The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

No description available.

This thesis focuses on control strategies for intralogistics transport systems. It evaluates how switching from central to decentral dispatching approaches influences the performance of these systems. Many ideas and prototypes for implementing decentral control have been suggested by the scientific community. But usually only the qualitative advantages of this new paradigm are stated. The impact on the performance is not quantified and analyzed. Additionally, decentral control is often confused with distributed algorithms or uses the aggregation of local to global information. In the case of the latter, the technological limitations due to the communication overhead are not considered. The decentral prototypes usually only focus on routing. This paper takes a step back and provides a generic simulation environment which can be used by other researchers to test and compare control strategies in the future. The test environment is used for developing four truly decentral dispatching strategies which work only based on local information. These strategies are compared to a central approach for controlling transportation systems. Input data from two real-world applications is used for a series of simulation experiments with three different layout complexities. Based on the simulation studies neither the central nor the decentral dispatching strategies show a universally superior performance. The results depend on the combination of input data set and layout scenario. The expected efficiency loss for the decentral approaches can be confirmed for stable input patterns. Regardless of the layout complexity the decentral strategies always need more vehicles to reach the performance level of the central control rule when these input characteristics are present. In the case of varying input data and high throughput the decentral strategies outperform the central approach in simple layouts. They require fewer vehicles and less vehicle movement to achieve the central performance. Layout simplicity makes the central dispatching strategy prone to undesired effects. The simple-minded decentral decision rules can achieve a better performance in this kind of environment. But only complex layouts are a relevant benchmark scenario for transferring decentral ideas to real-world applications. In such a scenario the decentral performance deteriorates while the layout-dependent influences on the central strategy become less relevant. This is true for both analyzed input data sets. Consequently, the decentral strategies require at least 36% to 53% more vehicles and 20% to 42% more vehicle movement to achieve the lowest central performance level. Therefore their usage can currently not be justified based on investment and operating costs. The characteristics of decentral systems limit their own performance. The restriction to local information leads to poor dispatching decisions which in return induce self-enforcing inefficiencies. In addition, the application of decentral strategies requires bigger storage location capacity. In several disturbance scenarios the decentral strategies perform fairly well and show their ability to adapt to changed environmental conditions. However, their performance after the disturbance remains in some cases unpredictable and relates to the properties of self-organizing complex systems. A real-world applicability has to be called into question.

This thesis presents a study on efficient dispatching rules in ambulance dispatching. By efficient dispatching rules, we mean such dispatching rules that lower response times for priority 1 calls while keeping response times for priority 2 calls at an adequate level. A Markov process and a simulation model were developed in order to evaluate the performance of several existing and newly designed dispatching rules. On four different response areas, five different dispatching rules were tested and their performances were compared. Particular focus was put upon the dispatch rule currently used by the Swedish emergency service provider SOS Alarm; the Closest rule. Our findings indicate that the four priority-based dispatching rules all outperform the Closest rule in decreasing the mean response time for calls of priority degree 1. Furthermore, implementing restrictions on the travel time for priority 2 calls was proven an efficient way to control the trade-off between the mean response time of priority 1 and 2 calls. The conclusion was drawn that the possibilities for more efficient ambulance dispatching are many and that SOS Alarm should consider implementing priority-based dispatching rules, alike the ones presented in this thesis, in their dispatching process. A study of the ambulance operator and controller profession, and the operator’s and controller’s interplay with the decision support system used by SOS Alarm in the ambulance dispatching process, was conducted in parallel. The properties of the interaction dynamics between operator and automation and the dangers linked to it were mapped out, described and analyzed. / Denna kandidatexamensuppsats behandlar effektiva dirigeringsstrategier inom ambulansdirigering. Effektiva dirigeringsstrategier åsyftar dirigeringsstrategier som lyckas sänka svarstiden för inkommande prioritet 1-samtal, samtidigt som svarstiden för prioritet 2-samtal hålls på en tillfredsställande nivå. I syfte att utvärdera olika dirigeringsstrategier utvecklades både en Markovsk modell och en simuleringsmodell. På fyra olika geografiska områden testades och jämfördes. Fem olika dirigeringsstrategier, varav två existerande och tre nyutvecklade. Särskilt fokus riktades mot Closest rule, vilket är den dirigeringsstrategi som används i SOS Alarms verksamhet idag. Från resultaten kunde utläsas att de prioritets-baserade dirigeringsstrategierna resulterade i en lägre genomsnittlig svarstid för prioritet 1-fall än Closest rule. Dessutom konstaterades det att en begränsning av svarstiderna för prioritet 2-samtal var ett effektivt sätt att kontrollera balansen mellan de genomsnittliga svarstiderna för samtal av prioritet 1, respektive 2. Slutsatsen drogs att möjligheterna för att utveckla nya effektiva dirigeringsstrategier är många och att SOS Alarm bör överväga att implementera prioritetsbaserade dirigeringsstrategier likt dem som presenterats i denna uppsats. Parallellt studerades ambulansoperatörens och -dirigentens yrkeskunnande, samt operatörens och dirigentens samspel med det beslutsstödssystem som används i SOS Alarms dirigeringsverksamhet. Interaktionen mellan operatör och automatisering samt de relaterade riskerna kartlades, beskrevs och analyserades.

Call number: LD2668 .T4 1985 E34 / Master of Science

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

現今企業在面臨市場需求快速變化、接單多樣化，及交貨期短的多重壓力下， 無不致力於改善作業流程以求獲利極大化，尤其是現場生產排程規劃一直是相當複雜而難以管理的部分。現行的軟體例如：企業資源規劃(ERP)、供應鏈管理(SCM)，即是希望藉由資訊科技以解決複雜的生產排程問題。 然而，大部分企業在使用這些系統時都發現，由於缺乏有效的現場管制功能(Shop Floor Control)，使得這些系統的效能受到很大的限制，而目前大多數的現場排程問題為零工式生產問題(Job Shop Problem)。 過去在零工式生產問題的理論上雖有許多傑出的研究，但研究與現場實際的問題之間有許多差異。其中最大的差異在於過去研究所使用的零工式生產問題假設所有的工作會以不同的流程經過所有的機器，然而在現場實際的零工式生產問題中卻顯示每一機器所處理的工作數目變異非常大。現場零工式生產生產排程問題包括兩個主要特性：1、每一工件可以擁有不等之操作數目；2、事先知道某些機器為瓶頸機器。本研究分別針對此兩個特性設計實驗一：產生工件擁有’等操作數’與’不等操作數’的問題；與實驗二：’無瓶頸機器’與’有瓶頸機器’的問題。在實驗中，我們以三個因素：工件數、機器數、和操作時間變異，模擬產生18種不同的狀況。然後以7種評量準則比較50個常用的派工法則在不同狀況下的表現。 研究結果發現，在實驗一與實驗二中，理論與實際現場排程問題在派工法則的表現上確實有極大的差異。本研究將這些其差異加以分析，並嘗試整理出一些規則以提供現場的使用者在面對不同狀況下選擇適當之派工法則的依據。我們相信本研究的成果不論對理論研究者、現場工程師、或生管軟體系統開發者都有極大價值。

Each year the Railway Applications Section (RAS) of the Institution for Operations Research and the Management Sciences (INFORMS) posits a research problem to the world in the form of a competition. For 2012, the contest involved solving the Train Dispatching Problem (TDP) on a realistic 85 edge network for three different sets of input data. This work is an independent attempt to match or improve upon the results of the top three finishers in the contest using mixed integer programming (MIP) techniques while minimizing the use of heuristics. The primary focus is to partition the network in a manner that reduces the number of binary variables in the formulation as much as possible without compromising the ability to satisfy any of the contest requirements. This resulted in the ability to optimally solve this model for RAS Data Set 1 in 29 seconds without any problem-specific heuristics, variable restrictions, or variable fixing. Applying some assumptions about train movements allowed the same Data Set 1 solution to be found in 5.4 seconds. After breaking the larger Data Sets 2 and 3 into smaller sub-problems, solutions for Data Sets 2 and 3 were 28% and 1% better, respectively, than those of the competition winner. The time to obtain solutions for Data Sets 2 and 3 was 90 and 318 seconds, respectively.