Sustainable cities are currently undergoing unprecedented transformative changes in light of the recent paradigm shift in science and technology brought on by big data science and analytics These marked changes are motivated by the increased need to tackle the problematicity surrounding sustainable cities as quintessential complex systems in terms of their development planning, operational management, and fragmentary design strategies and technology solutions That is to say, sustainable cities are increasingly embracing and leveraging what smart cities have to offer in terms of big data technologies and their novel applications in an attempt to effectively deal with the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities” This new area is a significant gap in and of itself—as it is still in its infancy—that this paper seeks to fill together with to what extent the integration of sustainable urbanism and smart urbanism is addressed and what directions and forms it takes Using a compelling evidence synthesis approach, this paper provides a comprehensive state-of-the-art literature review of the flourishing field of data-driven smart sustainable cities This study corroborates that big data technologies will change sustainable urbanism in fundamental and irreversible ways, bringing new and innovative ways of monitoring, understanding, analyzing, planning, and managing sustainable cities It reveals that the evolving development planning approaches and operational management mechanisms enabled by data-driven technologies are of crucial importance to increase and maintain the contribution of sustainable cities to the goals of sustainability in the face of urbanization However, what smart urbanism entails and the way it functions raises several critical questions, including whether the policy and governance of data-driven smart sustainable cities of the future will become too technocentric and technocratic respectively, but also with regard to other aspects of social and environmental sustainability Addressing these important contemporary concerns is of equal importance in achieving the desired outcomes of sustainability This review and critique of the existing work on the prevailing and emerging paradigms of urbanism provides a valuable reference for scholars and practitioners and the necessary material to inform them of the latest developments in the burgeoning field of data-driven smart sustainable cities In addition, by shedding light on the increasing adoption and uptake of big data technologies in sustainable urbanism, this study seeks to help policymakers and planners assess the pros and cons of smart urbanism when effectuating sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on the topic

Data-driven smart sustainable cities of the future: An evidence synthesis approach to a comprehensive state-of-the-art literature review

Accurate estimation of the state of charge (SOC) is critical for the normal use of lithium-ion battery equipment like electric vehicles. However, the SOC of lithium-ion battery is not available by direct measure, but can only indirectly be estimated by measurable variables. According to the nonlinear characteristics between the measured values and SOC during the working period of lithium-ion batteries, we propose a method to estimate the SOC of lithium-ion batteries with Temporal Convolutional Network (TCN). The measured values of voltage, current, and temperature during the use of lithium-ion batteries can be directly mapped to accurate SOC in this method without using a battery model or adaptive filter. The network can self-learning and update parameters by being fed datasets collected under various working conditions and then obtain a model that can correctly estimate SOC under different estimation conditions. In addition, it can also be applied to different types of lithium-ion batteries through transfer learning with only a small amount of battery data. At various ambient temperature conditions, the average MAE estimated by the proposed method is 0.67% for all the tests, which proves that the TCN network is an effective tool to estimate the SOC of lithium-ion batteries.

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State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

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Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning


 Accurately estimating the state of charge (SOC) of lithium-ion batteries is critical for developing more reliable and efficient electric vehicles. However, the commonly used models cannot simultaneously extract effective spatial and temporal features from the original data, leading to an inefficient SOC estimation. This paper proposes a novel neural network method for accurate and robust battery SOC estimation, which incorporates the temporal convolutional network (TCN) and the long short-term memory (LSTM), namely TCN-LSTM model. Specifically, the TCN is employed to extract more advanced spatial features among multivariate variables, and the LSTM captures long-term dependencies from time-series data and maps battery temporal information into current SOC and historical inputs. The proposed model performs well in various estimation conditions. The average value of mean absolute error, root mean square error, and maximum error of SOC estimation achieve 0.48%, 0.60%, and 2.3% at multiple temperature conditions, respectively, and reach 0.70%, 0.81%, and 2.7% for a different battery, respectively. In addition, the proposed method has better accuracy than the LSTM or TCN used independently and the CNN-LSTM network. The computational burden with varying length of input is also investigated. In summary, experiment results show that the proposed method has excellent generalization and robustness.

State of Charge Estimation for Lithium-Ion Batteries Based on TCN-LSTM Neural Networks

The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing.

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

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Zeshen Wang

Papers

Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction