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Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Multi-modal transportation system"

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Flórez, José, Álvaro Torralba Arias de Reyna, Javier García, Carlos Linares López, Ángel García-Olaya, and Daniel Borrajo. "Planning Multi-Modal Transportation Problems." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 66–73. http://dx.doi.org/10.1609/icaps.v21i1.13466.

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Анотація:
Multi-modal transportation is a logistics problem in which a set of goods have to be transported to different places, with the combination of at least two modes of transport, without a change of container for the goods. The goal of this paper is to describe TIMIPLAN, a system that solves multi-modal transportation problems in the context of a project for a big company. In this paper, we combine Linear Programming (LP) with automated planning techniques in order to obtain good quality solutions. The direct use of classical LP techniques is difficult in this domain, because of the non-linearity of the optimization function and constraints; and planning algorithms cannot deal with the entire problem due to the large number of resources involved. We propose a new hybrid algorithm, combining LP and planning to tackle the multi-modal transportation problem, exploiting the benefits of both kinds of techniques. The system also integrates an execution component that monitors the execution, keeping track of failures and replans if necessary, maintaining most of the plan in execution. We also present some experimental results that show the performance of the system.
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2

Wu, Fanyou, Cheng Lyu, and Yang Liu. "A personalized recommendation system for multi-modal transportation systems." Multimodal Transportation 1, no. 2 (June 2022): 100016. http://dx.doi.org/10.1016/j.multra.2022.100016.

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3

KAWAKAMI, Shogo, Yasuhiro HIROBATA, and Kwang-Suk SEO. "AN EVALUATION METHOD OF MULTI-MODAL TRANSPORTATION SYSTEM." Studies in Regional Science 18 (1987): 125–44. http://dx.doi.org/10.2457/srs.18.125.

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4

Chaturvedi, Manish, and Sanjay Srivastava. "Multi-Modal Design of an Intelligent Transportation System." IEEE Transactions on Intelligent Transportation Systems 18, no. 8 (August 2017): 2017–27. http://dx.doi.org/10.1109/tits.2016.2631221.

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5

Shi, Hong Yun, Xiao Qing Zeng, Dong Bing Shi, Wen Chen Yang, and Kai Xiang Cao. "The Design and Application of the Multi-Modal Transportation System for Large-Scale Events." Applied Mechanics and Materials 209-211 (October 2012): 938–44. http://dx.doi.org/10.4028/www.scientific.net/amm.209-211.938.

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Анотація:
Large-scale events are characterized by temporally large-scale passengers and spatially short-term aggregation. To transport high intensity passengers aggregated in short term safely and efficiently, the key is to build a reliable, flexible, and green sustainable pattern of the multi-modal transportation system. This paper presents a multi-modal transportation system oriented for large- scale events, and intensive transportation system, coordinated non-Motorized traffic system and self-improving auxiliary traffic system is designed respectively. Based on statistical data of travel modes and passenger satisfaction questionnaire during Shanghai Expo, the multi-modal transportation system case of Shanghai Expo is studied, and the application effects and enlightenments of this system are discussed in detail, aiming at offering the reference for the design of urban multi-modal transport system for large-scale events.
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6

Liu, Yang, Cheng Lyu, Zhiyuan Liu, and Jinde Cao. "Exploring a large-scale multi-modal transportation recommendation system." Transportation Research Part C: Emerging Technologies 126 (May 2021): 103070. http://dx.doi.org/10.1016/j.trc.2021.103070.

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7

Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (November 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.

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Анотація:
Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.
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Dzemydienė, Dalė, Aurelija Burinskienė, and Arūnas Miliauskas. "Integration of Multi-Criteria Decision Support with Infrastructure of Smart Services for Sustainable Multi-Modal Transportation of Freights." Sustainability 13, no. 9 (April 22, 2021): 4675. http://dx.doi.org/10.3390/su13094675.

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Анотація:
The problems of developing online decision support systems concern the complexity of the transportation management processes, the multi-dimensionality of data warehouses (DWs), and the multi-criteria of decision making. Such issues arise in the management processes by supporting them with adaptable information and communication infrastructure for the management of the freight’s transportation processes. Our research domain involves the assurance of infrastructure for the provision of smart services for the operative management of multi-modal transportation of freights under the conditions of the heterogeneity of communication channels and multi-criteria decision support. The complexity of managing multi-modal transportation processes raises some problems for ensuring the infrastructure for finding more optimal management solutions by the requirements of green development. This research aims to develop the approach for providing the infrastructure of intelligent smart services for adaptable implementation in management processes of online multi-modal freight transportation. The objectives are forwarded to construct the multi-criteria decision-making components and integrate them into the multi-layered, computer-based information management of intelligent systems. We present the steps of extension of the structure of the knowledge-based system with a multi-criteria decision support method, and the possibilities to work under the conditions of wireless networks and the recognition of context-aware situations. The results are demonstrated for the analysis of alternatives by assessing the multi-modal transportation corridors between the East and West regions of Europe, and the opportunities to evaluate them for more effective managerial perspectives under sustainable development requirements.
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9

LARIOUI, Jihane. "Multi-Agent System Architecture Oriented Prometheus Methodology Design for Multi-modal Transportation." International Journal of Emerging Trends in Engineering Research 8, no. 5 (May 25, 2020): 2118–25. http://dx.doi.org/10.30534/ijeter/2020/105852020.

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10

Sandhya, G., S. Suvetha, S. Swathi, and R. Shwetha. "Analysis on Multi Modal Transportation System Using Spatial Domain Inverse." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012108. http://dx.doi.org/10.1088/1742-6596/1916/1/012108.

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Дисертації з теми "Multi-modal transportation system"

1

Wang, Jinghui. "Multi-modal Energy Consumption Modeling and Eco-routing System Development." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78624.

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Анотація:
A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs). Specifically, heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses are modeled. The models are developed based on the Virginia Tech Comprehensive Power-based Fuel consumption Modeling (VT-CPFM) framework. The results demonstrate that the model estimates are highly consistent with field observations as well as the estimates of the Comprehensive Modal Emissions Model (CMEM) and MOtor Vehicle Emissions Simulator (MOVES). It is also found that the optimum fuel economy cruise speed ranges between 32 and 52 km/h for the tested trucks and between 39 and 47 km/h for the tested buses on grades varying from 0% to 8%, which is significantly lower than LDVs (60-80 km/h). The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The dynamics model varies throttle and brake level with running speed rather than assuming constants as was done by previous studies, and the energy consumption model considers instantaneous energy regeneration. Both models can be easily calibrated using non-engine data and implemented in simulation systems and eco-transit applications. The results of the dynamics modeling demonstrate that the proposed model can adequately capture instantaneous acceleration/deceleration behavior and thus produce realistic train trajectories. The results of the energy consumption modeling demonstrate that the model produces the estimates consistent with the National Transit Database (NTD) results, and is applicable for project-level analysis given its ability in capturing the energy consumption differences associated with train, route and operational characteristics. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). Given that the car-following model within a micro-simulator controls longitudinal vehicle motion and thus determines the resulting vehicle trajectories, the research effort mainly focuses on the performance of the built-in car-following models from the energy and environmental perspective. The vehicle specific power (VSP) distributions resulting from each of the car-following models are compared to the field observations. The results demonstrate that the Rakha-Pasumarthy-Adjerid (RPA) model (implemented in the INTEGRATION software) outperforms the Gipps (AIMSUM), Fritzsche (PARAMICS) and Wiedemann (VISSIM) models in generating accurate VSP distributions and fuel consumption and emission estimates. This demonstrates the advantage of the INTEGRATION model over the other three simulation models for energy and environmental analysis. A new eco-routing model, comprehensively considering microscopic characteristics, is then developed, followed by a numerical experiment to test the benefit of the model. With the resulting eco-routing model, an on-road vehicle dynamic eco-routing system is constructed for in-vehicle navigation applications, and tested for different congestion levels. The results of the study demonstrate that the proposed eco-routing model is able to generate reasonable routing suggestions based on real-time information while at the same time differentiate eco-routes between vehicle models. It is also found that the proposed dynamic eco-routing system achieves lower network-wide energy consumption levels compared to the traditional eco-routing and travel time routing at all congestion levels. The results also demonstrate that the conventional fuel savings relative to the travel time routing decrease with the increasing congestion level; however, the electric power savings do not monotonically vary with congestion level. Furthermore, the energy savings relative to the traditional eco-routing are also not monotonically related to congestion level. In addition, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler's age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives.
Ph. D.
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2

Thuillier, Etienne. "Extraction of mobility information through heterogeneous data fusion : a multi-source, multi-scale, and multi-modal problem." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCA019.

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Анотація:
Aujourd'hui c'est un fait, nous vivons dans un monde où les enjeux écologiques, économiques et sociétaux sont de plus en plus pressants. Au croisement des différentes lignes directrices envisagées pour répondre à ces problèmes, une vision plus précise de la mobilité humaine est un axe central et majeur, qui a des répercussions sur tous les domaines associés tels que le transport, les sciences sociales, l'urbanisme, les politiques d'aménagement, l'écologie, etc. C'est par ailleurs dans un contexte de contraintes budgétaires fortes que les principaux acteurs de la mobilité sur les territoires cherchent à rationaliser les services de transport, et les déplacements des individus. La mobilité humaine est donc un enjeu stratégique aussi bien pour les collectivités locales que pour les usagers, qu'il faut savoir observer, comprendre, et anticiper.Cette étude de la mobilité passe avant tout par une observation précise des déplacements des usagers sur les territoires. Aujourd'hui les acteurs de la mobilité se tournent principalement vers l'utilisation massive des données utilisateurs. L'utilisation simultanée de données multi-sources, multi-modales, et multi-échelles permet d'entrevoir de nombreuses possibilités, mais cette dernière présente des défis technologiques et scientifiques majeurs. Les modèles de mobilité présentés dans la littérature sont ainsi trop souvent axés sur des zones d'expérimentation limitées, en utilisant des données calibrées, etc. et leur application dans des contextes réels, et à plus large échelle est donc discutable. Nous identifions ainsi deux problématiques majeures qui permettent de répondre à ce besoin d'une meilleure connaissance de la mobilité humaine, mais également à une meilleure application de cette connaissance. La première problématique concerne l'extraction d'informations de mobilité à partir de la fusion de données hétérogènes. La seconde problématique concerne la pertinence de cette fusion dans un contexte réel, et à plus large échelle. Nous apportons différents éléments de réponses à ces problématiques dans cette thèse. Tout d'abord en présentant deux modèles de fusion de données, qui permettent une extraction d'informations pertinentes. Puis, en analysant l'application de ces deux modèles au sein du projet ANR Norm-Atis.Dans cette thèse, nous suivons finalement le développement de toute une chaine de processus. En commençant par une étude de la mobilité humaine, puis des modèles de mobilité, nous présentons deux modèles de fusion de données, et nous analysons leur pertinence dans un cas concret. Le premier modèle que nous proposons permet d'extraire 12 comportements types de mobilité. Il est basé sur un apprentissage non-supervisé de données issues de la téléphonie mobile. Nous validons nos résultats en utilisant des données officielles de l'INSEE, et nous déduisons de nos résultats, des comportements dynamiques qui ne peuvent pas être observés par les données de mobilité traditionnelles. Ce qui est une forte valeur-ajoutée de notre modèle. Le second modèle que nous proposons permet une désagrégation des flux de mobilité en six motifs de mobilité. Il se base sur un apprentissage supervisé des données issues d'enquêtes de déplacements ainsi que des données statiques de description du sursol. Ce modèle est appliqué par la suite aux données agrégés au sein du projet Norm-Atis. Les temps de calculs sont suffisamment performants pour permettre une application de ce modèle dans un contexte temps-réel
Today it is a fact that we live in a world where ecological, economic and societal issues are increasingly pressing. At the crossroads of the various guidelines envisaged to address these problems, a more accurate vision of human mobility is a central and major axis, which has repercussions on all related fields such as transport, social sciences, urban planning, management policies, ecology, etc. It is also in the context of strong budgetary constraints that the main actors of mobility on the territories seek to rationalize the transport services and the movements of individuals. Human mobility is therefore a strategic challenge both for local communities and for users, which must be observed, understood and anticipated.This study of mobility is based above all on a precise observation of the movements of users on the territories. Nowadays mobility operators are mainly focusing on the massive use of user data. The simultaneous use of multi-source, multi-modal, and multi-scale data opens many possibilities, but the latter presents major technological and scientific challenges. The mobility models presented in the literature are too often focused on limited experimental areas, using calibrated data, etc., and their application in real contexts and on a larger scale is therefore questionable. We thus identify two major issues that enable us to meet this need for a better knowledge of human mobility, but also to a better application of this knowledge. The first issue concerns the extraction of mobility information from heterogeneous data fusion. The second problem concerns the relevance of this fusion in a real context, and on a larger scale. These issues are addressed in this dissertation: the first, through two data fusion models that allow the extraction of mobility information, the second through the application of these fusion models within the ANR Norm-Atis project.In this thesis, we finally follow the development of a whole chain of processes. Starting with a study of human mobility, and then mobility models, we present two data fusion models, and we analyze their relevance in a concrete case. The first model we propose allows to extract 12 types of mobility behaviors. It is based on an unsupervised learning of mobile phone data. We validate our results using official data from the INSEE, and we infer from our results, dynamic behaviors that can not be observed through traditional mobility data. This is a strong added-value of our model. The second model operates a mobility flows decompositoin into six mobility purposes. It is based on a supervised learning of mobility surveys data and static data from the land use. This model is then applied to the aggregated data within the Norm-Atis project. The computing times are sufficiently powerful to allow an application of this model in a real-time context
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3

Bevrani, Bayan. "Multi-criteria capacity assessment and planning models for multi-modal transportation systems." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122895/1/Bayan_Bevrani_Thesis.pdf.

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Анотація:
This research provides a comprehensive set of methods for the capacity assessment of multi-modal transportation systems, which are easy to apply and can significantly reduce the time to perform a capacity analysis. This thesis developed multi-objective mathematical models that can evaluate the effect of parametric and structural changes and can assist planners to update and reconfigure multi-modal transportation systems considering the trade-off in different aspects of cost, time and functionality. The methods proposed in this thesis can help planners and decision-makers to identify the performance and capability of multiple modes of an integrated transportation system.
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4

Doshi, Siddharth. "Designing a multi-modal traveler information platform for urban transportation." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37167.

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Анотація:
Urban transportation networks are inefficient due to sub-optimal use by travelers. One approach to counter the increase in urban transportation demand is to provide better information to travelers, which would allow them to make better use of the network. Existing traveler information systems do this to a certain extent, but are limited by the data available and the scope of their implementation. These systems are vertically integrated and closed so that using any external elements for analysis, user interfacing etc. is difficult. The effects of such traveler information systems are reviewed via a comparative analysis of case studies available in the literature. It is found that information availability has a definite positive effect, but the social and environmental benefits are difficult to quantify. It is also seen that combining data by integrating systems can lead to additional uses for the same data and result on better quality of service and information. In this thesis, a regional platform for multi-modal traveler information is proposed that would support the development of traveler information systems. The architecture incorporates a central processing and storage module, which acts as an information clearinghouse and supports receiving, managing and sending data to and from multiple sources and interfaces. This setup allows sharing of data for analysis or application development, but with access control. The components are loosely coupled to minimize inter-dependencies. Due to this, the source, analysis, user interface and storage components can be developed independently of each other. To better develop the requirements and understand the challenges of the proposed concept, a limited implementation of the system is designed for the midtown Atlanta region, incorporating multiple data sources and user interfaces. The individual elements of the system are described in detail as is the testing and evaluation of the system.
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5

Goswami, Subhajit. "One-Shot Coordination of First and Last Mode Service in Multi-Modal Transportation." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4535.

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Анотація:
In this thesis, we propose a coordination of the rst and last mode connectivity in a multi- modal transportation system. In particular, we consider a one-shot problem wherein the passengers must be transported to or from a common location before a fixed deadline. We consider a macroscopic model, wherein we model a region of interest by a graph and consider flows of vehicles and volumes of passenger demand and vehicle supply. We then consider the problem of operator's profit maximization through optimal pricing and allocations of feeder vehicles given the knowledge of demand and supply distributions. Due to the speci c nature of the problem, we can first determine the optimal prices, subsequent to which the overall problem becomes a linear program. We first study the problem of one-shot feed-in". Given the large scale or scope of the problem and the need for a near real-time implementation, we rst seek to reduce the problem size. We propose an o -line route elimination algorithm that given a route- set returns another reduced" route-set by eliminating the routes that would never be used in an optimal solution irrespective of the demand and supply distributions. In simulations on a 24 node graph, our proposed route reduction algorithm reduced the number of optimization variables to nearly one third of the original number. Such a reduced route-set could then be utilized for faster computation of the optimal solutions of the feed-in problem when the demand and supply distributions are revealed. We then analyse the supply optimization problem for one-shot feed-in for a given demand distribution. In this problem, given a total supply, we are interested in optimally distributing the supply so as to maximize the operator's pro ts. With this formulation, we give the closed form expression of the \absolute maximum pro ts" of the operator over all supply distributions given the demand distribution. Next, we go on to show that the one-shot feed-out" problem is equivalent to the supply optimization problem for one-shot feed-in" and that similar results can be drawn using the equivalence analysis. Finally, we propose a simple framework for determining the prices. With this framework, we are able to analyse the cost of a rst or last mode feeder service relative to the best alternative transportation at which the feeder service becomes pro table. We demonstrate our analytical results with a suite of simulations, including a com- parative study of our model with respect to a macroscopic single-depot single-window routing problem.
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6

"Data organization for routing on the multi-modal public transportation system: a GIS-T prototype of Hong Kong Island." 2001. http://library.cuhk.edu.hk/record=b5890808.

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Анотація:
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
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Книги з теми "Multi-modal transportation system"

1

Modelling Intelligent Multi-Modal Transit Systems'. Taylor & Francis Group, 2016.

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Nuzzolo, Agostino, and William H. K. Lam. Modelling Intelligent Multi-Modal Transit Systems. Taylor & Francis Group, 2017.

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Nuzzolo, Agostino, and William H. K. Lam. Modelling Intelligent Multi-Modal Transit Systems. Taylor & Francis Group, 2017.

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4

Nuzzolo, Agostino, and William H. K. Lam. Modelling Intelligent Multi-Modal Transit Systems. Taylor & Francis Group, 2021.

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Nuzzolo, Agostino, and William H. K. Lam. Modelling Intelligent Multi-Modal Transit Systems. Taylor & Francis Group, 2017.

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6

Nuzzolo, Agostino, and William H. K. Lam. Modelling Intelligent Multi-Modal Transit Systems. Taylor & Francis Group, 2017.

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Частини книг з теми "Multi-modal transportation system"

1

Juozapavičius, Aušrius, and Stanislovas Buteliauskas. "Multi-modal Transportation System Using Multi-functional Road Interchanges." In Sustainable Solutions for Railways and Transportation Engineering, 133–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01911-2_12.

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Larioui, Jihane, and Abdeltif Elbyed. "A Multi-Agent Information System Architecture for Multi-Modal Transportation." In Embedded Systems and Artificial Intelligence, 795–803. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_75.

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3

Fan, Haodong, and Y. I. Baldric. "Urban Intelligent Transportation Solution Based on Road Monitoring System." In Application of Intelligent Systems in Multi-modal Information Analytics, 209–16. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05484-6_26.

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Yu, Tzu-Yang, Christopher Niezrecki, and Farhad Ansari. "Multi-modal Remote Sensing System for Transportation Infrastructure Inspection and Monitoring." In Advanced Research in Applied Artificial Intelligence, 95–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31087-4_11.

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Aissat, Kamel, and Sacha Varone. "Carpooling as Complement to Multi-modal Transportation." In Enterprise Information Systems, 236–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-29133-8_12.

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Zhao, Yue, Changfu Yuan, Jiabao Du, and Yi Wang. "ICT-Based Information Platform Architecture of Maritime Cargo Transportation Supply Chain." In Application of Intelligent Systems in Multi-modal Information Analytics, 9–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51431-0_2.

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Ye, Qingpin, and Wenying Wu. "Optimization Model and Algorithm for Rail-Highway Combined Transportation of Dangerous Goods." In Application of Intelligent Systems in Multi-modal Information Analytics, 264–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05484-6_33.

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Hansen, Rebecca Grüner, and Giovanni Pantuso. "Pricing Car-Sharing Services in Multi-Modal Transportation Systems: An Analysis of the Cases of Copenhagen and Milan." In Lecture Notes in Computer Science, 344–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00898-7_23.

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9

"Multi-Modal Transportation." In Global Supply Chains and Multimodal Logistics, 110–41. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8298-4.ch005.

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This chapter highlights concepts, dimensions, issues, and challenges associated with multimodal transportation of goods. It gives a decision-making framework for selection of modes and nodes based on the dimensions of multimodal system. Thirteen dimensions of multimodal transportation have been identified and its impact on selection of logistics route has been discussed. A computational framework for selecting the best multimodal route has been proposed in this chapter.
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10

Pal, Kamalendu. "Software Agents Mediated Decision Simulation in Supply Chains." In Emerging Applications in Supply Chains for Sustainable Business Development, 40–56. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5424-0.ch003.

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The concept of software agent has become important in both artificial intelligence and mainstream computer science. Multi-agent systems (MAS) are providing the way to design and implement information system solutions that exhibit flexibility, adoptability and reconfigurability in a distributed environment, which are main benefits over traditional centralized software systems. The analysis, design, deployment and testing of such distributed agent-based software systems, particularly those exhibiting intelligent decision-making properties, are usually a challenging task. Simulation plays a key role to analyse the behaviour of MAS solutions during the analysis and design phase of automated software solution. This chapter uses the concept of multi-agent computing and presents software architecture for green supply chain management, in particular carbon footprint assessment planning for a multi-modal transportation problem. In this architecture, all the software agents' operations are governed by a hybrid knowledge-based which utilizes case-based reasoning (CBR) and rule-based reasoning (RBR). The describe architecture accepts a transportation service request and plans a transportation strategy with a minimum environmental impact (i.e. CO2 footprint), by retrieving best practices (from a carbon footprint perspective) for each route leg, from a repository of best practiced cases. Carbon footprint best practices from each route leg in a multi-modal transportation scenario are used to minimize environmental impact and thus demonstrate system functionality.
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Тези доповідей конференцій з теми "Multi-modal transportation system"

1

Ferreira, Joao C., Porfirio Filipe, and Alberto Silva. "Multi-Modal Transportation Advisor system." In 2011 IEEE Forum on Integrated and Sustainable Transportation Systems (FISTS). IEEE, 2011. http://dx.doi.org/10.1109/fists.2011.5973636.

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Liu, Yihan, Guangming Xu, Linhuan Zhong, and Yao Xiao. "Multi-Modal Transport Logic Architecture Analysis Based on Autonomous Transportation System." In 22nd COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2022. http://dx.doi.org/10.1061/9780784484265.043.

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Wu, Ping, Yanyan Chen, Guanghou Zhang, and Wen Wu. "The Coordination Evaluation Research for Multi-Modal Public Transport System." In Tenth International Conference of Chinese Transportation Professionals (ICCTP). Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41127(382)268.

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Dehzangi, Omid, Vaishali Sahu, Mojtaba Taherisadr, and Scott Galster. "Multi-modal system to detect on-the-road driver distraction." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569893.

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Zhang, Xin, and Gang-len Chang. "The multi-modal evacuation system (MES) for Baltimore metropolitan region." In 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338592.

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Hong, Shaozhi, Jinxin Li, and Huanxi Xu. "Development and Evaluation Index System for Sustainable Urban Multi-Modal Transit Development." In 17th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784480915.383.

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Menychtas, Andreas, Dimosthenis Kyriazis, George Kousiouris, and Theodora Varvarigou. "An IoT enabled point system for end-to-end multi-modal transportation optimization." In 2013 5th IEEE International Conference on Broadband Network & Multimedia Technology (IC-BNMT 2013). IEEE, 2013. http://dx.doi.org/10.1109/icbnmt.2013.6823942.

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Zavattero, David, and Wei Wu. "Design of the Gary-Chicago-Milwaukee Gateway: A Multi-Modal Traveler Information System." In Second International Conference on Urban Public Transportation Systems. Reston, VA: American Society of Civil Engineers, 2004. http://dx.doi.org/10.1061/40717(148)7.

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Wang, Zhong, Meng Zhang, and Huiyuan Liu. "A Utility-Based Method for Urban Transportation System Multi-Modal Level of Service Evaluation." In 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.292.

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Eckert, Johannes, David Lopez, Carlos Lima Azevedo, and Bilal Farooq. "A blockchain-based user-centric emission monitoring and trading system for multi-modal mobility*." In 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS). IEEE, 2020. http://dx.doi.org/10.1109/fists46898.2020.9264892.

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Звіти організацій з теми "Multi-modal transportation system"

1

Agrawal, Asha Weinstein, and Hilary Nixon. Investing in California’s Transportation Future: 2022 Public Opinion on Critical Needs. Mineta Transportation Institute, July 2023. http://dx.doi.org/10.31979/mti.2023.2158.

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This study surveyed 3,821 adults living in California about their general travel behaviors and resources, use of ride-hailing, performance ratings for the transportation system and agencies responsible for transportation, transportation system improvement priorities, and preference for how transportation funds are allocated. Key findings include the following: • Californians are multi-modal: Although driving was the most common mode, respondents reported that in the previous 30 days 66% had made a walk trip, 28% had used ridehailing, 25% had used public transit, and 22% had bicycled. • Although many respondents had at least once substituted ride-hailing for transit, walking, or bicycling and micromobility, the impact on those modes was nuanced. For example, although 64% of respondents who used ride-hailing had done so at least once when transit was available, only about a quarter of ride-hailers (27%) felt that they used transit less once they started ride-hailing. Another 16% of ride-hailers said they rode transit more after they started ride-hailing. • Virtually all respondents—over 90%—wanted the state to work towards better safety and maintenance; reduced congestion, greenhouse gas emissions, and air pollution; and convenient multimodal travel options. • Large majorities of respondents placed a medium or high priority on transportation spending options to support all modes.
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