5. Conclusion: Outreach, limitations and some recommendations
This brief state of art in modelling transportation systems involving AVs is summarized in Figure 1. We observe that major models focus on the supply operations and set-ups without detailing the demand side beyond statistical and spatial description in the form of an origin-destination matrix of trip-flows.
On the supply side, agent-based approaches allow to assess AV fleet size required while optimizing the waiting time and empty VKT. Furthermore, these models permit to reproduce in a realistic, detailed and robust way movements of vehicles considering several strategies. However, studies considering the real network are very scarce (ITF, 2015; Adnan, et al., 2016; Anderson, et al., 2014; Azevedo, et al., 2016). Further, urban constraints which determine the locations of stations and their capacities are not considered at all, even in the case of electric vehicles. Similar to car sharing, two servic e configurations are possible: stationbased and free-floating. The free -floating configuration is employed for a broader variety of uses than the station-based one (Bereck, et al., 2016). The combination of fixed stations and free-floating (while respecting the conditions of accessibility (Ciari, et al., 2015)) could reduce waiting time and locating stations in low dense areas (which is also economically attractive). Using dynamic parking cost (relevant to area’s configuration and state of congestion) could be explored as well. Assignment strategies of vehicles to customers should be optimized as well. Indeed, almost all studies are based on a FCFS strategy; a strategy that could be optimized using heuristic insertion or simulated annealing (Jung, et al., 2013). In addition, all aforementioned models are applied on urban centres of cities. It would be interesting to explore the service potentialities in suburban zones, freeways and around major train stations.
