Thesis (PhD)--University of Stellenbosch, 2001. / ENGLISH ABSTRACT: Public and private transport system planning and operation have tended to be fragmented functions. In particular, public transport is often planned and operated independently of the "private" transport system. South African government policy now requires that comprehensive, strategic transport plans be prepared by metropolitan transport authorities. These plans are expected to conform to national strategic objectives as well as including local current and longterm objectives. This planning is required in the environment of a multi-modal, multi-operator, public-private partnership scenario that is new for most of the role players. The lack of experience is accompanied by a lack of any existing model for dealing with this scenario. This dissertation describes such a model. The model is based on the principles of strategic logistics management commonly employed in commerce and industry, including service-oriented industries. The modelling process is thus based on achieving a combination of customer service and long-term objectives. The model comprises a number of separate components and steps: • A transport network model (Emme/2). ~ A multi-class, generalised-cost assignment of private and public transport demand onto a network, modified to be modeless to the public transport users, is performed. This assignment allows for the imposition of generalised-cost reflecting urban-planning objectives in addition to more conventional costs such as travel cost. In this assignment, the interaction of public and private transport is accounted for and results in an associated modal choice. ~ A series of single-class generalised-cost assignments is then used to "focus" public transport demand to create corridors of demand adequate to justify public transport routes. This process can be enhanced to develop a design promoting switching from private to public transport. It also allows for multi-period route design. ~ The results of this modelling process are output to a text file and then subject to the processes described below. The results of these processes are then input into the network model where a standard transit assignment is performed and used to modify the proposed lines and update the network design data with respect to boardings and alightings at nodes. This information is used to design fixed infrastructure. • A Microsoft Access database and route extraction program. );> The network model data is drawn into the database where it is subject to a route extraction program that converts the assignment results from the network model into a set of mode specific potential public transport route definitions. These route definitions are based on paths of maximum demand. The extraction process is controlled by parameters specified by the planner, such as minimum route lengths and the demand level for various categories of service. );> After route extraction, vehicle allocation, and transit assignment, the database provides details of the boardings and alightings and number and details of transit lines using each node and link in the network. This data is used to design fixed infrastructure. • A Microsoft Excel spreadsheet vehicle operating cost model. );> For each vehicle type, the operating cost given the anticipated vehicle mileage and operating speed is determined. This is used to guide the choice of vehicles for different routes. • A Lingo goal-programming model. );> The potential routes and the available or potential fleet are subjected to a goalprogramme in which the optimum choice of vehicle allocation is determined. The allocation parameters can be controlled by the planner. These parameters may include costs, energy, fuel consumption, and vehicle and route limitations amongst others. Multiperiod design is included in the modelling process so that the optimum design may be for the operating period, daily, or weekly cycle. The modelling process provides two main outputs: • A set of fully described and costed transit lines ill terms of both routing and vehicle allocation. These transit line definitions can be output to the level of driver instructions if necessary. • Details of the type and location of infrastructure to be provided on the network. / AFRIKAANSE OPSOMMING: Openbare en private vervoerstelsel-beplanning en -bedryf IS geneig om gefragmenteerde funksies te wees. Dit is veral waarneembaar in die openbare vervoerstelsels waarvan die beplanning en bedryf onafhanklik van die "private" vervoerstelsels plaasvind. Die beleid van die Suid-Afrikaanse regering vereis dat omvattende strategiese vervoerplanne deur die metropolitaanse vervoer owerhede voorberei word. Daar word van hierdie planne verwag om aan die nasionale strategiese doelwitte, asook die plaaslike bestaande en langtermyn doelwitte te voldoen. Hierdie beplanning word vereis deur 'n omgewing wat nuut is vir die meeste rolspelers en bestaan uit multi-modale, multi-operateur en openbare-private vennootskap scenario's. Die tekort aan ondervinding gaan gepaard met 'n tekort aan 'n bestaande model wat gebruik kan word om hierdie scenario's te hanteer. So 'n model word deur hierdie verhandeling beskryf. Die model is gebasseer op die beginsels van strategiese logistieke bestuur wat algemeen gebruik word in die handel en industrie, insluitende die diens-georïenteerde industrieë. Die modelleringsproses wil dus 'n kombinasie van diens aan kliënte en langtermyn doelwitte bereik. Die model bestaan uit onderskeie komponente en stappe: • 'n Vervoernetwerkmodel (Emmel2) }i;> 'n Multi-klas, veralgemeende-koste toedeling van private en openbare vervoeraanvraag op 'n netwerk, aangepas om modusloos te wees vir die openbare vervoergebruiker, word uitgevoer. Hierdie toedeling laat nie net die heffing van meer konvensionele kostes, soos reiskoste toe nie, maar ook veralgemeende kostes wat staatsbeplarmingsdoelwitte reflekteer. In hierdie opdrag word die interaksie van openbare- en private vervoer ondersoek waarvan die uiteinde 'n geassosieerde modale keuse is. }i;> 'n Reeks enkelklas veralgemeende koste toedelings word dan gebruik om op openbare vervoeraanvraag te fokus en daardeur korridors van aanvraag, wat gepas is om openbare vervoerroetes te regverdig, te skep. Hierdie proses kan verfyn word om 'n plan te ontwikkel wat die verskuiwing van private vervoer na openbare vervoer sal bevorder. Dit laat ook die ontwerp van multi-periode roetes toe. }i;> Die resultate van hierdie modelleringsproses word uitgevoer na 'n tekslêer en dan aan die prosesse, wat hier onder beskryf word, onderwerp. Die resultate van hierdie prosesse word dan ingevoer in die netwerkmodel waar 'n standaard publieke vervoertoedeling uitgevoer word. Dit word dan gebruik om die voorgestelde roetes te wysig en die netwerk data, met betrekking tot die aantal persone wat op en af klim by nodes, op te dateer. Hierdie inligting word gebruik vir die ontwerp van infrastrukture. • 'n Microsoft Access databasis en roete-ontrekkingsprogram );> Die netwerkmodel data word in die databasis ingetrek waar dit aan 'n roeteontrekkingsprogram onderwerp word. Hierdie program skakel die toedelingsresultate van die netwerkmodel om na 'n stel potensiële modus spesifieke openbare vervoerroete definisies. Hierdie roete definisies word gebasseer op paaie van maksimum aanvraag. Die ontrekkingsproses word deur parameters, soos minimum lengte van roetes en die vlak van aanvraag van verskeie kategorieë van diens, wat deur die beplanner gespesifiseer word, gekontroleer. );> Na die ontrekking van roetes, voertuigtoekenning en vervoertoedeling, voorsien die databasis besonderhede van die aantal persone wat op en af klim asook die aantal en details van vervoerroete wat elke node en skakel in die netwerk gebruik. Hierdie data word gebruik om infrastrukture te ontwerp. • 'n Microsoft Excel sigblad voertuig bedryfskoste model )i> Vir elke tipe voertuig word die bedryfskoste, volgens die verwagte afstand en spoed van die spesifieke voertuig, bepaal. Die resultate word gebruik om die keuse van voertuie vir verskillende roetes te bepaal. • 'n Lingo doelprogrameringsmodel );> Die potensiële roetes en die beskikbare of potensiële vloot word onderwerp aan 'n doelprogram waarin die optimum keuse van voertuigtoekenning bepaal word. Die toekenningsparameters kan deur die beplanner gekontroleer word. Die parameters kan onder andere kostes, energie, brandstofverbruik en voertuig- en roete beperkings, insluit. Multi-periode ontwerp is ingesluit in die modelleringsproses sodat die optimum ontwerp vir die bedryfsperiode, daaglikse of weeklikse siklusse, kan wees. Die modelleringsproses lewer twee hoofuitkomste: • 'n Stel volledig beskrywende en koste berekende vervoerroete wat, indien nodig, na die vlak van bestuurder instruksies, uitgevoer kan word. • Details van die tipe en plek van infrastruktuur wat benodig word deur die netwerk.

published_or_final_version / abstract / toc / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

A research report submitted in partial fulfilment of the requirements for the Degree of Master of Architecture to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, 2018 / This study applied a case study approach to analyse assumed human behaviour models applied in the conceptualisation and implementation of the promotion campaigns for public and non-motorised transport in the Gauteng City Region and how this might have influenced the transition towards public transport and non-motorised transport. Besides the primary data collected through interviews with diverse participants, secondary data from reports and media articles were captured and analysed. The study found a diverse range of promotion campaigns for public and non- motorized transport such as You make Joburg great and the Ecomobility Festival. The related promotion campaign activities included educational campaigns and billboard messaging. Using behavioural insights such as prospect theory and rational choice theory, the study analysed the activities and tools of the promotion campaigns in order to understand the predominant assumed model. The study finds that the rational agent model of human behaviour was the most assumed model for the promotion campaigns. Due to the fact that the outcomes of the campaigns were not systematically evaluated, specific transition-impacts of the assumed model could not be analysed and therefore no relevant finding could be made on the related sub-question. However, secondary data sources clearly indicate that IMT use continues to grow in Gauteng City Region in spite of the ongoing campaigns. The study therefore went on to identify gaps within the delivered campaign activities and considered better ways to improve such campaigns in the context of the non-rational model. The study finds that in spite of close to over three decades of scientific questioning of the rational model, the model remains as the predominant framework in the promotion campaigns for PT and NMT. Although there might be other contributing factors, this predominance of the framework possibly undermines the anticipated impacts, and in particular, inhibits the responses to such campaigns and overall transitioning towards public and non-motorised transport. Key words: econs, framing, non-motorised transport, non-rational model, nudging, promotion campaign, public transport, rational model / GR2019

Overwhelming evidence throughout the literature has shown that bus overhead and maintenance (O&M) costs increase as buses age. This has implications toward a fleet manager's decision of when one should buy, use, or sell buses to minimize total fleet costs. Unfortunately, there are uncertain market conditions associated with bus fleets that cloud the manager's ability to make appropriate decisions. Using integer programming (IP), O&M trends and changing market conditions are integrated into a model to better analyze bus fleets. Due to recent budget constraints of transit agencies, needs for a bus fleet replacement model have arisen. King County in Washington State has supplied cost aggregated data of their New Flyer (NF) and NF hybrid buses. These data have been analyzed to create statistical relationships based on rising O&M costs per mile with age, which are then integrated with the IP model to determine the impact of changing diesel prices, potential carbon dioxide (CO2) emissions costs, uncertain maintenance costs, and bus purchase cost subsidies. The goal is to aid fleet managers to determine the costs of early or delayed suboptimal bus replacement timing and the impacts of market variability on fleet costs and optimal replacement timing. The optimal replacement age for NF and NF hybrid buses based on King County data and current fuel prices of $3.99/gal are 16.7 and 18.3 years, respectively. It has been consistently observed that greater expense is incurred when buses are replaced earlier rather than later from optimal. To minimize total CO2 emissions (including operation and construction emissions), buses should be replaced slightly before the optimal replacement time without considering CO2 emissions. High diesel prices and CO2 emissions had little or no effect, on when buses should be replaced. However, higher maintenance costs reduced the optimal replacement time by almost two years. Although NF hybrid buses have been found to have no economic advantage over conventional buses, this finding may be a consequence of the different costs associated to the different routes operated by hybrid and conventional buses. Due to the lack of detailed King County's route level historical data, a study of the economic competitiveness of NF hybrids against conventional buses is outside the scope of this thesis. If buses are used less with age, the optimal replacement age is reduced. The optimal replacement age also dropped significantly when the Federal Transit Agency's procurement assistance is applied into the model. The procurement assistance can be up to 80% of the capital costs and can be considered a purchase subsidy from the transit agency viewpoint. If purchase subsidies decrease bus purchase prices by 1%, the optimal replacement age drops approximately 1.5%. When the bus purchase price is reduced by 80%, the optimal bus replacement age is less than 12 years, the FTA's minimum replacement age.

South Africa provides a fascinating interface between the developed and the developing world and poses a multitude of opportunities for enhancing the sustainable development of local cities. The concept of City Logistics is concerned with the mobility of cities, and entails the process of optimizing urban logistics activities by considering the social, environmental, economic, financial, and energy impacts of urban freight movement. Vehicle routing and scheduling has the potential to address a number of these key focus areas. Applying optimization to vehicle routing and scheduling results in a reduced number of trips, better fleet utilization, and lower maintenance costs; thereby improving the financial situation of the fleet owner. Improved fleet utilization could have a positive environmental impact, while also improving the mobility of the city as a whole. Energy utilization is improved while customer satisfaction could also increase through on-time deliveries and reliability. The Vehicle Routing Problem (VRP) is a well-researched problem in Operations Research literature. The main objective of this type of problem is to minimize an objective function, typically distribution cost for individual carriers. The area of application is wide, and specific variants of the VRP transform the basic problem to conform to application specific requirements. It is the view of this dissertation that the various VRP variants have been researched in isolation, with little effort to integrate various problem variants into an instance that is more appropriate to the South African particularity with regards to logistics and vehicle routing. Finding a feasible, and integrated initial solution to a hard problem is the first step in addressing the scheduling issue. This dissertation attempts to integrate three specific variants: multiple time windows, a heterogeneous fleet, and double scheduling. As the problem is burdened with the added constraints, the computational effort required to find a solution increases. The dissertation therefore also contributes to reducing the computational burden by proposing a concept referred to as time window compatibility to intelligently evaluate the insertion of customers on positions within routes. The initial solution algorithm presented proved feasible for the integration of the variants, while the time window compatibility decreased the computational burden by 25%, and as much as 80% for specific customer configurations, when using benchmark data sets from literature. The dissertation also improved the quality of the initial solution, for example total distance traveled, by 13%. Finding an initial solution is the first step in solving vehicle routing problems. The second step is to improve the initial solution iteratively through an improvement heuristic in an attempt to find a global optimum. Although the improvement heuristic falls outside the scope of this dissertation, improvement of the initial solution has a significant impact on the quality of improvement heuristics, and is therefore a valuable contribution. / Dissertation (MEng)--University of Pretoria, 2007. / Industrial and Systems Engineering / MEng / Unrestricted

Yu Hongbo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 130-138). / Abstracts in English and Chinese. / ABSTRACT IN ENGLISH --- p.i-ii / ABSTRACT IN CHINESE --- p.iii / ACKNOWLEDGEMENTS --- p.iv-v / TABLE OF CONTENTS --- p.vi-viii / LIST OF TABLES --- p.ix / LIST OF FIGURES --- p.x-xi / Chapter CHAPTER I --- INTRODUCTION / Chapter 1.1 --- Problem Statement --- p.1 / Chapter 1.2 --- Research Purpose --- p.5 / Chapter 1.3 --- Significance --- p.7 / Chapter 1.4 --- Methodology --- p.8 / Chapter 1.5 --- Outline of the Thesis --- p.9 / Chapter CHAPTER II --- LITERATURE REVIEW / Chapter 2.1 --- Introduction --- p.12 / Chapter 2.2 --- Origin of GIS --- p.12 / Chapter 2.3 --- Development of GIS-T --- p.15 / Chapter 2.4 --- Capabilities of GIS-T --- p.18 / Chapter 2.5 --- Structure of a GIS-T --- p.19 / Chapter 2.5.1 --- Data Models for GIS-T --- p.19 / Chapter 2.5.2 --- Relational DBMS and Dueker-Butler's Data Model for Transportation --- p.22 / Chapter 2.5.3 --- Objected-oriented Approach --- p.25 / Chapter 2.6 --- Main Techniques of GIS-T --- p.26 / Chapter 2.6.1 --- Linear Location Reference System --- p.26 / Chapter 2.6.2 --- Dynamic Segmentation --- p.27 / Chapter 2.6.3 --- Planar and Non-planar Networks --- p.28 / Chapter 2.6.4 --- Turn-table --- p.28 / Chapter 2.7 --- Algorithms for Finding Shortest Paths on a Network --- p.29 / Chapter 2.7.1 --- Overview of Routing Algorithms --- p.29 / Chapter 2.7.2 --- Dijkstra's Algorithm --- p.31 / Chapter 2.7.3 --- Routing Models for the Multi-modal Network --- p.32 / Chapter 2.8 --- Recent Researches on GIS Data Models for the Multi-modal Transportation System --- p.33 / Chapter 2.9 --- Main Software Packages for GIS-T --- p.36 / Chapter 2.10 --- Summary --- p.37 / Chapter CHAPTER III --- MODELING THE MULTI-MODAL PUBLIC TRANSPORTATION SYSTEM / Chapter 3.1 --- Introduction --- p.40 / Chapter 3.2 --- Elaborated Stages and Methods for GIS Modeling --- p.40 / Chapter 3.3 --- Application Domain: The Multi-modal Public Transportation System --- p.43 / Chapter 3.3.1 --- Definition of a Multi-modal Public Transportation System --- p.43 / Chapter 3.3.2 --- Descriptions of the Multi-modal Public transportation System --- p.44 / Chapter 3.3.3 --- Objective of the Modeling Work --- p.46 / Chapter 3.4 --- A Layer-cake Based Application Domain Model for the Multi- modal Public Transportation System --- p.46 / Chapter 3.5 --- A Conceptual Model for the Multi-modal Public Transportation System --- p.49 / Chapter 3.6 --- Logical and Physical Implementation of the Data Model for the Multi-modal Public Transportation System --- p.54 / Chapter 3.7 --- Criteria for Routing on the Multi-modal Public Transportation System --- p.57 / Chapter 3.7.1 --- Least-time Routing --- p.58 / Chapter 3.7.2 --- Least-fare Routing --- p.60 / Chapter 3.7.3 --- Least-transfer Routing --- p.60 / Chapter 3.8 --- Summary --- p.61 / Chapter CHAPTER IV --- DATA PREPARATION FOR THE STUDY AREA / Chapter 4.1 --- Introduction --- p.53 / Chapter 4.2 --- The Study Area: Hong Kong Island --- p.63 / Chapter 4.2.1 --- General Information of the Transportation System on Hong Kong Island --- p.63 / Chapter 4.2.2 --- Reasons for Choosing Hong Kong Island as the Study Area --- p.66 / Chapter 4.2.3 --- Mass Transit Routes Selected for the Prototype --- p.67 / Chapter 4.3 --- Data Source and Data Collection --- p.67 / Chapter 4.4 --- Geographical Data Preparation --- p.71 / Chapter 4.4.1 --- Data Conversion --- p.73 / Chapter 4.4.2 --- Geographical Data Input --- p.79 / Chapter 4.5 --- Attribute Data Input --- p.86 / Chapter 4.6 --- Summary --- p.88 / Chapter CHAPTER V --- IMPLEMENTATION OF THE PROTOTYPE / Chapter 5.1 --- Introduction --- p.89 / Chapter 5.2 --- Construction of the Route Service Network --- p.89 / Chapter 5.2.1 --- Generation of the Geographical Network --- p.90 / Chapter 5.2.2 --- Setting Attribute Data for the Route Service Network --- p.95 / Chapter 5.3 --- A GIS-T Prototype for the Study Area --- p.102 / Chapter 5.4 --- General GIS Functions of the Prototype --- p.104 / Chapter 5.4.1 --- Information Retrieve --- p.104 / Chapter 5.4.2 --- Display --- p.105 / Chapter 5.4.3 --- Data Query --- p.105 / Chapter 5.5 --- Routing in the Prototype --- p.105 / Chapter 5.5.1 --- Routing Procedure --- p.108 / Chapter 5.5.2 --- Examples and Results --- p.110 / Chapter 5.5.3 --- Comparison and Analysis --- p.113 / Chapter 5.6 --- Summary --- p.118 / Chapter CHAPTER VI --- CONCLUSION / Chapter 6.1 --- Research Findings --- p.123 / Chapter 6.2 --- Research Limitations --- p.126 / Chapter 6.3 --- Direction of Further Studies --- p.128 / BIBLIOGRAPHY --- p.130