Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
60,544
result(s) for
"Transportation networks"
Sort by:
Socioeconomic and usage characteristics of transportation network company (TNC) riders
The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.
Journal Article
Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco
Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.
Journal Article
Digital social networks and travel behaviour in urban environments
\"This book brings together conceptual and empirical insights to explore the interconnections between social networks based on Information and Communication Technologies (ICT) and travel behaviour in urban environments. Over the past decade, rapid development of ICT has led to extensive social impacts and influence on travel and mobility patterns within urban spaces. A new field of research of digital social networks and travel behaviour is now emerging. This book presents state-of-the-art knowledge cutting-edge research, and integrated analysis methods from the fields of social networks, travel behaviour and urban analysis. It explores the challenges related to the question of how we can synchronize among social networks activities, transport means, intelligent communication/information technologies and the urban form. This innovative book encourages multidisciplinary insights and fusion among three disciplines of social networks, travel behaviour and urban analysis. It offers new horizons for research and will be of interest to students and scholars studying mobilities, transport studies, urban geography, urban planning, the built environment, and urban policy\"-- Provided by publisher.
Book
Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh
Transportation network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve transportation network efficiency.
Journal Article
Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models
Cloud manufacturing is an emerging service-oriented manufacturing paradigm that integrates and manages distributed manufacturing resources through which complex manufacturing demands with a high degree of customization can be fulfilled. The process of service selection optimization and scheduling (SSOS) is an important issue for practical implementation of cloud manufacturing. In this paper, we propose new mixed-integer programming (MIP) models for solving the SSOS problem with basic composition structures (i.e., sequential, parallel, loop, and selective). Through incorporation of the proposed MIP models, the SSOS with a mixed composition structure can be tackled. As transportation is indispensable in cloud manufacturing environment, the models also optimize routing decisions within a given hybrid hub-and-spoke transportation network in which the central decision is to optimally determine whether a shipment between a pair of distributed manufacturing resources is routed directly or using hub facilities. Unlike the majority of previous research undertaken in cloud manufacturing, it is assumed that manufacturing resources are not continuously available for processing but the start time and end time of their occupancy interval are known in advance. The performance of the proposed models is evaluated through solving different scenarios in the SSOS. Moreover, in order to examine the robustness of the results, a series of sensitivity analysis are conducted on key parameters. The outcomes of this study demonstrate that the consideration of transportation and availability not only can change the results of the SSOS significantly, but also is necessary for obtaining more realistic solutions. The results also show that routing within a hybrid hub-and-spoke transportation network, compared with a pure hub-and-spoke network or a pure direct network, leads to more flexibility and has advantage of cost and time saving. The level of saving depends on the value of discount factor for decreasing transportation cost between hub facilities.
Journal Article
Multi-objective metaheuristics for hub location problem: A case study of rail network planning
Purpose: Hub location problems have been utilized in various applications including rail transportation network planning, where hub serves as a key transit point within the network. In this paper, we focus on determining the optimal location for a rail transportation hub, where cost and service are trade-off.Design/methodology/approach: The problem is formulated as a multi-objective programming model with the objectives of minimizing total transportation costs and minimizing maximum travel time. A case study of rail transportation network hub planning in Thailand is presented. Given the complexity and large scale of the real-world case study, we develop and compare the Multi-Objective Tabu Search (MOTS) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem.Findings: The proposed algorithms yield efficient performance in terms of computational time and solution quality. Performance comparison is further analyzed to see the difference in both algorithms.Originality/value: The results offer valuable managerial insights for decision-makers in rail transportation hub network design.
Journal Article
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks
This paper evaluates the robustness of a railway network with respect to operational delays. It assumes that trains in the network operate on fixed routes and with reference to a timetable. A stochastic delay propagation model is proposed for identifying primary (externally imposed) delays and for computing the resultant secondary (knock-on) delays. Delay probability distributions are computed for each train at each station on its journey, using timetable and infrastructure data for identifying potential station resource conflicts with other trains. The delay predictions are used to evaluate schedule robustness using two newly proposed metrics.
Individual robustness
measures the ability of trains to limit the adverse effects of their own primary delays. On the other hand,
collective robustness
measures the ability of the network as a whole, to limit the knock-on effects of primary delays imposed on a small fraction of trains. The two metrics provide stochastic guarantees on the punctuality of trains when the published schedule is put in operation. The applicability of the proposed methodology is validated using empirical data from a portion of the Indian Railways network, containing more than 38,000 train arrival/departure records. While a railway network is used as a case study, the same ideas can be applied to any scheduled transportation network.
Journal Article