Optimizing Customized Transit Service considering Stochastic Bus Arrival Time

Abstract

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

Workflow scheduler optimization using an enhanced hybrid genetic algorithm

“…The term chromosome is used to symbolize the answer, which is an integer string made up of a series of substrings [1]. Each substring represents a list of nodes that a bus has visited.…”

“…The fittest geneLists were selected from the best chromosomes with the fitness function. The improvement introduced by the proposed e-HGA model was described as follows: elitism (selection of the top 10% fittest individuals) was used to calculate individuals' fitness at each level before the best fit was selected for mating [1].…”

“…Task scheduling is a difficult problem, especially in parallel and distributed systems. A hybrid method allows the strengths of many algorithms to complement one another, resulting in superior results [1]. Task scheduling in distributed computing environments is a critical aspect that directly impacts the overall performance and efficiency of the system.…”

“…Grid computing and cloud computing resources offer optimal solutions that can cater to user requirements, providing scalability and flexibility for the considered applications [12]. However, there are distinct differences in task scheduling between cloud computing and grid computing: (1) Resource sharing: Cloud computing leverages advanced services by utilizing resource sharing through virtualization technologies and internet-based concepts [13]. This enables real-time allocation, maximizing resource utilization and enhancing the elasticity of cloud services.…”

Transit Signal Priority for an Integrated Traffic Signal System

“…To consider these factors, we assume first that we know when the buses enter the network (t 1 and t 2 in Figure 6). Extensive studies have confirmed that the arrival distribution of buses follows a normal, log normal, or gamma distribution [38][39][40]. Because this study deals with the near-side stop, which has the highest uncertainty in bus dwell time, and a stochastic arrivaltime distribution based on a normally distributed stochastic dwell time has been adopted [30].…”