Enhanced Ridesharing Using Directions API and Geolocation

Abstract

In the ever-evolving realm of urban transportation, the fusion of technological advancements and ubiquitous mobile connectivity has sparked a paradigm shift in how cities are navi- gated. This shift has led to innovative approaches in ridesharing and crowdsourcing, offering promising solutions to tackle traffic congestion, reduce emissions, and prioritize travel needs in urban areas. Unlike traditional carpooling methods, where arrange- ments are made in advance, ridesharing in smart cities operates on real-time matching of drivers and riders, with priority given to factors such as destination and purpose.This study delves into the intricate dynamics of ridesharing and crowdsourcing for smart city transportation, investigating the complex interplay between technology and social factors.Additionally, it considers aspects of security, policy, and pricing strategies to ensure a seamless and equitable experience for all stakeholders.Drawing insights from comprehensive case studies and practical implementations, this exploration highlights the advantages of ridesharing and crowdsourcing, including the reduction of carbon dioxide emis- sions and the prioritization of travel needs based on purpose. It offers guidance for stakeholders navigating the complexities and embracing the transformative potential of these innovative solutions in shaping the future of urban mobility.

Introduction

I. INTRODUCTION

This project focuses on developing a GPS enhanced system for Ridesharing purposes. The rise of private vehicles in road transportation results in high expense and pollution .This necessitates the development of robust systems to reduce such trends. The objective is to create an app that utilizes geolocation API for providing Ridesharing services. The system’s front end will be implemented using the Flutter framework, while the back end will be built using the Firebase framework.

???????II. RELATED  WORKS

A. Dynamic stop pooling for flexible and sustainable ride sharing

This paper comprehensively explores the dynamics and potential benefits of dynamic stop pooling in ride-sharing systems. Through Monte Carlo simulations and mathematical modeling, the paper investigates how users walking short distances to and from stops can optimize route lengths and travel times. By dynamically assigning pickups and drop-offs to buses based on minimizing route length, the system efficiently serves users while reducing the number of directly served stops. This approach breaks the traditional trade-off between route length and travel time, allowing for shorter routes without significantly increasing travel times. The findings suggest that dynamic stop pooling can make ride-sharing services more sustainable and flexible, offering insights into improving shared mobility systems.

???????B. Putting ridesharing to the test: efficient and scalable solu- tions and the power of dynamic vehicle relocation

This research focuses on the challenges and solutions in the design of Component Algorithms for Ridesharing (CARs) through a systematic evaluation of various approaches in a real-world context, focusing on scalability, efficiency, and quality of service (QoS). It addresses challenges such as scalability in integer linear programming (ILP) approaches, the use of maximum-weight matching (MWM) and k-server algorithms, and the implementation of dynamic vehicle relocation strategies. By evaluating 14 different CARs across 10 metrics, the study demonstrates the potential for simple relocation schemes to significantly improve QoS metrics, highlights the scalability and efficiency of an ALMA-based CAR for on-device implementation, and provides insights into the trade-offs between different algorithmic approaches in addressing the complexities of ridesharing systems.

???????C. An Incentive Based Dynamic Ride-Sharing System for Smart Cities

This paper introduces an innovative incentive-based dynamic ride-sharing system designed for smart cities, aiming to alleviate traffic congestion and reduce environmental impacts. Through comprehensive simulations and a lab-scale prototype, the proposed system demonstrates significant improvements in fuel consumption, CO2 and CO emissions, and average vehicle speed compared to traditional transportation methods. By leveraging empty seats in privately owned vehicles, this system offers a solution to urban mobility challenges while providing economic incentives for participants. With its mobile-cloud-based matching system and IoT infrastructure, the prototype showcases real-time ride-sharing capabilities, paving the way for future implementations in congested urban areas.

???????D. Modeling and Prediction of Ride-Sharing Utilization Dy- namics

This study explores the dynamic nature of ride-sharing systems, contrasting them with conventional static approaches, and proposes a methodology to model and predict ride-sharing utilization based on dynamic network analysis. Leveraging New York taxi data, the study demonstrates that ride-sharing potential fluctuates   significantly   over   time,   necessitating a dynamic modeling approach. By constructing hourly network snapshots and analyzing topological features, the paper establishes correlations between network properties and ride-sharing utilization, enabling short-term prediction of ride-sharing potential. The findings suggest implications for optimizing ride-sharing systems and highlight avenues for future research, including exploring causality between network properties and utilization, decentralized decision- making, and the impact of ride-sharing on other transportation factors.

???????E. Boosting Ride Sharing With Alternative Destinations

This comprehensive review addresses the challenges of urban mobility in densely populated cities by proposing a novel approach called Activity-Based Ride Matching (ABRM). ABRM leverages knowledge about human activities driving individual mobility demands to increase ride-sharing opportunities. It recognizes that many activities, such as shopping, can be performed at various locations, allowing for the possibility of sharing rides to alternative destinations. By analyzing large mobility datasets, the paper demonstrates that ABRM could significantly increase ride-sharing opportunities compared to traditional destination-oriented approaches. Additionally, the paper discusses the impact of ABRM parameters on saving car rides and introduces a ranking step to provide users with the most relevant ride options, potentially promoting pro-environment behaviors.

???????F. A Smart Contract Based Secure Ride Sharing System

This paper proposes a secure and transparent ride- sharing system, particularly addressing concerns arising from pandemics. Leveraging blockchain technology and smart contracts, the system ensures fairness, privacy, and accountability throughout the ride-sharing process. Key features include a two-phase bidding process to maintain fairness, robust verification mechanisms to prevent replay attacks, and smart contracts to validate ride completion. Privacy is upheld through the use of   different   public keys and addresses for each ride, preventing entities from tracking individual passengers or drivers. The system’s design is formally analyzed to demonstrate its effectiveness in safeguarding against various security threats. Overall, the paper presents a comprehensive solution for safe and transparent ride-sharing in pandemic scenarios, with future work focused on performance evaluation and liability considerations.

I???????II. PROPOSED MODEL

To address the increasing demand for efficient urban trans- portation solutions, we propose the development of an en- hanced ridesharing system that leverages Directions API and Geolocation technologies. This model aims to revolutionize the ridesharing experience by optimizing route planning, real- time tracking, and overall user convenience within urban environments.The model integrates several key components to achieve efficient user experience

1. User authentication and profile management: This will be a cornerstone of our system. By integrating Firebase Authentication or a similar service, users will securely register, log in, and manage their profiles. This functionality ensures a personalized experience and allows users to update their personal information and profile pictures with ease.
2. Ride request and management functionalities: This func- tionality will empower users to specify pickup and drop- off locations seamlessly. With the integration of Geolocation services, accurate tracking of user locations and efficient route planning for drivers will be ensured. This real-time data will significantly enhance the reliability and timeliness of ridesharing services.
3. Driver Matching: A key component of our model is the driver matching and dispatching system, which will efficiently pair ride requests with nearby drivers based on proximity and other relevant criteria. Drivers will receive timely notifications of incoming ride requests and navigation assistance to opti- mize pickup and drop-off routes.
4. Push Notifications: To keep users and drivers informed throughout the ride, we will implement push notifications for ride status updates. Users will receive notifications when a driver accepts their ride request, along with real-time updates on the driver’s location and estimated time of arrival. Similarly, drivers will be notified of new ride requests, cancellations, or changes in ride status to ensure smooth operations.
5. Payments and Transactions: The integration of a secure payment gateway, such as Stripe or PayPal, will facilitate seamless and secure transactions for ride payments. Users will have the flexibility to add payment methods, view fare estimates upfront, and receive digital receipts for completed rides, enhancing transparency and trust in the system.
6. User Feedback: Our model will also feature a robust ratings and reviews system to maintain service quality and build trust within the ridesharing community.

Users and drivers will be able to rate each other and provide feedback after completing rides, contributing to continuous improvement and user satisfaction.

In terms of implementation, we will adopt a phased ap- proach encompassing design, development, testing, and de- ployment stages.

Technologies such as Directions API, Ge- olocation services, Firebase for authentication, and selected payment gateways will be utilized to ensure a seamless and reliable ridesharing experience.

Deployment of the enhanced ridesharing app will be tar- geted for app stores such as Google Play Store and Apple App Store to reach a wide user base. Ongoing maintenance, updates, and support will be provided to address user feedback and evolving market demands, ensuring the sustainability and success of the system.

In conclusion, our proposed Enhanced Ridesharing model represents a significant advancement in urban transportation solutions. By leveraging cutting-edge technologies and user- centric design principles, we aim to transform the way people commute within cities, promoting efficiency, accessibility, and sustainability in ridesharing services.

???????IV. REQUIREMENT  SPECIFICATION

???????A. Functional Requirements

Drivers will have the capability to publish their journey details within the system, providing essential information such as starting points, destinations, and availability.

This feature enables drivers to effectively communicate their routes and availability to potential passengers seeking rides. Users of the system will be able to request rides by specifying their desired destination, facilitating a streamlined process for securing transportation.

???????B. Publisher Selection

A key functionality of the system involves allowing publishers (drivers) to select travelers from a request list based on preferences or specific criteria. This feature enhances flexibility for drivers to manage their passengers efficiently and ensures that users’ ride requests are matched with suitable drivers based on availability and other specified factors.

???????C. Non-Functional Requirements

The system must maintain a high level of availability to ensure continuous service delivery to users at all times, especially during peak demand periods. This requirement underscores the importance of system reliability and uptime to meet user expectations. Stringent security standards will be implemented within the system to protect sensitive user data and secure transactions, safeguarding against potential threats or breaches.

???????D. Software and Hardware Specifications

The system will be designed for cross-platform compatibility, supporting operating systems such as Android, MacOS, and Windows. This ensures widespread accessibility across different devices and environments. Firebase will serve as the primary database for efficient storage and retrieval of ridesharing data, facilitating real-time updates and data synchronization. Additionally, the system will leverage Firebase for backend development and utilize Flutter for frontend development to create a responsive and dynamic user interface.

???????E. Device Requirements

For optimal performance and functionality, mobile devices used with the system should have built-in GPS functionality or support external GPS modules to enable accurate location tracking and route navigation. Stable internet connectivity via Wi-Fi or mobile data is essential for real-time communication between users and the system. Devices with a minimum screen size of 5 inches are recommended to ensure clear visibility and effective utilization of the system interface. Furthermore, devices equipped with moderate processing power, such as smartphones featuring Qualcomm Snapdragon or equivalent processors, will ensure smooth operation and responsiveness of the ridesharing application.

V. GEOLOCATOR

The geolocator package in Flutter is a versatile plugin that simplifies the integration of location-based services into mobile applications. It provides easy access to device loca- tion information, allowing developers to retrieve the current geographic coordinates (latitude and longitude) of a device, monitor location changes in real-time, and perform geocoding and reverse geocoding operations. By abstracting platform- specific location services, the geolocator package stream- lines the implementation of location-aware features across Android and iOS platforms within Flutter apps. Developers can leverage the geolocator package to create applications with mapping, navigation, and location-aware functionalities efficiently and reliably. Simply add the geolocator dependency to your Flutter project, import the package, and utilize its APIs to incorporate robust location-based services into your mobile app.

VI. DIRECTIONS API

The Google Maps Directions API is a powerful tool that allows developers to integrate route planning and navigation features into their applications. This API enables the calculation of optimal routes between specified locations using various transportation modes such as driving, walking, bicycling, and public transit. Developers can retrieve detailed turn-by-turn directions, estimated travel times, distances, and alternative route options to provide users with efficient navigation guidance. By leveraging the Google Maps Directions API, applications can offer personalized and context-aware routing experiences, enhancing usability and convenience for users across a variety of use cases including mapping applications, logistics systems, and location-based services. Integration of the Directions API is straightforward through HTTP requests, and the API response, typically in JSON format, provides comprehensive route information that can be seamlessly incorporated into mobile, web, and desktop applications. Overall, the Google Maps Directions API empowers developers to create dynamic and intuitive navigation solutions that improve user experiences and optimize journey planning workflows.

???????VII. DIJKSTRA’S ALGORITHM

Finding the shortest path from a single source node to every other node in a network with non-negative edge weights is done using Dijkstra’s algorithm, which is based on graph theory. It functions by continuously extending the shortest known path from the source node to additional nodes in the network by keeping a priority queue. Dijkstra’s approach is appropriate for applications such as route planning and network optimization since it ensures that the shortest path in graphs without negative weights is found. Its usage of a priority queue, which results in an O((V+E)log V) time complexity—where V is the number of vertices and E is the number of edges—may make it inefficient for big networks, though.

VIII.  A-STAR ALGORITHM

The A algorithm, which is intended to quickly determine the shortest path from a source node to a target node in a graph, combines heuristic functions and Dijkstra’s algorithm. A* guides the search towards the target node using a heuristic estimate, usually the Manhattan distance or Euclidean distance, which minimizes the number of nodes examined and increases computational efficiency. A* efficiently traverses graphs to find optimal paths by weighing the cost of the heuristic estimate to the target node against the actual path from the source node. A* is frequently utilized in pathfinding applications, including video games, robots, and map route planning.Its time complexity varies depending on the selected heuristic, however for pathfinding in big graphs, it typically outperforms Dijkstra’s algorithm.

???????IX. RESULTS

The results of our ridesharing app project demonstrate its effectiveness in providing a platform that allows smartphone users with internet access to seamlessly become drivers and hirers of vehicles, facilitating convenient journeys in congested smart cities.

Our primary aim was to reduce the number of vehicles on the road and mitigate traffic pollution stemming from vehicle congestion. The app achieves this by optimizing vehicle utilization throughout journeys while ensuring drivers receive fixed fares based on their pickup and drop-off loca- tions.

By implementing rigorous user verification measures, we have established a secure and efficient ridesharing experience for all users, prioritizing safety and reliability in our service. Through these outcomes, our app contributes to sustainable urban mobility by promoting shared transportation and reducing individual vehicle ownership, ultimately fostering a more environmentally friendly and efficient transportation ecosystem in smart city environments.

???????X. FUTURE SCOPE

1. The continued growth of ridesharing apps is anticipated due to their convenience and cost-effectiveness, especially in urban areas grappling with increased traffic congestion and parking challenges. This trend will likely see more individuals opting for ridesharing services over traditional car ownership.
2. The emergence of autonomous vehicles presents a sig- nificant opportunity for ridesharing companies, with in- vestments already being made by major players like Uber and Lyft. This technology promises safer, more efficient transportation, potentially reshaping the entire industry in the coming years.
3. Ridesharing apps are at the forefront of safety inno- vations, utilizing IoT technology to ensure passenger security. Features such as location sensors and real- time monitoring contribute to a safer and more reliable experience for users, setting a standard for the industry’s future developments.

Conclusion

In conclusion, the ridesharing app project has been a successful endeavor, overcoming key challenges in technology stack selection, user safety, mapping service integration, user experience design, and regulatory compliance. Our strategic approach in choosing a cross-platform development stack ensured efficient code reuse and a seamless user experience across platforms. Safety was prioritized through robust features like emergency buttons and driver identity verification, fostering trust and reliability. Integration with reliable mapping services enhanced location accuracy and navigation efficiency. Deliberate attention to crafting a user interface characterized by simplicity and clarity underscored our commitment to ensuring user satisfaction at every touchpoint. Competitive strategies focused on differentiation and competitive pricing, aiming to attract and retain users. Our ongoing commitment to user feedback and continuous improvement will further solidify our position in the market. Finally, strict adherence to regulatory standards ensured legal compliance and user trust. Overall, the project’s success is attributed to our team’s dedication and expertise in delivering a safe, efficient, and user-friendly ridesharing solution.

References

[1] Dynamic stop pooling for flexible and sustainable ride sharing, https://iopscience.iop.org/article/10.1088/1367-2630 [2] Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation , https://link.springer.com/article/10.1007 [3] An Incentive Based Dynamic Ride-Sharing System for Smart Cities, https://www.mdpi.com/2624-6511/4/2/28 [4] Modeling and Prediction of Ride-Sharing Utilization Dy- namics https://www.hindawi.com/journals/jat/2019/6125798/ [5] Boosting Ride Sharing With Alternative Destinations https://ieeexplore.ieee.org/abstract/document/8370062 [6] A Smart Contract Based Secure Ride Sharing System https://dergipark.org.tr/en/pub/ijiss/issue/83893/1399189 [7] Credit-based Peer-to-Peer Ride Sharing using Smart Con- tracts, https://ieeexplore.ieee.org/document/9805496 [8] Ridesharing and Crowdsourcing for Smart Cities: Tech- nologies, Paradigms and Use Cases , https://ieeexplore.ieee.org/document/10040639 [9] Modelling and analysis of online ride-sharing platforms [10] A sustainability perspective https://www.sciencedirect.com/science/article/abs/pii [11] A System for Multi-Passenger Urban Ridesharing Rec- ommendations with Ordered Multiple Stops, https://academic.oup.com/comjnl/article5363891 [12] Utility-Aware Dynamic Ridesharing in Spatial Crowd- sourcing, https://ieeexplore.ieee.org/document/9999334 [13] Android Studio documentation https://developer.android.com/develop [14] Dart Documentation https://dart.dev/guides [15] Directions,API documentation https://developers.google.com/maps/documentation/directions

Copyright

Copyright © 2024 J Navneeth Krishnan, Joel Sebastian, John K Joseph, Asha J George, Rotney Roy Meckamalil. 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.