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Article

Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning

by
Okkie Putriani
1,2,*,
Sigit Priyanto
2,
Imam Muthohar
2 and
Mukhammad Rizka Fahmi Amrozi
2
1
Department of Civil Engineering, Universitas Atma Jaya Yogykarta, Yogyakarta 55281, Indonesia
2
Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 672; https://doi.org/10.3390/su15010672
Submission received: 29 November 2022 / Revised: 20 December 2022 / Accepted: 26 December 2022 / Published: 30 December 2022
(This article belongs to the Special Issue Transport Sustainability and Resilience in Smart Cities)
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Abstract

:
The need to solve public transport planning challenges using 5G is demanding. In 2019, the world started using 5G technology. Unfortunately, many countries have no equipment that is compatible with 5G infrastructures. There are two main deployment options for countries willing to accept 5G. They can directly venture to install relatively expensive infrastructure, called 5G SA (standalone access). However, more countries use the 5G NSA (non-standalone access) alternative, a 5G network supported by existing 4G infrastructure. One of the considerations for choosing NSA 5G is that it still performs 4G equalisation in its area. The data throughput is faster but still uses the leading 4G network. Interestingly, there are three types of 5G: low-band (sub-6), middle-band (sub-6), and high-band (millimetre-wave (mmWave)). The problem is determining the kind of 5G needed for public transport planning. Meanwhile, mobile network big data (MNBD) requires robust and stable internet access, with broad coverage in real time. MNBD movement includes the movement of people and vehicles, as well as logistics. GPS and internet connections track the activity of private vehicles and public transportation. The difference between mmWave and sub-6 5G can complement transportation planning needs. The density and height of buildings in urban areas and the affordability of the range of the connections determine 5G. This study examines the literature on 5G and then, using the bibliographic method, matches the network coverage obtained in Indonesia using nPerf data services. According to the data, urban areas are becoming more densely populated. Thus, this could show the differences in the data quality outside of metropolitan areas. This study also discusses the current conditions in terms of market potential and the development of smart cities and provides an overview of how real-time mobile data can support public transport planning. This article provides beneficial insight into the stability and adjustment of 5G, where the connectivity can be adequately maintained so that the MNBD can deliver representative data for analysis.

1. Introduction

In 2019, South Korea was the first country to offer 5G cellular services [1,2]. The decline in new 5G launches from 2021 carried over into the first half of 2022. In the first eight months of 2022, only 20 new commercial 5G launches were reported, bringing the total number of operators offering commercial 5G services in 85 markets to 220 worldwide [3]. 5G connections offer high network speeds for mobile experiences and daily activities. 5G simplifies and expands big data analysis, such as through the Internet of Things (IoT), robotics, artificial intelligence, augmented reality, and mobile cloud sensing [4,5,6]. Public transportation that is well-connected with 5G is expected to become more integrated and interconnected to achieve adequate mobility, low costs, optimal comfort, and safety [7]. As a representative developing country, Indonesia received 5G for the public in June 2021 [8]. In its implementation, Indonesia still found many obstacles to the availability of frequency bands that support 5G. The technology, communication, and information entering Indonesia continue to advance quickly. Infrastructure readiness should also support the rapid development of communication devices and computers that support 5G [9].
Many smart gadgets or things are connected to the Internet, enhancing the digital world’s scope in recent years. These intelligent items include physical devices and vehicles that can perceive real-world physical objects, collect data, and communicate with others. The objects are linked via the Internet, leading to the phrase Internet of Things (IoT), which is supported by cellular services. In public transportation, smart mobility plays a critical role in citizens’ activities [10]. The Internet of Things (IoT) revolution has transformed transport vehicles into veritable computers on wheels (mobility) with thousands of sensors. These sensors allow data on movements, vehicle status, fuel consumption, abnormalities, and the number of passengers [11].
This level of technological complexity will only contribute to the development of sustainable transportation if the 5G network’s internet connection is uniformly and sufficiently accessible [12,13,14]. In this research, we sought to understand the potential for managing public transport with the availability of the 5G network and its implementation in Indonesia.
2. 5G (Fifth Generation) Broadband Cellular Network
5G is the fifth generation of cellular networks [15]. Before this, there were 4G, 3G, 2G, and 1G. Each generation of network technology sets the communication signal standards for mobile devices according to the guidelines set by the International Telecommunication Union (ITU) [16]. Comparisons of 2G, 3G, 4G, and 5G [12,17] are shown in Table 1.
While 4G is being expanded, base transceiver stations (BTS) are available throughout the country. 5G has started to offer the advantages of its existing features. 5G offers higher internet speeds than the previous generation, namely, 4G. The three main differences between 4G and 5G are related to speed, capacity, and latency [19].
Numerous important drivers are required to realise the vision of the 5G future, as shown in Figure 1. The most crucial factor is connectivity. Within physic and economic limits, the mobile industry will push for data speeds of up to 1 Gbps and latencies of less than ten milliseconds for 5G networks. The underlying broadband capabilities of the 5G era will be provided by this higher technical performance, paired with the continuous evolution and availability of 4G networks, as well as suitable alternative network technologies. While 5G is expected to lower the cost per MB, the industry hopes it will also stimulate top-line growth for operators who can profit from the new value created. Operators will construct an agile, on-demand 5G core network to coordinate an ecosystem of a heterogeneous [20], multi-access network infrastructure to deliver connectivity in three usage scenarios to realise the vision of unbounded connection:
  • Indoors: using 5G macro cells (if on low-frequency bands), 5G small cells (for capacity or if on high-frequency bands or as a fixed wireless access unit), and integrating other heterogeneous networks, such as Wi-Fi, fibre and device-to-device communication;
  • In densely populated places, relying on the new 5G RAN, which includes 5G small cells in high-traffic zones (e.g., train stations, stadia, and shopping malls);
  • In economically depressed locations, a mix of 5G RAN (if accessible on low-frequency bands), 4G RAN, low earth orbit (LEO) satellites, and other alternative network technologies will be used.
5G will transform the mobile broadband experience in early deployments and drive new intelligent automation use cases in later phases [11]. It will evolve and leverage a variety of spectrum ranges. 5G will start as an urban-focused technology and integrate with 4G to provide exceptional connectivity from rural areas to metropolitan cities. An on-demand 5G core network for the coordination of an ecosystem of heterogeneous, multi-access network infrastructure to provide connectivity in three usage scenarios will be implemented, as shown in Figure 1, for indoors, outdoors in dense urban areas, and outdoors in economically challenging regions. According to earlier study, 5G offers a heterogeneous network environment to create an unrestricted, fast, and trustworthy network that can serve vital services for society [20]. But in reality, 5G appears to be divided into many use cases based on location and environmental factors. Figure 1 depicts a 5G scenario based on a localised setting.
From Table 2, 5G will supplement rather than replace 4G in mobile broadband. Using carrier aggregation, long-term evolution advanced (LTA+) and LTE advanced pro networks are planned to provide peak data rates of up to 1 Gbps, which is sufficient for many applications. To enable bandwidth-intensive applications, such as live streaming and augmented reality, 5G will bring substantially greater peak network speeds—up to approximately 10 Gbps [21].
5G deployments are classified into non-standalone (NSA) and standalone (SA). 5G non-standalone (NSA) is a 5G deployment supported by the existing 4G infrastructure. Meanwhile, 5G standalone (SA) is a 5G deployment fully supported by 5G infrastructure. Usually, 5G NSA is installed in areas where the population still needs to be, mainly using 5G or infrastructure that still needs to be improved. For example, Indonesia uses an NSA 5G network. The cost of 5G NSA is cheaper because it is not set-up from scratch, but in terms of the benefits of 5G, SA is much more comprehensive [22].
Type 5G is divided into low-, mid-, and high-bands [23]. Low-band can travel the farthest of the three. While it is the slowest 5G option, it is still faster than 4G. Mid-band occupies the best spot between speed and coverage. A limited number of cell phone carriers can use this type. High-band offers the fastest 5G speeds but cannot travel very far. It is best suited for covering a small area. High-band is also known as millimetre wave (mmWave) 5G.
5G is available in two different variants: sub-6 and mmWave [24,25]. These influential modes are named for their distinct type of wireless spectrum, with sub-6 referring to data transferred below the 6 GHz spectrum and mmWave referring to data sent above the 6 GHz spectrum. Spectrum is the medium via which cell towers and smartphones interact, and carriers can achieve different results with their services by employing different spectrum wavelengths.

3. Mobile Network Big Data (MNBD)

3.1. Big Data Transportation

Big data is a general term that refers to the technologies and techniques for processing and analysing large amounts of data, whether structured, semi-structured, or unstructured [26]. Big data is commonly used in all fields, including transportation [27,28]. The ability to process this data is used for decision making, from big data to analysis and prediction. This prediction is obtained using machine learning through patterns, regression, classification, and others. Big data analytics aid the public transportation industry in accurately forecasting passenger loads, congestion, poor weather, breakdowns, and customer feedback.

3.2. Real-Time Mobile Data

Data speed is essential because data can be sent directly for processing. There is no delay for navigation or tracking, and real-time data can now be monitored. The advantage of processing data in real-time is that the decision making is faster than for conventional data [10]. The forecasting and prediction using real-time data are expected to achieve more realistic results [29]. In real-time transportation, data are obtained from the movement of people and vehicles, as well as logistics. Mobile network big data (MNBD) is this movement in large numbers obtained from cell phones or GPS activities [30,31]. Table 3 shows an example of data movement from two IDs with the same relative time. In big data, there is mastery of data privacy (not including personal information). Activity is seen from one position of time and place to the next. When processed into data movement, the potential of this data can be a pattern of transportation trips, as shown in Figure 2, which will be included in transportation planning [32]. By entering the primary data acquired, this straightforward mapping takes advantage of the Google Maps platform. So that the movement from one point to another can be translated and modified for the environment. The spatial mapping area in this illustration is located in Yogyakarta, Indonesia, in Sendowo, Sinduadi, Mlati District, and Sleman Regency.
Figure 2 shows the travel patterns of two people using the digital footprints obtained from mobile phone data, which were geospatially documented based on the location and time difference. Human mobility is the term for this movement. According to the symbols in the image, the green line represents a person walking from one place to another, and the purple line depicts the bicycle transportation movement. Collecting this movement data will allow for big data options in the study of transportation.

4. Public Transportation Planning

4.1. Smart Cities

Smart cities are developing rapidly worldwide with the Internet of Things (IoT). Namely, bright things, such as sensors and actuators, and mobile device applications and installations that change the daily lives of citizens and interconnected parties. Smart cities generate enormous data sensor flows, while citizens help with web or mobile devices that use social networks [33]. The smart city concept places information technology as the key to city governance in managing all aspects. This collaboration includes government, energy, mobility, water, public services, buildings, and data centres, such as shown in Figure 3 [34]. Smart cities combine human intelligence and artificial intelligence on people, processes, and technology to create a sustainable city [35]. Interestingly, as shown in Figure 3, managing smart cities requires two things: integration and collaboration [36]—the integration between cities of all essences, and information and the cooperation of all parties from planning, implementation, and business development.
The goal of smart cities is to increase inhabitants’ quality of life sustainably, leading to the perfect integration of all stakeholders in these communities. IoT, the foundation of smart cities, depends on 5G implementation, making the goal of smart cities a reality.
With 5G, more devices may be connected to the internet regardless of place or time. A connected car and intelligent metering can cover the whole spectrum of business activities across vertical industry sectors.

4.2. Intelligent Transportation System (ITS)

Transportation problems cause multidimensional economic losses in time, health, and the environment. The cause of the transportation problem is the severe congestion of public transport. Systematically, various parties are involved, from road authorities and traffic controllers, but this alone cannot solve the problem. Gradually, the information system helps smooth transportation problems including scheduling, ticketing, traffic management, accident management, and control systems. This information system is called the intelligent transportation system (ITS) [12]. One of the advantages is profit. Figure 4 provides real-time travel information and shows the management model of all modes of transportation [37]. An ITS integrates closed circuit television (CCTV), global positioning system (GPS), video management systems, and content management systems technology [38]. Information and communication technology (ICT) is integrated with the transportation system, cars, and road users. The use of ITS can aid in handling the issue of traffic congestion. The advanced traffic signal control system (ATCS), electronic toll system, integrated traffic management centre, and the currently under construction multi-lane free flow are just a few of the ITS technologies that have already been implemented in Indonesia. Regarding ITS, several innovations have been put into practice, including those related to mobility, self-driving cars, real-time location, road safety, intelligent traffic lights, connectivity, smart logistics, and innovative trains. One subject is connected to another, as shown in Figure 4. A company’s 5G network is constructed to provide an internet connection to unify this line.

4.3. IoT (Internet of Things)

The Internet of Things (IoT) is the foundation for connecting various things, sensors, actuators, and other intelligent technologies [39]. IoT refers to physical objects equipped with sensors, computing power, software, and other technologies to connect and exchange data with other devices and systems over the Internet or other communication networks. It makes it easier to communicate. IoT is the collaboration of devices from vehicles and home appliances, with the data, resulting in connectivity and interaction [40]. They improve transportation through maintaining vehicle performance, enabling cars, optimising fleet operations, keeping traffic moving, smart roads, parking, bridge sensors, bicycle and pedestrian monitoring, micronavigation in public transport, and others. IoT widely connects with many objects to collect general data, integrates with digital smart cities, affects large volumes of data generation, and is called big data [41]. A smart city is based on the IoT, which employs big data analyses when harvesting real-time data from the city [42,43]. Sensors are used in smart homes, smart parking, vehicle networks, surveillance, weather, air monitoring systems, etc., to collect real-time data. In addition to smart digital city services, a complete system is proposed that uses architecture and implementation, which provides information and traffic data from the past. The system collects several types of data and manages the creation and management of data, aggregation, filtration, classification, pre-processing, computing, and decision making.

4.4. MaaS

MaaS (mobility as a service) refers to the shift away from personally owned modes of transportation that prioritises mobility as a service [44]. With the MaaS concept, it is hoped that there will be more public transportation users, with better service quality. MaaS is accomplished by merging public and private transportation services through a unified gateway that creates and administers the journey and allows customers to pay for it with a single account [45]. Users can pay for trips individually or pay a monthly subscription for a set distance. The primary idea behind MaaS is to provide travellers with mobility options tailored to their specific travel requirements. Figure 5 shows that the MaaS approach starts with the ease of offering transportation modes, ticketing, reduced waiting times, minimised delays, and access to change of transportation use [46].
The mobility as a service (MaaS) idea is depicted in Figure 5, with customer serving as the main focal point. Public transit, logistics, e-mobility, aeroplanes, and shipping are the different ways individuals reach customers. Connected life refers to connectivity while travelling from home to a place. Citizens use propulsion, automated cars, or drones for demand-based transportation in cities. Information sharing is essential to improve the quality of MaaS, facilities, and access in the logistics industry. The speed and reliability of the internet connectivity made possible by 5G are the only links in mobility as a service.

4.5. Sustainable Urban Transport (SUT)

Supposing that a 5G network is more uniformly spread and has more robust signalling in different places; in this case, the ten principles of sustainable urban transportation investigated by the Sustainable Urban Transport Project (SUTP) [48] will be better optimised. Urban planning [49] that is integrated and people-centred must put people first. For a city to be built around public transportation, integration and communication must work together effectively [50]. Information on parking availability and traffic statistics, including information on how quickly public transportation operates. Ticketing is offered, but it is convenient and everywhere. Data and information concerning vehicle sharing and public transportation are integrated. Until additional policies are oriented toward solutions for road users [51], the governing apparatus can connect to parking signs on the road in real-time.

5. Materials and Methods

This study conducted a literature review on topics related to 5G’s impact on mobile network big data’ readiness. Focusing on when 5G will be implemented, the two types mmWave and sub-6 were compared. Metadata from prior studies were first gathered for the literature review using Publish or Perish (PoP), which yielded 2120 metadata and 193 pre-existing studies in the literature. To enhance the visualisation of the research metadata and to aid in advancing the investigation, this evaluation incorporated a second bibliographic review using VOSviewer. This resulted in 1520 studies that were acquired after sorting based on duplicates, automation tools, and other factors. After filtering the data several times regarding the research topic, theory, completeness of data, and year of research, there were 62 references related to this topic. A flow chart was made using the PRISMA flow diagram [52] based on the systematic review, as shown in Figure 6.

6. Results

6.1. 5G in Indonesia

The implementation of 5G is the embodiment of digital transformation in Indonesia. A trial was carried out starting in 2017 related to the 2018 Asian Games. 5G operates based on IMT-2020 (International Mobile Telecommunication-2020), with a frequency band of 2300 MHz or 2.3 GHz. The 5G network service, which has been present in Indonesia since March 2022, is available in 9 major cities (Table 4) out of 514 cities in Indonesia. The presence of 5G does not necessarily displace the 4G services that the community has used. A 4G network is also needed as a 5G operational base, for implementing 5G NSA (non-standalone).
Optimising 5G services requires the allocation of a frequency spectrum with three layers: low-band, middle-band, and high-band. This 5G technology is flexible and can be applied to mobile broadband, fixed broadband, or fixed wireless access services. Frequency bands in the low- and middle-band layers, such as the 700 MHz, 2.6 GHz, and 3.5 GHz bands, are suitable for 5G mobile broadband services. Currently, 5G mobile broadband will be prioritised for development in Indonesia [54].

6.2. Network Coverage: 2G, 3G, 4G, and 5G

The following are spatial data visualisations [55] from telecommunications providers in Indonesia. Indonesia’s five most significant providers are Telkomsel, XL-Axiata, Indosat, Smartfren, and 3-Tri. Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11 show the regional mapping of the signal distributions of 2G, 3G, 4G, 4G+, and 5G. A service called nPerf uses a bitrate test on binary files with concurrent connections and a latency test run ten times to provide information about the quality of an internet connection [56]. This program gathers geo-localized statistics; it does not gather personal information, because it is carried out anonymously. The network of the operator is then described using the available data. Geolocation, network details, device model and type, system version, and test results are some examples of the associated data.
Figure 7 shows the Telkomsel provider with 5G distribution in several cities. Telkomsel was the largest provider of coverage area at this time in the implementation of 5G. Its distribution exists throughout Indonesia, although it is still centred in the east and central parts. This can be seen from the mappings in Figure 8 for the XL Axiata provider and Figure 9 for the Indosat provider. These two providers were still relying on the coverage area of western and central Indonesia. 5G was yet to be widely available, but it already existed in several cities on the island of Java, as provided in Figure 10 for Smartfren and Figure 11 for 3 Tri. These two connections focused on 3G, 4G, and 4G+ networks.
Figure 12 depicts the location of the 5G internet network in Indonesia and demonstrates that it has yet to be deemed successful. Only a few major cities in Indonesia have 5G services. Indonesia lags considerably behind Singapore, Thailand, and the Philippines regarding 5G implementation compared to other Southeast Asian nations. Figure 13 depicts the distribution of 5G internet networks worldwide, demonstrating that developed nations like East Asia, Europe, and America already have access to these networks.

6.3. Deployment 5G mm and Sub-6

The frequency spectrum is a critical resource for introducing 5G technology. High data speeds and vast bandwidth are provided by a broad range, allowing various innovative devices, applications, and services to suit the needs of multiple domains. On the other hand, a high spectrum has a smaller range (10–100 m2) than a sub-6 GHz spectrum. As a result, 5G adoption using high frequencies necessitates careful planning (mmWave). Therefore, this study planned to deploy a 5G new radio (NR) network using a 28 GHz mmWave frequency. Because of its market potential and infrastructure support, Central Jakarta was used as a pilot project to help Indonesia prepare for 5G deployment [57]. The sub-6 GHz range is used for network control and generally stable communications, while the mmWave spectrum is used for high-throughput applications [24]. Sub-6 GHz is a 5G technology that uses low-band frequencies below 6 GHz. Meanwhile, mmWave (Millimetreil-wave) is a relatively high-frequency band, with speeds between 24 and 40 GHz [25]. The high bandwidth generated by mmWave makes this technology offer faster access speeds. However, mmWave’s area coverage is not as comprehensive as sub-6 GHz. To use mmWave, users must be within approximately 100 m of a signal transmitting tower (BTS). Therefore, mmWave is quite expensive, because it requires many BTS (base transceiver stations) to cover a reasonably large area [58]. Due to the fact of its minimal characteristics, mmWave is suitable for use in dense urban areas. In contrast, it is claimed that sub-6 GHz is a better setup for rural or suburban areas. In addition to having a more comprehensive range and being better at penetrating objects, sub-6 GHz costs much less. In controlling public transportation, the 5G mm and sub-6 innovations enable vehicles to communicate with other vehicles more comprehensively with high internet access (vehicle-to-vehicle (V2V)) [59].

6.4. MNBD and 5G Implementation

Technological innovations based on the IoT and artificial intelligence using MNBD can produce several breakthroughs. Traffic control will react automatically to pedestrians. Automatic number plate readers will capture CCTV (closed-circuit television) images of the license plates of passing vehicles. The intelligent bus can detect routes by requesting a drone camera to monitor traffic. Automated toll lanes will follow the traffic and congestion patterns. Pedestrian and traffic sensors, by measuring traffic, will contribute to city planning optimisation. Private vehicle arrangements will be managed by RFID (radio frequency identification) tags. There will be V2V vehicles and voice navigation of real-time locations for people with visual impairments. An innovative railway will be designed for trains, and light rail transit (LRT), mass rapid transit (MRT), and high-speed trains can automatically report damage. A mobile application for passengers of buses will allow for ordering and waiting and will be used promptly and conveniently [60]. Examples can be seen in Figure 14.

6.5. Bibliometric Visualization

Bibliometric visualisation analysed the mapping of the metadata obtained during the literature review. Using VOSviewer [61], it was easier to review the research topics. Figure 15 shows the relationship between the transportation research and the Internet, and 5G can still be optimised. Figure 16 shows a timelapse of the related study, and the research related to the latest information technology was discussed but can still be reviewed further. Figure 17 shows that there is still room for research between subtopics, including transportation and 5G.

7. Discussion

Cities can improve public transportation operations and planning with 5G connections, even introducing dynamic transport planning, which minimises traffic congestion and reallocates space for cycling and pedestrians. 5G can help monitor the effectiveness of public transport vehicles in real time. Public transport management of user demand results will be conducted in more synchronisation between supply and demand. With the convenience of 5G, it is possible to create an origin–destination (OD) matrix close to real-time to increase the efficiency of transportation operators [62]. A real-time OD matrix can avoid operations with empty or overloaded vehicles, thereby improving the quality of service for users. With the convenience of 5G, the increased multimodal connectivity between modes of transport will integrate all mobility options into a single MaaS platform. As a bonus, public transportation users will have the convenience of onboard entertainment and a better user experience. Moreover, it can tighten security against violence as well as assault or limit sexual activity while traveling. Public transit passengers, bikers, pedestrians, and drivers of private automobiles are all considered transportation users.This connectivity is a harmonious embodiment of an intelligent transportation system [12].
Figure 18 illustrates the emerging risks in the transportation sector. Data availability, latency, the number of connected devices, and data sharing are all features of 5G. Vehicle-to-everything communication (V2X), intelligent networking, and real-time cargo and passenger monitoring are only a few of the transportation technology applications, such as information on autonomous vehicles, clever logistics, human mobility, and public transportation. One of them is the private sector’s management of MaaS development. Public transit and private MaaS will need digitalisation, which calls for an innovative government. Otherwise, public transportation will be plagued by inefficient trips, wasted energy, parking management, congestion fees, the impact of using walkways, and other problems. This is a challenging task for underdeveloped nations. The digital divide will widen even further as 5G technology advances [63]. The data gathered enable transportation users, operators, and authorities to make decisions and control traffic, which will benefit all parties.
Understanding the position of 5G on public transportation planning provides an awareness of the urgency of the internet network offered. The emphasis in future work related to subsequent research will be on how mobile network big data (MNBD) can help with integration between urban and rural areas. Driving travel behavior patterns [64,65] Prof Sigit, Pak Fahmi), mode selection for public transportation [66] (Pak Imam Muthohar), and implementation of big data transportation [67].

8. Conclusions

Each country must keep up with the 5G development of technology and information to be well connected. These linkages include the connectedness of disadvantaged, rural, and urban areas to the scope of a country. Further research is needed to complete the implementation and mapping of 5G mm Wave and sub-6 to equalise 5G connections so that the realisation of public transportation (e.g., public bus, LRT, MRT, train, and high-speed train) can be well integrated into an all-time situation. 5G mmWave is suitable for densely populated areas with high-rise buildings, and sub-6 is more appropriate for more comprehensive and quieter areas, such as rural or suburban areas. Sub-6 GHz is also much cheaper. Connectivity between public transport management, intelligent transport systems, mobility as a service, and big data analysis are how internet access can be reached in every region. With the stability and adjustment of 5G, connectivity can be adequately maintained so that the MNBD can deliver representative data for analysis.

Author Contributions

Conceptualization, S.P.; data curation, O.P.; methodology, M.R.F.A.; formal analysis, I.M.; project administration, O.P.; investigation, O.P.; resources, O.P.; software, O.P.; writing—original draft, O.P.; writing—review and editing, O.P., S.P., I.M. and M.R.F.A.; visualisation, O.P.; supervision, S.P., I.M. and M.R.F.A.; funding acquisition, S.P.; validation, I.M. and M.R.F.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Directorate of Research, Universitas Gadjah Mada grant number 089/E5/PG.02.00.PT/2022; 1931/UN1/DITLIT/Dit-Lit/PT.01.03/2022 and The APC was funded by Universitas Gadjah Mada.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data was obtained from and are available with the permision.

Acknowledgments

We would like to thank the Department of Civil & Environmental Engineering—Faculty of Engineering; Directorate of Research, Universitas Gadjah Mada; Directorate General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research, and Technology, Indonesia for supporting the research process.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. 5G as the centre of a heterogeneous network environment.
Figure 1. 5G as the centre of a heterogeneous network environment.
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Figure 2. An example trip pattern using mobile network big data using Google Map.
Figure 2. An example trip pattern using mobile network big data using Google Map.
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Figure 3. The general concept of smart cities [34]. Adapted by the author.
Figure 3. The general concept of smart cities [34]. Adapted by the author.
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Figure 4. Intelligent transportation system. Adapted by the author.
Figure 4. Intelligent transportation system. Adapted by the author.
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Figure 5. Mobility as a service. Elaborated by the author, based on [47].
Figure 5. Mobility as a service. Elaborated by the author, based on [47].
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Figure 6. PRISMA flow diagram.
Figure 6. PRISMA flow diagram.
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Figure 7. No signal, 2G, 3G, 4G, 4G+, and 5G network coverage: Telkomsel. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 7. No signal, 2G, 3G, 4G, 4G+, and 5G network coverage: Telkomsel. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 8. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: XL Axiata. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 8. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: XL Axiata. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 9. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: Indosat. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 9. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: Indosat. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 10. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: Smartfren. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 10. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: Smartfren. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 11. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: 3 Tri. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 11. No signal and 2G, 3G, 4G, 4G+, and 5G network coverage: 3 Tri. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 12. 5G coverage map for all of Indonesia. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 12. 5G coverage map for all of Indonesia. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 13. 5G coverage map worldwide. Adapted with permission from Ref. [56]. 2022, nPerf.
Figure 13. 5G coverage map worldwide. Adapted with permission from Ref. [56]. 2022, nPerf.
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Figure 14. 5G implementation smart transportation using MNBD. Adapted by the author.
Figure 14. 5G implementation smart transportation using MNBD. Adapted by the author.
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Figure 15. Network visualization using bibliometric analysis using VOSviewer.
Figure 15. Network visualization using bibliometric analysis using VOSviewer.
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Figure 16. Overlay visualization bibliometric analysis using VOSviewer.
Figure 16. Overlay visualization bibliometric analysis using VOSviewer.
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Figure 17. Density visualization bibliometric analysis using VOSviewer.
Figure 17. Density visualization bibliometric analysis using VOSviewer.
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Figure 18. 5G use cases in public transport planning. Modified from Ref. [62]. 2021, World Bank.
Figure 18. 5G use cases in public transport planning. Modified from Ref. [62]. 2021, World Bank.
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Table
Table 1. Comparison of 2G, 3G, 4G, and 5G.
Table 1. Comparison of 2G, 3G, 4G, and 5G.
Comparison2G3G4G5G
Introduced [18]1993200120092018
Bandwidth [17]30–200 KHz25 MHz100 MhZ30 GHz–300 GHz
Access SystemTDMA, CDMACDMACDMAOFDM, BDMA
TechnologyGSMWCDMALTE, WiMaxMIMO, mmWaves
AdvantageSMS, MMS, Internet Access, SIM IntroducedHigh Security, International RoamingSpeed, High-Speed Handoffs, Global MobilityExtremely High Speed, Low Latency
Application [17]Voice Calls, Short MessagesVideo Conferencing, Mobile TV, GPSHigh-Speed Applications, Mobile TVs, Wearable DevicesHigh-Resolution Video Streaming, Remote Control, Robots
Speed64 Kbps2 Mbps
21.6 Mbps (HSPA+ (Evolved High Speed Packet Access)		50 Mbps10 Gbps
Table
Table 2. 4G vs. 5G performance capabilities.
Table 2. 4G vs. 5G performance capabilities.
Capabilities4G5G
Latency10 milliseconds<1 millisecond
Data Traffic7.2 exabytes/month50 exabytes/month
Peak Data Rates1 Gb/s20 Gb/s
Available Spectrum3 GHz30 GHz
Connection Density100 thousand connections/km21 million connections/km2
Table
Table 3. Example of mobile network big data.
Table 3. Example of mobile network big data.
Anonymous IDDateTimestampLatitudeLongitude
A012B4861 June 202207:12:33−7.7713794110.3753058
A012B4861 June 202207:13:57−7.7718940110.3768761
A012B4861 June 202207:20:49−7.7722358110.3784207
C008D1291 June 202207:12:07−7.7676464110.3725624
C008D1291 June 202207:13:44−7.7676314110.3725608
C008D1291 June 202207:35:26−7.7697228110.3730843
Table
Table 4. 5G in Indonesia, last updated March 2022. Adapted from Ref. [53]. 2022, CNBC.
Table 4. 5G in Indonesia, last updated March 2022. Adapted from Ref. [53]. 2022, CNBC.
CityProvinceStatusTime Zone
JakartaDKI JakartaAccessibleUTC + 7
BogorWest JavaAccessibleUTC + 7
DepokWest JavaAccessibleUTC + 7
TangerangBantenAccessible UTC + 7
BekasiWest JavaAccessibleUTC + 7
BatamRiau IslandsAccessibleUTC + 7
MedanNorth SumateraAccessibleUTC + 7
SurabayaEast JavaAccessibleUTC + 7
SurakartaCentral JavaAccessibleUTC + 7
MakassarSouth SulawesiAccessibleUTC + 8
BalikpapanEast KalimantanAccessibleUTC + 8
DenpasarBaliAccessibleUTC + 8
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Putriani, O.; Priyanto, S.; Muthohar, I.; Amrozi, M.R.F. Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning. Sustainability 2023, 15, 672. https://doi.org/10.3390/su15010672

AMA Style

Putriani O, Priyanto S, Muthohar I, Amrozi MRF. Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning. Sustainability. 2023; 15(1):672. https://doi.org/10.3390/su15010672

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Putriani, Okkie, Sigit Priyanto, Imam Muthohar, and Mukhammad Rizka Fahmi Amrozi. 2023. "Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning" Sustainability 15, no. 1: 672. https://doi.org/10.3390/su15010672

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