Posted by: Denis Guilhot, Senior EU Project Manager, Worldsensing
Introduction
The open, secure, decentralized, multi-stakeholder management framework that is being developed in the mF2C project is expected to set the foundations for a novel distributed system architecture.
The proof-of-concept system and platform will be tested and validated in three real-world use cases: Smart Boat [1], Smart Fog-Hub Service [2] and Emergency Situation Management in Smart Cities.
Use Case scenario
The main services of this use case will be:
(a) decision-making according to an inclination sensor that monitors emergencies in infrastructures and
(b) to provide Emergency Situation Management in a Smart City context by processing information and triggering the intervention of the relevant emergency services.
LoadSensing is a commercial solution proposed by Worldsensing for connecting and wirelessly monitoring infrastructures. If a sensor reports a value higher than an alarm threshold, the alarm manager will report an emergency situation to the cloud software that will trigger its alert methods. Furthermore, in order to improve the solution’s security, the alarm manager is able to detect whether the LoadSensing and the Gateway are communicating (using a LoRa interface) with each other. In the scenario presented here, the mF2C system would act upon the emergency vehicles but also the traffic light system, to optimise the intervention.
Why Loadsensing?
Loadsensing is a data acquisition and monitoring system which combines state-of-the-art wireless monitoring and advanced software tools. It is widely recognized as one of the leading solution for connecting and monitoring infrastructures. Loadsensing devices are battery-powered and equipped with long-range, low-power wide area network (LPWA) radio communications and are compatible with a wide range of geotechnical sensors. The software suite is web-based and facilitates real-time data capture and analytics. It is also possible to set automatic alarms to make operations safer.
Construction and mining companies and operators of bridges, tunnels, dams, railways and many other inaccessible assets can now work with reliable data. Having access to this information and real-time insights enables operators to anticipate needs, manage their workforce, diminish risks, and even prevent disasters.
The wireless configuration also eliminates the need for manual monitoring and expensive cabling thus contributing to capex and opex savings.
Fig. 1: Worldsensing Loadsensing commercial system
How do traffic light work and why do they turn green?
There is a number of reasons for traffic lights to turn green. Some network changes are set at specific intervals, and often synchronised to turn green in a wave sequence. Smarter traffic light might use a camera or an underground sensor to detect cars and optimise the timings of both directions of traffic. Others may react to inputs such as pedestrian push buttons, public transport arrival, such as trains or buses, and, in some cases, to Emergency Vehicles approaching.
Fig. 2: Worldsensing Emergency Situation Management system
Why optimise?
Most traffic light systems are not optimised with regards to the flux of vehicles at a given time, although it takes into consideration estimates of traffic at different times. Each intersection is configured with values for the main parameters such as minimum and maximum green durations, pedestrian indication requirements, gap and extension times, overlaps, cycle, offset… All these parameters being considered for traffic light as a whole system for part or the totality of a city and modified accordingly to the calculations for that moment.
The flux of vehicles varies greatly over the day, as well as from one day to the other, so an estimation is performed using averages for each intersection. This estimation is rarely optimal as an average situation rarely occurs, due to the large variability in the number of cars at each traffic light [3].
By optimising the traffic light network, savings on time and gasoline consumption have been demonstrated. For instance, the city of Portland reported savings of over six million litres of petrol each year, resulting in reduction of over fifteen thousand tons of carbon dioxide, through optimisation of 135 intersections [4].
What about Emergency Vehicles?
In the case of emergency vehicles who assist people in critical situations, the response time is critical and a short amount of time can make a big difference, reducing the severity of an injury, with the corresponding impact on wellbeing and healthcare costs, and sometimes even avoiding deaths, as 66% of those happen in the first twenty minutes, in the case of car crashes [5]. For this reason, lots of efforts have been dedicated to this topic. For instance, Qin et al. [6] report on control strategies of traffic signal timing transition for emergency vehicle pre-emption so that the approaching emergency Vehicle can cross the intersection safely at its operating speed instead of having to slow down at each intersection [7]. Also, companies like GTT have released the commercial Opticom system solution that deals with priority control of traffic lights for emergency vehicles [8]. Actually, numerous patents have been applied for and/or granted over the years for such systems. [9,10].
Fig.3: Emergency Vehicle Pre-emption (Copyright fhwa) [11]
Case Study: Barcelona
The city of Barcelona installed what is called “Corredores Verdes de emergencia” or Green Emergency Corridors as a means to increase the emergency intervention efficiency of the firemen service.
There are 5 fire stations in Barcelona. A section of the city is attributed to each one of these fire stations. Approximately fifty green corridors are defined, starting from these five locations and optimising the time it takes for the fire engines to get to any location in the city. Corridors are adapted to the time of vehicle required depending on the location and the type of emergency. When an emergency is identified and located, the optimum green emergency corridor is attributed and the traffic lights network of the city is notified and placed on stand-by. Once the fire engine or the convoy is ready to exit the fire station, the firemen press a switch which activates the corridor. The traffic lights around the fire station turn red except the first two or three traffic lights that will be used by the emergency service. Each corridor comprises up to 30 crossways and an estimation of the time it takes the convoy to reach each one of them is used to turn the traffic lights green when appropriate. For the shortest corridors, the estimation is generally precise enough, nevertheless in the case of large corridors, there can be an offset between the estimated time and the actual arrival of the convoy which can result problematic as drivers might become frustrated and go through a red light, blocking the convoy or even in the worst-case scenario, creating an accident. To improve this system and avoid offsets, the estimation can be refined depending on the day of the week, the time of the day, the season, but traffic is intrinsically hard to predict a long time in advance and this solution will never be as optimum as having a real-time monitoring of the emergency fleet and ad-hoc traffic light changes.
Nevertheless, the existing system is not currently applicable to police and ambulances as they do not initiate their intervention path always from the same point but from their current location at the time the alarm is raised.
Fig.4: Emergency Situation in Barcelona
Another aspect is that, although the intervention mechanisms are very efficient in standard situations, in case of a big emergency such as terrorist attacks or earthquakes, the emergency fleet from the city might not be sufficient. In such cases, emergency vehicles from the surrounding towns would be asked to intervene, but they do not necessarily have access to the protocol, green emergency corridors or might not possess an extensive knowledge of the city, allowing them to chose the fastest route to the emergency zone. Of even more concern is the evacuation of the victims towards hospitals. As of today, there are not any pre-determined evacuation routes or priority systems except for the use of sirens or police escorts. Ambulances coming from other towns might not know the best path to the hospitals so the system proposed could provide a priority and guidance system. Also, the hospitals could be automatically noticed in advance of when an ambulance would arrive, which type (whether there is an on-board physician) and which attention is required in order to optimise the service.
An added factor to take into account is that in case of emergency one of the first services to become unavailable are electricity and communications. Using mF2C can restore communication through agents’ interaction instead of standard telco protocols.
Conclusions
The Emergency Situation Management in Smart City use case presented in the mF2C project is deemed credible as a real-life possibility by taking into account the current protocols established in the city of Barcelona. The technology proposed could potentially be applied to the existing network and could provide important benefits such as extending the protocol to other type of emergency vehicles or enabling a system similar to that of the Green Emergency Corridors to the evacuation from the emergency location.
Acknowledgments: The authors would like to express their gratitude to Ares Gabás Masip from the Ajuntament de Barcelona and Sergio Delgado, Deputy director for coordination and emergency management – Civil Protection Directorate – Generalitat de Catalunya for their generous help and fruitful discussions.
Refs:
[1] http://www.mf2c-project.eu/smart-boat-use-case/ [2] http://www.mf2c-project.eu/use-cases/ [3] https://www.wired.com/2010/09/traffic-lights-adapt/ [4] https://www.c40.org/case_studies/optimizing-traffic-signal-timing-significantly-reduces-the-consumption-of-fuel [5] http://www.itene.com/blog/i/1863/239/reduccion-de-los-tiempos-de-respuesta-de-los-vehiculos-de-emergencia-a-traves-de-un-sistema-integrado-de-gestion-y-prior [6] https://www.sciencedirect.com/science/article/pii/S0968090X1200054X [7] https://doi.org/10.1016/j.trc.2012.04.004 [8] http://www.gtt.com/emergency-response/ – https://www.youtube.com/watch?v=XsrdqEluKtk [9] US4443783 in 1983 [10] US3881169 in 1975 [11] https://ops.fhwa.dot.gov/publications/fhwahop08024/chapter9.htm