Enhancing passenger comfort and operator efficiency through multi-objective bus timetable optimization

The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system.