Modeling Departure time choice of Metro Passengers: A case study of Delhi metro

Abstract

Delhi metro is suffering from severe congestion due to booming travel demand. Differential pricing is an important and effective measure in traffic demand management (TDM). Whether pricing strategies work depends strongly on the responses of travelers to fare changes. Normally, the fare elasticity of demand is used to describe the relationship between demand changes and fare changes at an aggregate level. It is useful to estimate demand changes for system-wide and long-term pricing policies. However, for regional and short-term (valid time window is short) fare strategies, it is hard to capture the reactions just by the fare elasticity of demand. The retiming elasticity decreases greatly with increasing shift time, and 30 minutes is almost the maximum acceptable shift time for passengers. Moreover, the retiming elasticity of passengers in the middle term is approximately twice that in the short term. Applications of fare optimization are also executed, and the results suggest that optimizing the valid time window of the discount fares is a feasible way to improve the congestion relief effect of the policy, while policymakers should be cautious to change fare structures and increase discounts. Passengers' travel responses to fare changes are very complex and related to various external factors, such as service quality, travel preference, and socioeconomic factors. Past works on travel responses are usually specific to a certain region or transit system and assume that the external factors remain the same before and after the policy. Flexi fare is a successful measure in Beijing suburban railway and it handles the booming congestion of suburb rail. Modeling in departure time choice plays a vital role in caters the congestion in the metro and distributing the commuters at different intervals of time. The objective of this study is to understand the ridership pattern, mode-choice behaviour, trip purposes of users, and willingness to shift due to fare differential during the peak and non-peak hours in the metro rail system of Delhi. To know the different modeling departure time choice, it is necessary to know the willingness to shift to an off-peak hour or hike in peak hour fare. The study result shows that the users with work trips are willing to pay more but unwilling to shift from peak hour travel, and users with leisure trips are willing to shift to non-peak hour travel if the fare in peak hour changes. Therefore, new fare strategies should be recommended.