TY - JOUR
T1 - A bi-level framework for heterogeneous fleet sizing of ride-hailing services considering an approximated mixed equilibrium between automated and non-automated traffic
AU - Fan, Qiaochu
AU - van Essen, J. Theresia
AU - Correia, Gonçalo H.A.
PY - 2024
Y1 - 2024
N2 - Ride-hailing companies will face the emergence and gradual expansion of AVs-only zones in urban areas where only automated vehicles (AVs) are allowed to circulate. When owning a mixed fleet (automated and conventional taxis), a ride-hailing company has to determine the optimal fleet size as a function of the gradually expanding coverage of AVs-only zones while taking into account interactions with privately-owned human-driven vehicles. To model this problem, we propose a bi-level framework in which the lower level captures the mixed routing behaviour of the vehicles and the endogenous traffic congestion, and the upper level determines fleet sizes to maximise profit. A parallel genetic algorithm is introduced to solve this bi-level framework, which is embedded with a tailored algorithm for solving the lower-level model. Numerical experiments are conducted on instances based on a small network and the network of the city of Delft, The Netherlands, to demonstrate the performance of the proposed solution method and investigate the impacts of AVs-only zones on traffic and ride-hailing operations. Results indicate that the fleet size of automated taxis increases nonlinearly with the expansion of the AVs-only zone while that of conventional taxis decreases as demand shifts from human-driven vehicles to automated taxis. The fleet size decision depends heavily on the fleet's cost structure, the location and the distribution of parking depots. Furthermore, the existence of an AVs-only zone leads to detours for human-driven vehicles in the early stages, but it will bring major benefits by reducing congestion as its size increases.
AB - Ride-hailing companies will face the emergence and gradual expansion of AVs-only zones in urban areas where only automated vehicles (AVs) are allowed to circulate. When owning a mixed fleet (automated and conventional taxis), a ride-hailing company has to determine the optimal fleet size as a function of the gradually expanding coverage of AVs-only zones while taking into account interactions with privately-owned human-driven vehicles. To model this problem, we propose a bi-level framework in which the lower level captures the mixed routing behaviour of the vehicles and the endogenous traffic congestion, and the upper level determines fleet sizes to maximise profit. A parallel genetic algorithm is introduced to solve this bi-level framework, which is embedded with a tailored algorithm for solving the lower-level model. Numerical experiments are conducted on instances based on a small network and the network of the city of Delft, The Netherlands, to demonstrate the performance of the proposed solution method and investigate the impacts of AVs-only zones on traffic and ride-hailing operations. Results indicate that the fleet size of automated taxis increases nonlinearly with the expansion of the AVs-only zone while that of conventional taxis decreases as demand shifts from human-driven vehicles to automated taxis. The fleet size decision depends heavily on the fleet's cost structure, the location and the distribution of parking depots. Furthermore, the existence of an AVs-only zone leads to detours for human-driven vehicles in the early stages, but it will bring major benefits by reducing congestion as its size increases.
KW - Routing
KW - Fleet sizing
KW - Approximated mixed equilibrium
KW - AVs-only zone
KW - e-hailing services
UR - http://www.scopus.com/inward/record.url?scp=85184027907&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2024.01.017
DO - 10.1016/j.ejor.2024.01.017
M3 - Article
SN - 0377-2217
VL - 315
SP - 879
EP - 898
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 3
ER -