Effects of Temporal Data Aggregation on Performance Measures and Other Intelligent Transportation Systems Applications

2010 ◽  
Vol 2160 (1) ◽  
pp. 96-106 ◽  
Author(s):  
Alexander Y. Bigazzi ◽  
Helene Siri ◽  
Robert L. Bertini
Keyword(s):  
Performance Measures ◽  
Intelligent Transportation Systems ◽  
Data Aggregation ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Temporal Data

PLANiTS: Structuring and Supporting the Intelligent Transportation Systems Planning Process

1997 ◽  
Vol 1588 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Asad J. Khattak ◽  
Adib Kanafani
Keyword(s):  
Performance Measures ◽  
Intelligent Transportation Systems ◽  
Planning Process ◽  
Intelligent Transportation ◽  
Quantitative Information ◽  
Transportation Systems ◽  
Case Based Reasoning ◽  
Systems Planning ◽  
Trade Offs ◽  
Computer Based

PLANiTS (Planning and Analysis Integration for Intelligent Transportation Systems) is a process-based computer system that supports a series of mutually interdependent steps progressing toward developing and programming transportation improvement projects. It is a tool that translates problems and goals to performance measures, examines possible competing and complementary transportation improvement actions, systematically evaluates the impacts of actions using models and knowledge, and supports human interactions between stakeholders. The PLANiTS methodology is nonincremental because it integrates existing knowledge about transportation with analysis using models with deliberation and issue resolution. To link planning and modeling, PLANiTS has a policy base that contains contemporary performance measures, an action base containing conventional and Intelligent Transportation Systems actions, a methods base that facilitates modeling, a case that has qualitative and quantitative information about historical cases, and a set of computer-based communications tools. This comprehensive methodology will likely expedite the implementation of intelligent technologies by systematically examining their trade-offs with more conventional transportation improvement actions. PLANiTS’s structure, functionality, and application are described. Transportation improvement projects are represented as planning vectors in PLANiTS. A vector permits users to examine the effects of chosen transportation actions in terms of performance measures within an environment. Users must specify the actions, performance measures, and the environment, each in terms of their spatial, temporal, and user dimensions. Then they can analyze the planning vector with models and case-based reasoning. During the process of planning vector specification and analysis, stakeholders at different locations can communicate by sending and receiving messages and sharing the planning vector. Users at different locations can examine and review the results and iterate in an open and deliberative planning environment. Overall, PLANiTS facilitates transportation planning processes by combining analysis and deliberation.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

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