Urban road networks are typical complex systems, which are crucial to our society and economy. In this study, topological characteristics of a number of urban road networks purely based on physical roads rather than routes of vehicles or buses are investigated in order to discover underlying unique structural features, particularly compared to other types of transport networks. Based on these topological indices, correlations between topological indices and small-worldness of urban road networks are also explored. The finding shows that there is no significant small-worldness for urban road networks, which is apparently different from other transport networks. Following this, community detection of urban road networks is conducted. The results reveal that communities and hierarchy of urban road networks tend to follow a general nature rule.

The frequency, destruction and costs of natural and human-made disasters in modern highly-populated societies have resulted in research on emergency evacuation and wayfinding, which has drawn considerable attention. The subject is now a multidisciplinary area of research where information and communication technologies (ICT), and in particular the Internet of Things (IoT), have a significant impact on sensing and computing dynamic reactions that mitigate or prevent the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to share ongoing research results, identify the research gaps and address the need for future research. We present a comprehensive review of research on emergency evacuation and wayfinding, focusing on the algorithmic and system design aspects. Starting from the history of emergency management research, we identify the emerging challenges concerning system optimisation, evacuee behaviour optimisation and data analysis, and the additional energy consumption by ICT equipment that operates the emergency management infrastructure.

The rapid progress in informatisation and electrification in transportation has gradually transferred public transport junctions such as metro stations into the nexus of pedestrian flows, information flows, computation flows and energy flows. These smart environments that are efficient in handling large volume passenger flows in routine circumstances can become even more vulnerable during emergency situations and amplify the losses in lives and property owing to power outage triggered service degradation and destructive crowed behaviours. On the bright side, the increasingly abundant resources contained in smart environments have enlarged the optimisation space of an evacuation process, yet little research has concentrated on the joint optimal resource allocation between transportation infrastructures and pedestrians. Hence, in the paper, we propose a queueing network based resource allocation model to comprehensively optimise various types of resources during emergency evacuations. Experiments are conducted in a simulated metro station environment with realistic settings. The simulation results show that the proposed model can considerably improve the evacuation efficiency as well as the robustness of the emergency response system during emergency situations.

The Covid-19 pandemic has hit hard on the highly-organised yet risk-vulnerable modern societies, and has introduced new characteristics to large-scale emergencies, which feature long persistence in duration, high frequency in occurrence, large sensitivity to individual behaviours, and extreme high hazard propagation rate owing to the highly-efficient transport networks. This has raised new challenges on long-term emergency preparedness of urban transportation systems in terms of safety, efficiency, robustness and sustainability. Non-cooperative behaviours of transport participants could result in severe performance degradation in emergency preparedness, and mandatory restrictions in human activities can be economical costly and difficult in operation. In addition, current arrangement models for the disaster financial assistance have not been elaborately designed for civilian behaviour optimisation although with great potential as an economical instrument. Hence, in this paper, we propose a reward based traffic control mechanism to generate cooperative behaviours and optimise resource allocation in an urban transportation system for emergency preparedness via distributing credit coins, which can also be treated as a financial assistance approach during long-term disasters. A queueing theory based analytic model is employed to mimic the behaviours of civilians in the transportation system under the emergency preparedness state and a probability choice model is utilised to optimise the emergency preparedness strategies of the system. The experimental results show that the introduction of the incentive based traffic control mechanism can significantly reduce hazard response time, travel delay as well as the energy usage of the urban transportation system at the expense of monetary rewards.

Nowadays, public transportation junctions (PTJs) such as metro stations and railway stations can involve up to half of urban traffic and account for a large portion of energy usage in urban areas. The tremendous traffic and energy flows in PTJs have induced severe problems in safety and efficiency. Thus, in this paper, we present a comprehensive edge computing enabled navigation framework to aid the emergent evacuation processes within PTJs with respect to energy and safety restrictions. From the energy-saving aspect, due to the resource restraints of the on-site Internet of Things (IoT) based environmental monitoring system, a queueing network model is utilised to balance the energy utilisation and reduce the congestion of the sensing and navigation process during emergency. From the safety aspect, three edge computing aided cooperative strategies are proposed to dynamically assign evacuees into several groups to adapt their course of action with regard to their physical conditions and immediate environments. Simulation results show that the use of the queueing network model can reduce and balance the energy utilisation of the on-site IoT network. Experiments also show that the use of cooperative strategies to adjust the evacuees’ category and the associated routing algorithm can achieve higher survival rates.