Assessing the Impacts of Autonomous Bus-on-Demand Based on Agent-Based Simulation: A Case Study of Fuyang, Zhejiang, China

Abstract

This paper envisions and assesses the performance of an autonomous bus-on-demand (ABoD) system. We take Fuyang, Zhejiang, China, as the study area to investigate the spatiotemporal distribution of bus travel demand during workdays, and we propose replacing inefficient bus routes with the ABoD system. Agent-based models with various bus dispatching and operation control strategies are constructed to evaluate the performance of the ABoD system. The behaviors and interactions of the agents, passengers, autonomous buses, and a control center are designed. After the verification of the simulated bus travel demand with real-world demand, a series of scenarios with various ABoD operation strategies are simulated. The simulation results show that, in comparison with both current fixed-schedule bus services and the optimized bus dispatching strategies, the ABoD system occupies fewer road resources and utilizes bus vehicles more efficiently. Besides, the system is adaptive to the sudden surge in bus travel demand and is economically sustainable.

As long as I don't have to drive myself: The influence of trait anxiety in the context of fear of giving up control on the acceptance of autonomous shuttle buses

“…Another approach may be to give people the feeling that they have influence over the bus route, e.g., by selfefficaciously requesting a bus via app. These on-demand services are currently being simulated and tested for several cities, e.g., Hamburg, Germany [51] or Fuyang, China, [52], and might have a positive impact on the perceived self-efficacy. Providers should also emphasize the benefits of AVs and inform passengers specifically, e.g., opportunities related to driving safety or optimizing traffic flow [12,53].…”

Autonomous Minibus Service With Semi-on-Demand Routes in Grid Networks

“…The model found savings in total passenger travel time and the number of vehicles over existing routes. Similarly, ordinary fixed routes with skipping capabilities were proposed based on origin–destination generated from payment data in Zhejiang, China ( 16 ). Agent-based simulation, which incorporated the interaction of passengers, buses (in operation/idle), and control center, concluded that the proposed model offered higher utilization of buses and better adaptiveness to changes in demand.…”

How Will the Technological Shift in Transportation Impact Cities? A Review of Quantitative Studies on the Impacts of New Transportation Technologies

“…Wagner [23] demonstrates through traffic simulation studies that autonomous systems reduce intersection delays by 5-80%. Zhai et al study the impact of replacing one bus line with an automated bus on-demand system in Fuyang prefecture in China, and find the system overwhelmingly advantageous, with less road resource occupation, shorter passenger waiting times and more efficient utilization of vehicle capacity [24]. However, a microscopic traffic simulation by Arnaout and Arnaout [25] suggests that capacity gains are not significant under a low to moderate penetration rate of AVs.…”

“…In an agent-based simulation of Manhattan, the estimated cost for the operation of an SEAV fleet is roughly ten times lower than a regular taxi fare, as a result of savings due to electrification, the elimination of driver cost and efficiency of a single-operator, smartphonebased system [32]. Zhai et al compare a traditional bus system with an automated bus on-demand (ABoD) system and find that if the travel demand exceeds five multiples of the current demand, the total cost of conventional buses increases sharply, while the cost of the ABoD increases almost linearly, which indicates that the ABoD system is more adaptive to the change in travel demand than the conventional buses [24].…”

“…Location Method Vehicles [11] United States Mathematical Analysis Estimation of impacts of automation, connectivity, sharing and electrification. [23] Braunschweig Microscopic Simulation All passenger vehicles as AV [51] No geographic location Location Choice Modelling Private and Shared AV as additional modes [55] Rome Traffic Simulation All vehicles fully automated and electric [52] The Netherlands Simulations All vehicles automated (cars and transit) [57] Lisbon Greenwich, UK Agent-based Simulations AVs as taxis and for pooled rides [26] No geographic location Microscopic traffic simulations AVs with varying levels of automation [59] Singapore Agent based simulations Automated mobility on demand as an additional mode [60] Gunma, Japan Agent-based Simulation with MATSim SAV and PAV as additional modes [24] Fuyang, Zheijiang, China Agent-based Simulations ABoD as additional mode PAV: Private Automated Vehicles; CAV: Connected Automated Vehicles; SAV: Shared Automated Vehicles; SEAV: Shared Electric Automated Vehicles, ABoD: Autonomous Bus on Demand.…”