**Table 4.** Parameter and Macro-Parameters for Transportation (Source: Web of Science).

Figure 21 provides a taxonomy of the transportation domain extracted from academia. The taxonomy was created using the parameters and macro-parameters discovered from the Web of Science dataset. The macro-parameters are shown on the first level of branches, the discovered parameters are shown on the second level of branches, and the most representative keywords are shown on the third level of branches.

**Figure 21.** Taxonomy extracted from Web of Science dataset.

#### *6.2. Quantitative Analysis (Web of Science)*

This section discuss the term score, word score, intertopic distance map, hierarchical clustering, and similarity matrix.

Figure 22 depicts that only the top 13 keywords in each parameter actually represent the parameter when we evaluate the keywords (see Section 3.8). Because the probabilities of all the other possibilities are so close to one another, their ranking becomes more or less pointless. When we investigated the top keywords per parameter to label the parameter, we used this information to focus on the top 10 or so keywords in each parameter.

**Figure 22.** Term score (Web of Science).

Figure 23 shows the top five keywords for each parameter. The importance score, or c-TF–IDF score, is used to order the keywords (see Section 3.8). There are 50 subfigures and in each subfigure, the horizontal line shows the importance score, and the vertical line shows the parameter keywords.

Figure 24 shows the intertopic distance map (see Section 3.8), where nine groups of parameters are clearly identified. In the bottom left corner, one group of parameters contains more parameters than the others. There are two small-size parameters on the right side, which are comparatively small. However, we notice that the BERT model clusters are not very well clustered, so we used domain knowledge and other information to label them. Additionally, we manually labelled the parameters into six macro-parameters.

**Figure 23.** Academic article parameter with keywords c-TF–IDF score.

**Figure 24.** Intertopic distance map (Web of Science).

Figure 25 describes the hierarchical clustering of the 50 clusters and systematically pairs them based on the cosine similarity matrix (see Section 3.8). This automated hierarchical clustering grouped the clusters correctly, with some exceptions.

Figure 26 visualises the similarity matrix among the parameters (see Section 3.8). We use the same configuration as discussed in Figure 11. The dark blue colour represents the highest similarity relationship between parameters, while the light green represents the lowest similarity. For example, Cluster 12, labelled as traffic flow modelling, and Cluster 17, labelled as traffic congestion, have high similarity scores as the main focus on traffic. There is another high similarity between cCusters 2 and 30, and both are labelled as VANET.

**Figure 25.** Hierarchical clustering (Web of Science).

#### *6.3. Policy, Planning and Sustainability*

The macro-parameter policy, planning and sustainability discusses the policy, services, planning, and development related to transportation. It includes 15 parameters, including land-use planning, smart cities, low-income neighborhoods, urban mobility, road safety, traffic congestion, subways, vehicles ownership, electric vehicles and charging, bike sharing and ridesourcing, children and schools, gender and race equality, parking, tolling, and tourism.

The land-use planning parameter discussed how to manage and map the transportation area based on several criteria. For example, analysing the usage of spatial metrics and population data to map the urban transportation areas [328] or the impact of walkability to the metro station on retail location choice [329], types of suburbanisation [330], etc. The smart cities parameter refers to urban areas that have experienced rapid growth and are technologically sophisticated. Crowd management [331], green transportation [332], and improving the quality of transportation systems [333] are the important areas of smart cities. With the progress of civilisation and rapid industrialisation, urban mobility becomes one of the key research areas in transportation. The investigation of urban mobility is the key factor for policy makers, and transport planners. The Road safety parameter is the combination of Parameters 33 and 38. This parameter is related to the research on road accidents and crashes, traffic collisions, pedestrian walking and safety [334], sidewalks, etc.

**Figure 26.** Similarity matrix (Web of Science).

Transportation affordability is one of the major issues in low-income neighbourhoods. Extensive research work has been conducted to analyse the issue and co-relation between transportation and low-income households. In [335], the authors examined transportation services and policies between 1960 and 2000 to determine the relationship between poverty and public transportation.In another study, the transportation affordability of low-income people in US cities was examined, and it was discovered that transit-rich neighbourhoods are more inexpensive than auto-oriented ones owing to reduced transportation costs [336]. With the advancement of civilisation and fast industrialisation, urban mobility has become one of the major research areas in transport research. For policy makers and transportation planners, investigating urban mobility is fundamental [337].

Hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), plug-in electric vehicles (PEVs), and plug-in hybrid electric vehicles (PHEVs) are the most prevalent forms of electric vehicles. There are two types of charger systems: off-board and on-board, with uplink and downlink power transmission. A study of battery charger layouts, infrastructure for PHEVs, and charging voltage levels is provided by [338]. Zhang et al. [339] demonstrated efficient planning of PEVs fast-charging stations while taking into account interactions between transportation and electrical networks. In other research, Fernandez et al. [340] evaluated the impact of PEVs on distribution networks. The PHEVs batteries are charged at home from the outlet or corporate car park, which has an extra impact on the distribution grid (i.e., voltage deviations and power loss) [341].

Recently, research on bike sharing and ridesourcing are dramatically increased after COVID-19. There is a lot of ongoing research work to improve the quality of resourcing and bike-sharing systems. For example, the survey of ridesourcing research [342–344], shared autonomous vehicles [344–346], the survey of bike share [347], and traveller experience after using the ride-sharing services, such as Uber [348].

#### *6.4. Transportation Modes*

The macro-parameter transportation modes includes four parameters: road transport, rail transport, air transport, marine transport. The road transport parameter is related to public transportation. Normally, there are three types of public transportation in rural areas such as intercity buses, demand-responsive transit, and deviated fixed-route transit which is discussed in this study. One of the keys to bringing sustainable growth and a higher quality of life to urban areas is better public transit planning, control, and management [349]. Public transit uses road space more effectively than private traffic and creates fewer accidents and pollutants. Furthermore, owing to the impractical nature of public transit in many places, many choose to use private transportation rather than public transportation [350]. Much research has been conducted to improve the quality of public transportation. For example, evaluation of road transportation in Brazil [351], analysing passenger satisfaction with public transportation in Romania [352], passenger flow prediction in public transport based on the timetable in India [353], effective system design for public transport in the USA [354], etc.

The rail transport parameter shows key research areas in rail transportation such as high-speed railways [355], rail freight transportation [356], risk management [357], railway transit traffic [358], and train services [359]. The objective of high-speed rail (HSR) is to enhance mobility via inter-city conveyance and facilitate financial growth. In October 1964, Japan's first modern high-speed rail line opened, linking Tokyo and Osaka at a maximum speed of 210 km/h [360]. In 1976, British Railways established a high-speed train link between London and Bristol. Following that, France started its first high-speed rail service between Paris and Lyon in 1981. Since then, several European countries, including Spain, Germany, Italy, Belgium, and the Netherlands, have built HSR lines. South Korea built its first high-speed rail line between Daegu and Seoul in 2004 which was later extended to Busan in 2009, while Taiwan began service between Taipei and Kaohsiung in 2007. China declared in 2016 that they would have about 38,000 km of HSR tracks by 2025 [361].

The air transport parameter is represented by keywords airport, airline, flight, air, aircraft, passenger, aviation, air transportation, and others. It discusses the environmental impact of air transportation with respect to air pollution, noise, and climate change [362]. The marine transport parameter is defined by the keywords ship, port, maritime, container, shipping, sea, vessel, ferry, model, cargo, etc. Marine transportation accounts for 80–90% of worldwide trade, transporting about 10 billion tonnes of cartons and solid–liquid weighty cargo across the world's oceans each year [363]. Some research has been conducted on marine transportation trade [364,365], waterway or marine transportation systems [33,34], waterway traffic flow modelling [366], and so on,

#### *6.5. Logistics and SCM*

The macro-parameter logistics and SCM comprises three parameters, the Clusters 44 and 45 are combined as freight and logistics, food supply chain, and cost and pricing. The logistics and SCM parameter contains the following keywords, business, company, industry, service, freight, and others. The parameter relates to freight transportation, where companies or industries are using technologies to evaluate and improve their business models, logistics services, and activities. For example, marine transportation contributes 75% of all international freight [36,367], and as international freight increases, seaports need to improve their infrastructure to maintain the services and market demand. International freight transportation should be expanded to promote the growth of the logistics industry [368,369].

The food supply chain parameter is represented by keywords including food, chain, product, meal, supply, supply chain, food supply, waste, food waste, temperature, etc.

This parameter is associated with research aimed at enhancing the quality and safety of industrial food supply chain management. In[370], the authors presented an architecture for goods supply chain management with three key elements: real-time surveillance of the goods to reduce lost and perishable goods; anticipating the heat of the package; and triggering an alarm if the goods are not reserved under the acceptance conditions. Fresh food transportation is another research area in the food SCM parameter. A mathematical model was proposed by [371] to distribute the fresh food to the end customer. The distributed ledger and blockchain technologies are also used in the agri-food supply chain [372].

The cost and pricing parameter is an important research area in transportation. For example, the spatial price policy under transportation asymmetry [373], reducing transportation carrier cost [374], and analysing the global transportation system [375].

#### *6.6. Pollution*

The macro-parameter pollution contains eight parameters, namely air quality and pollution, soil pollution, noise pollution, fuel types and effects, coal transport and consumption, waste management, wastewater treatment, and incident and risk management. The transportation industry is responsible for around 30% of the greenhouse emissions that contribute to global warming [376]. In recent times, air pollution in transportation has become a major public concern. Consequently, it is crucial to enhance transport efficiency to keep urban air quality [377]. A number of studies have examined the levels of exposure to presumed air pollutants such as VOCs (volatile organic compounds) [378]. Traffic congestion also has an impact on air pollution and quality [379].

Transportation is a major contributor to soil pollution. Railway transport, for example, pollutes nearby soils with heavy metals. Heavy metal contamination has a significant detrimental influence on the natural environment, including decreased enzyme activity in soil and ecosystem destruction [380]. Noise pollution, which is mostly caused by transportation and automobile traffic [381], is a significant environmental contaminant because of its physical and mental impacts on humans.

The fuel types and effects parameter includes several types of fuels keywords such as diesel, biodiesel, gasoline, oil, biofuel, etc. Biofuel use has an impact on carbon dioxide emission in the United States transportation [382]. To determine more sustainable fuel is another area of research. In [383], the authors analyze the factors which help to choose between ethanol and gasoline for the flex-fuel vehicles. Bayramo˘glu et al. [376] analyze the valve lift effects on the diesel engine.

#### *6.7. Technologies*

ITS, computer vision, smartphone sensing, VANET, autonomous vehicles and taxis, IoT security, physical layer communication, and robot mobility are the eight parameters of macro-parameter technologies.

Intelligent transportation systems (ITS) which have seen substantial advancement in the recent decade have been recognised as viable solutions to improve the transportation system's safety and reliability [384].

The computer vision parameter is the state-of-the-art research area including vehicle detection, vehicle type recognition, vehicle colour recognition [385], automatic license plate recognition [90], tracking moving vehicles, incident detection through the image processing task, vehicle counting system, traffic light detection, pedestrian detection, traffic sign recognition, etc.

The smartphone sensing parameter is another research area in the transportation field. Transport organisations actively promote public transportation to alleviate the traffic congestion produced by private vehicles. To increase the number of passengers using public transport, transport authorities can evaluate their local and premium services and market policies by analysing the travel patterns and regularity. For example, Ma et al. [386] proposed a data-mining process to identify the travel patterns in China based on the temporal and spatial features of Chinese smart card transaction data.

VANET referred to as vehicular ad-hoc networks, encloses V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communication architectures to enhance navigation, road safety, and other transport services. We noticed that VANET has been the most active research field for more than 10 years. The top three survey papers for VANET are [89,387,388] which provide an overview of VANET.

The parameter autonomous vehicles, also known as self-driving cars or driverless cars, have the ability to reduce travel time, increase road safety, improve fuel efficiency, and provide automatic parking facilities [389]. More research work is ongoing to improve the performance and quality of AV systems. For example, Haboucha et al. [390] discussed user preference concerning AV. In other research, a cost analysis of several types of AV was presented by [391].

#### *6.8. Modelling*

The macro-parameter modelling includes two parameters traffic flow modelling, and transportation modelling algorithms. Traffic flow models are important for analysing the performance of ITS applications. Extensive research work has been conducted on traffic flow models. For example, traffic flow prediction [392], traffic speed prediction [393], traffic violation detection, traffic sign detection, traffic congestion prediction, traffic data collection methods and passenger flow prediction [394]. Traffic coordination and monitoring depend on the traffic flow model. To solve the transportation modelling problems, i.e., fuzzy transportation problems where transportation source, destination, and time are represented as exponential fuzzy numbers, transportation scheduling problems, transport shipment problems, transport parameters (such as cost, capacity, speed) problem, and transportation network design problems, the researcher proposed several algorithms. For example, the genetic algorithm was used to solve the solid transportation problem [395].

#### *6.9. Temporal Analysis (Web of Science)*

In this section, we will analyse how the parameters have developed over time. Figure 27 shows the temporal progression of the parameter, which is distributed into eight subfigures. The first two subfigures represent the temporal progression of the policy, planning and sustainability parameter by showing seven and eight parameters, respectively. The next five macro-parameters show the temporal progression of transportation modes, logistics and SCM, pollution, technologies, and modelling, respectively. The last subfigure depicts the temporal progression of all macro-parameters.

The vertical line of the graph indicates the number of articles, which is defined as the intensity. The temporal progression of the macro-parameter policy, planning and sustainability is depicted in Figure 27a,b for better visualisation. The EV and Charging parameter has a higher intensity compared to the other parameters. Figure 27c shows that the rail transport intensity was increasing over time until 2017, and also more research was conducted on this parameter.

The intensity of articles for the macro-parameter logistics and SCM which includes three parameters: cost and pricing, freight and logistics and food supply chain is depicted in Figure 27d. Freight and logistics has the highest peak of about 14 in 2017. The temporal progression of macro-parameter pollution which includes eight parameters is shown in Figure 27e. We observed that there are more articles related to air quality and pollution and coal transport and consumption compared to others. Figure 27f shows the parameters of the technologies macro-parameter, where we observed that there was more research conducted on the VANET parameter. The intensity of articles for the macro-parameter modelling which includes two parameters: traffic flow modelling and transportation modelling algorithms is depicted in Figure 27g. We noticed that more research work was conducted on the transport flow modelling parameter. The temporal progression of all macro-parameters is summarised in Figure 27h. We discovered that n 2019 the macro-parameter policy, planning and sustainability had the highest peak value of 350. Pollution had the highest peak in 2021, about 250. The macro-parameter technologies and

transportation modes intensity was about 150 between 2016 and 2020. There has been less research on macro-parameter modelling, logistics and SCM.

**Figure 27.** Temporal progression of parameters (Web of Science): (**a**) policy, planning and sustainability (i); (**b**) policy, planning and sustainability (ii); (**c**) transportation modes; (**d**) logistics and SCM; (**e**) pollution; (**f**) technologies; (**g**) modelling; (**h**) macro-parameters.

#### **7. Discussion**

The broader aim of our work is to investigate the use of ICT technologies for solving pressing problems in smart cities and societies. Specifically, in this paper, we investigate the use of digital methods to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national and international governance. We brought together a range of deep learning, big data, and other technologies to discover transportation parameters from three different perspectives using three different types of data sources, viz. a newspaper (*The Guardian*), a transportation technology magazine (*Traffic Technology International)*, and academic literature on transportation (from Web of Science). These three types of transportation parameters were described in detail in Sections 4–6, respectively.

We discovered a total of 25 parameters from *The Guardian* dataset and grouped them into 6 macro-parameters, namely road transport; rail transport; air transport; crash and safety; disruptions and causes; and employment rights, disputes, and strikes. Figure 28 depicts the word cloud of the keywords of *The Guardian* parameters discovered by the BERT modelling, where the size of each keyword denotes its frequency that can be related to the importance of the keywords. The figure shows that *The Guardian* is primarily concerned with the government, trains, passengers, the general public, and accidents. As *The Guardian* is a UK-based newspaper, the train is one of the most used public transport in the UK which is clearly seen in the word cloud by the following keywords: train, rail, and railtrack. Additionally, some major transportation issues, such as accidents, delays, injuries, traffic, safety, and crashes, are also shown in this figure. Heathrow airport is the central international airport in the UK and is visible in the word cloud.

**Figure 28.** A word cloud generated from the transportation parameter keywords (*The Guardian*).

A total of 15 parameters were discovered from the *TTI* dataset, and we grouped them into 5 macro-parameters, namely industry, innovation, and leadership; autonomous and connected vehicles; sustainability; mobility services; and infrastructure. Figure 29 depicts the word cloud of the keywords of the *TTI* parameters discovered by the BERT modelling, where the size of each keyword denotes its frequency. The word cloud for transportation magazines is heavily dominated by the transportation industry. The keywords such as

technology, project, system, traffic, solution, autonomous vehicles are highly related to the transport industry field. Datum is a well-known organisation that has been providing solutions and services in the transportation sector since 1999, especially in the UK and Europe, where the majority of transport infrastructure and projects are conducted under this organisation.

**Figure 29.** A word cloud generated from the transportation parameter keywords *(TTI* magazine).

We discovered 49 transportation parameters from the academic dataset and grouped them into 6 macro-parameters. These are policy, planning and sustainability; transportation modes; logistics and SCM; pollution; technologies; and modelling. Figure 30 depicts the word cloud of the keywords of the academia parameters. Academic transportation research covers a wide range of topics such as public transportation, various pollution impacts and solutions, intelligent transportation systems, traffic, supply chain management, cost, and so on.


**Figure 30.** A word cloud generated from the transportation parameter keywords (Web of Science).

Reflecting on the multi-perspective view (public, governance, political, industrial, and academic) of transportation gained through the discovery of parameters from three different sources of datasets (see Figure 1; more detailed taxonomies and analyses are provided in the respective sections), we note that the newspaper parameters show more of a public and political view of transportation with the focus on the road, rail, and air transportation issues highlighting public discontent, accidents, cycling, pollution and effects on climate, road congestion, fuel prices, travel costs, privatisation and its impacts, dangerous driving, deaths, extreme weather impacts, and employment rights. Rail transport has a very high significance in the newspaper dataset due to the importance of the London underground and intercity rail transport in the UK. It may vary from country to country depending on the nature of transport in that country being used for intra- and intercity travel. Megacities tend to rely on rapid transit systems, but it can vary. The transport magazine parameters show an industrial view of transportation with great deal of the focus on autonomous and connected vehicles owing to several trials in progress around the world. The spotlights are also on crash and safety, tolling, parking, pollution, and electric vehicles. The Web of Science parameters show an academic view of transportation with various foci on policy, planning, sustainability, equity, children and schools, vehicle ownership, tourism, four transportation modes, freight and logistics, a wide range of pollution and its sources, modelling methods, and several technologies.

The three views show that there are many important problems such as transportation operations and public satisfaction that industry and academia seem not to place their focus on, or perhaps if they do, the solutions do not get much attention from policy makers and industrialists. For example, the fare-related parameters for public transport in *The Guardian* dataset show the importance of this topic for the public while it is not visible in the *TTI* and academic parameters. This could show the need for research in this area to model fares, provide insights, provide solutions for people for lower fares, and so on. Similarly, other parameters, issues, and contents such as the need for better employment conditions for drivers discovered from the public view (*The Guardian*) could be studied by academics and addressed by the industry, and this could set new trends where the technology-focused literature could bridge the gaps between social sciences, sustainability, and technology. We can also see that academia produces much broader and deeper knowledge on the subject, while many important issues such as a wide range of pollution affecting people and the planet do not reach the public eye. Our deep journalism approach could find the gaps and highlight them to the public and other stakeholders. We called this approach Deep Journalism because it allows capturing and reporting a relatively deeper view of a topic (e.g., transportation) from multiple perspectives, dimensions, stakeholders, and depths. Second, we use deep learning to automatically discover multi-perspective parameters about a topic.

Commenting on the relationship and contributions of this work to the specific field of journalism, we make the following observations. Traditional journalism has failed at its core purpose of providing citizens impartial information to maintain 'free' societies due to many reasons such as difficulties in maintaining the freedom and impartiality of media organisations and funding cuts, leading to public mistrust. The mistrust in traditional journalism coupled with the rise of digital technologies has given a rapid rise to citizen journalism. While citizen journalism solves some of the problems of traditional journalism, it comes with its own problems such as subjectivity and lack of regulations, standards, quality, and responsibility. In this work, we aimed to contribute to improving journalism through academic integrity and rigour. Academics should be in the vanguard of objective information, sincerity, impartiality, equity, and other ideals, and therefore the academics should search, pursue, propagate, and defend these ideals. If the academics fail to do so, then the responsibility lies on common people to pursue and be in the vanguard of the ideals needed to maintain a free society. An academic is not by profession and job but rather by action. The responsibility to maintain ideals lies with all people, and therefore everyone has the responsibility and needs to work towards upholding honesty, sincerity, equity, freedom, and other ideals. Through deep journalism, we aim to make impartial, cross-sectional, and multi-perspective information available to people, bring rigour to the journalism field by nurturing responsibility in people, make it easy for people to generate information for public benefit using deep learning, and make tools and information available to common people so they can search and defend ideals of free societies, including social, environmental, and economic sustainability. As we have explained earlier, the methods and tools developed in this work are not limited to journalism and can be used by engineers, scientists, policy makers, and others to understand the parameters for the design and operations of any sector such as healthcare, supply chain management, etc.

#### **8. Conclusions**

We live in a complex world characterised by complex people, complex times, and complex social, technological, and ecological environments. There is clear evidence that governments are failing at most public matters. The recent COVID-19 pandemic is a major example of global governance failure both at preventing such a pandemic and managing it. It is time that all of us take responsibility for both success and failure rather than criticising our governments and look into ways of collaboratively improving the governance of public matters.

While there are many reasons for government failures, we believe the lack of information availability is a fundamental reason that limits governments' abilities to act smartly and allows the lack of transparency to creep into policy and action, leading to corruption and failure. To this end, this paper introduced the concept of deep journalism, a data-driven deep learning-based approach for discovering multi-perspective parameters related to a topic of interest. We examined the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool, DeepJournal (Version 1.0), that implements our proposed approach. We built three datasets specifically for the work presented in this paper. These datasets will be provided openly to the community for further research and development. We elaborated on 89 transportation parameters and hundreds of dimensions, reviewing 400 technical, academic, and news articles.

The work presented in this paper and the investigation into the proposed Deep Journalism approach is far from perfect both in terms of its definition and scope as well as its exploration in this paper such as the different types of media, the use of deep learning and other computing methods, the investigations into the specific media and sources used in this paper, and more. We work in a range of research topics including smart cities [396–400], cloud, fog, and edge computing [401–403], big data [404,405], high performance computing [406,407], education [408,409], and healthcare [410,411], and plan to benefit from these technologies and topics for extending and improving Deep Journalism in the future.

Regarding NLP (natural language processing), more research is needed to understand the clustering performance of BERT and other clustering methods. For example, some clusters have some similarities, and it is not clear why these clusters are separated by the clustering algorithms or why an article from one topic is included in another, the quality of cluster boundaries, and so on. One obvious reason is that documents are related to all clusters, some more and others less. Another reason by example is a train crash that could also be linked to a road crash because there is something about crashes and similar vocabulary there. Death and injuries can be other linked topics. Another matter related to topic modelling, NLP, and BERT performance is to investigate the effects of language used by different communities and sectors such as in the case of newspapers using a different vocabulary because dramatisation is important for them.

The aim of the work presented in this paper is partly to understand the transportation domain comprehensively and use this understanding to improve the transportation sector. Our research concerns multiple sectors and disciplines such as healthcare and smart cities,

with a focus on developing interdisciplinary methods and technologies allowing for the minimisation of silos and inefficiencies through policy and action integration and the facilitation of holistic designs and optimisations. The proposed approach can also be utilised to create disciplines and curricula for teaching in schools and universities, training for staff in industry and government institutions, and more. Given ICT is penetrating all spheres of our lives, be it transport, politics, corruption investigations, legal actions, and more, capturing such details automatically is useful because it can lead to the development of automated algorithms for cross-disciplinary exploratory analysis, real-time investigations, decision making, and more.

Finally, a broader direction of investigation in the future would be to find how deep learning can further help develop multi-perspective and deeper insights into different issues, sectors, etc., related to our societies. We wish to investigate whether it is possible to develop rigorous methodologies and processes to gain deeper insights into various issues, the processes that could be automated and followed easily by the public to objectively investigate matters of civic concerns and produce impartial information that could be consumed by the public to develop informed societies. We wish to explore whether it is possible to cultivate informed societies through such processes (deep journalism), i.e., by enabling and nurturing the public to study matters of public concern through automated, rigorous, 'deep', processes and contribute to the debate through objective processes and impartial information. The definition of deep journalism proposed in this paper also entails that the scope of journalism should not be limited and should extend to any matters affecting the public and society.

We believe the possibilities for utilisation and potential impacts of the work presented in this paper and the proposed approach are significant and endless; we invite all communities interested in transportation and otherwise to investigate our proposed approach for a sustainable and joint future for all.

The bottom line is that a society is as informed as it wishes to be. This statement does not relieve oppressed governments and institutions from the responsibility for their oppressive actions against the public. It only highlights the fact that if individuals do not take responsibility for the governance of their environment, opportunists will rise and will blind them through misinformation and enslave them.

**Author Contributions:** Conceptualization, I.A. and R.M.; methodology, I.A. and R.M.; software, I.A.; validation, I.A. and R.M.; formal analysis, I.A. and R.M.; investigation, I.A., R.M., F.A. and E.A; resources, R.M., F.A. and E.A.; data curation, I.A.; writing—original draft preparation, I.A. and R.M.; writing—review and editing, R.M., F.A. and E.A.; visualization, I.A.; supervision, R.M. and F.A.; project administration, R.M.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors acknowledge with thanks the technical and financial support from the Deanship of Scientific Research (DSR) at the King Abdulaziz University (KAU), Jeddah, Saudi Arabia, under Grant No. RG-11-611-40.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used in this paper will be made publicly available.

**Acknowledgments:** The work carried out in this paper is supported by the HPC Center at the King Abdulaziz University. The training and software development work reported in this paper was carried out on the Aziz supercomputer.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*Article*
