Abstract
Since the bus deregulation (Transport Act 1985) the patronage for bus services has been decreasing in a county in South of England. Hence, methods that increase patronage, focus subsidies and stimulate the bus industry are required. Our surveys and market research identified and quantified essential factors. The top three factors are price, frequency, and dependability. The model was further enhanced by taking into account real time passenger information (RTPI), socio-demographics and ticket machine data along targeted bus routes. These allowed the design of predictive models. Here, feature engineering was essential to boost the solution quality. We compared several models such as regression, decision tress and random forest. Additionally, traditional price elasticity formulas have been confirmed. Our results indicate that more accuracy can be gained using prediction methods based on the engineered features. This allows to identify routes that have the potential to increase in profitability - allowing a more focused subsidy strategy.