Keywords

1 Introduction

Urbanisation and movement of masses towards cities have led towards concentration of population in smaller areas. This has put forward various challenges in front of planning commissions of states and countries. The continuously increasing vehicular traffic on the road is one of the major issues in urban areas. The increase in the number of vehicles on the road congestion is the main concern in urban areas, especially in rush hours. The major demerits related to road congestion include wastage of fuel, degradation of vehicular performance, anomalous driving behaviour, etc. Intelligent Transportation Systems (ITS) aim at utilising the sensing, analysing, disseminating, and computing abilities to provide solutions to transportation-related issues [1]. ITS are also being used for supporting other causes like data offloading, energy trading, etc. The mobility of the vehicles is a major difficulty in data offloading, which can be tackled using various approaches like Software-defined network-based controller [2]. Moreover, electric vehicles are also being put to use for energy trading in smart cities, which can be secured by using concepts like block chain [3]. Highly advanced deep learning networks are also being put to use to optimise the network traffic for enhancing the quality of service in smart vehicles [4,5,6]. There are a plethora of other highly advanced support services as well that contribute towards improving the quality of life in society, but the most haunting side effect of ITS is the road congestion. The root cause of road congestion is observed to be inefficient traffic control at traffic lights [7]. To manage the traffic, we use static traffic light method on single and multiple intersection. The static traffic light has a fixed time of lights. There are three lights in a traffic light, which are red, yellow, and green. The red light means stop, the yellow light means wait, and the green light means go. The static traffic light approach has fixed time slots for each side of the intersection irrespective of the density of each side. The magnitude of the losses incurred increases manifold in country like India where in January 2019, more than a million-and-half (1,607,315) new vehicles were registered. The bifurcation of this new registration includes 74% of the vehicles being two wheelers (1,187,998) and more than 80% of the total vehicles consuming petrol [8]. This method is not efficient in terms of cost, performance, and maintenance of traffic light [8].

The average waiting time of vehicle increases at the intersection due to static traffic light approach. To overcome this problem, intelligent traffic light system (ITLS) needs to be deployed to manage the traffic automatically on the intersection of roads. ITLS has varying time period based on analysis performed instead of fixed time slots. This method manages the traffic and aids in minimising the fuel consumption of vehicles running on the road. ITLS minimises the average waiting time and congestion of vehicles on the intersection as well. The ITS consist of surveillance system, communication system, traffic light control system, and energy efficiency system [9]. The focus of the existing ITLS has been mainly on these factors only, but lesser consideration is given on adding parameter of high-priority vehicles and their early clearance from intersections. Thus, there is a need of an architecture of ITS working in light of ITLS for better management with respect to emergency vehicles. The different types of emergency vehicles are fire brigades, police cars, and ambulances. A scheme also needs to be established for multiple lane clearance only in case of conflicting emergency vehicle lane sides based on prior declared non-overlapping of the paths chosen. The whole paper consists of 4 parts. Section 9.2 discusses the proposed architecture of ITS in light of intelligent traffic light. Section 9.3 explains the proposed scheme to give priority to emergency vehicles. Section 9.4 discusses the conclusion and future enhancement of this work.

2 Architecture

As shown in Fig. 9.1, the architecture of ITLS is in the form of top-down stack where the flow of information starts from the top layer and reaches the bottom layer. The decisions based on the analysis are propagated back to the top layer. The proposed architecture of ITLS contains four layers, i.e., hardware layer, network layer, analytical layer, and application layer. The hardware layer represents the different types of sensors that perform specific functions such as count of vehicles, motion, weight, location and type of vehicle, and an interface manager that helps in maintaining the state of different sensors.

Fig. 9.1
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Architecture of intelligent traffic lighting system

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2.1 Hardware Layer

This layer includes all the sensors that are going to be the first point of contact/exchange for vehicles as the first and foremost requirement of ITLS implementation is the ability to sense traffic attributes. The vehicles are also a part of the hardware layer as they will be receiving the information via backward propagation from the bottom layer. The different types of sensors available that could be used to implement ITLS are intrusive sensors and non-intrusive sensors and are shown in Fig. 9.2.

Fig. 9.2
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Traffic light sensors classification

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2.1.1 Intrusive Sensors

Intrusive sensors are installed in the pipeline so that they intersect with the process flow of the entities under investigation. These are called intrusive sensors because of the fact that they intrude the process flow either via direct contact or indirect contact. These have high equipment and maintenance cost. The different types of intrusive sensors are:

  • Inductive Loop Detector (ILD) Sensor: The inductive loop detector sensor is used in traffic management. ILD detects the vehicle arrival and passing at a certain point before the intersection point of traffic light. ILD consists of inductive metallic wire to form a loop under the surface of the road. When the vehicle passes through the loop, electric current is produced in the loop which transmits the detection information of vehicle to the control station. The frequency range is 10–200 kHz. The different variations of ILD can even help in detecting the type of vehicle that passes through them. Thus, an ILD can help to count the total number of vehicles passing through a road segment as well as help in bifurcating the type of vehicles passed.

  • Magnetometer Sensor: The magnetometer sensor is used with the secondary sensor to detect the large loaded weighted vehicles like trucks, rail cars on the road before the traffic light. It uses a passive approach of sensing where change in the ambient magnetic field corresponds to the detection of vehicle. Therefore, during installation of magnetometer sensor, it must be ensured that no vehicle is present when the magnetometer is being installed and taught about the ambient magnetic conditions.

  • Pneumatic road tube sensors: The pneumatic road tube sensor is detecting the number of vehicle count passes through a particular range on the road. When the vehicle passes over the road tube, it sends the burst pressure, which produces the electric signal that is transmitted to analysis software. If the requirement of the scenario is to evaluate the wrong direction vehicle movement, then a pair of tubes can be drawn on various lanes. Hence, on the basis of which tube is pressed first can help to decide the direction from which the vehicle came.

  • Piezoelectric sensors: The piezoelectric sensor can also be used in traffic management. The basic principle of piezoelectric sensors is the conversion of mechanical energy into the electric energy. The installation of piezoelectric sensors includes making a groove cut in the road and fitting the sensor in it. When a car crosses the groove cut, it applies pressure on the sensor that further transforms it into electrical energy, and hence, this data may be transmitted through RS232 connection or an Ethernet connection to the server.

2.1.2 Non-intrusive Sensors

The non-intrusive sensors are installed at different places on the roads. It does not interfere with the flow profile. It gives the information of the number of vehicles in a lane at the intersection, weather conditions, and traffic conditions on the road [10]. It has high maintenance cost. The different types of non-intrusive sensors are:

  • Ultrasonic Sensor: Ultrasonic sensor is used for automation purposes in traffic management on the road. The ultrasonic sensor is placed at one side of the road near the intersection of traffic light. It covers some particular area where the vehicle is restricted to pass during the red light on ITL [11]. The max range of ultrasonic sensor is 4 m.

  • Radio Frequency Identification (RFID) Sensor: The RFID sensor is placed at the vehicles. The RFID reader is installed at the road-side units (RSU). When a vehicle approaches the RSU, the RFID reader installed on the RSU reads the active RFID tag of the car. The data stored in the RFID tag is mostly a unique number that corresponds to all the information related to the car. It is used to count the number of vehicles in a specific duration [12].

  • Radar Sensor: These radar sensors detect the motion of vehicle either the vehicle is moving or stationary. It is also not affected by the weather condition. The max range of radar sensor is 40 m.

  • Acoustic Array Sensors (AASs): AAS is used for traffic monitoring. The acoustic sensors are deployed in a geometrical pattern where the sound sensed by an array of sensors is collaborated and analysed to evaluate the number of vehicles passed as well as the type of vehicle passed. The major challenge in the acoustic sensors in the country like India is unavailability of real-life data pertaining to sound produced by electric vehicles when passing through AAS [13].

  • Video Camera Sensor: The video camera sensor is used to capture the vehicles near the intersection of traffic lights. It checks the speed of vehicles. The video camera sensor has a large amount of variants available with different capabilities like motion detection. The amount of data generated by video camera sensors is large and, hence, requires a high amount of computational costs to process and analyse the data [14].

  • Infrared Sensor: The infrared sensor detects the length of vehicle and also checks the speed of vehicles. The infrared sensors could be either active or passive. The active infrared sensor works on spectral range, whereas passive infrared sensor works on thermal range. The max range of infrared sensor is 200 m [15].

The types mentioned above are not exhaustive of all the available sensors but cover most of the sensors being used in traffic management scenarios. For the purpose of implementing ITLS, either single sensor or a combination of sensors could be used. The decision related to placement of the sensors also plays a big role in the overall performance of the system. The sensors are not confined to be placed only on the intersection points but are required to be placed on selected RSUs for predictive analytics to be fed into the ITLS.

2.2 Network Layer

The second layer in the architecture is the network layer that is responsible for all the communication between all the entities of the ITS structure. The various entities involved are: vehicles, sensors, road-side units, processing units, and application units. The different types of communication that are possible amongst entities are wired communication and wireless communication. The various intrusive sensors can be connected to Fog and/or Edge devices installed on the RSU via wired channels like Ethernet/RS232 connections. The fact that the sensors deployed are immobile makes the selection of wired channels ideal for the case as it will provide dedicated and congestion-free channels with fastest possible transfer speeds.

The network layer is also responsible for transmitting various information broadcasts and control messages to the vehicles for ensuring optimised traffic flow. The wireless communication system is embedded on the surface of the road near traffic light at the intersection and at RSU. The wireless communication has low maintenance cost as compared to the wired communication system. The wireless communication system helps to communicate between the vehicle to vehicle and vehicle to infrastructure. ITS use wireless communication system amongst the vehicles and traffic light/RSU. The wireless communication is of two types: short-range communication system and long-range communication system. The main communication system of using IEEE 802.11 protocols is Wireless Access in Vehicular Environments (WAVE), the Dedicated Short-Range Communications standard (DSRC), Wifi, WiMax, etc. [16]. The control messages to normal vehicles would require to be sent via wireless channels when an emergency vehicle is about to approach the road segment so that the normal vehicles provide passage clearance for fast passing through emergency vehicles. Thus wireless communication channels are used for data exchange between vehicle to vehicle and between vehicles and the RSU or traffic light.

2.3 Analytical Layer

The third layer of the architecture is the analytical layer. This layer is responsible for performing all the analysis on the data saved on the Edge/Cloud device based upon the request of the application layer. Replacing road-side units with Edge devices has been demonstrated to increase efficiency in the smart vehicle environments [17]. The functionalities provided by analytical layer are directly mapped with the application layer services. Thus both the layers work hand in hand, but there is a level of encapsulation maintained between these layers just like MVC architecture for easier maintenance of the system. Analytical layer would be executing various algorithms for different application layer requirements. For Example: application layer needs to propagate the route information to the emergency vehicles for fastest possible commute to the location of emergency. This further requires analysis of traffic densities at different road segments, real-time information about any possible road blockages due to accidents/herds of cattle, ongoing road maintenance work, religious processions/parades, etc. The raw information sensed from the physical layer would be transmitted via network layer to the edge/cloud devices for storage. Analytical layer would trigger functional units based upon the request received from the application layer.

2.4 Application Layer

The final layer of the ITS architecture is the application layer. This layer represents the different applications of ITS that need to be acted upon. The various types of domains are Road Maintenance Tasks, Road Blockage Clearance Tasks, Traveller Safety Tasks, Emergency Vehicle Lane Clearance, Traffic Light Management, Public Transportation Management, etc., under which a plethora of applications exists. The application layer tasks are shown with respect to the frequency of data collection, permissible latency, and processing unit being used for both emergency vehicles and normal vehicles as shown in Table 9.1. The processing unit could either be Fog device, Edge device, or Cloud device. The permissible latency for different applications could be near real time, very low, low, medium, or high. The frequency of data collection from the sensors could be per second, per minute, per hour, per day, and so on as per the need of the analysis to be performed. The different types of data being produced by sensors that would be required to be saved on the temporary or persistent storage devices could be binary, text-based, image-based, video-based, audio-based, and binary large object-based. All the unit values depend on whether the application is required by an emergency vehicle or a normal vehicle. Hence, the values of all the attributes are being explained for both.

Table 9.1 Major applications of ITS
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3 Proposed Scheme

This chapter proposes a scheme for emergency vehicles lane clearance and multiple emergency vehicles route intersection avoidance. The proposed scheme consists of different phases.

3.1 Centralised Light-Weight Reporting

The proposed scheme focuses on providing priority to the emergency vehicles in ITS, i.e., ambulance, fire brigade, and police as compared to public vehicles and private vehicles [18]. The reason for prioritising emergency vehicle over other vehicles is the reason that saving lives always have higher priority as compared to poorer public/private transportation performance. The first step for achieving this objective is to ensure easier and effective reporting. The data type of the report should also be as light as possible. The scheme proposes the use of PON data type as it is evaluated to be a very light data interchange format [19]. Moreover, the reports are also proposed to keep a minimum number of attributes. The attributes selected in the approach are destination coordinates, a number of persons affected, and severity of incident. This report is filled by the emergency stations where actual incident is reported. The hands-on information is provided to immediate response team following which the data will be entered by data entry operator at the emergency stations and the updated information will be transferred to immediate response teams as soon as it is synced to cloud device. The application layer executes different required algorithms and dispatches the information to response team for actions.

Figure 9.3 shows how a light-weight report is transferred to a centralised repository.

Fig. 9.3
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Light-weight reporting

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3.2 Real-Time Route Information Dissemination to Emergency Vehicles

The information shared at time instance t with response team is based on the predictive analytics performed on the date received till t-x where x is the minimum taken for processing the data received and generating results. The selection of route for transit of emergency vehicles is made by feeding the above-mentioned data in the Dijkstra’s algorithm. The predictions made using models with high accuracy can falter due to the fact that the traffic is highly volatile in nature. Thus information received from RSU lying on all the candidate routes also needs to be transmitted continuously to cloud. In case connectivity of RSU to the cloud fails, the RFID sensors installed at the RSU will detect the near emergency vehicle in lane through RFID that is embedded in vehicles and pass on the information to all the neighbouring RSS for emergency lane clearance as the information related to the updated route is not amongst the RSUs. Similarly if the connectivity of emergency vehicle with the cloud fails, then the RSU will feed the recent most information received from the cloud to them. This information will continuously be processed for generation of routes from emergency vehicle’s road segment’s starting point/ending point to destination. The ILD detects the number of vehicles passing through it and submits the information to the RSU. The RSU lying on the selected route will continuously update the road density to cloud and broadcast information to the normal vehicles about clearing the emergency lane for the incoming emergency vehicle. The RSU nearest to the traffic light will keep on updating the recent most information about the emergency vehicle position to the traffic light controller. As soon as the emergency vehicle enters the last road segment before the traffic lights; the traffic lights for the lane would be turned to green and will be kept green till the vehicle passes the traffic light. The intelligent traffic light system that works on the density parameter of the different lanes will continue to work as proposed in the literature but will be overridden in the case of an approaching emergency vehicle [20].

3.3 Multiple Emergency Vehicles Intersection Crossing

Emergency vehicles include fire brigades, ambulances, and police cars. For single emergency vehicle, network cloud gives priority on the intersection of the road to turn on the green light on ITLS till the emergency vehicle crosses the traffic light. The third phase of the proposed scheme highlights the fact that it is not always possible that multiple emergency vehicles would not need to cross the intersection in a short difference of time span. In such cases, a mechanism needs to be developed for taking the decision about how to ensure that the maximum number of people in emergency situation could be saved. For multiple emergency vehicle approaching traffic signal from multiple directions at same time, then the selection of the traffic lane to be given prioritised green traffic signal, the below equation is to be used, where x is the intersection crossing impact,

$$\displaystyle \begin{aligned} f(x)=(a*.3)*(b*.7), \end{aligned} $$
(9.1)

a =  the number of persons affected and b =  incident severity. The weightage assigned to the incident severity is high, and this is due to the below mentioned reasons. In case the number of persons involved is very large but the incident severity is very low, the overall criticality of the event is considerably low. Similarly even if the number of persons involved is low but incident severity is very high, then the criticality of the event is considerably high as the untimely reaching of emergency vehicle may lead to loss of lives.

Another case of multiple emergency vehicles reaching the intersection point could be that the emergency vehicles are coming from opposite directions and need to go straight ahead. Thus, in such case strict straight green traffic light signal could be turned on for both the sides.

Final case could be that multiple emergency vehicles are coming from all the sides of the intersection. In such scenario, collective intersection crossing impact of different combinations needs to be evaluated such that intersection crossing impact is maximised and the emergency vehicles crossing the intersection do not collide with each other.

4 Conclusion and Future Scope

This chapter discusses a four-layered architecture for ITS as well as proposes a scheme for implementation of ITLS while considering various parameters related to the emergency vehicles and emergency situations. The proposed scheme suggests the usage of PON data notation for light-weight communication of reporting data and explains the various factors related to the need of real-time route evaluation and dissemination for emergency vehicles. The scheme takes into consideration that either emergency vehicle may lose connection to cloud/edge or RSU may lose connection to the cloud/edge and proposes a solution when only one of them fails. The proposed scheme needs to be evaluated by using real-time data or simulated data for the validation of the benefits proposed. Furthermore, research may be carry-forwarded for offline–online sync-based information dissemination mechanisms in the smart cities.