DEPART: Dynamic Route Planning in Stochastic Time-Dependent Public Transit Networks

Abstract

Abstract-While providing intelligent urban transportation services for commuters is one of the key enablers for developing smart cities, existing route planners mainly rely on static schedules and hence fall short in dealing with uncertain and timedependent traffic situations. In this paper, by leveraging a large set of historical travel smart card data, we propose a method to build a stochastic time-dependent model for public transit networks. In addition, we develop DEPART 1 -a dynamic route planner that takes the stochastic models of both bus travel time and waiting time into account and optimizes both the speediness and reliability of routes. Experiments on real bus data set for the entire city confirm the quality and accuracy of the routes returned by DEPART in comparison to state-of-the-art route planners.

Dynamic on-line multimodal route planning with least expected travel time in the stochastic and time-dependent bus network

“…In [3], the approach only takes the range of uncertainty according to the historical data and experience of the decision-makers, and the route with the greatest robustness is found as the optimal path in STD network. In [4], the authors develop a dynamic route planner based on the stochastic timedependent transportation network model, which is built by leveraging a large set of historical travel smart card data. However, the estimation of travel time in the above researches is only based on possibility distribution or historical data, and ignores the real traffic condition.…”

Multi-criteria journey planning for enhancing user experience in multimodal transportation systems

“…For example, Gothere.sg returns a travel time of 30 minutes for a journey consisting of 30 bus stops. However, the actual journey takes at least 50 minutes [11].…”

“…For example, some of the trajectories overlap, which violates the First-In-First-Out (FIFO) property. It is noteworthy that a network without FIFO property may not have optimal substructures, i.e., the concatenation of the shortest paths from A to B and from B to C is not necessarily the shortest path from A to C [11]. It has been proven that the link travel times in stochastic time-dependent networks satisfy the stochastic FIFO property [225].…”

“…1 line[7][8][9][10][11][12][13][14][15][16][17][18][19][20], TEAE searches all reachable routes for the given journey request 𝑟 𝑖 =< 𝑜 𝑖 , 𝑑 𝑖 , 𝑡 𝑖 >. With the transfer graph 𝑇𝐺, the algorithm TEAE searches the journey routes in several iterations.…”