Performance Measures for Multimodal Transportation Systems

1996 ◽  
Vol 1518 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Richard H. Pratt ◽  
Timothy J. Lomax
Keyword(s):  
Land Use ◽  
Performance Measurement ◽  
Travel Time ◽  
Performance Measures ◽  
System Performance ◽  
Transportation Systems ◽  
Regional Networks ◽  
Multimodal Transportation ◽  
Decision Context ◽  
Multimodal Systems

Transportation systems analyses have been evolving as the decision context for improvement projects and programs has changed. The increased emphasis on the movement of persons and goods, and a recognition of the importance of system performance measures that address the needs and interests of the audiences for mobility information, will result in a very different set of procedures for evaluating transportation and land use infrastructure and policies. Some of the key underlying concerns of performance measurement for multimodal systems are presented. Definitions are included for congestion, mobility, and accessibility that are used to guide the development of performance measures. Travel time–based measures are seen as the most readily understandable quantities, and examples are used to show how mobility can be measured for locations, corridors, transit analyses, and regional networks.

Advancing Urban Multimodal Transportation System Performance Management

2017 ◽  
Vol 2646 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Stephanie Dock ◽  
Ryan Westrom ◽  
Kevin Lee ◽  
Burak Cesme
Keyword(s):  
Performance Management ◽  
Performance Measures ◽  
System Performance ◽  
Urban Areas ◽  
Transportation System ◽  
Visualization Tool ◽  
Department Of Transportation ◽  
Policy Makers ◽  
Multimodal Transportation ◽  
The Public

As in many cities, congestion in Washington, D.C., is one of the top concerns of residents, businesses, travelers, and policy makers. Monitoring and communicating system performance from a mobility perspective is challenging, particularly when dealing with the multimodal nature of travel in urban areas. The District Department of Transportation has identified a set of performance measures for multimodal mobility—including congestion—that are based on available data and is making these metrics available to the public and to policy makers through an innovative visualization tool. The study’s approach, measures, and visualization component provide a model for other jurisdictions to consider adopting when seeking to better understand and convey the challenges, opportunities, and interdependencies of multimodal travel.

Planning-Level Areawide Multimodal Level-of-Service Analysis: Performance Measures for Congestion Management

2003 ◽  
Vol 1858 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Martin Guttenplan ◽  
Beverly Davis ◽  
Ruth Steiner ◽  
Demian Miller
Keyword(s):  
Land Use ◽  
Performance Measures ◽  
Land Use Planning ◽  
Local Governments ◽  
Congestion Management ◽  
The State ◽  
Sustainable Growth ◽  
Service Performance ◽  
Level Of Service ◽  
Multimodal Transportation

The state of Florida has been experiencing an explosion of growth, and it is one of the fastest growing states in the country. While the state has been in the forefront of growth management initiatives, the results have been less than satisfactory. The state recognized the need to integrate land use planning efforts with transportation planning efforts. Legislation was passed that allows the formation of multimodal transportation districts, which focus on appropriate land use mixes and densities that would enhance the use of transportation modes other than the automobile. There are several key elements in the evaluation of these districts, using both land use analysis and the recently adopted multimodal level-of-service performance measures. To validate these evaluation techniques, especially focusing on the level-of-service performance measures, several case studies were undertaken. The guidelines for the formation of a multimodal transportation district provide local governments with a template for enhancing existing and new development. These guidelines also provide a blueprint for sustainable growth and the promotion and development of livable communities through the integration of transportation, land use, and urban design.

Performance measurement for supply chain management and quality management integration

2019 ◽  
Vol 27 (7) ◽  
pp. 2130-2147 ◽  
Author(s):  
Marcio C. Machado ◽  
Renato Telles ◽  
Paulo Sampaio ◽  
Maciel M. Queiroz ◽  
Ana Cristina Fernandes
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Quality Management ◽  
Performance Measurement ◽  
Performance Measures ◽  
Chain Management ◽  
Content Type ◽  
Theoretical Perspectives ◽  
Overall Performance ◽  
Key Aspects

Purpose The purpose of this paper is to present a conceptual framework for performance measurement (PM) for the integration of supply chain management (SCM) and quality management (QM). Design/methodology/approach A systematic literature review on SCM and QM was conducted to develop key performance measures related to six areas of integration between QM and SCM: leadership; continuous improvement and innovation; sustainability performance; stakeholders; information system; and management and strategic planning. Findings Supported by the literature concerning to supply chain quality management (SCQM) integration, a set of nine propositions about performance measures, that contribute to the integration of SCQM, were developed. Originality/value This study contributes to QM practices within a supply chain environment from an integrated perspective. Additionally, the propositions have significant implications from both managerial and theoretical perspectives. This study also extends the concept of supply chain quality integration by focusing on key aspects of PM that may help to improve the overall performance of the supply chain.

Systematic Approach to Measuring ILI Tool Performance

2014 ◽  
Author(s):  
Sean Keane ◽  
Karmun Cheng ◽  
Kaitlyn Korol
Keyword(s):  
Performance Measurement ◽  
Performance Measures ◽  
New Technologies ◽  
Efficient Reduction ◽  
Tool Performance ◽  
Typical Performance ◽  
Substantial Investment ◽  
Integrity Management ◽  
Insight Into

In-line inspection (ILI) tools play an important role within integrity management and substantial investment is made to continuously advance performance of the existing technologies and, where necessary, to develop new technologies. Performance measurement is typically focused for the purpose of understanding the measured performance in relation to the ILI vendor specification and for the determination of residual uncertainty regarding pipeline integrity. These performance measures may not provide the necessary insight into what type of investment into a technology is necessary to further reduce residual uncertainty regarding pipeline integrity, and beyond that, what investment, as an operator, results in an effective and efficient reduction in uncertainty. The paper proposes a reliability based approach for investigating uncertainty associated with ultrasonic crack ILI technology for the purpose of identifying efficient investment into the technology that results in an effective and measurable improvement. Typical performance measures and novel performance measurement methods are presented and reviewed with respect to what information they can provide to assist in investment decisions. Finally, general observations are made regarding Enbridge’s experience using ultrasonic crack ILI technology and areas currently being investigated.

Tollbooth tandem queues with infinite homogeneous servers

2015 ◽  
Vol 52 (4) ◽  
pp. 941-961 ◽  
Author(s):  
Xiuli Chao ◽  
Qi-Ming He ◽  
Sheldon Ross
Keyword(s):  
Performance Measures ◽  
System Performance ◽  
Stochastic Ordering ◽  
Tandem Queues ◽  
Poisson Arrival ◽  
Arrival Processes ◽  
Poisson Arrivals ◽  
Number Of Customers ◽  
Steady State Solutions ◽  
Transient And Steady State

In this paper we analyze a tollbooth tandem queueing problem with an infinite number of servers. A customer starts service immediately upon arrival but cannot leave the system before all customers who arrived before him/her have left, i.e. customers depart the system in the same order as they arrive. Distributions of the total number of customers in the system, the number of departure-delayed customers in the system, and the number of customers in service at time t are obtained in closed form. Distributions of the sojourn times and departure delays of customers are also obtained explicitly. Both transient and steady state solutions are derived first for Poisson arrivals, and then extended to cases with batch Poisson and nonstationary Poisson arrival processes. Finally, we report several stochastic ordering results on how system performance measures are affected by arrival and service processes.

scholarly journals Lost in transportation: Information measures and cognitive limits in multilayer navigation

2016 ◽  
Vol 2 (2) ◽  
pp. e1500445 ◽  
Author(s):  
Riccardo Gallotti ◽  
Mason A. Porter ◽  
Marc Barthelemy
Keyword(s):  
New York ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Traditional View ◽  
Multimodal Transportation ◽  
Large Cities ◽  
Amount Of Information ◽  
Information Measures ◽  
Transportation Modes ◽  
Metropolitan Networks

Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder whether it is possible to quantitatively characterize our difficulty navigating in them and whether such navigation exceeds our cognitive limits. A transition between different search strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of a limit associated with cognitive overload and caused by a large amount of information that needs to be processed. In this light, we analyzed the world’s 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the “Dunbar number,” which represents a limit to the size of an individual’s friendship circle, our cognitive limit suggests that maps should not consist of more than 250 connection points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks. In large cities such as New York, Paris, and Tokyo, more than 80% of the trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and, consequently, the traditional view of navigation in cities has to be revised substantially.

Origin–Destination-Based Travel Time Reliability

2017 ◽  
Vol 2643 (1) ◽  
pp. 139-159 ◽  
Author(s):  
Shu Yang ◽  
Chengchuan An ◽  
Yao-Jan Wu ◽  
Jingxin Xia
Keyword(s):  
Travel Time ◽  
Performance Indicator ◽  
Transportation Systems ◽  
Travel Time Reliability ◽  
Primary Difference ◽  
Route Information ◽  
Time Periods ◽  
Approximate Confidence Intervals ◽  
Deviation Coefficient ◽  
Insight Into

Travel time reliability (TTR) is an important performance indicator for transportation systems. TTR can be generally categorized as either segment based or origin–destination (O-D) based. A primary difference between the two TTR estimations is that route information is implied in segment-based TTR estimations. Segment-based TTR estimations have been widely studied in previous research; however, O-D–based TTR estimations are used infrequently. This paper provides detailed insight into O-D–based TTR estimations and raises three new issues: ( a) How many routes do travelers usually take and what are the TTR values associated with these routes? ( b) Do statistical differences exist between route-specific and non-route-specific (NRS) TTR values? ( c) How can O-D–based TTR information be delivered? Two processes were proposed to address the issues. Three TTR measures—standard deviation, coefficient of variation, and buffer index—were calculated. The bootstrapping technique was used to measure the accuracy of the TTR measures. Approximate confidence intervals were used to investigate statistically the differences between route-specific and NRS TTR measures. A large quantity of taxicab GPS-based data provided data support for estimating O-D–based TTR measures. The results of O-D–based TTR measures showed that no statistically significant differences existed between route-specific and NRS TTR measures for most of the time periods examined. Statistically significant differences could still be found in some time periods. Travelers may take advantage of these differences to choose a more reliable route. Access to both numeric TTR values and route preference, instead of just to TTR information on segments of interest, can be beneficial to travelers in planning an entire trip.

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