Domain knowledge is gradually renovating its attributes to exhibit distinct features in autonomy, propelled by the shift of modern transportation systems (TS) toward autonomous TS (ATS) comprising three progressive generations. The knowledge graph (KG) and its corresponding versions can help depict the evolving TS. Given that KG versions exhibit asymmetry primarily due to variations in evolved knowledge, it is imperative to harmonize the evolved knowledge embodied by the entity across disparate KG versions. Hence, this article proposes a siamese-based graph convolutional network (GCN) model, namely SiG , to address unresolved issues of low accuracy, efficiency, and effectiveness in aligning asymmetric KGs. SiG can optimize entity alignment in ATS and support the analysis of future-stage ATS development. Such a goal is attained through (a) generating unified KGs to enhance data quality, (b) defining graph split to facilitate entire-graph computation, (c) enhancing a GCN to extract intrinsic features, and (d) designing a siamese network to train asymmetric KGs. The evaluation results suggest that SiG surpasses other commonly employed models, resulting in average improvements of 23.90% and 37.89% in accuracy and efficiency, respectively. These findings have significant implications for TS evolution analysis and offer a novel perspective for research on complex systems limited by continuously updated knowledge.

With the growing concerns on data security and user privacy, a decentralized mechanism is implemented for federated data mining (FDM), which can bridge data silos and collaborate diverse devices in ubiquitous IoT (Internet of Things) systems and services to extract global and shareable knowledge, i.e., encoded in deep neural networks (DDNs). Moreover, compared with FDM in synchronous mode, asynchronous FDM (AFDM) is more suitable to accommodate devices with diversified computing resources and distinguishable working statuses. However, as AFDM is still in its infancy, how to harness heterogeneous resources and biased knowledge of learning participants within the asynchronous context remains to be addressed. Such that, this paper proposes an adaptive and integrated mechanism, named AiFed, in which, a layer-wise optimization of AFDM is implemented based on the integration of two dedicated strategies, i.e., an adaptive local model uploading strategy (ALMU), and an adaptive global model aggregation strategy (AGMA). As shown by the evaluation results, AiFed can outperform five state-of-the-art methods to reduce communication costs by about 61.76% and 56.88%, improve learning accuracy by about 1.66% and 3.05%, and accelerate learning speed by about 22.16% and 37.81% under IID (independent and identically distributed) and Non-IID settings of four standard datasets, respectively.

The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability.

With the explosion of social networks, people more often share their opinions on-line, which provides a great opportunity to detect the public sentiment of a place in an automatic and timely way comparing to the conventional approaches, e.g., surveys, workshops and interviews. Even through the application of social sentiment analysis is widely discussed in many domains, e.g., politics, e-commerce, economy, and health and environment, to the best of our knowledge, no research has ever studied the effects of public sentiments of social networks in the domain of place design. In order to fill this vacancy, a sentiment analysis service, called geo-sentiment analysis service, is required, whose cores are 1) a social sentiment analysis engine, and 2) an intuitive and interactive visualization service. Thus, this paper firstly proposes CGSA: a Crowd-calibrated Geo-Sentiment Analysis mechanism, which can 1) start the sentiment analysis process based on the design of CTS (Compound Training Samples), and SSF (Social Sentiment Features), 2) perform three analyses, namely sentiment, clustering and time series analysis on geotagged social network messages, and 3) collect crowd-labelled data based on a crowdsourced calibration service to gradually improve the classification accuracy. As proved by two detailed analyses, SSF has the best accuracy in training sentiment classifiers, and the performance of the calibrated classifier increases gradually and significantly from 74.71% to 80.05% in three calibration cycles. Moreover, as a part of a big project “Liveable Places”, “Sentiment in places” service with two visualization modes, namely 2D sentiment dashboard and 3D sentiment map, is implemented to support local authorities, urban designers and city planners better understand the effects of public sentiments regarding place (re)design in the testbed area: Jurong East, Singapore.

As a novel concept, "Informed Design" is being practiced in the Future Cities Laboratory at the Singapore-ETH Centre to innovate place design from empirical to evidential by harnessing geo-referenced "Big Data" for a responsive design. Initially, potentials of people sensing data derived from multi-sources, such as social networks, dedicated applications, sensors, etc., shall be explored to measure place utilization for a better understanding of design contexts and elicitation of design requirements. Therefore, an "Activities in Places" service is required to detect frequently used places, called hot places (HPs), and measure their utilizations in various dimensions. In order to fulfill emerging requirements and properly handle big and heterogeneous geo-located data in a near-real time manner for a responsive design, a unsupervised method, called Scalable "Activities in Places" Analysis Mechanism (SAPAM), is proposed with two main analysis mechanisms, namely 1) a scalable density-based spatial clustering of applications with noise (SDBSCAN), which dramatically improves the performance of DBSCAN through concurrent clusterings on data partitions, 2) a hot place detection procedure (HPDP) to extract HPs from clusters based on a continuous place usage pattern (CPUP), and analyze performed activities through a topic model trained by a corpus of daily documents of places. As proved by a comprehensive evaluation, 1) SDBSCAN, indeed, greatly improves the performance as shown by its best performance 4.71s, which is 11 times faster than DBSCAN, 2) HPDP can precisely detect HPs with a high recall of Singaporean regional centers, main transportation hubs and famous attractions, and 3) the utilization of HPs can be unveiled by three indicators, namely the number of visitors, the size of influence area, and the density of people, and also by performed activities in HPs. As a case study, three top 10 HP lists of three utilization indexes are created, and performed activities in a regional center Jurong East are analyzed.

People sensing data have been successfully utilized in various domains to support a more livable place with on-demand transport system, green environment, profitable economy and interactive governance, however, their potentials in supporting the design of places are not widely studied and explained. As an on-going multidisciplinary project in Singapore, “Livable Places” mins valuable insights from these data through a novel mechanism, called Scalable “Activities in Places” Analysis Mechanism (SAPAM), which conducts three kinds of analyses on their spatial, temporal and textual information respectively to reveal frequently used places, called Hot Places (HP), and to measure their utilization quantitively and qualitatively for a better understanding of design contexts. Accordingly, three analysis mechanisms are designed and implemented, namely 1) a Scalable sPace Clustering Mechanism (SPCM) based on spatial information to cluster geo-referenced data, 2) a Hot Place Detection Mechanism (HPDM) based on temporal information to detect frequently used places, and 3) a Discussing Topic Detection Mechanism (DTDM) based on textual information to explore people's activities in a place. As proved by a comprehensive evaluation, 1) SPCM, which implements a scalable version of DBSCAN, indeed, can dramatically improve the clustering performance from the baseline 53.57s to less than 5s; 2) HPDM can precisely detect HPs with a high recall of Singaporean regional centers, main transportation hubs and famous attractions; and 3) DTDM classifies discussing topics of a given HP with a high precision about 85%. As the project testbed, Jurong East is detailedly investigated, and comparing to other Singaporean regional centers, it is marked as a growing regional center with a prosperous and stable commercial ecosystem.

Crowdsourcing implies user collaboration and engagement, which fosters a renewal of city governance processes. In this article, we address a subset of crowdsourcing, named citizen-sourcing, where citizens interact with authorities collaboratively and actively. Many systems have experimented citizen-sourcing in city governance processes; however, their maturity levels are mixed. In order to focus on the service maturity, we introduce a city service maturity framework that contains five levels of service support and two levels of information integration. As an example, we introduce CITY FEED, which implements citizen-sourcing in city issue management process. In order to support such process, CITY FEED supports all levels of the maturity framework (publishing, transacting, interacting, collaborating, and evaluating) and integrates related information relationally and heterogeneously. In order to integrate heterogeneous information, it implements a threefold feed deduplication mechanism based on the geographic, text semantic, and image similarities of feeds. Currently, CITY FEED is in a pilot stage.