| China’s economy and urbanization level has been increasing fast since the reform and opening up, which obviously improved people’s life. However, traffic congestion is becoming more and more serious nowadays, resulting in the severe environmental pollution as well. Thus, it is now very important to alleviate traffic congestion and improve environment. Congestion pricing has been widely accepted as an efficient traffic management policy to ease the traffic situation and has been implemented in many cities, e.g. Singapore, London and Stockholm. Furthermore, in China, Beijing, Shanghai, Shenzhen and Guangzhou have also discussed the possibility of carrying out this program.Although abundant studies have been focused on different aspects of congestion pricing, such as optimal toll rate, minimum toll revenue, equity effects, selection of charging locations and public acceptance, little attention in the previous literatures has been paid to a specific problem that whether taxis should be tolled in the pricing zone. In fact, it is a non-negligible issue when designing road pricing policies by taking account of the two opposite effects:on the one hand, taxis are component part of public transit and thus it would hurt travelers’benefit if taxis are charged in the pricing zone; on the other hand, taxis have accounted for higher and higher percentage of the overall traffic demand in urban area over years. And taxi always operates on the street searching for the next customer even when it is vacant, thus its impact on the traffic congestion is consistent. Therefore, taxis should also pay the congestion charge as private car. Furthermore, part of taxi demands would shift to public transit if charging taxis with toll, which can alleviate traffic congestion.This study aims at investigating the impact and feasibility of charging taxis with toll fee in the pricing zone when designing congestion pricing scheme. The optimal taxi fleet structure so as to maximize the profit of each taxi firm under various market regimes has also been addressed. The paper is structured as follows.In Chapter 2, we analyzed the principles of economics in congestion pricing, which is the fundament to investigate the pricing policy of taxis in road pricing zone. We gave the definition and measurement of traffic congestion, and provided the reasons resulting in the traffic congestion in view of the traffic demand, supply, management and other factors. The marginal cost pricing theory was discussed, and the static and dynamic congestion pricing models were also introduced. Finally, we explained how road pricing alleviates the traffic congestion.In Chapter 3, we investigated the issue that whether taxis should be charged in the pricing zone when there are only private car and taxi in the system. A bi-level programming model was developed to compare the maximum social welfares before and after the congestion charge is imposed on taxis. The lower-level was a combined network equilibrium model (CNEM) formulated as an optimal problem, which considered the logit-based mode split, route choice, elastic demand and vacant taxi distributions. The upper-level was to maximize the social welfare when toll rates vary. The bi-level problem can be solved by the Genetic Algorithm, whereas the lower-level was solved by a heuristic algorithm. An application with numerical examples was conducted to demonstrate the effectiveness of the proposed model and algorithm and to promote some interesting findings. We also discussed the sensitivity of parameters in models.Chapter 4 extended the models proposed in Chapter 3. The extensions included incorporation of the mass transit mode into the model and consideration of the competition among private car, taxi, and public transit. The customers’waiting time for taxi and the waiting/searching time of vacant taxi are also taken into account, which has a significant impact on the mode split. And we also introduced the taxi service time constraint in this chapter. Additionally, the delay-based taxi fare was incorporated in the model. Due to the asymmetric interactions of network flows, a variational inequality (VI) program was used which is equivalent to the combined network equilibrium model (CNEM). We still presented the Genetic Algorithm to solve the bi-level problem, whereas the lower-level was solved by the block Gauss-Seidel decomposition approach together with the method of successive averages and diagonalization algorithm. The numerical example proposed in Chapter 3 was used to illustrate the effectiveness of the proposed model and algorithm. Furthermore, we compared the results with that obtained in Chapter 3.In Chapter 5, we investigated the optimal taxi fleet size structure under monopoly and oligopoly market regimes when taxis are charged with the link-based toll. We proposed a bi-level programming model to take account of the interaction between taxi fleet size and different traffic modes in the network. The upper-level was to determine the optimal taxi fleet structure so as to maximize the profit of each taxi firm. In particular, when taxi market is an oligopoly the upper problem can be described as an n-player, non-cooperative game. The lower-level was a combined network equilibrium model (CNEM) representing the travelers’response to the equilibrium taxi fleet size structure when congestion toll is imposed on taxis. We showed that the lower-level problem can be formulated as an equivalent variational inequality formulation, which considered the hierarchical logit-based mode split, route choice, elastic demand and vacant taxi distributions. The bi-level problem can be solved by an iterative heuristic solution algorithm, whereas the lower-level model was solved by the block Gauss-Seidel decomposition approach together with method of successive averages. An application with numerical examples was presented to demonstrate the effectiveness of the proposed model and algorithm, and some interesting findings were also provided.
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