## 5 Future Research Directions  
Although, information sharing initiatives such as Sustainability Mobility and Accessibility Research and Transformation (SMART) [93], have been aimed at carefully analysing the trade-offs between investment decisions, sustainability and innovative urban mobility. This is primarily because infrastructure investments are high budgeted and may become locked-in once the decisions have been made, and cities may be stuck with irreversible bad decisions, which predicament platforms such as SMART aim to avoid. Other programmes such as the global mobility start-up database Mobi [1] and smartphone app-powered commercial door-to-door shuttle service of Transdev also aim to provide an alternate and multi-modal solution to the urban transportation problem. Nevertheless, at present most government agencies around the world have yet to catch up to the technology of automated transit. For example, as [PERSON] et al. [1] report, the vagueness in the Federal Transit Administration and ITS Joint Program Office-led, on-demand mobility programme's description of newer multi-modal mobility concepts and the absence of autonomous mobility from the U.S. Department of Transport strategic plan [94]. Due to the decreasing reliance of millennial travellers on single-occupancy vehicles and the divergence of trip load towards PT, researchers such as [PERSON] et al. [1], [PERSON] et al. [27], [PERSON] [95] and [PERSON] [96] have commented that transportation agencies should also focus on long-term  
Figure 5: Public responses on mode choices: conventional vs. autonomous buses under the same route parametersautonomous transit, SAVs, connected AVs and exclusive rights-of-way for AVs. Moreover, building upon the success of projects like the Morgan Town People mover [97], ideas like automated rapid bus transit, an automated vehicle serving \(>15\) travellers on dedicated lanes [5; 98], may become the new mode choice for mass transit. Although at present the procurement and purchase costs of private and public AVs may be a concern [31] for practical implications, the research covered in Table 1 above shows that these high initial costs may be offset by life-cycle ownership costs, specifically fuel cost savings. Environmental impacts notwithstanding, the travel time and ride comfort factors may be the primary drivers of AV adoption for public transport and ride-sharing transit systems, and as such, should be focused in future research. It is also uncertain how the mode choice of travellers shall be affected by the introduction of ITS and AV-based transportation systems. Similarly, the detailed impact of AV-PT, private AV cars and SAVs on the daily operation of traffic fleet as well as life-cycle implications need to be empirically calculated. Scenario analyses of various AV adoption rates, AV adoption methods and fuel sources should be performed with the simultaneous engagement of stakeholders (government, users and private sectors). Additionally, the following research directions are critical for the real-world application of artificially intelligent and sustainable transport systems in line with the literature studies covered in Table 1.  
* SAV prioritization, vehicle occupancy and sharing technique (dynamic vs. serial ride-sharing). * Mode-shift and mode choices. * Variation of traffic density due to AV use in traffic fleet. * Renewable electric power grid and alternate fuel sources. * Long-term traffic and population growth. * Use of actual traffic count data and growth models for simulations and empirical calculations. * High-resolution (per vehicle level) calculation of cost, energy use and pollutant emissions. * The penetration rate of vehicles following new emission regulations (e.g., distribution percentages of Euro 1-6 emission standards). * System-wide analysis and modelling of the transportation network. * Dynamic vehicle simulation and dynamic choice-modelling. * Urbanism, urban relocation and zonal developments (TODs) integration with AVs and ITS.