scholarly journals Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub

2017 ◽  
Vol 29 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Baozhen Yao ◽  
Qingda Cao ◽  
Lu Jin ◽  
Mingheng Zhang ◽  
Yibing Zhao

The building density of Central Business District (CBD) is usually high. Land for a bus terminal is insufficient. In this situation, passengers in CBD have to walk far to take a bus, or take a long time to wait for a taxi. To solve this problem, this paper proposes an indirect approach: the design of a circle line of shuttle bus as a dynamic bus terminal in CBD. The shuttle bus can deliver people to the bus station through a circle line. This approach not only reduces the traffic pressure in CBD, but also saves travel time of the passenger. A bi-objective model is proposed to design a circle line of a shuttle bus for CBD. The problem is solved by non-dominated sorting genetic algorithm (NSGA-II). Furthermore, the Dalian city in China has been chosen as the case study to test the proposed method. The results indicate that the method is effective for circle line optimization of shuttle bus in central business district without a bus terminal.

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 159 ◽  
Author(s):  
Jackie Parker ◽  
Greg Simpson

The widely applied Importance-Performance Analysis (IPA) provides relatively simple and straightforward techniques to assess how well the attributes of a good or service perform in meeting the expectations of consumers, clients, users, and visitors. Surprisingly, IPA has rarely been applied to inform the management of urban public green infrastructure (PGI) or urban nature (UN) spaces. This case study explores the visitor satisfaction levels of people using a PGI space that incorporates UN, close to the central business district of Perth, Western Australia. With diminishing opportunities to acquire new PGI spaces within ever more densely populated urban centers, understanding, efficiently managing, and continuously improving existing spaces is crucial to accessing the benefits and services that PGI and UN provide for humankind. An intercept survey conducted within the Lake Claremont PGI space utilized a self-report questionnaire to gather qualitative and quantitative data (n = 393). This case study demonstrates how the IPA tool can assist urban planners and land managers to collect information about the attributes of quality PGI and UN spaces to monitor levels of service, to increase overall efficiency of site management, to inform future management decisions, and to optimize the allocation of scarce resources. The satisfaction of PGI users was analyzed using the IPA tool to determine where performance and/or resourcing of PGI attributes were not congruent with the expectations of PGI users (generally in the form of over-servicing or under-servicing). The IPA demonstrated that a majority of PGI users perceived the study site to be high performing and were satisfied with many of the assessed attributes. The survey identified the potential for some improvement of the amenity and/or infrastructure installations at the site, as well as directing attention towards a more effective utilization of scarce resources. Optimizing the management of PGI spaces will enhance opportunities for individuals to obtain the physiological, psychological, and emotional benefits that arise from experiencing quality urban PGI spaces. This case study promotes the important contribution that high-quality PGI spaces, which include remnant and restored UN spaces, make to the development of resilient and sustainable urban centers.


Author(s):  
F. Al-Abri ◽  
E.A. Edirisinghe ◽  
C. Grecos

This chapter presents a generalised framework for multi-objective optimisation of video CODECs for use in off-line, on-demand applications. In particular, an optimization scheme is proposed to determine the optimum coding parameters for a H.264 AVC video codec in a memory and bandwidth constrained environment, which minimises codec complexity and video distortion. The encoding/decoding parameters that have a significant impact on the performance of the codec are initially obtained through experimental analysis. A mathematical formulation by means of regression is subsequently used to associate these parameters with the relevant objectives and define a Multi-Objective Optimization (MOO) problem. Solutions to the optimization problem are reached through a Non-dominated Sorting Genetic Algorithm (NSGA-II). It is shown that the proposed framework is flexible on the number of objectives that can jointly be optimized. Furthermore, any of the objectives can be included as constraints depending on the requirements of the services to be supported. Practical use of the proposed framework is described using a case study that involves video content transmission to a mobile hand.


Author(s):  
M. Kanthababu

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.


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