In the realm of expressway development, the importance of vehicle identification technology is steadily rising. However, conventional systems often struggle to maintain optimal recognition accuracy in adverse weather conditions such as heavy rain, snow, or haze. This study proposes an enhanced approach to license plate recognition utilizing radar fusion technology to synthesize multiple information bands and improve image visibility. By addressing the challenges posed by inclement weather, our algorithm aims to overcome limitations observed in conventional de-fogging methods. Through cooperative image processing, the proposed algorithm automatically identifies and enhances license plate regions, subsequently employing a character recognition model to identify clipped characters. This enhanced technique effectively mitigates the impact of complex backgrounds and noise, thus boosting recognition accuracy. Simulation analyses conducted in MATLAB validate the efficacy of our approach. Through simulation analysis, it is found that the recognition accuracy rate can reach 97%, demonstrating its superior recognition accuracy even in adverse weather conditions.

The suspension system design has been one of the most challenging tasks for engineers due to the importance of its role in reducing the road vibrations transmitted to the vehicle, which have a harmful effect on the human body. This research aims to use and compare different optimization techniques used to design the passive suspension parameters, including the spring stiffness (Ks), damping coefficient (Cs), and tire stiffness (Kt), to minimize body accelerations and subsequently enhance ride comfort for vehicles. The quarter car is modelled as a two-degrees-of-freedom system by using MATLAB/Simulink. Different optimization techniques were introduced and used, such as Taguchi, Genetic Algorithms (GA), and Simulated Annealing (SA), to design the passive suspension parameters. The results showed that the optimal design parameters for suspension systems were obtained using GA and SA methods, which reduced the value of the root mean square of vertical vibration by approximately 44% and the peak of acceleration by approximately 60% compared to the original values. The Taguchi approach reduces the value of the root mean square by approximately 32% and the peak of the acceleration by approximately 28% compared to the original values.

Dynamic pricing on seat selection contributes good revenue during demand, and forecasting of income using events occupancy is lagging are to be addressed by novel methods. The passengers, who may not have the purchasing capability of a 4 wheeler must prefer to share the facility for savings. The crucial aspects are customer comfort and knowing price drops in advance. The proposed system is a hybrid upfront that consists of modified ARIMA, and GBM to accurately forecast income based on filled events occupancy. This system not only provides integrated features such as applying discounts on specific scenarios, a dynamic seat selection, individual track of the vehicle, and predicting income of future days. The Firsts Come First Serve (FCFS) and seat ranking have been integrated into the evolution of a hybrid method that also outputs good revenue. The evaluation is compared against existing approaches. The outcome is an effective way of selecting the seat as per their choice, and the event occupancy cut-off is reached. This feature guides the driver community to raise seat prices or drop prices would generate revenue as well as maximize passenger comfort.

Indonesia is generous in renewable energy resources since the country benefits from abundant solar energy. There has been an increase in the energy demand, and thus, it is essential to consider shifting to renewable energy sources to sustain future energy demand. This research looks at how renewable energy could be formed in an airport, specifically alleviating the use of fossil fuel-powered vehicles. Among them is the engineering of a standalone Public Electric Vehicle Charging Station (SPKLU) powered by other energies. The researcher applies a HOMER-Grid simulation approach to design an approximate daily electrical load of 424.25kW. According to the projections made in the simulation, approximately 254,078kWh of electricity will be produced annually from this renewable energy system. The percentage contribution of the energy from this system to the total energy load is 26.11%. Harnessing renewable energy at the airport is about developing a green technology approach, which can reduce the operational carbon footprint efficiency of the airport and help make the operation more sustainable.

Driver identification is vital in connected transport and has various benefits like usage-based insurance, personalized assisted driving, fleet management, etc. The data collected from behind the wheel makes it possible to identify unique driving styles as an alternative to adding extra costs or compromising drivers' biometric fingerprint privacy, such as facial recognition. The variable nature of drivers causes problems for traditional techniques because they become less accurate when faced with new drivers. This paper presents an innovative method of driver identification using few-shot learning techniques based 1D CNN-LSTM Attention model that can effectively solve the N-driver identification problem, given very few training examples on driving. Our findings reveal that this model can be generalized correctly from just a few examples, making it essential in real-life situations. We compare our proposed method with several baseline models such as LSTM Attention, LSTM, CNN, and ANN. Furthermore, applying our model to 3-way and 5-way classification problems using 1-shot and 5-shot methods further evidences its effectiveness in changing environments. Consequently, from this research, it is clear that knowledge based on the training dataset could be applied successfully to new drivers. Impressive results obtaining when trained on all raw databases but still getting correct identifications even with a small number of instances per driver label.

Sustainable urban development stands as a pivotal imperative in contemporary discourse. One of the world's largest megacities, Dhaka, faces immense social sustainability challenges due to rapid urbanization for both present and future generations. As Dhaka's urbanization accelerates, transportation availability has become a pressing concern for socially sustainable urban areas. This study aims to investigate the current conditions of transportation availability and its impact on socially sustainable urban development, particularly in developing cities. This study used a quantitative research approach, and Dhaka is considered a representative city in a developing region. A multistage sampling method was used to collect 564 responses from residents of Dhaka through a structured questionnaire survey. The results indicated that four indicators of transport availability in Dhaka city exhibit low satisfaction levels among residents. Additionally, transportation availability statistically impacts Dhaka's socially sustainable urban development, as determined using four indicators cross-section OLS and Poisson regression analysis. The results guide governmental bodies, legislators, and urban planners in pursuing socially sustainable cities. Moreover, transportation availability indicators offer actionable policy insights for urban sector strategies in developing nations, bridging urbanization and sustainability to advance SDG-11 (Sustainable Cities and Communities) in the 2030 Agenda.

The conventional belief that mobile phone usage adversely affects driving performance, particularly through driver distraction leading to accidents, underscores the importance of understanding its impact within the context of varying traffic environments. This study aims to investigate the individual and interactive effects of mobile phone use and traffic environment on driving performance. Two distinct experiments were conducted in a simulated driving environment, employing a 2² factorial design to examine these factors. Mobile phone use was assessed in both hands-free and hand-held modes, while traffic environments comprised rural and urban routes. Driving performance was evaluated using four measures: driver mental workload, error frequency, average speed, and lateral position changes. Our findings reveal that mobile phone use significantly affects all performance measures, while traffic environment predominantly influences average speed and lateral position changes. Specifically, both hands-free and hand-held modes are statistically significant in influencing mental workload. Additionally, the interaction between traffic environment and hand-held phone use notably affects error frequency. These results provide insights into the complex interplay between mobile phone use, traffic conditions, and driving performance.

Parking management systems often need to improve due to outdated infrastructure, inefficient manual processes, and the lack of automation, resulting in increased congestion and poor user experience. Traditional parking management systems, reliant on manual license plate entry and outdated payment methods, need help to meet modern demands. In response, this research introduces a novel Intelligent Parking System (IPS) that leverages automatic License Plate Recognition (LPR) with a YOLO model for real-time detection and recognition of vehicle license plates. By eliminating the need for sensors and manual data entry, our system enhances accuracy, reduces maintenance costs, and optimizes parking operations. Furthermore, integrating QR code-based payment simplifies and accelerates the payment process, reducing wait times and improving user experience. This approach addresses the growing need for scalable and adaptable parking solutions in smart city initiatives. Through this research, the IPS provides a scalable, intelligent alternative to conventional parking systems, contributing to smarter urban environments.

Technology has significantly improved transportation services through information and communication technologies (ICT), and it has enhanced efficiency, accessibility, security, and sustainability in transportation systems. This study aims to comprehensively understand how ICT impacts the transportation system through a systematic literature review. Moreover, this study systematically reviews and synthesizes literature on the impact of Information and Communication Technologies (ICT) on various facets of transportation. It specifically investigates the transformative potential of ICT, including blockchain and IoT technologies, in replacing traditional transportation systems. This review followed the PRISMA guidelines for reporting systemic reviews and meta-analyses. The review process involves several stages, including initial search queries, screening studies, eligibility assessments, and the final selection of articles. The study used only one database, Scopus and it found 425 articles, only 10 papers matched the selection criteria. The Study findings suggest that information and communication technologies have played an important role in transportation services, like smart traffic management, real-time data analysis, and enhanced user interfaces. Despite this, data privacy, infrastructure integration, and equitable access remain challenges. This review contributes to a deeper understanding of the relationship between ICT and transportation. The findings offer valuable insights for policymakers, researchers, and practitioners striving to harness the potential of ICT for creating more efficient, sustainable, and user-centric transportation systems. Based on the study, future research should explore the integrated impact of blockchain, IoT, and AI on transportation efficiency, user acceptance, regulatory adaptations, and environmental implications.

The Indonesian government, through the Directorate General of Railways, Ministry of Transportation, plans to operate the trans-Sumatra Rail Way or train transportation mode (TSRW), so that cities on the island of Sumatra will be fully connected. Meanwhile, on the one hand, the construction costs are very high, starting from the rails and bridges, stations and trains themselves, and on the other hand, cities in Sumatra are already operating with satisfactory service to travelers using road transportation modes, both ICIP buses and Small passenger cars (LPC) have very easy access, while on trains each passenger has to go to the station first or access is low, so it is feared that once this Trans Sumatra Rail Way (TSRW) or train operates, it will not be of interest to people traveling between cities on the island of Sumatra. So, to ensure that the Trans Sumatra Rail Way (TSRW) or train is in demand by people traveling between cities and provinces on the island of Sumatra, it is necessary to carry out a study by presenting a new service attribute that is not yet available in other modes of transportation besides the Trans Sumatra Rail Way (TSRW) or train and also so far. Currently, there are no studies that discuss this mode choice which includes this new service attribute, namely continuous integration between trains and online transportation such as Go-Car, Grab and Maxim by combining the payment of one ticket on the train ticket so that train passengers can be picked up at home and delivered to the train station for free which is called seamless service. The results of the study show that with the existence of this new service attribute as a new variable, it turns out that this trans Sumatra rail-way mode has a great opportunity to be used by people traveling between cities on the island of Sumatra with great opportunities with a market share of 81 percent.

Traffic congestion in Egyptian cities is a major issue due to unplanned and private vehicles, impacting land use, housing, and development. A study aims to improve accessibility and mobility by implementing a multimodal transportation system, reducing private vehicle usage, and attracting sustainable transit investments. The research questions include mode of transport, destination distance, passenger walking distance, integration principles, and multimodal effects on land use and attraction. The study employs a qualitative approach, utilizing data collection, literature reviews, and analytical examples. It employs a geographic information system (GIS) analysis program to examine existing and proposed cases, demonstrating the effectiveness of multimodal transportation in connecting intercity, promoting smart urban growth, and attracting investments. It transforms land use from mono-use to diverse, contributing alternative downtown and activating job opportunities. This approach revitalizes neglected lands, reduces traffic congestion, and creates green spaces. It also encourages the TOD concept. Furthermore, it improves the environment and health and reduces urban congestion, contributing to urban development. Overall, multimodal transportation is a significant contributor to urban growth. Moreover, future policies should consider various transportation modes, public-private partnerships, infrastructure investment, technology integration, accessibility, and sustainable modes, with community engagement and policy support prioritizing multimodal transportation and raising awareness.