Smart Transportation System Research Articles

Smart transportation planning and its challenges in the Kingdom of Saudi Arabia

In the 21st century, transport has become an essential factor defining countries' progress or failure. The century has also witnessed a shift from conventional styles of transport to modernized and smart models that facilitate the effective movement of people between various destinations. Smart transportation refers to an approach that integrates modern technologies into the transportation system to enhance urban mobility efficiency. This paper examined the cornerstones and benefits of smart transportation planning and analyzed the challenges of implementing smart transportation in the KSA. The methodology comprises identifying, screening and analyzing the peer-reviewed articles on smart transportation in KSA. Databases such as Elsevier, Google Scholar, and Springer and Science Direct were used. During the assessment, it became evident that safety, convenience, and speed are the factors that inspire KSA to shift towards smart transport. Smart transportation is beneficial as it provides employment opportunities, encourages investments and improves people's living standards. The findings offered helpful insights regarding the different issues that affect the effective implementation of smart transportation systems in KSA, including lack of resources, regulatory framework and skilled personnel. The recommendations that are evident from the study comprise stakeholder involvement and intensive planning. The country must adopt policy-driven strategies, standardization, and inclusive development with clear private, public, and research collaboration commitments. Close adherence to these recommendations will help KSA attain the benefits of smart transportation systems.

A Case Study of Existing National Highway no. 5 & 6

Abstract: The case study of National Highways 5 and 6 provides insights into the infrastructure development, economic impact, and challenges associated with these critical transportation corridors. National Highway 5 stretches from Chennai to Kolkata, while National Highway 6 runs from Mumbai to Kolkata, serving as vital arteries connecting major cities and facilitating trade and commerce across regions. Both highways underwent significant upgrades and expansions to meet the growing demands of transportation and enhance connectivity between key economic hubs. This involved road widening, modernization of toll systems, and the implementation of safety measures to improve the overall quality of the highways. The case study may conclude by discussing future prospects for NH 5 and 6, including potential expansions, technology integration for smart transportation systems, and strategies to address emerging challenges such as climate change resilience and sustainable mobility.

THE ROLE OF STARTUPS IN PROMOTING THE DEVELOPMENT OF SMART CITIES – DUBAI AS A MODEL

Objective: The objective of this study is to learn about the role of start-ups in promoting the development of the smart city of Dubai, since these companies are always seeking innovative solutions to the challenges facing cities and rely on the latest methods and software used, thus helping cities to achieve the indicators that guarantee them important rank among other smart cities. Theoretical Framework: In this study, the basic concepts of the variables of the study were addressed by identifying the concept of start-ups, their characteristics and their importance in general, as well as by highlighting the subject of smart cities by listing some definitions and mentioning their advantages and dimensions. To then link the two variables by clarifying the relationship between startups and smart cities. Method: The methodology adopted for this research includes a sample of the most important start-up companies in Dubai selected from STARTUPRANKING, and the services provided by each company and its area of operation have been identified. To then determine the impact of the services provided on any indicators on which cities rely to become smarter (smart governance, smart transport, smart energy, smart technology, smart infrastructure, smart health, smart buildings and intelligent populations). Through which the role of these companies in the development of smart cities is determined. Results and Discussion: The results obtained revealed that Start-ups promote the development of cities by contributing to the challenges facing cities, communities and individuals by: - Ensuring a clean environment and smart transport systems that help to facilitate the movement of citizens and tourists through the use of telephones and at the lowest cost; - Strengthening cyber security to protect digital systems from threats; - Developing programs and techniques used in the educational sector, and focusing on developing the capabilities of individuals to ensure optimal use of technology; - Finding energy and environmental solutions to ensure optimum use of available resources. Originality/Value: This study sheds light on the importance of modern technology-based start-ups in promoting the development of smart cities. Dubai has been chosen as a model for a leading and developing Arab city that ranks first globally compared to other smart cities, and can be used as a successful experiment. In addition, the importance of both start-ups and smart cities, which are the pillars for the economic development of countries, they rely on advanced technology and the swift provision of services, ultimately facilitating life and enhancing the standard of living for their citizens.

Road Network Traffic Flow Prediction Method Based on Graph Attention Networks

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities.

Challenges and Methods for Smart Transport System using Big Data Analytics and IOT

Challenges and Methods for Smart Transport System using Big Data Analytics and IOT

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

Enhancing smart city operation management: Integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study.

Smart transportation systems using learning method for urban mobility and management in modern cities

The pressing demand for an escalation in renewable energy production arises from the surge in power consumption, escalating environmental concerns, and the exhaustion of fossil fuels. Microgrids, featuring dispersed power sources, exhibit superior resilience, efficiency, and stability in contrast to conventional power grids. Noteworthy investments in eco-friendly initiatives have been made by various major corporations, particularly in institutional structures. The current research advocates for the integration of photovoltaics, energy storage, and electric vehicles (EVs) as pivotal elements in a smart grid. This study introduces an optimal energy management system (EMS) designed to efficiently allocate power from existing sources. To address the challenges within an uncertain and reliable environment, a stochastic framework leveraging the whale optimization algorithm and unscented transform has been formulated. All simulations have been executed using the digital twin of the smart grid components. Results suggest that the proposed EMS ensures uninterrupted power supply, contributing to a substantial 45% reduction in energy costs. Furthermore, the potential of EVs as storage devices is explored. When employed as energy sources, EVs led to a noteworthy 45.58% reduction in power prices, while their use as loads still yielded a significant 19.33% decrease. The study also delves into the analysis of outage effects on the continuous power supply cost.

Smart and Sustainable Public Transportation - A Need of Developing Countries

A sustainable transport system is one which offers economical solutions and causes no/little damage to the environment. We usually categorize transport into three sections - roads, airways and water. Such systems need to be set up in order to have a transportation medium or ecosystem with minimum environmental impact. Public buses are an excellent example of sustainable road transportation. They can accommodate a large number of people economically and help ease the financial burden on individuals. Unfortunately, the majority of public buses are powered by non-renewable energy sources such as diesel. This results in the release of hazardous pollutants, leading to a degraded environment. Although the cost of using this form of transportation is relatively affordable, it nonetheless has an adverse effect on the environment. Therefore, it cannot be considered sustainable in that regard. We are constantly pushing the boundaries of innovation by introducing new technologies such as IoT, AI, Blockchain and 5G into our smart and sustainable transportation system. This way we can ensure its sustainability from all angles. The impact of transportation on our society is undeniable- both in terms of economic, social and environmental implications. To ensure a better quality of life for all, we must shift to smart and sustainable transport solutions. This research seeks to present a clever and eco-friendly transport system that brings economic and social advantages for developing countries.

Alcohol and Drowsiness Detection in Smart Transportation System

Abstract: Drunk driving is a major contributing factor in the rising number of unintentional deaths that occur in the modern day. Drunk driving is an extremely dangerous practice that can result in car accidents and injuries on the road. Numerous preventative actions had been implemented, including license suspension, fines, and the seizure of motor vehicle permits. Notwithstanding the numerous measures implemented, the proportion of collisions caused by intoxicated drivers continues to rise. The purpose of this module is to shield people from needless deaths brought on by intoxicated and sleep-deprived drivers in traffic accidents. This module includes a Wi-Fi module and an alcohol detection sensor (MQ-3) based on the Raspberry Pi model 3. We are incorporating an automated drowsy driver monitoring and accident prevention system as part of the design of our major project.

A survey on drowsiness detection system with advanced face tracking

To address the increasing dangers associated with driver and worker fatigue, this project introduces an advanced Drowsiness Detection System featuring state-of-the-art face-tracking capabilities. The pressing need for fatigue detection is evident in the alarming figures of 800 annual fatalities and 50,000 injuries resulting from drowsy driving incidents. This research expands the application of the technology to industrial workplaces, where the consequences of drowsiness are equally severe. Our comprehensive approach involves real-time monitoring of facial features, with a focus on eye movements and eyelid patterns. This system goes beyond traditional boundaries, covering drivers and industrial workers operating heavy machinery. By incorporating facial landmarks and introducing the innovative Eyes Aspect Ratio parameter, our technology offers a precise assessment of weariness in individuals within the current frame. This approach enhances safety measures in smart transportation systems and industrial settings. Integrating facial landmarks allows for a nuanced understanding of fatigue, recognizing subtle changes in facial expressions and movements indicative of drowsiness. The Eyes Aspect Ratio parameter, a novel addition, improves weariness assessment precision by considering factors such as eye closure duration, blink frequency, and gaze direction. These outcomes signify a significant contribution to road safety and broader workplace security. By mitigating inherent risks associated with drowsiness among industrial workers, our technology aims to reduce accidents, prevent injuries, and save lives across various occupational settings. The potential impact extends beyond individuals, influencing organizational safety protocols and contributing to the overall well-being of workers in high-risk environments.

Advanced Privacy Scheme to Improve Road Safety in Smart Transportation Systems

In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.

Calculation of the Level of Death Risk in Traffic Accidents Based On Aggregate Loss Costs

The transportation sector is undergoing transformative changes, driven by the emergence of autonomous vehicles and advancements in smart transportation systems. However, these innovations pose novel challenges, particularly in mitigating the risk of fatalities in traffic accidents. This study focuses on estimating death risk in traffic accidents by analyzing aggregate loss costs within the context of traffic safety. Employing a quantitative approach, the research utilizes the Poisson distribution to model the frequency of fatal incidents and the exponential distribution function to depict the distribution of associated losses. The study's objective is to calculate aggregate losses, offering insights into the potential severity of risks. Through a comprehensive analysis, the results affirm the efficacy of the Poisson and exponential distributions in assessing death risk in traffic accidents, with the highest estimated aggregate loss cost reaching Rp3.734.832,09.

IOT Based Accident and Analysis System Using Edge Computing

The increasing number of road accidents and their associated consequences necessitate the development of advanced systems for timely detection and analysis. This paper proposes an Internet of Things (IOT)-based Accident Detection and Analysis System that leverages Edge Computing for real-time processing and decision-making. The system integrates smart sensors, edge devices, and cloud infrastructure to create a comprehensive framework for efficient accident detection and analysis. The proposed IOT-based Accident Detection and Analysis System enhances road safety by enabling quick response to accidents and providing valuable insights for preventive measures. The integration of edge computing optimizes resource utilization, minimizes latency, and enhances the overall efficiency of the system. This research contributes to the evolving field of smart transportation systems, addressing the critical need for advanced technologies accident detection and analysis.

Use of Technology to Promote Road Safety: Public Health Perspective.

Road safety has been acknowledged as a public health concern of global significance owing to its direct association with human lives and their well-being. Road safety is a much broader concept than measures that are aimed to just prevent accidents, but it extends to improving the quality of life of members of the community, safeguarding environmental health, and improving the economy of the nation. With the passage of time, several technologies in road safety have surfaced, and their collective employment can aid in the establishment of a smart, safe, and efficient transportation system in nations. In fact, these technologies will account for a significant reduction in the number of accidents, better management of traffic, and improved road safety. In conclusion, ensuring road safety can save lives, promote responsible behavior, and create secure transportation system. There is an immense need to create awareness, engage the community, and embrace technology to minimize the possibility of road accidents and ensure safe journeys for all.

Machine Learning Driven Smart Transportation Sharing

In many urban areas, traffic congestion has become one of the most challenging issues of modern life, resulting in detrimental effects on the environment, productivity loss, fuel wastage, and longer travel times. As a solution, people are increasingly turning to shared transportation modes due to the convenience of multi-modal journeys facilitated by smart transportation systems. The last mile problem refers to the fact that, in large cities, buses and trains deliver passengers to transit stations close to retail and job areas, leaving them needing another form of transportation to reach their final destination. By promoting the use of public transportation and addressing this issue, a smart bike-sharing system can contribute to reducing traffic congestion. The study presents a review of various methods that are associated with the designing of the bike sharing system and suggests a model incorporating various methods to derive solutions, with a focus on utilizing clustering algorithms for the analysis of the provided time series dataset. The study reveals that the application of algorithms such as the K-Means algorithm, Fuzzy C-means, etc. would be very effective in visualizing the resulting clusters and improve the forecasting accuracy.

A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems

This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architecture tailored for edge computing. This architecture employs MQTT for efficient event transport and utilizes Elixir’s lightweight concurrency model for distributed processing. Robustness and scalability are further ensured through the EMQX broker. We demonstrate the effectiveness of our approach through two smart transportation case studies: a traffic light system for dynamically adjusting signal timing, and a cab dispatch prototype designed for high concurrency and real-time data processing. Evaluations on an Apple M1 chip reveal consistently low latency responses below 5 ms and efficient multicore utilization under load. These findings showcase the system’s robust throughput and multicore programming capabilities, confirming its suitability for real-time, distributed edge computing applications in smart transportation. Therefore, our work suggests that integrating Elixir with an event-driven model represents a promising approach for developing scalable, responsive applications in edge computing. This opens avenues for further exploration and adoption of Elixir in addressing the evolving demands of edge-based smart transportation systems.

Enhancing road safety through advanced predictive analytics in V2X communication networks

"SafeRouteX," which stands for "Enhancing Road Safety through Advanced Predictive Analytics in V2X Communication Networks," is a novel approach to Vehicle-to-Everything (V2X) networks. Despite advancements in vehicle and road safety technologies, traffic accidents continue to be a significant problem. To increase road safety, SafeRouteX uses cutting-edge machine learning to forecast traffic accidents in V2X networks. The first data is processed by a data ingestion layer, and then the temporal patterns and uncertainties in the traffic data are extracted using LSTM-VAE features. The Elastic Net Layer minimizes dimensionality while retaining predictive power after feature selection optimization. A RandomForest classifier, which performs well on complicated, high-dimensional datasets, is the main predictive model. Combining approaches guarantees a solid strategy for the complexity analysis of traffic data. Using 4-fold cross-validation, the accuracy and generalizability of SafeRouteX were thoroughly evaluated across a range of traffic scenarios. This validation approach demonstrated SafeRouteX's ability to anticipate traffic accidents, highlighting its potential to enhance road safety in smart transportation systems.

Spatial-temporal evolution analysis of the impact of smart transportation policies on urban carbon emissions

The impact of smart transportation policies on carbon emission control has become a new issue in urban development. Existing literature has primarily focused on smart transportation technology and new energy vehicles, while ignoring the impact of smart transportation policies. By using a quasi-natural experiment from China, this paper analyzes the impact of smart transportation policies on urban carbon emissions. The impact mechanism is analyzed from the perspectives of vehicle intelligence, road intelligence, and cloud; the empirical analysis of spatial difference is based on the panel data of 285 cities in China. The results show that: (1) smart transportation policies and vehicle-road-cloud collaboration have a positive inhibitory effect on urban carbon emissions. (2) The inhibitory effect of smart transportation policies on urban carbon emissions is influenced by the level of urban economic development but not by transportation infrastructure. (3) The spatial spillover effect of smart transportation policies on urban carbon emissions is influenced by transportation technology. Cities with higher transportation technology capabilities show inhibitory effects, while cities with low transportation technology capabilities show promoting effects. This study provides a reference for the sustainable development of urban smart transportation systems and environmental management.

Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments.