Performance assessment of public transport routes: A framework using revealed data

Abstract

To retain existing passengers and attract new users, a comprehensive evaluation of public transport services becomes an indispensable tool for transport planners and operators. The present study proposes a novel framework for assessing public transport service quality using secondary data sources. In this, a composite index is developed by incorporating key attributes, i.e., service availability, travel time reliability, occupancy, and environmental factors. The proposed framework uses the fuzzy AHP method to assign weights to each attribute; the resulting order of attributes is service availability (0.442), travel time reliability (0.293), occupancy (0.189), and environmental factor (0.075). Further, these weights are used as input for a composite scale developed. The study demonstrates the effectiveness of a composite index for three transit routes in Delhi by revealing various scenarios requiring attention, notably (1) routes with higher occupancy and with a higher variation in occupancy, (2) cases of low occupancy despite high service availability, and (3) situations of high occupancy with poor reliability. The composite scale facilitates data-driven optimization of various resources by quantifying the trade-offs between attributes. It helps determine precise resource allocation - whether deploying additional buses during peak periods, redistributing existing fleets, or reserved lanes. A sensitivity analysis is also performed to understand the interaction of different attributes and their influence on the overall service level. This ensures that any reallocation maintains or improves service quality on both the source and recipient routes by providing specific thresholds for each attribute that maintains the desired Level of Service. This systematic approach allows planners to optimize resources while ensuring service standards are not compromised on the route. With these valuable insights, policymakers can make more informed decisions about resource allocation and service improvements.