O NE of the most significant repercussions of coronavirus disease (COVID-19) pandemic will regard the transport sector and mobility, in particular public transportation (PT) systems in urban areas. A recent survey carried out in China [1] estimated that, as a consequence of the outbreak, the use of private cars will be roughly doubled, increasing from 34% to 66%, whereas the use of public transports (buses/metros) will be more than halved, dropping down from 56% to 24%. Furthermore, due to the lack of trust in PT systems, more than Manuscript received September xx, 2020; revised xxx yy, 2020; accepted zzzz 2020. Date of publication xxx 2020; date of current version yyy 2020. This work was supported in part by..... The associate editor coordinating the review of this manuscript and approving it for publication was Dr. X.
D. Darsena is with the Department of Engineering, Parthenope University, Naples I-80143, Italy (e-mail: darsena@uniparthenope.it).
I. Iudice is with Italian Aerospace Research Centre (CIRA), Capua I-81043, Italy (e-mail: i.iudice@cira.it).
G. Gelli and F. Verde are with the Department of Electrical Engineering and Information Technology, University Federico II, Naples I-80125, Italy [e-mail: (gelli,f.verde)@unina.it].
D. Darsena, G. Gelli, and F. Verde are also with the National Inter-University Consortium for Telecommunications (CNIT), 80125 Naples, Italy.
Digital Object Identifier xxxx/yyyyyyyy 70% of the surveyed people not owing a car declared their intention to buy a new one, with negative consequences on the environment (landscape and air pollution) in urban areas. In Italy, both national and regional authorities issued a number of rules aimed at limiting PT usage as well as avoiding bus and metro overcrowding, which severely limit their capacity to about 60% of the nominal capacity before the outbreak. The effects of such limitations are still not fully perceived, due to the breakthrough of mobility patterns associated to the widespread use of smart working and e-learning practices in education. However, it is expected that they will severely affect the fruition of PT in the recovery phase, when the transport demand will start to increase. Moreover, since new outbreaks might be coming in the next months/years, it is likely that some features of PT systems must be adapted to structurally cope with these limitations.
In many countries, to counteract the shift to private car usage, governments are favoring the use of individual sustainable mobility and micromobility means, such as bikes, electrical scooters, and segways, and they are planning and deploying the related infrastructures (emergency bike lanes) or powering vehicle sharing services, which can shift to this transport mode a certain percentage of short and medium-distance trips.
However, owing to the large number of passengers carried by PT systems in urban areas, it is of utmost importance to adopt measures aimed at assuring safe and reliable PT usage. To this aim, the first adopted measures (during the lockdown) were devoted to minimizing the contagion risk, such as backdoor boarding, cashless operation, frequent sanitization of vehicles and stations, enforcing social distances, requiring the passengers to wear personal protection equipment (e.g., face masks and/or gloves). Other medium-term measures are focused on intervening on PT system operations, such as modifying timetables, frequencies, paths, leveraging modal integration, and so on. Some of these measures, like increasing PT service frequency to compensate for reduced vehicle capacity, are seen by PT companies as effective [2] , but not sustainable in the long term, due to the limited number of drivers and vehicles.
Thus, in the re-opening phase, when a capacity-limited PT system must cope with increasing volumes of passengers (especially with the reprise of school and university activities), it will be mandatory to dispose in real-time of reliable and capillary information about the crowding of PT vehicles (e.g., bus, tram, and trains) and of the related access infrastructures (e.g., bus stops and railway stations). Such a knowledge will allow one to implement the necessary social distancing measures, while at the same time assuring an adequate level of service. Real-time knowledge of crowding data can be used for fast or even proactive adaptation of PT operations (e.g., increasing the service frequency, reallocating vehicles from one line to another, planning alternative routes), in order to cope with spatially and/or temporally localized crowding situations, which cannot be tackled by conventional (statistical) tools used in transportation system design, such as, e.g., analysis of origin-destination flows.
A significant performance improvement in crowd avoidance approaches can be ensured by exploiting information gathered by intelligent transportation systems (ITSs). In a modern ITS, commonly available data are automated vehicle location (AVL) -via global positioning system (GPS) -and automatic passenger count (APC) or automatic fare collection (AFC). In [3] , optimization and planning strategies based on such data are surveyed, at four different levels: strategic, tactical, operational, and real-time. In particular, it is mentioned that the lack of passenger arrival information, especially in realtime, is a limiting factor for accurate studies. It is also stated [4] that "the availability of real-time passenger demand data can significantly improve the performance of control models in case of overcrowding".
To overcome such limitations, and cope with COVID-19related issues, a fundamental role can be played by Information and Communication Technologies (ICT) and Internet of Things (IoT) technologies, some of which are already employed in state-of-the-art ITS. In particular, the fifth generation (5G) of cellular networks is potentially able to support massive IoT connections, where billions of smart devices can be connected to the Internet.
In [5] , the use of new technologies, such as IoT, unmanned aerial vehicles (UAVs), artificial intelligence (AI), blockchain and 5G, for managing the impact of COVID-19 in health applications has been considered. In [6] , [7] a review of technologies for social distancing has been provided, with emphasis on wireless technologies for positioning, including crowd detection and people density estimation.
To the best of our knowledge, the application of ICT and IoT, particularly crowd-detection techniques, to cope with COVID-19 effects in a PT system has not been considered yet. In this paper, we propose a new system concept, called SALU-TARY (Safe and Reliable Public Transportation System), aimed at integrating the legacy ITS, which is already available in many PT systems in urban areas, for fleet management and travel information purposes. Specifically, the SALUTARY system is built upon a distributed IoT subsystem, composed by a network of capillary active/passive sensor, aimed at measuring passenger crowding in the different segments of the PT system (buses, trams, trains, bus stops, and railway/metro stations). The acquired measures are transmitted in real-time by a communication infrastructure to a control subsystem, which performs optimized and proactive management of the PT system. The crowding data can also be reported (in aggregated form, for privacy concerns) to the PT users, by means of ad hoc mobile applications (apps) or by integrating it in the existing mobile transport apps (e.g., Moovit or proprietary operator applications). Since crowding on PT systems has a big impact on travel satisfaction [8] , we are confident that the added features will be of interest for improving the quality of service also when the COVID-19 limitations will be finally removed.
The paper is organized as follows. In Section II, related work on crowd analysis, detection, and prediction is discussed. An overview of the SALUTARY system concept and its architecture is given in Section III. Various sensing solutions are presented in Section IV for different indoor and outdoor scenarios of interest. Section V is devoted to highlight innovations and advantages provided by the SALUTARY system. Finally, conclusions are drawn in Section VI.
Estimating and predicting some features of a "crowd" in indoor and outdoor locations is an active research topic, with many applications, including surveillance and security, situation awareness, emergencies, and crowd management. In our application, the feature of interest is the number of components of the crowd and/or its density.
A review of crowd analysis techniques for urban applications, including transportation systems, is provided in [9] , where different data sources are compared and discussed, including information fusion approaches. When dedicated crowd detection infrastructures are not available, crowdsourcing or participatory sensing 1 applications have been developed to acquire such information [10] , [11] .
Applications of crowd analysis to PT systems is described in [12] and [13] . In [12] , in particular, it is described a lowcost hardware/software solution to estimate the current number of passengers in a vehicle, by counting the number of probe requests, i.e., medium access control (MAC) addresses, sent by WiFi-equipped (IEEE 802.11) devices in the vehicle. In [13] , such a passenger counting technique is incorporated in a bus navigation system capable of giving crowd-aware route recommendations: such a system has been tested in the municipal bus infrastructure of Madrid, Spain.
One of the problem inherent to the use of techniques based on radio frequency (RF) for crowd sensing is the ability to distinguish between people outside the bus and actual passengers. This issue was tackled in [12] , [13] by filtering the probes by a sliding window, aimed at removing MAC addresses that were not detected over a longer period of time. The system was able to detect only around 20% of the passengers, since several users may have turned off the WiFi interface. Moreover, the system is only tailored at crowd analysis in buses, and do not take into account other scenarios (trains, bus stops, and railway stations).
In [14] , a crowdsensing-based PT information service is described, called TrafficInfo, which exploits mobile or participatory sensing to enrich the static travel information without requiring deployment of a vehicle tracking infrastructure. A solution to count the people in a queue, based on Bluetooth low-energy (BLE) devices, is described in [15] . The system is completely passive and estimates the number of persons in the queue by analyzing the mean and variance of the received signal strength indicator (RSSI) values between a BLE beacon and a receiver covering a certain area. In general, RSSIbased approaches for crowd analysis exhibit good performance only for small monitored environments, where the propagation channel variation is mainly dominated by the attenuation caused by the present people. On the other hand, in a rich scattering environment, crowd analysis based on channel state information (CSI) provides a more reliable people counting. In this respect, the received WiFi and Long Term Evolution (LTE) downlink control signals are processed in [16] and [17] , respectively, to extract the changes of the propagation channel induced by the presence of different number of people.
Crowd prediction algorithms can be classified on the basis of the prediction horizon into short-term (less than 60 minutes) and long-term ones. They usually rely on three different types of information: (i) real-time load station data, (ii) real-time train load data, and (iii) historical data. The latter data aim to improve prediction by exploiting possible cyclic daily, weekly, or seasonal variations in passenger loads.
Prediction approaches can be further classified, on the basis of the prediction methodology, into model-based methods or data-driven ones. Model-based methods include time-series analysis, regression modeling or Kalman filtering models. In [18] , e.g., a traffic flow prediction algorithm based on timeseries is employed, and many variants have been subsequently proposed to improve the prediction accuracy. Due to the highly nonlinear and random nature of traffic flow, datadriven approaches have recently gained significant attention, including k-nearest neighbor (k-NN) methods and artificial neural networks techniques (see, e.g., [19] , [20] and references therein).
In some cases, simulation approaches, which employ traffic simulation tools to predict traffic flow, have been considered as well. Most of these techniques are mainly devoted to congestion management in urban transportation and can exploit information from a multitude of sensor sources, including inductive loops, radars, cameras, GPS, social media, etc. Passenger load prediction for PT systems, instead, is less common: recent studies are [21] , [22] , [23] .
The aim of the SALUTARY system is to integrate/augment the ITS system already available in a PT urban system with innovative functionalities, aimed at smart and proactive control and reduction of passenger crowding. It is based on the integration of heterogenous sensing and communication technologies, depending on the operation scenario and the ICT infrastructure available in the urban area where the system must be implemented. Detailed system design involves strong interdisciplinary skills, including transportation engineering, telecommunications, computer science, electronics, data analyis, artificial intelligence and machine learning (ML).
The SALUTARY system is composed (Fig. 1 ) by three subsystems:
1) the sensing and actuator subsystem (SASS); 2) the communication subsystem (CSS); 3) the monitoring, prediction and control subsystem (MPCSS). The proposed system introduces new data flows (marked in blue in Fig. 1 ) compared to existing data exchanges (in violet) found in state-of-the-art ITS systems for PT services. The core and main innovative part of the system is the SASS, with particular reference to the IoT sensing technologies, whereas the actuator part mainly encompasses the flow of information toward the users, such as audio speakers, variable-message panels or displays, which are typically already present in the PT system, or can be readily installed at the bus stops or in the railway station (and even inside the vehicles). Due to its centrality in our concept, we focus attention on such a subsystem in Section IV.
The characteristics of the CSS are strongly dependent on the communications infrastructure available in the urban area of interest, or the communication network owned by the PT service operator. In general, they encompass public wireless networks (such as cellular networks) and/or private wired and/or wireless networks owned by the operator, such as e.g. Global System for Mobile Railway (GSM-R) or LTE for Railway (LTE-R). To cope with this heterogeneity, it is envisioned that, at the protocol level stack, the CSS can be readily interfaced with the other subsystems by using standard interfaces and/or by means of simple adaptation layers.
The MPCSS performs data collection and real-time crowding prediction, also by means of AI and ML techniques. Based on such predictions, modifications to the transport services can be implemented in real-time, the related control data are sent to service operators (drivers, supervisors, etc.), whereas service information is sent to the passengers by means of displays and/or mobile transport apps. This information could be notified by the same applications to all the users of the PT, so as to discourage the access to overcrowded stations and/or bus stops, and propose alternative travel solutions. In our concept, the MPCSS is strongly integrated (and typically colocated with) the ITS control system of the PT service operator.
The transportation scenarios to be considered for the SASS are essentially of three types: 1) train and bus/tram (indoor scenario); 2) railway/metro station (indoor/outdoor scenario); 3) bus station (mainly outdoor scenario). In general, the sensing solutions to be adopted belong to the crowd detection family introduced in Sec. II-A. They can be classified [24] as visual-based (VB) solutions, based on still or moving images acquired by optical, thermal, or laser cameras, and non-visual based (NVB), which utilize techniques not based on optical sensors (mainly RF devices). The main characteristics of the available sensing solutions for the different scenarios are summarized in Table I and will be discussed in the forthcoming subsections.
Thanks to recent advances in AI, traditional optical, thermal and laser VB sensors are becoming "smart" and can detect, recognize, and identify persons. Optical cameras are widely used in private and public spaces for surveillance and security. Thermal cameras can detect people in low-light environments, complete darkness, or other challenging conditions, such as smoke-filled and dusty environments. Another option to detect and track persons is represented by laser imaging detection and ranging (LIDAR) sensors, especially in environments where there are several interacting people. However, these systems do not allow to estimate the number of present people with sufficient accuracy, due to possible obstructions, clutter, and poor light/weather conditions, and involve high deployment costs in complex environments. Moreover, they require electrical power supply and may involve privacy problems. The main advantage of VB technologies is that they do not require cooperation/participation of the users.
Among NVB technologies, sensing systems based on mobile devices (also called crowdsensing or mobile sensing [25] , [26] ) represent an interesting solution, due to the diffusion of smartphones and other portable/wearable devices, such as pedometers, smart watches, or biometrical sensors. On one hand, compared to VB solutions, data collected by means of device-aided NVB systems can be used not only to count persons, but also to gather additional information, e.g., planned routes, passengers using off-peak hours group ticket, and so on. On the other hand, a problem inherent to deviceaided systems is that they usually require the cooperation of the people who must agree to share their data (so called participatory or collaborative sensing).
To motivate participation, it is sometimes needed to introduce incentive or reward mechanisms, or introduce radical modifications in the procedures to access the PT service, such as an authentication phase to use the transportation service. Although this issue could raise significant privacy concerns, it could also be useful as a means to increase the overall safety of the PT systems, by reducing the risk that infected people can access the system or the execution of possible crimes from people already known to the police.
When device-aided NVB techniques cannot be used for crowd characterization, due to, e.g., lack of user cooperation and/or security issues, RF non-device aided approaches can be pursued, by analyzing the propagation channel variations of wireless signals (e.g. WiFi or LTE), which are induced by the people present in a given spatial area. The impact of the monitored people on the RF signal can be assessed either using traditional radar methodologies (range and Doppler analysis) or by analyzing features extracted by channel quality measurements, such as RSSI and CSI.
For vehicular scenarios, straightforward NVB solutions for passenger counting are passive infrared sensors (IR). In this case, a couple of IR emitter and receiver are mounted in correspondence of each gate of the vehicle. A passive IR (PIR) sensor can detect variations of the IR signal, depending on the temperature and the characteristics of the surface interrupting the beam. More generally, many technologies are available to solve such a "crowd counting" problem in scenarios characterized by a well delimited space with a limited number of accesses.
In the transportation community, APC systems [27] for rail and road vehicles are widespread and mainly based on simple technologies, such as counting from the number of AFC or by using independent sensors, typically infrared (active) or pyroelectric (passive) sensors, or combinations thereof, aimed at detecting the passage of people at the gates. Other techniques can be based on load sensors placed on the ground or on the suspensions, or pressure-sensitive switches ("treadle mats") placed on the steps, or weigh-in-motion (WIM) systems, which estimate the number of passengers by the ground loading of the vehicles detected before and after the stops. More sophisticated techniques can also be employed, such as camera-based systems or combined sensing (such as IR plus RF or IR plus ultrasound).
As indicated in Table I , even though more sophisticated NVB technologies may be used as well, their usage is not expected to lead to significant innovations in this scenario, compared to the aforementioned simpler solutions and systems (i.e., PIR, APC, AFC, and WIM).
For this scenario, both VB and NVB technologies can be applied, as reported in Table I . Among the latter ones, Near Field Communication (NFC) represents a viable solution that does not need to deploy special hardware [28] . Nowadays, Android and iOS phones/tablets support this technology, by allowing mobile devices to act as a NFC reader/scanner. When a device with NFC functionality appears in the reader's working range, which can be placed at the gates or at any other fixed access point, it "wakes up" and sends a signal containing encoded data. The reader receives and decodes such data, and sends them to a management entity.
Another interesting solution for such a scenario can be to install 802.11 (WiFi) access points in the stations, and the number of persons entering the stations can be estimated on the basis of the number of connections established by the user devices with the access points. In this case, the uplink signals transmitted by the user devices are sniffed to capture their network accesses and, thus, it can be categorized as a device-aided solution. In contrast, without requiring aid to the monitored devices, the number of users served by a WiFi access point can be estimated by sniffing the downlink control information transmitted by the access point and relying on the fact that, as expected, there is a strong correlation between the number of people in the considered area and the variations of some features (e.g., RSSI and CSI) of the signal received by a dedicated device designed as a people counter.
Similarly to WiFi networks, LTE signals can be used for device-aided and non-device aided crowd counting, thanks to their ubiquitous availability and good penetration in indoor environments. For instance, they could be available in areas where the WiFi coverage is not present, such as remote and small railways/metro stations.
This scenario is by far the most difficult to manage, since bus stops are usually located in outdoor space not well delimited by fixed gates. Also in this case, both VB and NVB systems can be used, but it is imperative to adopt costeffective, rugged and low-power solutions, in order to reduce the maintenance cost, as well as solutions that do not require significant infrastructures, since in many cases the bus stops are not equipped with shelters and are indicated by simple poles. Bluetooth can represent an efficient solution: recently, a low-energy consumption version of the standard, called BLE, has been introduced, which assures better communication performances with a limited power consumption. So-called Bluetooth beacons, namely, small transmitting devices that transmit BLE signals to compatible devices, can be easily installed at the bus stations, since they can be battery-powered and are characterized by simpler flexibility and simplified installation compared to WiFi. This solution relies on the same technology introduced by Google and Apple in recent versions of smartphone operating systems, and is used by many national contact-tracing apps (e.g., Immuni for Italy [29] ). LTE and 5G signals can also be exploited for counting people in bus stop environments. In particular, the deployment of 5G wireless technology over the coming decade is expected to rely on the introdution of many small cells working in the millimeter-wave (MMW) band. From the viewpoint of crowd analysis, the placement of small cells allows one to more precisely monitor spatially limited areas, like bus stops, with and without the aid of user devices. In addition, at bus stops, microwave SAR tomography techniques [30] , [31] could provide specific images from which more detailed crowd information can be extracted, by using complex image classification algorithms (i.e., count, distribution, mobility, etc.); model issues induced by the intrinsic near-field scenario (e.g., typical for bus stops) could be overcome by using specialized algorithms [32] .
The SALUTARY system can provide original and innovative functionalities that are not present in the legacy ITS: Proactive control of station access: In railway/subway applications, on the basis of the knowledge of the number of passengers aboard the arriving trains and the prediction of those getting off at the station, it will be possible to program the number of station accesses with a low error margin and in real-time. This number can be displayed at the users (by displays at the station gates or by the mobile transport apps) and can be used by the security operators to filter passengers at the turnstiles. Priority policies can be envisioned, such as taking into account the time already spent in queue, or the trip motivations (e.g., a priority could be assured to health workers, disabled or elder users, law enforcement, and teachers/students).
Vehicle access reservation: In bus/tram trips, a vehicle access reservation system can be implemented, wherein a sensor at the bus stop detects the user presence and exchanges information with its device (i.e, the smartphone), so as to grant him/her the access to board the first arriving bus (a virtual queuing system) or putting him/her in an overbooking list (with priority) to allow him/her to board the next one. The application can generate an e-ticket with the access grant (e.g., a QR-code) that can be validated on board at the ticket machine.
Crowding information dissemination: Users receive information related to capacity (in terms of number of seats or in percentage) of the bus/trains in arrival and/or the crowding at the bus stops/stations by means of displays installed in correspondence of the bus stops or at the entrance of the stations, or by alerts issued by mobile transport apps, so as to avoid unnecessary waiting and crowding and possibly reschedule their movements.
Crowd-aware route planning: Users can plan their trips on the mobile transport app, by taking into account not only geographical information and traveling times (static data), but also traffic and crowding information about vehicles and bus stops/stations during the trip (dynamic data). The app can suggest not necessarily the shortest route, but the least crowded one, taking into account also crowding levels measured during the trip. Such a feature not only help reduce crowds and the consequent infection risk, but also distribute more efficiently the load on the transportation network.
At first sight, the SALUTARY system can be seen as a technique aimed at enforcing social distancing measures. However, its scope is wider, since at the same time it tries to optimize the performance of the PT system. In practice, the aim of SALUTARY is to increase the flexibility and adaptability of the PT system, to partially recover the drawbacks and inefficiencies due to the adoption of rigid social distancing measures, and to avoid the use of private cars with the obvious negative impact on the air quality in urban areas. The main advantages of SALUTARY system are summarized in Tab. II.
In addition, the SALUTARY system can also be used at the end of the pandemic, allowing for more efficient planning and real-time control of the operation of the PT system, compared to static methods, like traditional survey-based compilation of origin-destination matrix flows. The large amount of generated data can be used by AI and ML algorithms to better understand and plan a series of aspects generally associated with improvements of the quality of life in urban areas and smart cities. The SALUTARY system can also be useful to allow schools and universities to open as safely and quickly as possible for in-person learning. Indeed, to enable schools and universities to open and remain open, it is important to adopt and implement actions to slow down the spread of COVID-19, not only inside the school, but also in the PT system, by optimally and dynamically adjusting transport timetables.
We presented in this paper the main functionalities and some potential applications of the SALUTARY system concept. In particular, system-level considerations were reported to corroborate the feasibility of integrating state-of-the-art wireless IoT technologies into the different segments of the PT infrastructure. The proposed system is based on some basic components and subsystems, to be used as building blocks to implement an evolved ITS, capable of real-time monitoring and predicting crowd situations, as well as disseminating useful information to users at the bus stops/stations and/or through mobile transport apps.
Some features of the SALUTARY system are similar to those of a contact tracing system, which can be implemented more easily if based on user cooperation. In this sense, the crowd detection functionalities can be incorporated in a more complex system, which can implement, besides the typical mobile transport app functionalities (like Moovit) also the possibility to buy tickets, to reserve the access to the vehicles, in conditions of particular crowding. This could represent a decisive incentive to the use of the system. However, the more appropriate crowd sensing solution must be singled out caseby-case, in dependence on the ICT infrastructure owned by the PT operator, of the socio-economic context, and the costbenefits ratio.
The potentials of the SALUTARY system go beyond the scope of dealing with typical social distancing problems, by potentially allowing real-time optimization and management of the PT system. For instance, the success in preventing the introduction and subsequent transmission of COVID-19 in schools and universities is strongly dependent on preventing disease transmission in the PT system used by students, families, teachers, school staff, and all community members.