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Passenger rejection by taxi drivers impacts the travel behaviour in many cities and suburban areas, often leaving those potential customers in non-popular zones stranded without access to taxis. To overcome this problem, many practices have been implemented, such as penalties to drivers, bans, and new pricing strategies. This paper presents a double auction taxi fare scheme, which gives both passengers and taxi drivers to influence the price, coupled with a clustering method to discourage strategic service rejection in the case study of Bangkok Metropolitan Region, Thailand, which has detailed data availability and uneven taxi journey distributions. The double auction mechanism is tailored to 2019 taxi trips, service rejection complaints, and local travel behaviour to boost transportation equity. To benchmark the performance of the new double auction scheme, a bespoke agent-based model of the taxi service in Bangkok Metropolitan Region at different rejection rates of 0%-20% was created. On one hand, the current rejection behaviour was modelled, and on the other, the double auction pricing strategy was applied. The results indicate that the double auction strategy generates a spatially distributed accessibility and leads to a higher taxi assignment success rate by up to 30%. The double auction scheme increases pickups from locations that are 20-40 km from central Bangkok by 10-15%, despite being areas of low profit. Due to the changing taxi travel landscape and longer taxi journeys, the total air pollutant emissions from the taxis increase by 10% while decreasing local emissions within central areas of Bangkok by upto 40%. Using a 5 Baht average surcharge, the total revenue drops by 20%. The results show that an equity-driven pricing strategy as an implementation of transport policy would be beneficial., Comment: 21 pages, 10 figures, 1 table, as accepted at Transportation Research Board Conference 2022

Ride-sourcing platforms enable an on-demand shared transport service by solving decision problems often related to customer matching, pricing and vehicle routing. These problems have been frequently represented using aggregated mathematical models and solved via algorithmic approaches designed by researchers. The increasing complexity of ride-sourcing environments compromises the accuracy of aggregated methods. It, therefore, signals the need for alternative practices such as agent-based models which capture the level of complex dynamics in ride-sourcing systems. The use of these agent-based models to simulate ride-sourcing fleets has been a focal point of many studies; however, this occurred in the absence of a prescribed approach on how to build the models to mimic fleet operations realistically. To bridge this research gap, we provide a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the fundamentals of agent-based modelling theory. We also introduce a model building sequence of the different modules necessary to structure a simulator based on our framework. To showcase the strength of our framework, we use it to tackle the highly non-linear problem of minimum fleet size estimation for autonomous ride-sourcing fleets. We do so by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. By modelling the ride-sourcing fleet function in the urban areas of Manhattan, San Francisco, Paris and Barcelona, we find that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. We also show that pickup wait times have a pivotal role in the estimation of the minimum fleet size in ride-sourcing operations, with agent-based modelling to be a more reliable route for their identification given the system parameters., Comment: 25 pages, 11 figures, 6 tables