Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning

Abstract

The project titled \\\"Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning\\\" aims to investigate and evaluate the methodologies employed by different ride-on-demand platforms to determine equitable pricing through the application of machine learning algorithms. The primary focus of this research is to assess the effectiveness, transparency, and adaptability of pricing mechanisms in the context of dynamic factors such as geographical location, time of day, cab type, source, destination and weather conditions. The project involves a comprehensive comparative analysis of various ride-on-demand services, exploring the diversity of machine learning models utilized for fair price detection. The study will delve into the accuracy of price predictions, considering real-time demand fluctuations and the adaptability of algorithms to dynamic operational environments. Transparency in pricing decisions will be a key parameter for evaluation, as clear and understandable explanations are crucial for establishing user trust. The research methodology includes data collection from multiple ride-on-demand platforms like Uber, Ola, Rapido and Indrive, analysis of pricing algorithms, and the development of performance metrics to assess the fairness and efficacy of each service. The project aims to provide insights into best practices for implementing machine learning in ride-on-demand services, with the ultimate main goal of enhancing user experience and fostering trust within the user community. The findings of this comparative analysis will contribute valuable knowledge to the field of transportation technology and assist in shaping future advancements in fair price detection mechanisms.

Introduction

I. INTRODUCTION

The "Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning" project delves into the dynamic landscape of ride-on-demand platforms, aiming to dissect and compare the methodologies employed for determining fair and equitable pricing through the integration of machine learning algorithms. In an era where transportation technology is evolving rapidly, understanding how these platforms leverage advanced technologies to optimize pricing becomes crucial for ensuring user satisfaction, market competitiveness, and the overall efficacy of the services.  The project is motivated by the increasing reliance on machine learning in the transportation sector, where pricing strategies are pivotal in meeting user demand while maintaining profitability for service providers. Ride-on-demand platforms, such as those offering car rides, bike shares, or similar services, deploy machine learning algorithms to adaptively adjust prices based on a myriad of factors. These factors may include geographic location, time of day, historical demand patterns, and user-specific preferences, creating a complex web of variables that influence the pricing dynamics. The primary objective of this research is to conduct a comprehensive comparative analysis of different ride-on-demand services, elucidating the diverse machine learning models employed for fair price detection.

II. LITERATURE SURVEY

1. Prediction of Dynamic Price of Ride-on-Demand Services Using Linear Regression:

Author: Kunal Arora, Sharanjit Kaur, Vinod Sharma.

Ride-on-demand services are becoming more and more common, such as Uber and OLA cabs. To help both drivers and customers, Ride-on-demand services use dynamic pricing to balance supply and demand in an attempt to increase service quality. Dynamic prices, however, often generate problems for passengers: often "unpredictable" prices prevent them from easily making fast decisions.

In order to address this problem, it is therefore important to give passengers more detail, and forecasting dynamic prices is a feasible solution. Finally, we predict dynamic prices using an efficient linear regression model based on evaluation results. Our hope is that the study helps to make passengers happier as an accurate forecast.

2. A Survey of Machine Learning-based Ride Hailing Planning:

Author: Dacheng Wen, Yupen Li, Francis C.M. Lau.

In ride-hailing, the platforms manage intelligently their vehicle resources to fulfil riders’ traveling requests. Compared to the traditional street-hailing mode in which the drivers operate all on their own, ride-hailing is more efficient. In street-hailing, without any intelligent strategies, an idle driver typically would just pick up the first rider s/he runs into. But with ride-hailing, the platforms can leverage advanced planning algorithms to manage their fleets to achieve higher efficiency in terms of metrics such as total vehicle miles travelled, vehicle capacity utilization rate, and rider waiting time. In this article, we present a comprehensive overview on latest developments of machine learning-based ride-hailing planning.

3. Comparative Analysis of Regression Models for Price Prediction of Ride-on-Demand Services:

Authors: Pooja Pranavi Nalamothu.

In recent years, Ride-on-Demand (RoD) services such as Uber, Ola, and Rapido have   emerged as popular alternatives to traditional taxi/cab services. We also evaluate the contribution of different features to dynamic pricing, determining which factors play the most significant role in determining fare prices. To accomplish our goal of reducing transportation fares and waiting times while enhancing transport accessibility, we utilize three different machine learning models: K-Nearest Neighbors, SVM and Random Forest. By comparing these models, we identify the best approach for predicting dynamic pricing and generating accurate forecasts for each individual order.

4. Predict the Price of Cab Trip using Classifiers and Regression:

Authors: S. Krishnaveni, A. Anjana.

The main intention of the objective is to layout a set of rules that facilitates to predict the fare of Uber rides for future rides. Machine learning knowledge of algorithms is used to expand regression fashions. Uber supplies carrier to a huge wide variety of clients every day. Now it becomes simply crucial to arrange their records well, to come up with new commercial enterprise thoughts to get the first-class effects. Eventually, it will become honestly vital to estimate the fare costs correctly.

5. Fine-grained Dynamic Price Prediction in Ride-on-demand Services: Models and Evaluations:

Authors:  Suiming Guo, Chao Chen, Jingyuan Wang, Yaxiao Liu, Ke Xu, Dah Ming Chiu.

Ride-on-demand (RoD) services use dynamic prices to balance the supply and demand to benefit both drivers and passengers, as an effort to improve service efficiency. However, dynamic prices also create concerns for passengers: the “unpredictable” prices sometimes prevent them from making quick decisions at ease. Finally, based on evaluation results, we provide discussions on model selection under different circumstances, and propose a way to combine the two models.

III. ANALYSIS

A. Existing System

As of the knowledge cutoff date in January 2022, the existing systems for fair price detection in ride-on-demand services using machine learning are diverse and continually evolving. These systems are implemented by various ride-hailing and ride-sharing platforms, each with its unique approach to pricing optimization. The following are some key characteristics and components commonly found in existing systems:

1. Dynamic Pricing Algorithms: Many ride-on-demand services leverage machine learning algorithms to implement dynamic pricing. These algorithms analyze real-time data, such as current demand, traffic conditions, and historical ride patterns, to adjust prices dynamically. Popular dynamic pricing models include surge pricing during peak hours or high demand.
2. Geographical and Temporal Factors: Existing systems often take into account geographical and temporal factors when determining prices. The cost of a ride may vary based on the pickup and drop-off locations, time of day, and day of the week. Machine learning models consider these factors to make pricing decisions that align with demand patterns.
3. User Behavior and Preferences: Some systems incorporate machine learning to analyze user behavior and preferences. This may include factors such as preferred routes, common destinations, and historical trip data. By understanding user preferences, platforms can tailor pricing to individual users or user segments.
4. Competitor Pricing Analysis: Certain ride-on-demand platforms use machine learning to perform competitive pricing analysis. This involves monitoring the pricing strategies of competitors and adjusting prices accordingly to remain competitive in the market. The ability to dynamically adapt to competitor pricing is a key feature in optimizing overall service competitiveness.
5. Transparency Features: In response to concerns about algorithmic transparency, some platforms have implemented features to enhance transparency. This may involve providing users with explanations or breakdowns of the factors influencing the current price, offering fare estimates before the trip, or notifying users about dynamic pricing conditions.
6. Feedback and Iterative Improvement: Machine learning models in existing systems often incorporate feedback loops. User feedback on pricing, as well as overall experience, is collected and used to iteratively improve the algorithms. This iterative process helps in refining pricing models over time.
7. Regulatory Compliance: Ride-on-demand platforms navigate a complex regulatory landscape. Existing systems may include features to ensure compliance with local regulations regarding pricing practices. This involves incorporating legal and regulatory constraints into the machine learning models.

B. Proposed System

The proposed system for the "Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning" project envisions an advanced and transparent pricing mechanism that addresses the complexities of dynamic demand, user preferences, and fairness considerations. The system aims to build upon existing models, incorporating novel features and methodologies to enhance user experience and ensure equitable pricing. The following outlines key components of the proposed system:

1. Enhanced Dynamic Pricing Algorithm: The proposed system will integrate an advanced dynamic pricing algorithm powered by machine learning. This algorithm will not only consider real-time factors such as demand-supply ratios and traffic conditions but will also incorporate predictive analytics to anticipate future demand patterns. This anticipatory approach aims to minimize sudden price spikes and enhance overall pricing stability.
2. Personalized Pricing Based on User Profiles: To cater to individual user preferences, the proposed system will incorporate machine learning models that analyze historical user data. By understanding the unique preferences, travel patterns, and behaviors of each user, the system can offer personalized pricing, creating a more tailored and satisfactory experience for riders.
3. Explanatory AI for Transparent Pricing: Transparency in pricing decisions is crucial for user trust. The proposed system will feature an explanatory AI component that provides users with clear and understandable explanations for pricing adjustments. This could include breakdowns of the factors influencing the fare, allowing users to make informed decisions and fostering a sense of trust in the pricing model.
4. Fairness Considerations and Bias Mitigation: Recognizing the importance of fairness in algorithmic decision-making, the proposed system will implement measures to mitigate biases. This includes regular audits of the machine learning models to identify and rectify potential biases in pricing. Fairness metrics will be integrated to assess and ensure equitable treatment across diverse user demographics.
5. User Feedback Mechanism for Continuous Improvement: The proposed system will include a robust user feedback mechanism to collect input on pricing satisfaction and overall user experience. This feedback will be instrumental in fine-tuning the machine learning models, allowing for continuous improvement and adaptation to changing user expectations.
6. Competitor Price Monitoring and Adaptive Pricing: Building on the concept of competitive pricing analysis, the proposed system will incorporate real-time monitoring of competitor pricing strategies. An adaptive pricing module will enable the system to dynamically adjust prices to remain competitive while ensuring fairness and avoiding sudden and unwarranted price changes.
7. Regulatory Compliance Module: A dedicated module will be integrated to ensure compliance with local regulations and ethical standards. The system will be designed to adhere to pricing regulations and provide transparency in its adherence to legal requirements.
8. Scalability and Resilience: The proposed system will be designed with scalability and resilience in mind. It should seamlessly adapt to varying operational scales, ensuring consistent performance even during peak demand periods or in different geographical regions.

Overall, the proposed system aims to set a benchmark for fairness, transparency, and user-centricity in ride-on-demand services by leveraging machine learning to optimize pricing strategies. Through continuous refinement based on user feedback and technological advancements, the system strives to enhance the overall ride-hailing experience for users while maintaining a competitive edge in the market.

C. Feasibility Description

The feasibility study for a comparative analysis of ride-on-demand services for fair price detection using machine learning involves a thorough investigation into the viability and potential outcomes of implementing data-driven pricing mechanisms in the ride-sharing industry. This study encompasses defining the scope and objectives, reviewing existing literature, collecting relevant data from ride-on-demand services, and developing machine learning models to predict fair prices based on various factors. Comparative analysis of different services is conducted to assess pricing accuracy, fairness, and responsiveness to external factors. Additionally, a cost-benefit analysis is performed to evaluate the economic implications, and potential risks associated with implementation are identified and addressed. Ultimately, the study aims to provide actionable recommendations for ride-on-demand services to enhance fairness and transparency in pricing through the adoption of machine learning-based approaches.

D. Algorithms

1. Cat Boost Regressor: CatBoostRegressor is a gradient boosting algorithm developed by Yandex, designed for regression tasks. It efficiently handles categorical features without preprocessing, thanks to its algorithmic approach. Hyperparameters such as learning rate, tree depth, and regularization parameters require tuning for optimal performance. CatBoost provides tools for model interpretation, including feature importance scores and visualization capabilities. Overall, CatBoost Regressor is a powerful and versatile tool for regression tasks
2. Linear Regression: Linear regression is a simple yet effective method for modeling the relationship between a dependent variable and one or more independent variables. It aims to minimize the sum of squared differences between observed and predicted values. It's widely used for tasks like predicting house prices, stock prices, and demand forecasting.
3. Hist Gradient Boosting Regressor: Histogram-based Gradient Boosting Regression is a variation of gradient boosting specifically optimized for large datasets. It's available in libraries like scikit-learn and offers competitive performance compared to other gradient boosting implementations.

IV. MODULE DESCRIPTION

V. IMPLEMENTATION AND TESTING

1. Data Collection Considerations: Data is collected from multiple ride-on-demand platforms to ensure a representative sample. This dataset, when appropriately designed and representative of real-world scenarios, will enable the effective training, evaluation, and comparison of machine learning models for fair price detection in ride-on-demand services.
2. Data Preprocessing:  Clean and preprocess the collected data to ensure uniformity and consistency. This may involve resizing images, normalizing pixel values, and augmenting the dataset to increase its size and diversity. Additionally, label encoding or one-hot encoding of emotions is necessary for model training.
3. Training Model: We divided the dataset into test data, train data for training the model. Firstly, we choose the linear regression model and trained with the dataset from the Kaggle is used for the implementation. Similarly we train the Linear regression model and the HistGradientboosting regressor. We used the scatter plot to better understand accuracy of the model.
4. Hyperparameter Tuning:  Fine-tune the model hyperparameters to optimize performance. This may involve adjusting learning rates, batch sizes, dropout rates, and other parameters to achieve better results
5. Python: We use the python programming language for detecting fair prices of ride on demand services. We choose python language because it contains different libraries such as pandas, numpy, seaborn, matplotlib which are used for reading the dataset and performing mathematical operations. Using different models, we can train  and test the data and calculate the performance of it.
6. Machine Learning: It is nothing but a approach to achieve artificial intelligence through software models that can learn from experience to find patterns in data. Using Machine Learning we can able to build the models which are used for fair price detection. These models includes random forest, linear regression, Decision Trees, gradient boost algorithm etc. and these models are classified into supervised learning and unsupervised learning.
7. Deployment and Scalability: Implement the proposed system within a controlled environment or collaborate with a partner platform for a real-world pilot. Evaluate the system's performance under varying loads and operational scales. Ensure that the system can handle peak demand periods without compromising performance.
8. Test Cases: Test cases are detailed instructions or procedures designed to verify the functionality, behavior, and performance of a software application. They are created based on requirements, specifications, and use cases, outlining the steps to be followed, the expected outcomes, and the criteria for determining whether the test has passed or failed. Test cases cover various scenarios, including normal, boundary, and error conditions, to ensure comprehensive testing of the system. Each test case is typically documented with a unique identifier, a description of the test scenario, preconditions, input data, expected results, and any additional information necessary for execution. Test cases play a crucial role in the testing process by providing a systematic approach to validate the software's functionality, detect defects, and ensure that the application meets user expectations and requirements.

Table 1. Test case 1

Table 2. Test Case 2

Conclusion

A. Conclusion The \\\"Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning\\\" project represents a significant step forward in leveraging advanced technologies to enhance transparency, fairness, and efficiency in the ride-on-demand services industry. Through the integration of machine learning algorithms, data analysis, the project aims to provide valuable insights into pricing strategies employed by various ride-on-demand platforms. In conclusion, the \\\"Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning\\\" project successfully achieves its objectives of providing a transparent, fair, and efficient analysis of ride-on-demand services. The outcomes and lessons learned during this project contribute to the ongoing discourse on the intersection of machine learning, fairness, and the optimization of pricing strategies in the evolving landscape of ride-on-demand services. As technology and industry practices continue to advance, the project lays the foundation for continued innovation and improvement in the analysis of pricing dynamics within the ride-on-demand sector. B. Future Scope The future scope for comparative analysis of ride-on-demand services for fair price detection using machine learning is promising and multifaceted. As machine learning algorithms continue to advance, they offer opportunities to enhance the fairness and transparency of pricing models in ride-hailing services. Future research could focus on developing more sophisticated machine learning models capable of analyzing vast amounts of data to accurately determine fair prices based on various factors such as demand, traffic conditions, and user preferences. Furthermore, the application of machine learning techniques can extend beyond fair pricing to address other challenges in the ride-hailing industry, such as optimizing driver allocation, improving route planning, and enhancing customer experience. Overall, the future of comparative analysis in this domain holds promise for leveraging machine learning to foster fairness, efficiency, and innovation in ride-on-demand services.

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Copyright

Copyright © 2024 Metta Dhana Lakshmi, Jani Revathi, Chichula Sravani, Maddila Adarsa Suhas, Balagam Umesh. 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.