In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city's canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human-operated vessels is penalized. Consequently, given a pair of origin and destination points, it finds vessel trajectories that resemble those of human-operated vessels. To formulate this, we adopt kernel density estimation (KDE) to build a nominal movement model of human-operated vessels from a prerecorded trajectory dataset, and use a Kullback-Leibler control cost to measure the deviation of the autonomous vessel's movements from the model. We establish an analogy between our trajectory planning approach and the maximum entropy inverse reinforcement learning (MaxEntIRL) approach to explain how our approach can learn the navigation behavior of human-operated vessels. On the other hand, we distinguish our approach from the MaxEntIRL approach in that it does not require well-defined bases, often referred to as features, to construct its cost function as required in many of inverse reinforcement learning approaches in the trajectory planning context. Through experiments using a dataset of vessel trajectories collected from the automatic identification system, we demonstrate that the trajectories generated by our approach resemble those of human-operated vessels and that using them for canal navigation is beneficial in reducing head-on encounters between vessels and improving navigation safety.

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In densely populated-cities, the use of private cars for personal transportation is unsustainable, due to high parking and road capacity requirements. The mobility-on-demand systems have been proposed as an alternative to a private car. Such systems consist of a fleet of vehicles that the user of the system can hail for one-way point-to-point trips. These systems employ large-scale vehicle sharing, i.e., one vehicle can be used by several people during one day and consequently, the fleet size and the parking space requirements can be reduced, but, at the cost of a non-negligible increase in vehicles miles driven in the system. The miles driven in the system can be reduced by ridesharing, where several people traveling in a similar direction are matched and travel in one vehicle. We quantify the potential of ridesharing in a hypothetical mobility-on-demand system designed to serve all trips that are currently realized by private car in the city of Prague. Our results show that by employing a ridesharing strategy that guarantees travel time prolongation of no more than 10 minutes, the average occupancy of a vehicle will increase to 2.7 passengers. Consequently, the number of vehicle miles traveled will decrease to 35% of the amount in the MoD system without ridesharing and to 60% of the amount in the present state.

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Self-driving technology is expected to enable the realization of large-scale mobility-on-demand systems that employ massive ridesharing. The technology is being celebrated as a potential cure for urban congestion and others negative externalities of individual automobile transportation. In this paper, we quantify the potential of ridesharing with a fleet of autonomous vehicles by considering all possible trade-offs between the quality of service and operation cost of the system that can be achieved by sharing rides. We formulate a multi-objective fleet routing problem and present a solution technique that can compute Pareto-optimal fleet operation plans that achieve different trade- offs between the two objectives. Given a set of requests and a set of vehicles, our method can recover a trade-off curve that quantifies the potential of ridesharing with given fleet. We provide a formal optimality proof and demonstrate that the proposed method is scalable and able to compute such trade-off curves for instances with hundreds of vehicles and requests optimally. Such an analytical tool helps with systematic design of shared mobility system, in particular, it can be used to make principled decisions about the required fleet size.@en

Vehicle sharing system consists of a fleet of vehicles (usually bikes or cars) that can be rented at one station and returned at another station. We study how to achieve guaranteed service availability in such systems. Specifically, we are interested in determining a) the fleet size and initial allocation of vehicles to stations and b) the minimum capacity of each station needed to guarantee that a) every customer will find an available vehicle at the origin station and b) the customer will find a free parking spot at the destination station. We model the evolution of number of vehicles at each station as a stochastic process and prove that the relevant probabilities in the system can be approximated from above using a computationally-tractable decoupled model. This property can be exploited to efficiently determine the size of fleet, initial distribution of vehicles to stations, and station capacities that are sufficient to achieve the desired service level. The applicability of the method is demonstrated by computing the initial vehicle stock and the capacity of each station that would be needed to avoid service failures in Boston's bike sharing system 'The Hubway'. Our simulation shows that the proposed method is able to find more efficient design parameters than the naive approach and consequently it can achieve the equivalent quality-of-service level with half of the vehicle fleet and half of the parking capacity.

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