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• W4285384510 endingPage "103792" @default.
• W4285384510 startingPage "103792" @default.
• W4285384510 abstract "• Proposes a MFD-based hierarchical dispatch method for CTaxis in large-scale traffic networks. • This method coordinates dispatch from macro road network to micro intersection. • This method provides an updatable way of match relationship between passengers and CTaxis. • Proposed method improves the service efficiency compared to NCD, CCD and MPCD. The dynamic dispatch of taxis is an important way to improve the service efficiency of taxis and reduce cruising distance. However, in a complex urban road network with extensive and stochastic roadside service demands, current macroscopic dispatch methods can hardly establish accurate and real-time matching relationships between taxis and passengers, and microscopic dispatch methods have the disadvantage of excessive computational complexity and nonupdatable match relationships. To this end, this paper proposes a hierarchical and cooperative macroscopic and microscopic dynamic dispatching approach for real-time urban network taxis in a connected taxi (CTaxi) information environment. First, network traffic flow dynamic evolution is described based on a macroscopic fundamental diagram (MFD), and the optimal macroscopic dispatching model is proposed to optimize the distribution of CTaxis for large-scale urban road networks with multiple MFD sub-regions. Second, to avoid the phenomenon of CTaxis blindly searching for passengers, a microscopic and relationship-updatable dispatching strategy between CTaxis and passengers is established within each MFD sub-region considering stochastic service demands and the signal timing of intersections. Finally, numerical experiments are conducted in a real road network to compare the performance of four methods: no CTaxi dispatch (NCD), conventional CTaxi dispatch (CCD), MPC dispatching (MPCD) and the dispatching strategy cooperating macro and micro (DSCMM) proposed in this paper. The results show that the DSCMM method can reduce the idle driving distance by 26.8%, 10.8% and 8.3% and the passenger waiting time by 11.0%, 13.4% and 6.4% compared to NCD, CCD and MPCD, respectively. Additionally, the market allocation of CTaxis under four scenarios is discussed, and the results demonstrate that the proposed DSCMM method is effective in improving the efficiency of CTaxis operation and reducing the waste of service resources in urban road networks with different service scales." @default.
• W4285384510 created "2022-07-14" @default.
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• W4285384510 date "2022-09-01" @default.
• W4285384510 modified "2023-10-02" @default.
• W4285384510 title "Dynamic dispatch of connected taxis for large-scale urban road networks with stochastic demands: An MFD-enabled hierarchical and cooperative approach" @default.
• W4285384510 cites W1804714834 @default.
• W4285384510 cites W1865325095 @default.
• W4285384510 cites W1908019361 @default.
• W4285384510 cites W1964612884 @default.
• W4285384510 cites W1988580225 @default.
• W4285384510 cites W2001303837 @default.
• W4285384510 cites W2010672263 @default.
• W4285384510 cites W2065195978 @default.
• W4285384510 cites W2075364600 @default.
• W4285384510 cites W2086376232 @default.
• W4285384510 cites W2092930278 @default.
• W4285384510 cites W2093776599 @default.
• W4285384510 cites W2105834060 @default.
• W4285384510 cites W2152865189 @default.
• W4285384510 cites W2161581167 @default.
• W4285384510 cites W2164863800 @default.
• W4285384510 cites W2166926417 @default.
• W4285384510 cites W2169761784 @default.
• W4285384510 cites W2195818062 @default.
• W4285384510 cites W2279201314 @default.
• W4285384510 cites W2293841092 @default.
• W4285384510 cites W2464962161 @default.
• W4285384510 cites W2509860226 @default.
• W4285384510 cites W2550789400 @default.
• W4285384510 cites W2553557005 @default.
• W4285384510 cites W2556996835 @default.
• W4285384510 cites W2569460227 @default.
• W4285384510 cites W2608770750 @default.
• W4285384510 cites W2623561804 @default.
• W4285384510 cites W2626630140 @default.
• W4285384510 cites W26726511 @default.
• W4285384510 cites W2723368897 @default.
• W4285384510 cites W2728013619 @default.
• W4285384510 cites W2749594142 @default.
• W4285384510 cites W2803952469 @default.
• W4285384510 cites W2806761102 @default.
• W4285384510 cites W2808810245 @default.
• W4285384510 cites W2883724395 @default.
• W4285384510 cites W2896628661 @default.
• W4285384510 cites W2899309741 @default.
• W4285384510 cites W2904430055 @default.
• W4285384510 cites W2924096405 @default.
• W4285384510 cites W2945802707 @default.
• W4285384510 cites W2953051921 @default.
• W4285384510 cites W2954989955 @default.
• W4285384510 cites W2962861257 @default.
• W4285384510 cites W2963858235 @default.
• W4285384510 cites W2964250193 @default.
• W4285384510 cites W2973455388 @default.
• W4285384510 cites W2990756245 @default.
• W4285384510 cites W3000016760 @default.
• W4285384510 cites W3002210958 @default.
• W4285384510 cites W3013050747 @default.
• W4285384510 cites W3029189025 @default.
• W4285384510 cites W3039360488 @default.
• W4285384510 cites W3042329331 @default.
• W4285384510 cites W3043533878 @default.
• W4285384510 cites W3095209644 @default.
• W4285384510 cites W3109149352 @default.
• W4285384510 cites W3114215610 @default.
• W4285384510 cites W3119457309 @default.
• W4285384510 cites W3126407732 @default.
• W4285384510 cites W3126898730 @default.
• W4285384510 cites W3130910809 @default.
• W4285384510 cites W3137694065 @default.
• W4285384510 cites W3141772097 @default.
• W4285384510 cites W3159661494 @default.
• W4285384510 cites W3166361176 @default.
• W4285384510 cites W3175186335 @default.
• W4285384510 cites W3197364647 @default.
• W4285384510 cites W3206147427 @default.
• W4285384510 cites W4200534728 @default.
• W4285384510 cites W4230397650 @default.
• W4285384510 cites W4231700625 @default.
• W4285384510 cites W4233845099 @default.
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• W4285384510 doi "https://doi.org/10.1016/j.trc.2022.103792" @default.
• W4285384510 hasPublicationYear "2022" @default.
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