Transactions in Urban Data, Science, and Technology (Volume 1 Issue 1-2, March-June 2022)

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Transactions in Urban Data, Science, and Technology (Volume 1 Issue 1-2, March-June 2022)

Transactions in Urban Data, Science, and Technology is an interdisciplinary, international, peer-reviewed journal. It seeks to publish innovative research in urban analytics on the new science of cities and the science of new cities with a focus on China.  The journal is a space for the analysis of Chinese cities and the study of Chinese urbanization, in local and in comparative context.

It focuses on - but is not limited to - topics like: smart city/infrastructure, future cities driven by disruptive technologies, urban modelling, planning/design support systems, big data and related analytics using emerging technologies, artificial intelligence, the internet of things, wearable devices, and applications in urban studies and planning. Research from the building scale to the international scale is welcomed.


Authors: Ying Long,Jiangping Zhou,Jianghao Wang,Yao Shen,Fan Zhang


Urban Data, Science, and Technology


Title: The shape of future cities: Three speculations

Authors: Michael Batty

Centre for Advanced Spatial Analysis (CASA), University College London (UCL), UK


Abstract: World population has grown dramatically since the Industrial Revolution began 250 years ago. Cities are key elements in this growth, but by 2100, we will all be living in cities of one size or another. Here we speculate what this world will look like. First, the Industrial Revolution represents a clean break from a past composed of hardly any cities to one which is completely dominated by cities. Second, cities will continue to change qualitatively as they get larger but size limits will emerge. Third, cities will no longer be classified by their size but by their internal dynamics.

Title: Rethink on Smart City: Technology Approach, Showcase Approach or Demand Approach?

Authors: Zhiqiang Wu

Tongji University, China


Abstract: The author reveals three current approaches to build Smart Cities, i.e., technology-oriented, showcase-oriented and demand-oriented, and analyzes the causes, advantages, shortcoming and influences of the three approaches. In the conclusion, a composite approach with the demand approach as its core is advocated, which returns to the demands of the city that supports problem-solving, innovation, intelligent governance, economic development and regional synergy.


Title: Mobility as a Service and urban infrastructure: From concept to practice

Authors: Si Qiao, Guan Huang and Anthony Gar

The University of Hong Kong, Hong Kong SAR, China


Abstract: The debate about how Mobility as a Service (MaaS) will revolutionize individual and collective mobility is gaining increasing attention from researchers, industries, and public sectors. MaaS is expected to create a techno-utopia with a new organization and operation of transport systems where residents have equal access to instant and ubiquitous mobility services. However, transport service is an artifact that is highly dependent on the construction of urban infrastructure. The success of MaaS requires the support of urban infrastructure that we categorize as (1) transport-flow infrastructure, (2) information-flow infrastructure, and (3) computing-flow infrastructure. The connotation of urban infrastructure here includes not only conventional concepts, such as transportation infrastructure, but also intelligent transportation concepts, such as high-speed communication networks and autonomous fleets. Moreover, travel behavior data collected by city sensors, communication networks, and intelligent vehicles require appropriate infrastructures to dynamically compute for fleet dispatch and demand-supply match. Based on the degree of integration of these infrastructures, MaaS projects will have different results in varying cities. From concept to practice, given that a vast disparity in infrastructure exists between cities, we need an inclusive, mobile, and global understanding of the MaaS concept to make it successful in different parts of the world.

Original Research Articles

Title: Evaluating spatial statistical and machine learning models in urban dynamic population mapping

Authors: Zhifeng Cheng, Jianghao Wang, Kaixin Zhu, Yong Ge, Chenghu Zhou


Abstract: Understanding population dynamics at fine spatiotemporal granularities are valuable to human-centered studies. With the increasing availability of high-frequency human digital footprint data, the past decades have witnessed numerous efforts in mapping populations at fine spatiotemporal scales. However, such research still lacks a unified standard in modeling strategy and auxiliary data selection, especially a systematic comparison between newly developed machine learning techniques and traditional spatial statistical methods under different covariates provisions. Here, we compared two spatial statistical models, the Bayesian space-time model and geographically and temporally weighted regression, with two machine learning techniques, random forest and eXtreme gradient boosting, in a case study of hourly population mapping at 100 m resolution in Beijing. We evaluated the model performance with varied covariates combinations and found that the Bayesian space-time model achieved the best in conjunction with urban function data. Leveraging the optimal model constructed, we mapped dynamic population distribution and concluded human activity patterns on diverse city amenities. This paper emphasizes the importance of spatiotemporal dependency information in fine temporal scale population mapping and the urban function covariates in urban population mapping.

Title: Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

Authors: Zhixiang Fang, Lupan Zhang, Meng Zheng


Abstract: New stations (such as metro stations) will bring remarkable changes to the local transportation and economic development. Understanding patterns of factors which importantly impact on public transit ridership in the surrounding areas of new stations is essential to their construction planning, like estimating the possible ridership. Built environment variables with high importance magnitude, which were thought applicable to estimate public transit ridership in other areas of the same category, were described as transferable variables (TVs) in this study. A transferability analysis method of the built environment for the ridership estimation was constructed by adopting partial least square regression (PLSR) based on available data. Taking Wuhan, China as an example, this study analyzed the changes and differences of the built environment variables in different categories of pedestrian catchment areas (PCAs) of metro stations on the importance and transferability magnitude for the metro and taxi ridership, based on the metro and taxi data of one week in January, April, and June. Performances of the ridership estimation based on TVs and all the built environment variables were compared. This study inferred that (1) most of the land use variables (about 85%) showed important influence on the metro and taxi ridership, while only about 18% of the other variables showed key impact. The importance magnitude of the built environment variables was mainly related to PCA categories and public transportation modes, but less related to time. (2) Highly important built environment variables also tended to be highly transferable. Transferability magnitude of the built environment variables for the ridership was related to PCA categories and types of public transport. (3) Compared to all the built environment variables, using TVs, the relative accuracy of the metro and taxi ridership estimation was around 20% and 18% higher respectively.

Title: On the feasibility of using canals for waste collection in Amsterdam

Authors: Snoweria Zhang,Fábio Duarte , Xuezhen Guo, Lenna Johnsen , Ruben van de Ketterij , Carlo Ratti


Abstract: Amsterdam, a growing city of over 800,000 people in the Netherlands, is struggling to collect waste. While residents in most districts of the city use underground bins to deposit their garbage, the historic Centrum district continues to rely on curbside collection. As such, the streets around the UNESCO-World-Heritage canals are lined with garbage bags as trash trucks rumble down roads centuries old and ill-fitted for vehicles of such size. This paper assesses the viability of moving Centrum trash collection to the canals with a fleet of tug boats and floating dumpsters. It does so by using a combination of GIS tools and integer programming to determine the quantity and optimal collection locations while ensuring that an average Centrum resident walks no farther than denizens of the other Amsterdam districts. Additionally, it proposes a schedule for emptying floating dumpsters based on one comparable to the current truck system. The results of this paper suggest that mobile trash collection using the canals is a viable solution that could reduce noise, pollution, and congestion, thus improving the quality of Amsterdam’s historic cityscape.

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