Bi-level decisions of vacant taxi drivers traveling towards taxi stands in customer-search: Modeling methodology and policy implications

• Propose a sequential logit model to predict taxi drivers׳ customer-search decisions.
• Determine the factors affecting the decisions on travel to taxi stands/stay there.
• Discuss the potential taxi policy implications.

This study adopts the sequential logit approach to modeling bi-level decisions of vacant taxi drivers in customer-search. The first level decision is about whether the drivers will travel to one of the nearest taxi stands after dropping off their customers and the second level decision is on whether the drivers will join the queue at the nearest taxi stand once they have arrived there. A stated preference survey was conducted to interview 258 urban taxi drivers about their choices of the two level decisions. The statistical test shows that search districts, travel distance from the customer׳s drop-off location to the designated taxi stand, the congestion level on the way of cruising, as well as the preference for traveling towards taxi stands are found to be the significant factors of the first level decision. This study also confirms that the queue lengths of both taxis and passengers at taxi stands, the expected customer-search distance after leaving taxi stands, and the preference of vacant taxi drivers for staying at taxi stands are found to be significantly influence the second level decision. The likelihood ratio tests for market segmentation analysis demonstrate the variations in preferences of taxi drivers operated in different taxi shifts and service areas. Some policy implications on introducing more taxi stands and improving the utilization rates of taxi stands are also discussed. We believe that the proposed sequential logit modeling approach, findings, and discussions are useful for developing micro-simulation models in terms of evaluating the performance of road traffic networks with taxi services and for developing simulation-based optimization models to answer policy questions related to taxi services.