Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: M. Chinna Babu, Bollemoni Vishal Nagraj, Badikala Ranadeep, Alakuntla Vamshi
DOI Link: https://doi.org/10.22214/ijraset.2024.65765
Certificate: View Certificate
This study presents the development and implementation of a Three-Wheeler Ride Service Application tailored to the unique needs of elderly individuals, people with physical disabilities, and women. The application aims to provide a safe, accessible, and user-friendly platform that addresses the mobility challenges faced by these vulnerable groups. Leveraging modern technologies like Flutter for a responsive interface, Firebase for real-time data management, and Google Maps API for route optimization, the app ensures seamless ride booking, live tracking, and affordability. Key features include a user-centric interface optimized for accessibility, real-time ride safety mechanisms such as driver verification and emergency contact integration, and specialized vehicle options for enhanced comfort and security. Additional functionalities, such as chatbot assistance, dynamic pricing based on distance, and multi-modal payment systems, further improve usability. Through the integration of advanced technological solutions and a focus on inclusivity, the application bridges gaps in the existing ride-hailing ecosystem. The platform\'s scalability and potential for future enhancements, such as voice-based assistance and eco-friendly vehicle options, underscore its broader social impact, offering a transformative approach to inclusive transportation services.
I. INTRODUCTION
In today's fast-paced world, accessible and reliable transportation is essential for mobility and independence. However, certain groups, including the elderly, individuals with physical disabilities, and women, often face significant challenges when using traditional public transportation or mainstream ride-hailing services. These challenges range from physical accessibility barriers and safety concerns to limited support for their unique mobility needs. Addressing these issues requires a transportation solution that prioritizes safety, convenience, and inclusivity.
The Three-Wheeler Ride Service Application is designed to bridge this gap by providing a dedicated platform tailored specifically for these vulnerable groups. Unlike conventional ride-hailing apps, this application offers features such as wheelchair-accessible vehicles, driver verification for safety, and ride-sharing options exclusively for women. Through its user-friendly interface, real-time GPS tracking, and emergency assistance features, the app ensures a seamless and secure transportation experience.
By leveraging modern technologies, including Google Maps API for route optimization, Firebase for real-time data management, and Flutter for cross-platform compatibility, the application not only provides technical efficiency but also enhances user satisfaction. The inclusion of chatbot integration and voice-assisted booking further simplifies the process for users who may face technological or physical constraints.
This introduction outlines the motivation, objectives, and technological framework behind the Three- Wheeler Ride Service Application, emphasizing its role as a transformative solution for improving mobility, safety, and accessibility for the elderly, handicapped, and women. The paper also explores the broader implications of such technology-driven initiatives in fostering inclusivity and equal access in transportation.
A. Challenges Faced Before
Before the development of the Three-Wheeler Ride Service Application, the target groups—elderly individuals, people with disabilities, and women—encountered several critical challenges in accessing safe, convenient, and affordable transportation. These challenges included:
1) Limited Accessibility in Traditional Transportation Systems
2) Safety Concerns
3) Inadequate Vehicle Adaptations
4) Complex User Interfaces
5) High Costs of Private Transportation
6) Inefficiency in Ride Availability
7) Lack of Emergency Support Features
8) Absence of Customization Options
B. Research Approach
The development of the Three-Wheeler Ride Service Application was guided by a systematic research approach, ensuring that the solution addresses the specific needs of the target audience—elderly individuals, people with disabilities, and women. The research methodology included the following phases:
1) Problem Identification and Analysis
This phase focused on understanding the limitations of existing transportation systems and ride-hailing applications:
2) User-Centric Design Approach
The research prioritized a user-centric design approach to ensure the platform’s usability and inclusivity:
3) Technological Feasibility Study
To determine the technical requirements, the feasibility study included:
4) Development and Testing
The solution was iteratively developed and refined based on user feedback and testing results:
5) Data Collection and Analysis
Data-driven insights were used to enhance the platform's features:
6) Evaluation of Impact
The final phase involved assessing the application’s effectiveness in addressing the identified challenges:
By combining user-centered research, technological innovation, and iterative testing, this research approach ensured that the Three-Wheeler Ride Service Application not only meets but exceeds the expectations of its target audience.
C. Feature Extraction
The Three-Wheeler Ride Service Application incorporates various features designed to meet the unique needs of elderly individuals, handicapped users, and women passengers. These features were identified, analyzed, and extracted through systematic research and user feedback. The extracted features are categorized based on accessibility, safety, usability, and technological innovation.
1) Accessibility Features
These features enhance usability for elderly and disabled users by addressing mobility and technological barriers:
2) Safety Features
Safety is a key focus of the application, particularly for women and vulnerable passengers:
3) Booking and Navigation Features
These features streamline the ride-booking process and enhance navigation efficiency:
4) Payment Features
The app provides flexible payment options to cater to a wide range of user preferences:
5) Chatbot and Assistance Features
A built-in chatbot enhances user support and simplifies the booking process:
6) Driver-Specific Features
Features designed to support drivers and improve service quality:
7) Data and Analytics Features
Data-driven functionalities improve the app's efficiency and provide valuable insights:
8) Scalability and Customization Features
The application is built to adapt to future needs and expansion:
The feature extraction process was critical in identifying functionalities that address the challenges faced by vulnerable groups in transportation. Each feature was designed with inclusivity, usability, and safety in mind, ensuring the app's effectiveness and user satisfaction.
II. OUTPUT
FIG : Mongodb Database
FIG 2: Firebase
FIG 3: Home Page
FIG 4: Result Screen
Fig 2: Sections
FIG 5: Result Screen
III. ADVANTAGES
The Three-Wheeler Ride Service Application offers numerous benefits for its target audience, including elderly individuals, people with disabilities, and women passengers. Key advantages include:
A. Enhanced Accessibility
B. Improved Safety
C. Cost-Effectiveness
D. Technological Integration
E. Tailored Services
F. Scalability and Sustainability
The Three-Wheeler Ride Service Application represents a significant step toward creating a safer, more accessible, and user-friendly transportation solution for elderly individuals, people with disabilities, and women. By addressing the unique challenges faced by these vulnerable groups—such as mobility limitations, safety concerns, and affordability—the application provides a reliable alternative to traditional ride-hailing platforms. Key features, including wheelchair-friendly vehicles, women-only rides, real-time ride tracking, and emergency support, demonstrate the app’s commitment to inclusivity and safety. The use of cutting- edge technologies, such as Google Maps API for route optimization, Firebase for real-time data handling, and AI-powered chatbots for assistance, ensures that the app is both efficient and easy to use. This project is not just a transportation service; it is a platform that empowers its users by prioritizing their needs and enhancing their independence. It fosters social inclusivity by bridging gaps in mobility, allowing users to navigate their daily lives with confidence and security. Future enhancements, including expanded regional coverage, eco-friendly vehicle options, and additional accessibility features, highlight the scalability and sustainability of the solution. The Three-Wheeler Ride Service Application is poised to set a new standard in transportation services, making mobility equitable and accessible for all.
[1] M. Irani, S. Peleg, “Improving Resolution by Image Registration,” CVGIP: Graphical Models and Image Processing, vol. 53, no.3, pp. 231-239, May 1991, 10.1016/1049-9652(91)90045-L. [2] C. Dong, C. Loy, K. He, X. Tang, “Learning a deep convolutional network for image super- resolution,” ECCV 2014 Lecture Notes in Computer Science, vol. 8692, pp. 184-199, 2014. [3] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, “Enhanced Deep Residual Networks for Single Image SuperResolution,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop, pp. 136-144, Jul. 2017. [4] Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, Chen Change Loy, “ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks,” ECCV Workshops, Lecture Notes in Computer Science, vol. 11133, pp. 63-79, Jan. 2019. [5] J. Liu and N. P. Chandrasiri, \"C-ESRGAN: Synthesis of superresolution images by image classification,\" 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), Genova, Italy, pp. 1-5, Dec. 2022. [6] Q. Huyuh-Thu, M. Ghanbari, “Scope of validity of PSNR in image/video quality assessment,” Electronics Letters, vol. 44, issue 13, pp. 800-801, Feb. 2008, 10.1049/el:20080522. [7] Joshi, Kamaldeep, Yadav, Rajkumar, and Allwadhi, Sachin, “PSNR and MSE based investigation of LSB,” International Conference on Computational Techniques in Information and Communication Technologies, pp. 280-285, Mar. 2016. [8] A. Horé and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” 2010 20th International Conference on Pattern Recognition, NW Washington DC, United States, pp. 2366-2369, Aug 2010, 10.1109/ICPR.2010.5790. [9] Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004, 10.1109/TIP.2003.819861. [10] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, \"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric,\" 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 586-595, 2018.
Copyright © 2024 M. Chinna Babu, Bollemoni Vishal Nagraj, Badikala Ranadeep, Alakuntla Vamshi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET65765
Publish Date : 2024-12-05
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here