research, including equivalent continuous A-weighted sound pressure level (LA), the difference between median noise and ambient background noise (L50 − L90), Sharpness (Sh), as well as satisfaction (Sat), the multivariable linear regression algorithm is used to further study the intrinsic correlation between the proposed “effective characteristics” and subjective perception. Then, a satisfaction evaluation model of the acoustic environment based on “effective characteristics” is built in this paper. Furthermore, the soundwalk evaluation experiment and the MATLAB numerical simulation experiment are carried out, which verify that the prediction accuracy of the proposed model is more than 92%, the consistency of satisfaction level is more than 88%, as well as the changes in the values of Sh and L50 − L90 have a significant impact on the satisfaction prediction of the proposed model. It shows that the proposed “effective characteristics” more comprehensively describe the quality level of the regional acoustic environment in urban open space compared with a single LA index, and the proposed acoustic environment satisfaction evaluation model based on “effective characteristics” has significant accuracy superiority and regional applicability.

A Data Envelopment Analysis Evaluation Study of Urban Crowd Sourcing Competitiveness Based on Evidence From 21 Chinese Cities.

In the era of the global village, crowd sourcing as a new model of service outsourcing is increasingly being valued by all walks of life. This study uses the data envelopment analysis (DEA) method to explain the crowd sourcing competitiveness of service outsourcing base cities by using input-output efficiency. The crowd sourcing competitiveness among crowd sourcing base cities is organized and analyzed by collating and analyzing the data of 21 service outsourcing base cities in China from 2016 to 2019. The results show that there is no significant difference in the competitiveness of 21 service outsourcing, the overall trend is that the competitiveness of crowd sourcing is not strong, the match between input and output is not high. Comparatively speaking, Beijing, Shanghai, Nanjing, Hangzhou, Suzhou and Wuxi are more competitive in crowd sourcing. Combining with the reality of China's economic development, taking the road of crowd sourcing with Chinese characteristics is a good choice for the development of China's crowd sourcing industry.

Sexual assault on public transport: crowds, nation, and violence in the urban commons

ABSTRACT This article investigates sexual assault on commuter trains in Tokyo to unravel the folk theorizing that passengers engage in to make sense of sexual violence in the urban commons. Through what everyday conceptual work do commutersconstitute sexual violence on transit systems as a persistent aspect of city life? The discussion identifies national cultural discourses of gender and city crowds as key explanatory categories through which commuters diagnose and grapple with sexual violence on mass transit. Scenes of sexual assault on commuter trains in Tokyo bespeak the need for geographical analysis of stranger violence to articulate itself from the intersections between urban forms and manifestations of the nation in cities. By tracing these social relations on-the-move, the article highlights the value of bringing feminist studies of violence into dialogue with a sociology of urban crowds and critical geographies of public transportation. First, a focus on urban form reveals how the social properties of mass transit mediate violent intimacies between strangers in the city. Second, precisely because violence produces social knowledge by discriminating between people, analysis of sexual assault on public transport enables a more precise conceptualization of the links between passengering and urban social inequality.

‘Surging Like the Sea’: Re-Thinking the Spectacle of the Crowd in Early Modern London

Many texts of early modern London are fascinated with crowds. The city’s growth and resulting crowdedness were essential to the city’s theatrical spaces, as the presence of crowds ‘turn[ed] London into a theater’. But even as it is celebrated as a symbol of civic life, evocations of the urban crowd brim with anxiety over its illegibility and unsettling of the very performance space it helps to create. This article re-assesses the London crowd as just such an essential but essentially unstable spatial and rhetorical phenomenon. Working with an array of period texts, this article reads their figuring of the urban crowd specifically in terms of Soja’s socio-spatial theory of Thirdspace and the improvisational power of urban spatial relations.

The use of public houses as a collective representation of the COVID-19 pandemic in Ireland

Pubs have served as a collective representation through which the collective identity of ‘Irish society’ has been articulated during the crisis of the coronavirus disease 2019 (COVID-19) pandemic. As such they offer a case study of how meaning making occurs in contemporary periods of social crisis. While the neoliberal era is widely interpreted as period involving a process of desymbolisation in which meaning giving traditions are undermined, in this period of social crisis long-established and authoritative narratives drawn from collective memory circulated to articulate the meaning of the pandemic for the collective identity in the sense of its nature, character, boundaries, ‘others’, and moral duties and sacrifices that membership implied. Highly stereotyped images of the sacred moral core of the collective as represented by publicans who embodied qualities of age and maturity, rural, cultural-nationalist identity and a post Land War ideal of community-oriented owner-proprieters appeared. Similarly conventional representations of the immoral enemies within, who threaten to morally and literally infect the community, can be seen in representations of venues and drinkers who embody youth, the urban crowd and mixing. The ‘other’ through which identity is articulated against was represented through Britishness, which was shown as the source of the undesirable aspects of modernity.

The crowd and citylife: Materiality, negotiation and inclusivity at Tokyo’s train stations

In the history of urban thought, density has been closely indexed to the idea of citylife. Drawing on commuters’ experiences and perceptions of crowds in and around Tokyo’s Shinjuku Station, this article offers an ethnographic perspective on the relationship between urban crowds and life in the city. We advance understandings of the relations between the crowd and citylife through three categories of ‘crowd relations’– materiality, negotiation and inclusivity – to argue that the multiplicity of meanings which accrue to people’s encounters with crowds refuses any a priori definitions of optimum levels of urban density. Rather, the crowd relations gathered here are evocations of citylife that take us beyond the tendency to represent the crowd as a particular kind of problem, be it alienation, exhaustion or a threshold for ‘good’ and ‘bad’ densities. The portraits of commuter crowds presented capture the various entanglements between human and non-human, embodiment and mobility, and multiculture and the civic, through which citylife emerges as a mode of being with oneself and others.