This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Improving the On-Vehicle Experience of Passengers Through SC-M*: A Scalable Multi-Passenger Multi-Criteria Mobility Planner Rongye Shi , Member, IEEE, Peter Steenkiste, Fellow, IEEE, and Manuela M. Veloso, Fellow, IEEE Abstract— The rapid growth in urban population poses signif- public transit system (IPTS) is one important member in the icant challenges to moving city dwellers in a fast and convenient ITS family, which has attracted increasing interest in recent manner. This paper contributes to solving the challenges from the years. viewpoint of passengers by improving their on-vehicle experience. Specifically, we focus on the problem: Given an urban public Traditional research on IPTS focuses on using information transit network and a number of passengers, with some of them about the state of the transportation networks for scheduling controllable and the rest uncontrollable, how can we plan for the and dispatching mass transit [1]. Although a lot of progress controllable passengers to improve their experience in terms of has been made improving the IPTS in terms of infrastructure their service preference? We formalize this problem as a multi- construction, information sharing and fleet scheduling, limited agent path planning (MAPP) problem with soft collisions, where multiple controllable passengers are allowed to share on-vehicle attention is paid to the people inside the vehicles, especially service resources with one another under certain constraints. to the travel experience of the passengers that use the public We then propose a customized version of the SC-M* algorithm to transits. For example, in public transit systems, different pas- efficiently solve the MAPP task for bus transit system in complex sengers have different preferences, even necessities, in terms urban environments, where we have a large passenger size and of public resources, such as seat availability or on-vehicle multiple types of passengers requesting various types of service resources. We demonstrate the use of SC-M* in a case study Wi-Fi. These factors seriously affect their satisfaction level of the bus transit system in Porto, Portugal. In the case study, while traveling. we implement a data-driven on-vehicle experience simulator for We advocate that, to develop an efficient IPTS, passenger- the bus transit system, which simulates the passenger behaviors centered research is a necessity, providing customized mobility and on-vehicle resource dynamics, and evaluate the SC-M* on plans directly to each passenger for a trip with a pleasant it. The experimental results show the advantages of the SC-M* in terms of path cost, collision-free constraint, and the scalability experience. This insight comes from the fact that an individual in run time and success rate. passenger can neither change the existing public transportation infrastructures nor the massive passenger and traffic flow, Index Terms— Public transit system, multi-agent systems, path planning, on-vehicle experience simulation, passenger behavior but rather, can choose an alternative well-designed strategy modeling, time-expanded graph. to accomplish the travel demand while making his or her experience pleasant. It is not easy to help passengers achieve this goal. On one hand, research to developing a model or a I. INTRODUCTION simulator that captures the road traffic, passenger behaviors OBILITY is a central service a city needs to provide to and other factors relevant to the on-vehicle experience is a Mthe people. To satisfy the mobility needs given the rapid prerequisite to the goal but has not been considered previ- growth in city population, Intelligent Transpiration System ously. Specifically, the on-vehicle experience is affected by (ITS) is proposed to utilize urban informatics and synergistic the surrounding passengers, traffic conditions, and facilities techniques to improve the transportation efficiency. Intelligent that support the public on-vehicle services. On the other hand, the mobility planner should consider the interactions Manuscript received August 8, 2019; revised November 19, 2019; accepted among passengers as they call for the same service, which is December 17, 2019. This research was funded by the Fundação para a Ciência e a Tecnologia (FCT), the Portuguese national funding agency, under the challenging in the field of planning and scheduling, especially Sensing and Serving a Moving City (S2MovingCity) project (Grant CMUP- on a city scale. ERI/TIC/0010/2014). The Associate Editor for this article was J. E. Naranjo. In this paper, for the first time, we address the challenges (Corresponding author: Rongye Shi.) Rongye Shi is with the Department of Electrical and Computer Engi- from the viewpoint of passengers by considering the important neering, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: role of passenger experience when planning for pleasant trips [email protected]). through the urban public transit systems. This paper con- Peter Steenkiste is with the Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA, and also with the Department of tributes a passenger-centered planner for multiple passengers Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, requesting multiple types of public services while limiting PA 15213 USA (e-mail: [email protected]). the level of interference among them. We assume that the Manuela M. Veloso is with the Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: [email protected]). total set of passengers is divided into two subsets – one with Digital Object Identifier 10.1109/TITS.2019.2961940 passengers who request planning service from the planner, 1524-9050 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS called client passengers, and the other non-client passengers criterion, are not enough. For public transit networks, other who do not. By viewing each client passenger as an agent, criteria are just as important, leading to a multi-criteria we formalize this problem as a multi-agent path planning planning problem. There are mainly two versions of multi- (MAPP) problem, where multiple client passengers may inter- criteria search: The Pareto set search and the scalarization- fere with one another in terms of some service resources. based search. The first one is to find a Pareto set (i.e., Grounded on this setting, we made the following contributions: a maximum set of incomparable paths [7]–[9]). This type • Model the collisions among passengers as soft collisions, of search treats each criterion as equally important, which leading to a soft-collision-based (SC-based) MAPP prob- incurs a high computational complexity and is not necessarily lem, where a collision among client passengers is soft, true in practice. An alternative is scalarization [10], using a quantified using a collision score, and different passen- linear combination of criteria as the optimization objective. gers have different scores according to their experiences. This approach is to transform the multi-criteria search to a Based on this setting, the soft-collision M* (SC-M*), single-criterion search using weighted sum of criteria, which which is originally proposed by the authors [2], can be is more practical and computationally feasible. customized and applied to solve the SC-based MAPP; As one case of multi-criteria planning, a multi-agent path • Customize the SC-M* to the bus transit system of planning (MAPP) finds a joint path with optimal objective for Porto in the simulator. Specifically, we propose the time- multiple agents conditioned on certain constraints. Approaches expanded expectation graph to model the Porto bus for MAPP can be folded into three main categories: coupled, network and introduce a technical detail to SC-M* (i.e., decoupled, and intermediate. Coupled approaches search the the forward-the-tail-agent approach) to handle the non- joint configuration space of the multi-agent system, which synchronization issue in the real system; is the tensor product of the free configuration spaces of all • Develop a data-driven on-vehicle experience simulator for the individual agents. Decoupled approaches plan for each the bus transit system of Porto, Portugal that simulates agent separately and then adjust the path to avoid collisions. the passenger behaviors and the dynamics of certain Algorithms from this category are generally faster because on-vehicle resources (i.e., Wi-Fi quality and space avail- the graph search and collision-avoidance adjustment are per- ability). We then apply the customized SC-M* to gen- formed in low-dimensional spaces. However, optimality and erate the mobility plans based on the time-expanded completeness are not guaranteed. Intermediate approaches lie expectation graph, which is constructed using the logs between coupled and decoupled ones because they dynam- of simulations driven by the historical data, and test the ically couple agents and grow the search space during the plans’ execution