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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
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