Intelligent Total Transportation Management System for Future Smart Cities

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

Prelims

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

Intelligent Transportation Systems (ITS) are quickly changing urban mobility in smart cities, leveraging cutting edge technologies to address critical challenges in urban transportation. This paper provides a comprehensive review of the integration of Internet of Things (IoT), Artificial Intelligence (AI), and autonomous vehicles in ITS for sustainable urban mobility. Through a systematic literature review, we analyze the latest developments in traffic flow prediction, real-time traffic control, and vehicle-to-infrastructure communication. Our findings show that IoT-enabled sensors and AI-driven models significantly improve traffic flow prediction accuracy, while autonomous and connected vehicles show promise in improving intersection management and overall system efficiency. However, critical challenges in cybersecurity, data privacy, and infrastructure readiness that must be addressed for successful implementation is identified. Analysis suggests that when properly implemented, these technologies can lead to safer, more efficient, and environmentally friendly urban transportation systems, thereby enhancing the overall sustainability of cities.

This study to analyze the development of Public Transportation in the application of smart cities in the United States, China, Italy, and Brazil. The result of public transportation services dramatically contributes to increase mobility, and safety, as well as having a positive impact in urban areas. This research method uses a qualitative approach to bibliometric analysis. The data source is taken from the Scopus and articles searched for the last ten years (2012-2021) using keywords smart city and public transportation. The data found, 293 articles, with the top four countries having the highest number of articles. United States by 41, China by 32, Italy by 23, and Brazil by 20. The data analysis technique in this study used the Nvivo12plus and Vosviwer. The study results there are three concept findings in the development of public transportation; mobility, policy, and connectivity. The four countries have different focuses on implementing public transportation. United States focuses on mobility, policy, and connectivity with indicators such as urban mobility, intelligent transportation management, and governance. China focuses on mobility and connectivity, smart urban mobility, criteria, infrastructure, economy, and the environment, China is not the policy concept. Italy and Brazil also focus on the idea of mobility and policy with indicators that focus on the progress of urban mobility analysis and efforts to improve the convenience of public transportation to increase demand. The concept of connectivity has not been a concern for Italy and Brazil. Within these four countries, the United States dominates the development of public transportation, because it has fulfilled the three concepts of public transportation, namely mobility, policy, and connectivity. It is hoped that from the analysis of the development of this research, the four countries can further improve technological advances based on the Intelligent Transport System (ITS) to make public transportation smarter.

Big data analytics helps in analyzing a huge set of data whereas IoT is about data, devices and connectivity. Internet of Things (IoT) involves connecting physical objects to the Internet to build smart systems and universal mobile accessibility advanced technologies like Intelligent Transportation System (ITS). IoT solutions are playing a major role in driving the global IoT in Intelligent Transportation System. Communication between vehicles using IoT will be a new era of communication that leads to ITS. IoT is a combination of storing and processing sensor data and computing using data analytics to achieve and assist in managing the Traffic system effectively. IoT based Intelligent transportation system (IoT-ITS) helps in automating railways, roadways, airways and marine which enhance customer experience about the way goods are transported, tracked and delivered. A case study on Intelligent Traffic Management System based on IoT and big data, which will be a part of, smart traffic solutions for smarter cities. The ITS-IoT system itself forms an eco-system comprising of sensor systems, monitoring system and display system. There are several techniques and algorithms involved in full functioning of IoT-ITS. The proposed case study will examine and explain a complete design and implementation of a typical IoT-ITS system for a smart city scenario set on typical Indian subcontinent. This case study will also explain about several hardware and software components associated with the system. How concepts like Multiple regression analysis, Multiple discriminant analysis and logistic regression, Cojoint analysis, Cluster analysis and other big data analytics techniques will merge with IoT and help to build IoT-ITS will also be emphasized. The case study will also display some big data analytics results and how the results are utilized in smart transportation systems.

As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three-tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better-coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS-based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers' traveling time.

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

Purpose: The aim of the article is to present the results of a study on the perception and evaluation of intelligent communication and transportation systems, as well as their management, by residents of cities in the Silesian Voivodeship who travel by car, bicycle (scooter), or on foot, as factors contributing to safe and fast movement within the city. Design/methodology/approach: The study was conducted using a proprietary survey based on the Consumer Satisfaction Index (CSI) methodology, and the analysis of the survey results was preceded by a brief literature review on the subject. Findings: The study made it possible to determine how road users evaluate the functioning of the intelligent communication and transportation system in their cities, which factors are the most and least satisfactory for them, as well as the importance they assign to these solutions. Research limitations/implications: The study had a limited scope as it was a one-time survey. It is advisable to continue and expand the sample size for future studies to allow for comparative analyses. Practical implications: The article may provide managers of intelligent communication and transportation systems in cities with knowledge about the expected outcomes of their operation for road users. Social implications: Utilizing the research results may enable better adaptation of ITS to the needs of road users and enhance road safety. Originality/value: The study included drivers, cyclists (scooter riders), and pedestrians. In contrast, most similar studies are limited to verifying the opinions of drivers. Keywords: smart city, smart mobility, management intelligent transports system, cyclists, drivers, pedestrians. Category of the paper: research paper.

The rapid increase in population has increased the traffic density in urban areas. In current scenario, the major challenges faced by transportation systems are congestion, accidents and pollution control, etc. In recent decade’s development of Internet of Things (IoT) and communication technologies and connected devices have paved way for the involvement of information and communication in all aspects of smart city. IoT has transformed the transportation system by enhancing customer experience, safety, and operational performance. The smart or intelligent transportation system is possible that can provide in vehicle transportation and navigation system and assistance. Various new techniques are required to further enhance the intelligence and capabilities of applications. Cyber-physical system is an integral part of intelligent transportation system. CPS can help in gathering information in distributed manner over various diverse networks and analysing useful information to control operations of Intelligent Transportation system. This paper provides the description of the main elements of traffic management system in intelligent transportation systems. The focus is on the algorithms used to increase the efficiency of transport management in Intelligent Transportation System.

Rapid urbanization and increasing population concentration in the cities can pose many challenges that need to be addressed intelligently. The smart city can be a proper answer to these issues. With the research and development made for the smart city, smart mobility is an important aspect that can solve everyday transportation challenges the citizens face. Smart mobility introduced the concept of connected vehicles that can sense their surroundings and make intelligent decisions based on the data collected. Such a concept must take decisions requiring a secure interface to reduce the latency in sharing information. This review and analysis of the future of 5G and IoT in smart mobility discusses the current trends in the transport system, autonomous vehicles, public transport, car sharing schemes (mobility as a service) mobility on demand. IoT connects all transport systems and communicates using 5G technology which facilitates fast communication and reduces latency, allowing millions of devices to be connected to the network. In addition, this paper discusses how 5G can cater to the needs of Internet of Things (IoT) technology for smart mobility, which looks into the aspects of smart mobility and 5G technology helping smart mobility. Lastly, this study showcases an overview of 5G that enables smart mobility.

Rapid urbanization and increasing population concentration in the cities can pose many challenges that need to be addressed intelligently. The smart city can be a proper answer to these issues. With the research and development made for the smart city, smart mobility is an important aspect that can solve everyday transportation challenges the citizens face. Smart mobility introduced the concept of connected vehicles that can sense their surroundings and make intelligent decisions based on the data collected. Such a concept must take decisions requiring a secure interface to reduce the latency in sharing information. This review and analysis of the future of 5G and IoT in smart mobility discusses the current trends in the transport system, autonomous vehicles, public transport, car sharing schemes (mobility as a service) mobility on demand. IoT connects all transport systems and communicates using 5G technology which facilitates fast communication and reduces latency, allowing millions of devices to be connected to the network. In addition, this paper discusses how 5G can cater to the needs of Internet of Things (IoT) technology for smart mobility, which looks into the aspects of smart mobility and 5G technology helping smart mobility. Lastly, this study showcases an overview of 5G that enables smart mobility.

With the acceleration of urbanization and the continuous increase in car ownership, urban transportation problems have become an important factor restricting urban development. The purpose of this paper is to explore the construction and application of an intelligent urban transportation system based on the Internet of Things (IoT) to address the increasingly severe urban transportation problems. To achieve this goal, a method of system modeling, experimental design, and verification was adopted. First, build a smart city transportation system model based on IoT, clarifying the functions and components of the perception layer, network layer, data layer, and application layer. Next, a real experimental environment was set up and an experimental plan was designed to verify the performance of the system. The results show that the system can improve traffic efficiency, reduce travel time, and to some extent reduce environmental pollution. The conclusion indicates that IoT-driven smart city transportation systems have high intelligence, real-time performance, and accuracy, and can comprehensively monitor and manage urban transportation, providing solid support for sustainable transportation development.

The Internet of Things (IoT), a solely original computer paradigm, first appeared years ago. Its significance is demonstrated by the way it links the physical and digital worlds to achieve a significant benefit. The IoT is a network of internet-connected devices that enables widespread connections and interactions with other embedded devices, services, and people. With the growing need for smart services in smart cities, this paper focuses on the development of intelligent vehicular management systems in these cities. Here, the devices used in IoT have embedded systems that take the data from the environment. The data is transferred through a wireless network and a gateway to a carrier network. All the data will be stored at Application Enablement Platforms (AEP) which seem like web servers. The data is analyzed through big data analytics platforms. Finally, the devices and gateway remote management platforms distribute the new versions and upgrades to all devices. The primary parameters for determining road conditions include traffic flow rates, air quality, meteorological characteristics, and signal status as indicated by the received signal intensity for both road and intra-vehicular settings. Bluetooth Low Energy (BLE) beacons and mobile applications are employed for smart parking and navigation system, public transportation system, traffic size monitoring, and health-related issues for passengers.

This paper contributes to building a systematic view of the mobility characteristics of smart cities by reviewing the lessons learned from the best practices implemented around the world. The main features of smart cities, such as smart homes, smart infrastructure, smart operations, smart services, smart utilities, smart energy, smart governance, smart lifestyle, smart business, and smart mobility in North America, Asia, and Europe are briefly reviewed. The study predominantly focuses on smart mobility features and their implications in newly built smart cities. As a case study, the modern city of Lusail located in the north of Doha, Qatar is considered. The provision of car park management and guidance, real-time traffic signal control, traffic information system, active-modes arrangement in promenade and busy urban avenues, LRT, buses, taxis, and water taxis information system, and multimodal journey planning facilities in the Lusail smart city is discussed in this study. Consequently, the implications of smart mobility features on adopting Intelligent Transportation Systems (ITS) will be studied. The study demonstrates that the implementation of Information and Communication Technologies (ICT) when supported by Intelligent Transportation Systems (ITS), could result in making the most efficient use of existing transportation infrastructure and consequently improve the safety and security, mobility, and the environment in urban areas. The findings of this study could be considered an initial step in the implementation of Mobility-as-a-Service (MaaS) in cities with advanced public transportation such as Doha, the capital of Qatar. Doi: 10.28991/CEJ-2022-08-10-09 Full Text: PDF