Rail and truck freight transportation systems provide vital logistics services today. Rail systems are generally used to transport heavy and bulky commodities over long distances, while trucks tend to provide fast and flexible service for small and high-value products. In this dissertation, we study two different distribution planning problems that arise in rail and truck transportation systems. In the railroad industry, shipments are often grouped together to form a block to reduce the impact of reclassification at train yards. We consider the time and capacity constrained routing (TCCR) problem, which assigns shipments to blocks and train-runs to minimize overall transportation costs, while considering the train capacities and shipment due dates. Two mathematical formulations are developed, including an arc-based formulation and a path-based formulation. To solve the problem efficiently, two solution approaches are proposed. The sequential algorithm assigns shipments in order of priority while considering the remaining train capacities and due dates. The bump-shipment algorithm initially schedules shipments simultaneously and then reschedules the shipments that exceed the train capacity. The algorithms are evaluated using a data set from a major U.S. railroad with approximately 500,000 shipments. Industry-sized problems are solved within a few minutes of computational time by both the sequential and bump-shipment algorithms, and transportation costs are reduced by 6% compared to the currently used trip plans. For truck transportation systems, trailer fleet planning (TFP) is an important issue to improve services and reduce costs. In this problem, we consider the quantities and types of trailers to purchase, rent, or relocate among depots to meet time varying demands. Mixed-integer programming models are developed for both homogeneous and heterogeneous TFP problems. The objective is to minimize the total fleet investment costs and the distribution costs across multiple depots and multiple time periods. For homogeneous TFP problem, a two-phase solution approach is proposed. Phase I concentrates on distribution costs and determines the suggested fleet size. A sweep-based routing heuristic is applied to generate candidate routes of good quality. Then a reduced mathematical model selects routes for meeting customer demands and determines the preferred fleet size. Phase II provides trailer purchase, relocation, and rental decisions based on the results of Phase I and relevant cost information. This decomposition approach removes the interactions between depots and periods, which greatly reduces the complexity of the integrated optimization model. For the heterogeneous TFP problem, trailers with different capacities, costs, and features are considered. The two-phase approach, developed for the homogeneous TFP, is modified. A rolling horizon scheme is applied in Phase I to consider the trailer allocations in previous periods when determining the fleet composition for the current period. Additionally, the sweep-based routing heuristic is also extended to capture the characteristics of continuous delivery practice where trailers are allowed to refill products at satellite facilities. This heuristic generates routes for each trailer type so that the customer-trailer restrictions are accommodated. The numerical studies, conducted using a data set with three depots and more than 400 customers, demonstrate the effectiveness of the two-phase approaches. Compared to the integrated optimization models, the two-phase approaches obtain quality solutions within a reasonable computational time and demonstrate robust performance as the problem sizes increase. Based on these results, a leading industrial gas provider is currently integrating the proposed solution approaches as part of their worldwide distribution planning software. / Ph. D.

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As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction in toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times for use in bike share systems (BSSs) using random forest (RF), least square boosting (LSBoost), and artificial neural network (ANN) techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation. / Doctor of Philosophy / As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction of toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times in bike share systems (BSSs) using machine learning techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation.

Trip planning is a well cited problem for which various solutions have been reported in the literature. This problem has been typically addressed, to a large extent, as a shortest distance path planning problem. In some scenarios, the concept of shortest path is extended to reflect temporal objectives and/or constraints. This work takes an alternative perspective to the trip planning problem in the sense it being situation aware. Thus, allowing multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the trip and the traveler. The work in this thesis introduces TSADA (Traveler Situation Awareness and Decision Aid) system. TSADA is designed as a modular system that combines linguistic situation assessment with user-centric decision-making. The trip planning problem is modeled as a graph G. The objective is to find a route with the minimum cost. Both hard and soft objective/attributes are incorporated. Soft objective/attributes such as safety, speed and driving comfortability are described using a linguistic framework and processed using hierarchical fuzzy inference engine. A user centric situation assessment is used to compute feasible routes and map them into route recommendation scheme: recommended, marginally recommended, and not recommended. In this work, we introduce traveler's doctrines concept. This concept is proposed to make the process of situation assessment user centric by being driven by the doctrine that synthesizes the user's specific demands. Hard attributes/objectives, such as the time window and trip monitory allowances, are included in the process of determining the final decision about the trip. We present the underline mathematical formulation for this system and explain the working of the proposed system to achieve optimal performance. Results are introduced to show how the system performs under a wide range of scenarios. The thesis is concluded with a discussion on findings and recommendations for future work.

Trip planning is a well cited problem for which various solutions have been reported in the literature. This problem has been typically addressed, to a large extent, as a shortest distance path planning problem. In some scenarios, the concept of shortest path is extended to reflect temporal objectives and/or constraints. This work takes an alternative perspective to the trip planning problem in the sense it being situation aware. Thus, allowing multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the trip and the traveler. The work in this thesis introduces TSADA (Traveler Situation Awareness and Decision Aid) system. TSADA is designed as a modular system that combines linguistic situation assessment with user-centric decision-making. The trip planning problem is modeled as a graph G. The objective is to find a route with the minimum cost. Both hard and soft objective/attributes are incorporated. Soft objective/attributes such as safety, speed and driving comfortability are described using a linguistic framework and processed using hierarchical fuzzy inference engine. A user centric situation assessment is used to compute feasible routes and map them into route recommendation scheme: recommended, marginally recommended, and not recommended. In this work, we introduce traveler's doctrines concept. This concept is proposed to make the process of situation assessment user centric by being driven by the doctrine that synthesizes the user's specific demands. Hard attributes/objectives, such as the time window and trip monitory allowances, are included in the process of determining the final decision about the trip. We present the underline mathematical formulation for this system and explain the working of the proposed system to achieve optimal performance. Results are introduced to show how the system performs under a wide range of scenarios. The thesis is concluded with a discussion on findings and recommendations for future work.

Travel demand models are a useful tool to assess transportation projects. Within travel demand, long-distance trips represent a significant amount of the total vehicle-kilometers travelled, in contrast to commuting trips. Consequently, they pay a relevant role in the economic, social and environmental impacts of transportation. This paper describes the development of a microscopic long-distance travel demand model for the Province of Ontario (Canada) and analyzes the sensitivity to the implementation of a new high speed rail corridor. Trip generation, destination choice and mode choice models were developed for this research. Multinomial logit models were estimated and calibrated using the Travel Survey for Residents in Canada (TSRC). It was complemented with location-based social network data from Foursquare, improving the description of activities and diverse land uses at the destinations. Level of service of the transit network was defined by downloading trip time, frequency and fare using the planning service Rome2rio. New scenarios were generated to simulate the impacts of a new high speed rail corridor by varying rail travel times, frequencies and fares of the rail services. As a result, a significant increase of rail modal shares was measured, directly proportional to speed and frequency and inversely proportional to price.

Les systèmes de transport sont un des leviers puissants du progrès de toute société. Récemment les modes de déplacement ont évolué significativement et se diversifient. Les distances quotidiennement parcourues par les citoyens ne cessent d'augmenter au cours de ces dernières années. Cette évolution impacte l'attractivité et la compétitivité mais aussi la qualité de vie grandement dépendante de l'évolution des mobilités des personnes et des marchandises. Les gouvernements et les collectivités territoriales développent de plus en plus des politiques d'incitation à l'éco-mobilité. Dans cette thèse nous nous concentrons sur les systèmes de transport public. Ces derniers évoluent continuellement et offrent de nouveaux services couvrant différents modes de transport pour répondre à tous les besoins des usagers. Outre les systèmes de transports en commun, prévus pour le transport de masse, de nouveaux services de mobilité ont vu le jour, tels que le transport à la demande, le covoiturage planifié ou dynamique et l'autopartage ou les vélos en libre-service. Ils offrent des solutions alternatives de mobilité et pourraient être complémentaires aux services traditionnels. Cepandant, ces services sont à l'heure actuelle isolés du reste des modes de transport et des solutions multimodales. Ils sont proposés comme une alternative mais sans intégration réelle aux plans proposés par les outils existants. Pour permettre la multimodalité, le principal challenge de cette thèse est l'intégration de données et/ou de services provenant de systèmes de transports hétérogènes. Par ailleurs, le concept de données ouvertes est aujourd'hui adopté par de nombreuses organisations publiques et privées, leur permettant de publier leurs sources de données sur le Web et de gagner ainsi en visibilité. On se place dans le contexte des données ouvertes et des méthodes et outils du web sémantique pour réaliser cette intégration, en offrant une vue unifiée des réseaux et des services de transport. Les verrous scientifiques auxquels s'intéresse cette thèse sont liés aux problèmes d'intégration à la fois des données et des services informatiques des systèmes de transport sous-jacents. / Multimodality requires the integration of heterogeneous transportation data and services to construct a broad view of the transportation network. Many new transportation services (e.g. ridesharing, car-sharing, bike-sharing) are emerging and gaining a lot of popularity since in some cases they provide better trip solutions.However, these services are still isolated from the existing multimodal solutions and are proposed as alternative plans without being really integrated in the suggested plans. The concept of open data is raising and being adopted by many companies where they publish their data sources to the web in order to gain visibility. The goal of this thesis is to use these data to enable multimodality by constructing an extended transportation network that links these new services to existing ones.The challenges we face mainly arise from the integration problem in both transportation services and transportation data

No description available.

近年來隨著全球化的發展，自助旅行的風氣蔚為風潮。背包客可以根據自己的喜好與條件，自己規劃旅遊路線與行程。一般規劃旅遊行程的過程費時而繁瑣，除了必須決定旅遊景點，還必須將景點開放時間、景點停留時間、交通方式、旅遊順序與路線、時間限制、住宿地點、經費等列入考量。 針對旅遊行程規劃，除了參考網友的行程規劃，目前已普遍有景點推薦、行程編輯系統，協助使用者規劃行程。但少有旅遊行程自動規劃系統。因此，本論文研發實作旅遊行程的自動規劃系統MyTripPlan。MyTripPlan具備景點推薦、景點停留與拜訪時間預估、路線規劃、行程規劃及行程調整的功能。 本系統MyTripPlan在離線時，先透過爬蟲程式由景點推薦網站取得熱門景點資訊、由相片分享網站取得景點相片時間以推估景點停留及拜訪時間、由交通查詢網站取得任兩景點間的推估交通時間。在線上時，當使用者輸入旅遊天數等時間限制、並由系統推薦景點勾選有興趣拜訪的景點及餐廳後，系統將運用定向排程演算法推論出符合時間限制的旅遊行程，呈現在地圖上，並結合交通網站，產生行程表。 經實驗效能驗證，以安排旅遊天數七天，五十六個景點所需要的行程規劃時間約為一秒鐘；使用者對於實作完畢的MyTripPlan系統與功能也都有滿意以上的評分。 / Travel planning process is time-consuming and tedious. To plan a trip, not only the attractions, but opening hours, timing cost, travel route, transportation, lodge, budget control and so on also need to be considered. While there are widespread attraction recommendation and itinerary editing systems to assist people to plan their trip, only few trip automated planning systems are developed. In this thesis, MyTripPlan, an automatic trip planning system, is designed and developed. MyTripPlan The system provides users the capability of attraction recommendation, visiting and stay time estimation, route planning, trip planning and itinerary adjustments. In offline process, MyTripPlan collects popular attraction information from web content for attraction recommendation, gets the timestamp of attractions from photo-sharing websites to estimate visiting and stay time, and crawls traffic information from public transportation query site to estimate the travel time between attractions. Given the trip duration, MyTripPlan recommends attractions and restaurants, schedules and produces the trip itinerary automatically based on the team orienteering scheduling algorithm. The generated itinerary takes the user-preferred attractions, visiting and stay time constraints, and transportation information into consideration. MyTripPlan presents the trip itinerary both in map and schedule list. The experiments show that the execution time for trip plan with fifty-six attractions in seven days requires about one second. Moreover, nineteen subjects were invited to evaluate the effectiveness of MyTripPlan. Most users were satisfied and gave excellent rating on the system performance.

With the technological advancement in computer science, Geographic Information Science (GIScience), and transportation, more and more complex path finding queries including category based queries are proposed and studied across diverse disciplines. A category based query, such as Optimal Sequenced Routing (OSR) queries and Trip Planning Queries (TPQ), asks for a minimum-cost path that traverses a set of categories with or without a predefined order in a graph. Due to the extensive computing time required to process these complex queries in a large scale environment, efficient algorithms are highly desirable whenever processing time is a consideration. In Artificial Intelligence (AI), a best first search is an informed heuristic path finding algorithm that uses domain knowledge as heuristics to expedite the search process. Traditional best first searches are single-variate in terms of the number of variables to describe a state, and thus not appropriate to process these queries in a graph. In this dissertation, 1) two new types of category based queries, Category Sequence Traversal Query (CSTQ) and Optimal Sequence Traversal Query (OSTQ), are proposed; 2) the existing single-variate best first searches are extended to multivariate best first searches in terms of the state specified, and a class of new concepts--state graph, sub state graph, sub state graph space, local heuristic, local admissibility, local consistency, global heuristic, global admissibility, and global consistency--is introduced into best first searches; 3) two bivariate best first search algorithms, C* and O*, are developed to process CSTQ and OSTQ in a graph, respectively; 4) for each of C* and O*, theorems on optimality and optimal efficiency in a sub state graph space are developed and identified; 5) a family of algorithms including C*-P, C-Dijkstra, O*-MST, O*-SCDMST, O*- Dijkstra, and O*-Greedy is identified, and case studies are performed on path finding in transportation networks, and/or fully connected graphs, either directed or undirected; and 6) O*- SCDMST is adopted to efficiently retrieve optimal solutions for OSTQ using network distance metric in a large transportation network. / Ph. D.