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Cited by 0Transportation Planning and OptimizationTraffic control and managementIoT and GPS-based Vehicle Safety Systems

An IoT-Based Bus Bunching Prevention Framework Using Real-Time Headway Monitoring and Driver Intervention Mechanisms

Open access
Mazwir, M.N., Ajib, N.A., Harum, N.·2026·International Journal of Research and Innovation in Social Science
DOI 10.47772/ijriss.2026.100800942
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Abstract

Bus bunching is one of the most persistent operational challenges in public transportation systems, causing irregular service intervals, prolonged passenger waiting times, vehicle overcrowding, and reduced service reliability. Existing solutions primarily focus on post-event analysis or complex optimization algorithms that are difficult to deploy in real-world transit environments. This study proposes an Internet of Things (IoT)-based bus bunching prevention framework that integrates real-time vehicle tracking, intelligent headway monitoring, automated driver intervention, and centralized fleet management within a unified architecture. The framework employs ESP32 microcontrollers and GPS modules installed on buses to continuously collect spatial and temporal vehicle data. A centralized web platform processes the data to calculate headways between consecutive buses and identifies potential bunching conditions when predefined thresholds are violated. Upon detection, automated alerts are transmitted to drivers through onboard notification devices and simultaneously displayed on an administrative monitoring dashboard, enabling immediate corrective actions. To evaluate the operational need for such a system, a survey was conducted among public transport users in Malaysia. Results revealed that 70.5% of respondents had experienced bus bunching, while 57.6% reported inconsistent arrival schedules as a major concern. Furthermore, 98.4% supported real-time driver notifications and 96.7% agreed that continuous fleet monitoring would improve service reliability. The findings demonstrate that integrating real-time monitoring with active intervention mechanisms provides a practical and scalable approach to preventing bus bunching and enhancing public transport performance.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper proposes a four-layer IoT-based bus bunching prevention framework using ESP32/GPS tracking, real-time headway monitoring, driver alerts, and a centralized fleet dashboard. A Malaysian commuter survey supports the need, with 70.5% reporting bus bunching and over 95% supporting real-time intervention features.

Key findings

  • 70.5% of survey respondents reported experiencing bus bunching, and 57.6% identified inconsistent bus schedules as the primary service issue; excessive waiting times (47.5%), overcrowding (45.8%), and congestion at bus stops (44.1%) followed.
  • Acceptance of intervention technologies was high: 98.4% supported driver notifications, 96.7% agreed real-time administrative fleet monitoring would improve operations, and 95.1% supported combined automated and manual intervention mechanisms.
  • The proposed framework has four layers: real-time data acquisition via GPS and ESP32, intelligent headway monitoring against predefined thresholds, active driver intervention through onboard buzzers/visual indicators and push-button acknowledgment, and centralized fleet management via a web dashboard.
  • Survey context: 80.3% of respondents were aged 18–25, 73.8% were students, 60% used public buses less than once per week, and 44% reported average one-way trips exceeding 1 hour.

Methods

  • Quantitative survey of public transport users across several Malaysian states, primarily Melaka and Selangor, using multiple-choice and Likert-scale questions on demographics, bus usage, bunching experiences, and perceptions of the proposed IoT system.
  • Data were analyzed using descriptive statistics, including frequencies and percentages, to assess operational challenges and stakeholder acceptance.
  • The framework design was derived from a literature review and stakeholder requirements analysis; no field deployment or prototype performance evaluation is reported.

Limitations

  • Stated: future work may focus on developing a full prototype implementation, conducting field trials with public transport operators, and integrating predictive analytics and artificial intelligence.
  • Evident from text: the evaluation is survey-based only, with no field trial, prototype testing, or operational performance results for the proposed framework.
  • Evident from text: the survey sample is skewed toward young respondents and students (80.3% aged 18–25; 73.8% students), geographically concentrated in Melaka and Selangor, and the sample size is not reported.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.47772/ijriss.2026.100800942

Cites

0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 3 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    70.5% of survey respondents reported experiencing bus bunching, i.e., situations where multiple buses arrived simultaneously.

    “Results revealed that 70.5% of respondents had experienced bus bunching”
    • Also stated in: § Validation of the Need for Bus Bunching Prevention Technology · plain
      “A total of 70.5% of respondents reported experiencing situations where multiple buses arrived simultaneously, confirming that service irregularity is a common operational challenge.”
    Quote in the paperSubstantial claim
  • Findingown data

    95.1% of survey respondents supported the use of both automated and manual intervention mechanisms.

    “Meanwhile, 96.7% agreed that real-time administrative fleet monitoring would improve public transport operations, and 95.1% supported the use of both automated and manual intervention mechanisms.”
    Quote in the paperSubstantial claim
  • Findingown data

    47.5% of survey respondents identified excessive waiting times as a service issue.

    “Additionally, 57.6% identified inconsistent bus schedules as the primary service issue, followed by excessive waiting times (47.5%),overcrowding (45.8%), and congestion at bus stops (44.1%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    44.1% of survey respondents identified congestion at bus stops as a service issue.

    “Additionally, 57.6% identified inconsistent bus schedules as the primary service issue, followed by excessive waiting times (47.5%),overcrowding (45.8%), and congestion at bus stops (44.1%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    98.4% of survey respondents supported driver notification features that provide alerts when buses are operating too closely to one another.

    “Furthermore, 98.4% supported real-time driver notifications”
    • Also stated in: § Validation of the Need for Bus Bunching Prevention Technology · plain
      “Results indicated that 98.4% of respondents supported driver notification features that provide alerts when buses are operating too closely to one another.”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. 12
  • May combine two claims. 7

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    80.3% of survey respondents were aged between 18 and 25 years.

    “A total of 80.3% of respondents were aged between 18 and 25 years, with 73.8% identifying as students.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    73.8% of survey respondents identified as students.

    “A total of 80.3% of respondents were aged between 18 and 25 years, with 73.8% identifying as students.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    60% of survey respondents reported using public buses less than once per week.

    “Regarding public transit frequency, 60% of respondents reported using public buses less than once per week, while a small proportion relied on buses daily.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisereasoning

    Current bus transportation management systems often lack continuous headway monitoring, automated detection of potential bus bunching events, real-time driver notification mechanisms, and integrated fleet management capabilities that enable operators to implement timely service recovery actions.

    “Current bus transportation management systems often lack continuous headway monitoring, automated detection of potential bus bunching events, real-time driver notification mechanisms, and integrated fleet management capabilities that enable operators to implement timely service recovery actions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisereasoning

    Many existing studies primarily focus on GPS-based tracking, post-event analysis, spatial-temporal assessments, or predictive modelling rather than providing integrated real-time operational intervention mechanisms for preventing bus bunching.

    “Many existing studies primarily focus on GPS-based tracking, post-event analysis, spatialtemporal assessments, or predictive modelling rather than providing integrated real-time operational intervention mechanisms for preventing bus bunching.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisereasoning

    Limited attention has been given to integrating real-time monitoring with immediate intervention mechanisms that support operational decision-making.

    “Limited attention has been given to integrating real-time monitoring with immediate intervention mechanisms that support operational decision-making.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The overwhelming support for real-time driver notifications (98.4%), administrative fleet monitoring (96.7%), and integrated alert mechanisms (95.1%) demonstrates strong user acceptance of proactive monitoring and intervention technologies.

    “Moreover, the overwhelming support for real-time driver notifications (98.4%), administrative fleet monitoring (96.7%), and integrated alert mechanisms (95.1%) demonstrates strong user acceptance of proactive monitoring and intervention technologies.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of public transport users in Malaysia.
    • Rests on an assumption: Stated user acceptance of the proposed monitoring and intervention system indicates that it would improve public transport operations and reliability.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The proposed framework transforms conventional fleet monitoring from a passive tracking tool into an active decision-support mechanism.

    “By transforming conventional fleet monitoring from a passive tracking tool into an active decision-support mechanism, the framework has the potential to enhance schedule adherence, reduce passenger waiting times, improve resource utilization, and increase overall public transport reliability.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The survey findings validate the relevance and necessity of the proposed framework.

    “The findings from the stakeholder survey further validated the relevance and necessity of the proposed framework.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of public transport users in Malaysia.
    • Rests on an assumption: Stated user acceptance of the proposed monitoring and intervention system indicates that it would improve public transport operations and reliability.

  • May combine two claims.

    Interpretationreasoning

    Upon detection of a potential bunching condition, automated alerts are transmitted to onboard notification devices and simultaneously displayed on an administrative monitoring dashboard.

    “Upon detection, automated alerts are transmitted to drivers through onboard notification devices and simultaneously displayed on an administrative monitoring dashboard, enabling immediate corrective actions.”
    • Also stated in: § Proposed Bus Bunching Prevention Framework · plain
      “Once a potential bus bunching event is detected, the system simultaneously generates driver alerts and administrative notifications, ensuring that both operational personnel and transportation managers are informed in real time.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    The framework has the potential to enhance schedule adherence, reduce passenger waiting times, improve resource utilization, and increase overall public transport reliability.

    “By transforming conventional fleet monitoring from a passive tracking tool into an active decision-support mechanism, the framework has the potential to enhance schedule adherence, reduce passenger waiting times, improve resource utilization, and increase overall public transport reliability.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Drivers will take corrective measures, such as adjusting speed or increasing spacing, when alerted to a potential bus bunching condition.
    • Rests on an assumption: Real-time monitoring, early detection, and timely driver intervention can minimize the occurrence of bus bunching before major service disruptions arise.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The proposed framework consists of four interconnected operational layers: real-time data acquisition, intelligent headway monitoring, active driver intervention, and fleet management.

    “The proposed framework consists of four interconnected operational layers that work collaboratively to maintain regular bus headways and improve service reliability.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The proposed system enables continuous observation of bus movements, automatic detection of potential bus bunching events, and immediate notification to both drivers and fleet administrators, thereby supporting timely corrective actions.

    “By utilizing GPS-enabled ESP32 devices and a centralized monitoring platform, the proposed system enables continuous observation of bus movements, automatic detection of potential bus bunching events, and immediate notification to both drivers and fleet administrators, thereby supporting timely corrective actions.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Drivers will take corrective measures, such as adjusting speed or increasing spacing, when alerted to a potential bus bunching condition.
    • Rests on an assumption: GPS modules connected to ESP32 microcontrollers provide sufficiently accurate and reliable real-time location and speed data for headway monitoring.
    • Rests on an assumption: Transport operators can establish predefined operational headway thresholds that reliably identify potential bus bunching conditions.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The framework was developed to overcome the limitations of existing bus monitoring solutions, which primarily focus on vehicle tracking, post-event analysis, or complex optimization techniques rather than providing proactive operational interventions.

    “The framework was developed to overcome the limitations of existing bus monitoring solutions, which primarily focus on vehicle tracking, post-event analysis, or complex optimization techniques rather than providing proactive operational interventions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    The proposed framework provides a practical, scalable, and cost-effective solution for mitigating bus bunching in public transportation systems.

    “By combining continuous data acquisition, automated headway analysis, immediate driver intervention, and centralized fleet supervision, the framework provides a practical, scalable, and costeffective solution for mitigating bus bunching in public transportation systems.”
    • Also stated in: § CONCLUSION · plain
      “Overall, the proposed framework contributes a practical, scalable, and cost-effective solution for improving bus service regularity and minimizing the operational impacts of bus bunching.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Drivers will take corrective measures, such as adjusting speed or increasing spacing, when alerted to a potential bus bunching condition.
    • Rests on an assumption: GPS modules connected to ESP32 microcontrollers provide sufficiently accurate and reliable real-time location and speed data for headway monitoring.
    • Rests on an assumption: Transport operators can establish predefined operational headway thresholds that reliably identify potential bus bunching conditions.
    • Rests on an assumption: Real-time monitoring, early detection, and timely driver intervention can minimize the occurrence of bus bunching before major service disruptions arise.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    The integration of these capabilities has the potential to improve schedule adherence, reduce passenger waiting times, minimize vehicle overcrowding, enhance resource utilization, and increase the reliability and efficiency of public transport services.

    “The integration of these capabilities has the potential to improve schedule adherence, reduce passenger waiting times, minimize vehicle overcrowding, enhance resource utilization, and ultimately increase the reliability and efficiency of public transport services.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Drivers will take corrective measures, such as adjusting speed or increasing spacing, when alerted to a potential bus bunching condition.
    • Rests on an assumption: Real-time monitoring, early detection, and timely driver intervention can minimize the occurrence of bus bunching before major service disruptions arise.
16 of 16

  • The paper relies on this without support.

    Drivers will take corrective measures, such as adjusting speed or increasing spacing, when alerted to a potential bus bunching condition.

    “These notifications inform drivers that their vehicles are approaching a critical headway condition and prompt them to take corrective measures, such as adjusting speed or increasing spacing from the preceding vehicle.”

  • The paper relies on this without support.

    The survey respondents are representative of public transport users in Malaysia.

    “Responses were collected from individuals with varying transportation usage frequencies and occupational backgrounds to ensure a broad representation of public transport users.”

  • The paper relies on this without support.

    Stated user acceptance of the proposed monitoring and intervention system indicates that it would improve public transport operations and reliability.

    “Meanwhile, 96.7% agreed that real-time administrative fleet monitoring would improve public transport operations, and 95.1% supported the use of both automated and manual intervention mechanisms.These findings validate the importance of integrating monitoring and intervention functions within a single framework.”

  • The paper relies on this without support.

    GPS modules connected to ESP32 microcontrollers provide sufficiently accurate and reliable real-time location and speed data for headway monitoring.

    “Each bus is equipped with a GPS module connected to an ESP32 microcontroller that captures and transmits realtime information, including vehicle location, speed, route position, and timestamp data.”

  • The paper relies on this without support.

    Transport operators can establish predefined operational headway thresholds that reliably identify potential bus bunching conditions.

    “These headways are compared against predefined operational thresholds established by transport operators.When the distance or time interval between buses falls below the acceptable threshold, indicating a potential bus bunching condition, the system automatically identifies the anomaly and generates an intervention alert.”

  • The paper relies on this without support.

    Real-time monitoring, early detection, and timely driver intervention can minimize the occurrence of bus bunching before major service disruptions arise.

    “Unlike conventional fleet monitoring systems that primarily provide location visualization and post-event analysis, the proposed framework emphasizes operational prevention by enabling continuous monitoring, early detection of service irregularities, and timely response mechanisms that can minimize the occurrence of bus bunching before major service disruptions arise.”
6 of 6

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 2

  1. Moodi, M., et al. (2023). Investigating Reliability and Stability Parameters of City Buses for Intra-city Transportation based on GPS. International Journal of Sustainable Transportation, 17(5), 485-498.
  2. Khalid, A., et al. (2016). A Bus Tracking Information System using Consumer Grade GPS: A Case Study. Journal of Telecommunication, Electronic and Computer Engineering, 8(4), 89-93.

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Related

Authors
Mazwir, M. N., Ajib, N. A., & Harum, N.
Affiliations
  • University of Kuala Lumpur (MY)
  • Universiti Teknikal Malaysia Melaka (MY)
Venue
International Journal of Research and Innovation in Social Science
Publisher
RSIS International
Published
2026
Volume
10
Issue
8
Pages
13759-13769
ISSN
2454-6186 (ISSN-L)
DOI
10.47772/ijriss.2026.100800942
OpenAlex
W7213952391
Cited by
0
References
0
FWCI
0.00
Type
journal article
Language
en
Open access
Locations
International Journal of Research and Innovation in Social Science (publishedVersion)
Topics
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
Keywords
Internet of Things, real-time vehicle tracking, driver intervention, public transport, fleet management, ESP32 microcontroller, GPS module, service reliability, passenger waiting time, user survey

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