Intelligent Transportation System Applicated in Smart Cities: Comparison
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The Intelligent Transportation Systems (ITS) was initially developed to help cities in attaining issues related to road traffic. However, due to its versatility, the system has been broadening to cover autonomous tolls fare collection, freight and fleeting system management, application of GIS, innovative satellite technologies, etc., especially in organized cities.

  • artificial intelligence
  • Internet of Things
  • smart cities

1. Internet of Things (IoT)-Based Intelligent Transportation System

The Intelligent Transportation Systems (ITS) was initially developed to help cities in attaining issues related to road traffic. However, due to its versatility, the system has been broadening to cover autonomous tolls fare collection, freight and fleeting system management, application of GIS, innovative satellite technologies, etc., especially in organized cities. Artificial intelligence is a driving factor for the management of transportation in smart cities. Its deployment will help in seamless vehicular movement, traffic detection, accident avoidance, obtaining real-time information of vehicles and other road users, enhancing security, adding efficiency to the system by converting it to be human-error-free, and providing prompt safety and support to the drivers. The objective of the already unveiled idea of sustainable traffic management using the ITS approach in smart cities is to establish robust transportation systems that could provide reliable and efficient networks, manage the overall travel time, minimize fuel consumption, and mitigate expected pollution-threatened environments. This can be achieved through consistent and reliable collection, organization, and analysis of data by presenting the result for proper and timely traffic decision-making processes, and these innovative ways have to be compatible with the versatile features of smart cities [27][1].

2. Applications of the Intelligent Transportation System in Smart Cities

2.1. Detecting Transportation Incidences

Entire transportation systems are vulnerable to the occurrence of unavoidable incidences due to human–machine interactions. ITS can be deployed as an aiding tool in detecting such incidences. The real-time data and location can be sent to/or communicated with the control center for effective management. These incidences can be accidents, traffic congestion, or a security threat. The detected information can be used to give road commuters an alternative route. To ascertain this detection capability of ITS, a relatively same concept was employed by Gothenburg’s tram system in Sweden [28][2], which indicates that these incidence detection devices and sensors have contributed immensely to energy saving, re-routing, and the management of traffic from the incidences. Therefore, adopting this in smart cities will serve as reliable tools for managing traffic [29][3].

2.2. Automated Ramp Control System

Most activities in smart cities are organized to be automated, and as part of an ITS-based IoT means of managing traffic, which works with sensing devices, the sensors will detect the traffic density, speed, and volume of a particular section of the road. The data fed through sensing the optimal level and spaces between the streams of traffic will be analyzed, and the output of the results will promptly decide on the volume and speed limit that will be expected based on the stream of the traffic through the usage ramping control [30][4].

2.3. Traffic Signal Management

As presented earlier, most ITS devices are detectors, and balancing the traffic supply and capacity of the road network is a challenging task that cannot be easily controlled manually. However, with ITS-based inductive detectors installed on the road’s surface, traffic volume, speed, and queue can be detected and give automatic solutions through communication with the central server of the main control room [31][5]. The baseline working condition is that the communication devices should be versatile and have full network coverage and processing capabilities to ensure timely data processing. This is where the full implementation of IoT-based devices comes into playing a vital role in the management of traffic; these devices with powerful processors were normally configured to give adequate cycle time and green times for each of the signals, and these detectors were designed to ensure that priority vehicles are given special consideration. The detection is usually conducted by integrating GPS devices in the systems linked with the central control rooms in the cities. These devices were in commercial usage, and they were introduced by the Sydney Coordinated Adaptive Traffic System and Split (SCATS), Cycle Time and Offset, Optimization Technique (SCOOT); these systems were practically deployed in Kingston, a suburb in London, UK, as a gating system [32][6].

2.4. Effective Parking Management Tools in Smart Cities

In smart cities, parking provisions and locations are crucial because non-proper and inappropriate parking may render some traffic management systems dysfunctional. However, parking in smart cities may be partially based on ITS. However, incorporating ITS into conventional parking methods will help commuters with information on parking guidance, payment methods, locations reserved as on-street parking lots, and space management [33][7]. An ITS parking payment was extensively employed in most cities of Europe, especially in Spain.

2.5. Demand-Responsive Transport Management (DRTM)

One of the areas in which ITS-based IoT will play an immense role in smart cities is Demand-Responsive Transport Management (DRTM). Conventionally, most public transport vehicles were designed to operate on specific routes, irrespective of the variability of traffic changes. To let passengers get real-time and exact routes, alternative routes, expected times of trips, and the number of commuters in particular vehicles, flexible scheduling, and booking systems is usually overlapped with the public transport systems [32][6].

2.6. Logistics Management

The characteristics of smart cities, and inter-city transportation of goods and services remain one of the backbones for socio-economic development, and smart cities are no exception in this regard. Fleet and freight management is of paramount importance to companies, and this includes an ability to track vehicles, predict routes, origins and destinations, trips schedules, alternative route detections, and the management of fuel consumption executed using satellite and radio technology. Due to the limitations and broad width of these technologies, they can be deployed within specific geographic locations, of which a smart city is a typical and precise example. In the research conducted, it was found that the implementation of these technologies to the logistic sector will help companies save up to 9% of the total running cost [33][7].

2.7. Special Provision to Vulnerable Road Commuters

To have considerations and maintain the equity for all classes of people in smart cities, people vulnerable to the threat of accidents need special treatment regarding their usage of transportation infrastructures, which conventional traffic management gives less attention to due to poor perceptions of road events. An IoT-based ITS can be used due to its sensitive nature to provide a system helping specific groups of people with limited capabilities on the road. These solutions can be used in pedestrian crossing areas and other public locations [34][8].

2.8. Route Guidance

As a segment of ITS, GPS is used for obtaining route information linked to origin and destination, and the system could be used to reduce energy consumption and travel time management. It will also help drivers find an alternative route in which real-time information gives congestion data of the particular route [30,35,36][4][9][10].

2.9. Cooperative Perception

The development of smart cities keeps growing with an unprecedented increasing ownership of automated vehicles, aiming to encourage sustainability by reducing fuel consumption and decreasing pollution, while providing more comfort to the vehicle users, helping attain the safety of the entire transportation system [37][11]. These vehicles should have a clear perception of the surrounding environment, usually achieved through installing sensors, cameras, radars, lidars, etc. [38][12]. However, these conventional devices and systems have built-in deficiencies of not working to the probable expectations, especially under extreme weather conditions [39][13]. To overcome these limitations of conventional systems and devices, a cooperative perception was developed, which works with the help of wireless communications between devices and systems, then transmits and transfers information between the vehicles and infrastructure nodes [40][14], and it served as an avenue for the timely detection of the surrounding environment and other temporary obstacles, especially unconnected infrastructure, impacting the roads [41][15].
To bridge the gap of blind spots in real-time maps, which are mainly caused by obstacles, improved systems of cooperative perception employed the application of GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) [42][16]. However, these tools have been in use for decades as navigational tools. Nevertheless, data fusion happens to be the major challenge for these integrated systems, usually addressed using the Kalman filter. Apart from the conventional Kalman filter, various improved Kalman filters have been developed to cover the faults of the conventional Kalman filter in the ability to detect only linear systems in the navigation field by providing optimum estimates using available parameters of the models and noise [43][17]. These include the Cubature Kalman filter (CKF) [44][18], the Unscented Kalman filter (UKF) [45][19], etc. Detection and sensing abilities are unique features of artificial intelligence tools, especially in autonomous driving, population counting, and agriculture, and different tools are available for that purpose [46][20]. However, unmanned aerial vehicles, UAV RGB, are cost-effective compared to other tools such as lidar [47][21], multispectral cameras [48][22], GNSS/INS systems [49][23], etc. The available object detection tools are designed for general purposes and can work on most platforms. However, most of these databases are facing challenges detecting smaller objects, in which tassel detection based on UAV is no exception. To have an improved tassel, it is necessary to have precise annotations and a versatile object detection algorithm. Therefore, a modified YOLOv5 architecture, called the YOLOv5 tassel, was developed to detect tassels [50][24].

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