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<p><strong>Abstract.</strong> Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2<span class="thinspace"></span>% precision, 58.5<span class="thinspace"></span>% recall and 73.4<span class="thinspace"></span>% harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4<span class="thinspace"></span>%), recall (68.8<span class="thinspace"></span>%) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.</p>

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

The smart city term has been widely used for a number of years and many pilot projects and limited scale, sector independent initiatives have been progressed, but comprehensive, long-term, city wide, multi-sector systems are much less evident. This paper examines one such case study in Newcastle, UK highlighting the challenges and opportunities that realizing “smart city” concepts at scale present. The paper provides the background to the Newcastle Urban Observatory project and discusses the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city. We discuss the organizational requirements, governance, data quality and volume issues, big data management and discuss the current and future needs of decision makers and other city stakeholders. Finally, we propose areas where smart cities can have a positive impact on public outcomes through the discussion of two case studies related to COVID-19 and pedestrianization initiatives.