Research Articles Open Access

Haazir Mechanic: Design and Functional Evaluation of a Location-Aware Mobile Platform for Roadside Vehicle Assistance

Zohaib Badar * , Hafiz Faizan Ahmed
* Corresponding author: Zohaib Badar ( L1F20BSSE0548@ucp.edu.pk )
Available online October 10, 2026
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Abstract

Getting roadside assistance is difficult as the driver has to locate a mechanic, explain the problem, agree on the time and then follow up the service request through multiple calls or informal contacts. Haazir Mechanic was developed as a mobile prototype to integrate those steps in one, location-aware workflow. The application is implemented using Flutter for mobile client, Node.js and Express for REST services and MySQL for persistent data. GPS and mapping services can help you locate nearby mechanics. It also includes a troubleshooting assistant, although the paper refers to this as rule based rather than trained diagnostic model, despite the “AI chatbot” name used in parts of the interface. The only evidence available for evaluation is the report of the final year project. It reports 20 functional test cases across authentication, mechanic search, booking, troubleshooting and reviews. We detail five end-to-end cases. The aggregate summary includes six defects, all of which are marked corrected. The results demonstrate that the documented prototype workflows were successfully executed, but they do not demonstrate field reliability, usability with real drivers and mechanics, performance at production scale, or diagnostic accuracy. Thus, this article considers Haazir Mechanic as an applied software prototype. The architecture and service flow are rebuilt, the degree of intelligence implemented is clarified and Verified functional behavior is distinguished from capabilities still requiring security, scalability, provider verification, payment and field evaluation.

Keywords

Roadside assistance location-based services mobile application mechanic discovery rule-based troubleshooting

Introduction

A vehicle break-down is a simple but urgent coordination problem. The driver needs to know who can help, where that help is, if the help is available, and how the request will be tracked once contact is made. Many of these steps are done by telephone, personal recommendation or separate service directories. This is more difficult to do and the driver is out of familiar territory.

Location based services can help to eliminate some of this uncertainty, by allowing a mobile application to combine the position of the driver with information on providers in the vicinity. In location-aware systems research, positioning, communication, evaluation, interface design, and privacy are not independent features, but related concerns . The same idea is used in a more concrete setting in roadside assistance applications. For instance, Rescuennect combines GPS, mapping, provider information, and help requests into one mobile application . Other recent systems also match vehicle owners and mechanics using location tracking and booking workflows .

The solution to this problem was to create Haazir Mechanic as a final year mobile software project. The prototype is an app that integrates mechanic discovery, service booking, status updates, troubleshooting help and post-service feedback. The user doesn’t have to move from map to directory to another communication channel to start a request. Same system on provider side, a mechanic can get a booking and respond to it and update the service status.

The system has to be described more accurately, but the source project has a terminology problem that must be solved first. In several early studies, price estimation with machine learning has been discussed. It is also named “AI chatbot” from the mobile interface. The implementation record in the latter is more detailed. A learning model was not implemented due to the lack of a suitable labeled data-set within the project constraints. But the troubleshooting module has built-in guidance based on a rule-based knowledge base. What follows describes the implemented system, not the language previously proposed. no claimed diagnostic performance, machine learning accuracy or learned price prediction

Hence, the present paper studies Haazir Mechanic as a software system in the field. In this paper, we answer three questions:

  • RQ1: What are the relationships of mechanic discovery, booking, service-status updates and troubleshooting support in the mobile architecture that has been implemented?

  • RQ2: The functional tests documented demonstrate the main mechanic and vehicle-owner flows.

  • RQ3: What are the technical and evaluation gaps before the prototype can be regarded as a production roadside-assistance service?

The contribution is intentionally limited. We use the project record to recreate the client-server architecture, describe the service life cycle in terms consistent with implementation, and present functional evidence without translating project requirements into measured results. That’s an important distinction because the docs have requirements for responsiveness, concurrentity, but no benchmark logs for them. Security, payment and cross-platform operation is the same.

The rest of the paper is organized as follows. In Section 2, we place the prototype into the context of previous work on location-based and roadside-assistance systems. Section 3 describes the actors, architecture, data model, workflow and the implemented troubleshooting logic. Section 4 provides functional evidence for evaluation. Section 5 interprets the results and discusses the remaining engineering gaps. Section 6 highlights the limitation of the paper. Finally, Section 7 concludes the study.

Complete Article

The complete article, including all figures, tables, equations and algorithms, is available in the official publication PDF.

Conclusion

Haazir Mechanic is a Flutter, Node.js/Express, and MySQL application featuring location-aware mechanic discovery, booking, service-status coordination, rule-based troubleshooting, and post-service feedback. The project record reports 20 functional test cases in 5 modules. The five end-to-end cases are detailed and aggregate summary records six defects, all marked corrected.

These results support a modest conclusion. The prototype implements and tests the main workflow for roadside assistance as documented by the project team. They do not demonstrate production readiness, large scale reliability, field usability, diagnostic accuracy or economic benefit. The paper does not contain the implementation or experiment to back up the earlier claims of machine learning and booking time.

Future work will include backend testing as it is required due to concurrent load, intermittent connectivity and failure recovery.

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