Internet-based transit services have the potential to enable fully connected, safe, reliable, efficient, and flexible service in the public transit system. The flexible transit system (FTS) allows passengers to make individual travel requests and receive customized door-to-door service by specifying their origins and destinations (OD), departure time range, and willingness to pay (WTP). Unlike the uniform and stable WTP in our previous paper , we consider an exponential decay function for the real pickup locations of passengers, and we proposed a joint design of two-level bus stops in temporal and spatial variations of travel demands. To ensure practical applications of this real-time FTS model, we combine the traversing method and the improved tabu search algorithm. A numerical example from Guangzhou is conducted to demonstrate the applicability of the proposed model and its effectiveness in reducing system cost.

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Optimization technology is widely applied to maximize economic profit under ecology constrains in environmental management systems. To tackle the inherent uncertainties, inexact optimization methods have been proposed. Interval linear programming (ILP) model has drawn increasing scholarly attention. ILP model describe uncertainty by one coarse scaled stochastic process. However, uncertainty often involves multiple stochastic processes when zooming into high resolution. ILP model may not satisfied fine scale constraints. A time variant interval linear programming (TVILP) model is developed to implement temporal downscaling, and likewise, a heuristic algorithm integrating dynamic programming is proposed for Markov chained TVILP. Dynamic programming can converts time complexity exponential to polynomial. In the current paper, the performance of TVILP model is analyzed based on the following three metrics: maximal profit (M_profit), constraint violation risk (CVR), and maximal profit path risk (MPR). The performance of TVILP is further compared with the performance of Best and Worst method, the classic ILP model, Interval linear programming contractor, and Interval-parameter multi-stage stochastic linear programming. Experimental results reveal that TVILP provides refined solutions on a smaller granularity whose decision space contracts based on the most possible transition paths. Unable to obtain the maximum profit, though, TVILP does pose decreased constraint violation risk and maximal profit path risk, facilitating more feasible and reliable decision-making on environmental management.

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