Rapid urbanisation has altered the vulnerability of urban areas to heat wave disasters. There is an urgent need to identify the factors underlying the effect of heat waves on human health and the areas that are most vulnerable to heat waves. In this study, we plan to integrate indices associated with heat wave vulnerability based on meteorological observation data, remote sensing data and point of interest (POI) data; analyse the influence of urbanisation on the urban vulnerability environment; and explore the relationship between the vulnerability environment and heat-wave-related mortality. Finally, we attempt to map the spatial distribution of high heat-wave-related mortality risk based on the results of heat wave vulnerability study and artificial society. The results reveal that 1) there are differences in the influence of urbanisation on heat wave exposure, sensitivity and adaptability; 2) the exposure and sensitivity level effects on the lower limit of health impacts and the adaptability level effects on the upper limit of the health impact from heat wave in a given study area; and 3) areas vulnerable to the effects of heat waves are not confined to the city centre, which implies that residents living in suburban areas are also vulnerable to heat waves. Finally, this study not only explores the factors contributing to the impacts of heat waves but also describes the spatial distribution of the risk of disaster-associated mortality, thereby providing direct scientific guidance that can be used by cities to address heat wave disasters in the future.

With the rapid growth of connected and autonomous vehicles (CAVs), vehicular crowdsensing (VCS) has emerged as an effective way in a wide range of applications, especially in intelligent transportation systems. However, centralized VCS frameworks have confronted many problems, such as privacy, security, utility, and dependability. To remedy these challenges, blockchain technology can be applied in VCS systems for effectively forming decentralized autonomous vehicles organizations and operations. This article briefly introduces blockchain-based VCS solutions addressing the current problems and present potential directions for future research.

Pedestrian evacuation simulation is vital for urban civil engineers. Although there exist many works addressing the issue of emergency evacuation, only a few study the phenomenon of people actively squeezing to pass through an exit. To model this behavior, a three-circle model is adopted to represent the shape of pedestrians. Active rotation torque (ART) is proposed to model the active rotation behavior of pedestrians turning their torsos in the desired direction. This torque occurs either when a pedestrian is not facing his velocity direction, or when he wants to pass through a bottleneck. The equation of ART is designed and regressed with real pedestrian experiments, in which a gyroscope was used to measure the angle of torso rotation. The proposed torque model is then applied to manifold scenarios with various door widths and different safety separation belt settings. Then, both microscopic and macroscopic indexes, including evacuation time, rotation angle, and crowd density, are obtained to show that the proposed model can simulate both non-competitive and competitive pedestrian behaviors near exit bottlenecks more accurately than the circular social force model. Thus, the evacuation time of the exit can be predicted more precisely, which helps to design optimal multi-exit assignment strategies.

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely cells), Sparse Mobile Crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current Sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in real-world, as the cost of sensing in a subarea can be diverse due to many factors, e.g. condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable bi-objective optimization problem under certain assumptions and approximation. Then, we presented two selection strategies, i.e. Pareto Optimization Selection (POS) and Generalized Cost-Benefit Greedy (GCB-GREEDY) Selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring datasets (Parking, Flow, Traffic, and Humidity) and three cost maps (i.i.d with dynamic cost map, monotonic with dynamic cost map and spatial correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-Query by Committee (QBC) in a sensing cycle. The findings show important implications in Sparse Mobile Crowdsensing for urban context properties.