Abstract With deterioration of global climate change and environmental pollution, low-carbon transportation is emerging as an important topic for promoting sustainable urban development. Meanwhile, with the continuous upgrading of artificial intelligence and autonomous driving algorithms, unmanned driving technology is moving from the laboratory to commercial implementation, which profoundly changes travel modes and has become a core trend for the global automotive industry. Based on this, we aim to develop an urban spatial model considering environmental pollution, and consider three travel modes including electric autonomous vehicles (EAVs), traditional gasoline vehicles (TGVs) and electric buses. Compared to traditional cars, EAVs do not produce exhaust pollution during driving, and are capable of autonomous parking, which saves the time of searching parking spots. Additionally, the research examines the impact of EAVs on residents’ travel patterns and urban system equilibrium, and further conducts a sensitivity analysis on parameters such as the automation level, speed, and fixed costs of EAVs. The results indicate that after the advent of EAVs, the number of people using traditional cars decreases significantly, leading to a reduction in urban pollution levels and an increase in housing pricing. In addition, the advent of EAVs leads to more concentrated residential density and thus a shrinkage of city size. Furthermore, the sensitivity analysis results show that the improvement of EAVs’ automation level and speed attracts more commuters choosing EAVs and thus leads to improve of resident utility level and expansion of city boundary.
Chen et al. (Mon,) studied this question.