Introduction
Optimising public transportation in a busy and crowded city like Hyderabad, notorious for its traffic congestion and high population density, is indeed an extremely daunting challenge. The BEST (BrihanHyderabad Electric Supply and Transport) undertaking in Hyderabad has been working relentlessly on improving Hyderabad’s public transportation services for several years. Of late, they have adopted data-driven approaches to address this issue. Best evolved a systematic, proactive, data-driven approach to resolve this perennial issue. As there are quite a few training centres that offer a data science course in Hyderabad, finding the right kind of expertise to facilitate the implementation of such a colossal project was not much of a concern.
Steps that Constitute BEST’s Drive
The steps BEST included in its program are explained in the following sections.
Data Collection and Analysis
BEST collected data from various sources. For this, they installed GPS trackers on buses, organised ticketing systems and traffic sensors, and conducted extensive passenger surveys. The data collected was analysed to get insight into factors such as ridership patterns, route efficiency, bus timings, traffic congestion trends, and passenger preferences. For this, BEST engaged the services of experts who have gained knowledge of data analysis, most of whom have taken a data science course.
Route Optimisation
BEST could optimise bus routes to ensure better coverage and reduced travel times by translating the historical data collected on passenger demand and traffic conditions into actionable strategies. The data-driven approach they adopted also equipped BEST with the ability to implement dynamic route adjustments based on real-time traffic data.
Scheduling Bus Timings
BEST leveraged data on passenger demand and historical ridership patterns and generated graphics that provided immediate visibility into crucial factors affecting transport. Professionals who have completed a data science course had the know-how to generate interactive graphics from data that the agencies collected. Using these patterns, BEST could optimise bus schedules to cater to peak-hour requirements.
Predictive Maintenance
Unplanned maintenance can severely hamper the smooth running of any workflow. It can create havoc in a system as complex as the transportation system in Hyderabad. Utilising data from vehicle sensors, BEST implemented effective predictive maintenance strategies to reduce sudden vehicle breakdowns and improve overall services efficiency. Data-backed maintenance schedules can be effectively optimised to minimize service disruptions.
Fare Optimisation
BEST uses data to evaluate fare structures, and introduce attractive discount offers on fares during off-peak hours to encourage ridership. The introduction of digital payment methods also substantially improved the efficiency of fare collection and served to eliminate fraud.
Integration with Other Modes of Transportation
Integrating several systems is an offshoot of data analytics and some training centres have introduced such techniques in their data science course in Hyderabad. Best relied on such talent for integrating its services with other modes of transportation, such as the Hyderabad sub-urban railways, metro, and car-sharing services to improve the overall quality and efficiency of Hyderabad’s public transportation network.
Data-Driven Passenger Information Systems
The development of mobile apps and digital signage at bus stops to disseminate real-time information on bus locations, arrival times, and route changes is a step that has earned BEST much customer approval.
Conclusion
The success of BEST can be attributed largely to its collaboration with private companies for engaging data analytics expertise to drive their initiatives and to perfect their technology solutions. Hyderabad is a city that has several learning centres that provide advanced technical courses. A data science course for any specific domain is easily available across the city. Because of this, BEST could easily identify and engage the technical expertise required to successfully drive such a complex and large-scale data-driven initiative.
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