Real-time city-scale ridesharing via linear assignment problems

Abstract

In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four times faster than the state-of-the-art, even if it is implemented on a less dedicated hardware, and achieves similar service quality. Current literature showcases the ability of state-ofthe-art ridesharing algorithms to tackle very large fleets and customer requests in almost near real-time, but the benefits of ridesharing seem limited to centralized systems. Our algorithm suggests that this does not need to be the case. The algorithm that we propose is fully distributable among multiple ridesharing companies. By leveraging two datasets, the New York city taxi dataset and the Melbourne Metropolitan Area dataset, we show that with our algorithm, real-time ridesharing offers clear benefits with respect to more traditional taxi fleets in terms of level of service, even if one considers partial adoption of the system. In fact, e.g., the quality of the solutions obtained in the state-of-the-art works that tackle the whole customer set of the New York city taxi dataset is achieved, even if one considers only a proportion of the fleet size and customer requests. This could make real-time urban-scale ridesharing very attractive to small enterprises and city authorities alike. However, in some cases, e.g., in multi-company scenarios where companies have predefined market shares, we show that the number of vehicles needed to achieve a comparable performance to the monopolistic setting increases, and this raises concerns on the possible negative effects of multi-company ridesharing.

A city-scale IoT-enabled ridesharing platform

“…In the Brainport simulations, we generate a fleet of 100 virtual vehicles serving between 360 and 1440 user requests during 1 hour of service. Vehicles are assigned to requests every 10 seconds: [8] showed that such frequency for batch assignment provides an optimum trade-off between level of service and computation time. We have several speed cameras connected to the oneM2M IoT platform which provide traffic events every 5 min.…”

“…Ridesharing is typically solved in a centralised fashion across the whole vehicle fleet and across customers, as to minimise the total travel time of the operating vehicles, while taking into account the customer pref-erences (pickup and drop-off times, whether sharing is desired or not, etc.) [8,7,3]. Ridesharing emerged in the past few decades amongst the shared-mobility services as a means to limit traffic congestion and achieve environmental benefits.…”

On the needs for MaaS platforms to handle competition in ridesharing mobility

“…In Simonetto et al (2019), it is highlighted that linear assignments can perform as good as more elaborated assignments, when run at a high enough sampling rate. Simonetto et al (2019) shows that optimization-based approaches for on-demand ridesharing can provide a high level of service while limiting the growth of the fleet size. Specifically, it is shown that 98% of the current trip demand in Manhattan, available via the New York Taxi dataset (NYC Taxi and Limousine Commission dataset 2019), can be satisfied with only 20% of the current taxi fleet (Alonso-Mora et al 2017a).…”

“…In order to evaluate advantages and limitation of the Cooperative and Competitive models, we compare them with a Centralized model, obtained by adapting the work of Simonetto et al (2019) to the multi-company setting. Hence, a suitable proxy for an efficient Centralized model for multi-company ridesharing systems is as follows: (i) the system collects batches of customer requests for a given time period, e.g., 10 s, (ii) the companies compute the costs for inserting requests in each vehicle route, by solving a DARP problem, (iii) those costs are shared with the broker, (iv) the broker solves the Linear Assignment Problem (LAP) to optimality and sends the updated assignments of vehicles to requests (v) the broker adopts a rebalancing strategy for the unserved requests, which means running steps (i)-(iv) with loose time constraints.…”

“…This process is repeated for every batch of requests. In particular, in Simonetto et al (2019), the authors highlight that one-to-one assignments of customer requests to vehicles provide very high service rates in a dynamic context, and suggest that a myopic search for optimal multipleto-one assignments of customer requests to vehicles is not necessary, and not beneficial from the computational point of view. In this context, the centralization of the ridesharing solution comes from the need for a broker that collects the costs and solves a centralized LAP.…”