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

Predicting bike sharing demand can help bike sharing companies to allocate bikes better and ensure a more sufficient circulation of bikes for customers. This paper proposes a real-time method for predicting bike renting and returning in different areas of a city during a future period based on historical data, weather data, and time data. We construct a network of bike trips from the data, use a community detection method on the network, and find two communities with the most demand for shared bikes. We use data of stations in the two communities as our dataset, and train an deep LSTM model with two layers to predict bike renting and returning, making use of the gating mechanism of long short term memory and the ability to process sequence data of recurrent neural network. We evaluate the model with the Root Mean Squared Error of data and show that the prediction of proposed model outperforms that of other deep learning models by comparing their RMSEs.

Predicting bike sharing demand using recurrent neural networks.

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

Public bike sharing systems require some kind of rebalancing to avoid too many rental stations of running empty or entirely full, which would make the system ineffective and annoy customers. Most frequently, a fleet of vehicles with trailers is used for this purpose, moving bikes among the stations. Previous works considered different objectives and modeled the underlying routing problem in different ways, but they all allow an arbitrary number of bikes to be picked up at some stations and delivered to other stations, just limited by the vehicles' capacities. Observations in practice, however, indicate that in larger well-working bike sharing systems drivers almost never pickup or deliver only few bikes, but essentially always approximately full vehicle loads. Many stations even require several visits with full loads. Due to budgetary reasons, typically only just enough drivers and vehicles are employed to achieve a reasonable balance most of the time, but basically never an ideal one where single bikes play a substantial role. Consequently, we investigate here a simplified problem model, in which only full vehicle loads are considered for movement among the rental stations. This restriction appears to have only a minor impact on the achieved quality of the rebalancing in practice but eases the modeling substantially. More specifically, we formulate the rebalancing problem as a selective unit-capacity pickup and delivery problem with time budgets on a bipartite graph and present a compact mixed integer linear programming model, a logic-based Benders decomposition and a variant thereof, namely branch-and-check for it. For the general case, instances with up to 70 stations, and for the single-vehicle case instances with up to 120 stations are solved to proven optimality. A comparison to leading metaheuristic approaches considering flexible vehicle loads indicates that indeed the restriction to full loads has only a very small impact on the finally achieved balance in typical scenarios of Citybike Wien. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 693, 270-289 2017

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Full-Load Route Planning for Balancing Bike Sharing Systems by Logic-Based Benders Decomposition and Branch-and-Check

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

As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the "last mile" of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.

https://www.mdpi.com/1424-8220/20/11/3072/pdf

Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City.

Internet of Things will play a vital role in the public transport systems to achieve the concepts of smart cities, urban brains, etc., by mining continuously generated data from sensors deployed in public transportation. In this sense, smart cities applied artificial intelligence techniques to offload data for social governance. Bicycle sharing is the last mile of urban transport. The number of the bike in the sharing stations, to be rented in future periods, is predicted to get the vehicles ready for deployment. It is an important tool for the implementation of smart cities using artificial intelligence technologies. We propose a DBSCAN-TCN model for predicting the number of rentals at shared bicycle stations. The proposed model first clusters all shared bicycle stations using the DBSCAN clustering algorithm. Based on the results of the clustering, the data on the number of shared bicycle rentals are fed into a TCN neural network. The TCN neural network structure is optimized. The effects of convolution kernel size and Dropout rate on the model performance are discussed. Finally, the proposed DBSCAN-TCN model is compared with the LSTM model, Kalman filtering model, and autoregressive moving average model. Through experimental validation, the proposed DBSCAN-TCN model outperforms the traditional three models in terms of two metrics, root mean squared logarithmic error, and error rate, in terms of prediction performance.

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Dazhou Li

Papers

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

Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City.

Short-Term IoT Data Forecast of Urban Public Bicycle Based on the DBSCAN-TCN Model for Social Governance