Page - (000427) - in Autonomes Fahren - Technische, rechtliche und gesellschaftliche Aspekte

Autonomous Mobility-on-Demand Systems for Future Urban Mobility412 represent about one third of GDP per capita. Hence, this analysis suggests that it is much more affordable to access mobility in an AMoD system compared to traditional mobility systems based on private vehicle ownership. 19.4 Future research directions This chapter provided an overview of modeling and control techniques for AMoD systems, and a preliminary evaluation of their financial benefits. Future research on this topic should proceed along two main dimensions: efficient control algorithms for increasingly more realistic models and eventually for real-world test beds, and financial analyses for a larger number of deployment options and accounting for positive externalities (e.g., increased safety) in the economic assessment. Such research directions are discussed in some details next, with a particular emphasis on the inclusion of congestion effects and some related preliminary results. 19.4.1 Future research on modeling and control A key direction for future research is the inclusion of congestion effects. In AMoD systems, congestion manifests itself as constraints on the road capacity, which in turn affect travel times throughout the system. To include congestion effects, a promising strategy is to study a modified lumped model whereby the infinite-server road queues are changed to queues with a finite number of servers, where the number of servers on each road represents the capacity of that road [13]. This approach is used in Figure 19.6 on a simple 9-station road network, where the aim is to illustrate the impact of autonomously rebalancing vehicles on congestion. Specifically, the stations are placed on a square grid, and joined by 2-way road segments, each of which is 0.5 km long. Each road consists of a single lane, with a critical density of 80 vehicles/km. Each vehicle travels at 30 km/hour in free flow, which means the travel time along each road segment is 1 minute in free flow. Figure 19.6 plots the vehicle and road utilization increases due to rebalancing for 500 randomly generated systems (where the arrival rates and routing distributions are randomly generated). The routing algorithm for the rebalancing vehicles is a simple open-loop strategy based on the Table 19.1 Summary of the financial analysis of mobility-related cost for traditional and AMoD systems for a case study of Singapore [18]. Cost [USD/km] Yearly cost [USD/year] COS COT TMC COS COT TMC Traditional 0.96 0.76 1.72 18,162 14,460 32,622 AMoD 0.66 0.26 0.92 12,563 4,959 17,522