Towards sustainable public transit: development of representative driving cycles for electric and CNG buses using naturalistic micro-trip data from Patna, India

Abstract

With the increasing emphasis on sustainable urban mobility, electric buses are rapidly being integrated into public transportation networks in Indian cities. A critical component in evaluating their energy consumption and operational efficiency is the development of representative driving cycles that reflect local driving conditions. This study presents a data-driven methodology to construct real-world driving cycles for electric and CNG buses operating on mixed urban and rural roads in Patna, India. High-frequency trajectory data, collected over a 1000 km stretch using VBOX GPS loggers, were segmented into micro-trips. The study proposed two methods, Modified Random Selection and k-means clustering, to identify representative segments for cycle construction. The resulting Patna Electric Bus Driving Cycle (PEBDC) and Patna CNG Bus Driving Cycle (PCBDC) are benchmarked against both Indian and international standards. It is observed that the Patna cycles exhibited lower average speeds and reduced time spent in acceleration and deceleration phases, when compared to driving cycles of other Indian cities.

Keywords

Driving cycle / Micro-trip / Modified random selection technique / K-means clustering / Electric Bus / CNG bus / GPS

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