scholarly journals Crash Classification by Congestion Type for Highways

2020 ◽  
Vol 10 (7) ◽  
pp. 2583
Author(s):  
Tai-Jin Song ◽  
Sangkey Kim ◽  
Billy M. Williams ◽  
Nagui M. Rouphail ◽  
George F. List
Keyword(s):  
Traffic Management ◽  
A Priori ◽  
Operational Conditions ◽  
Identification Schemes ◽  
Influence Area ◽  
Traffic Management System ◽  
Advanced Traffic Management System ◽  
Definition Of ◽  
Vehicular Crashes

Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

High density European Rail Traffic Management System (HD-ERTMS) for urban railway nodes: The case study of Rome

2021 ◽  
Vol 17 ◽  
pp. 100232
Author(s):  
Federico Cuppi ◽  
Valeria Vignali ◽  
Claudio Lantieri ◽  
Luca Rapagnà ◽  
Nicola Dimola ◽  
...  
Keyword(s):  
Traffic Management ◽  
Management System ◽  
High Density ◽  
Traffic Management System ◽  
Rail Traffic

scholarly journals Automated optimal flight planning based on the aircraft intent description language

2018 ◽  
Vol 233 (3) ◽  
pp. 928-948
Author(s):  
Daniel González-Arribas ◽  
Manuel Soler ◽  
Javier López-Leonés ◽  
Enrique Casado ◽  
Manuel Sanjurjo-Rivo
Keyword(s):  
Optimal Control ◽  
Traffic Management ◽  
Modular Design ◽  
Air Traffic Management ◽  
Generation Process ◽  
Trajectory Prediction ◽  
Description Language ◽  
Traffic Management System ◽  
Automated Tools

The future air traffic management system is to be built around the notion of trajectory-based operations. It will rely on automated tools related to trajectory prediction in order to define, share, revise, negotiate and update the trajectory of the aircraft before and during the flight, in some case, in near real time. This paper illustrates how existing standards on trajectory description such as the aircraft intent description language can be enhanced including optimisation capabilities based on numerical optimal control. The Aircraft Intent Description Language is a formal language that has been created in order to describe aircraft intent information in a rigorous, unambiguous and flexible manner. It has been implemented in a platform for a modular design of the trajectory generation process. A case study is presented to explore its effectiveness and identify the requirements and needs to generate optimised aircraft intents with higher automation and flexibility. Preliminary results show the suitability of numerical optimal control to design optimised aircraft intents based on the aircraft intent description language.

Implementation Vision for Distributed Control of Traffic Signal Subsystems

1996 ◽  
Vol 1554 (1) ◽  
pp. 43-47
Author(s):  
Darcy Bullock
Keyword(s):  
Distributed Control ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Adaptive Algorithms ◽  
Intelligent Transportation ◽  
Traffic Signal ◽  
Emergency Vehicle ◽  
Systematic Method ◽  
Traffic Management System ◽  
Advanced Traffic Management System

The developments that have led to the construction of the 2070 controller are reviewed. The intelligent transportation system community has proposed many features and user services that will likely use this new controller. In general, many of the functions proposed for this controller, such as emergency vehicle preemption, transit priority, weather monitoring, dynamic lane assignment, enhanced malfunction diagnostics, and adaptive algorithms, are all technically feasible. To achieve widespread deployment of systems that integrate several advanced traffic management system features, however, a systematic method for integrating a variety of distributed computing subsystems must be thoughtfully defined. The fundamental benefits of adopting a distributed control model for traffic signal subsystems are described and summarized.

Simulation Laboratory for Evaluating Dynamic Traffic Management Systems

2000 ◽  
Vol 1710 (1) ◽  
pp. 122-130 ◽  
Author(s):  
Qi Yang ◽  
Haris N. Koutsopoulos ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Traffic Management ◽  
Transportation Systems ◽  
Management Systems ◽  
Route Guidance ◽  
Dynamic Traffic ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Simulation Based ◽  
Traffic Management System

Advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS) are promising technologies for achieving efficiency in the operation of transportation systems. A simulation-based laboratory environment, MITSIMLab, is presented that is designed for testing and evaluation of dynamic traffic management systems. The core of MITSIMLab is a microscopic traffic simulator (MITSIM) and a traffic management simulator (TMS). MITSIM represents traffic flows in the network, and the TMS represents the traffic management system under evaluation. An important feature of MITSIMLab is its ability to model ATMS or ATIS that generate traffic controls and route guidance based on predicted traffic conditions. A graphical user interface allows visualization of the simulation, including animation of vehicle movements. An ATIS case study with a realistic network is also presented to demonstrate the functionality of MITSIMLab.

scholarly journals Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning

2021 ◽  
Vol 13 (16) ◽  
pp. 8924
Author(s):  
Silvia Zaoli ◽  
Giovanni Scaini ◽  
Lorenzo Castelli
Keyword(s):  
Community Detection ◽  
Traffic Management ◽  
Air Traffic ◽  
Computational Time ◽  
Acceptable Solution ◽  
Flight Planning ◽  
Detection Algorithms ◽  
Traffic Management System ◽  
Definition Of ◽  
The Cost

An environmentally and economically sustainable air traffic management system must rely on fast models to assess and compare various alternatives and decisions at the different flight planning levels. Due to the numerous interactions between flights, mathematical models to manage the traffic can be computationally time-consuming when considering a large number of flights to be optimised at the same time. Focusing on demand–capacity imbalances, this paper proposes an approach that permits to quickly obtain an approximate but acceptable solution of this problem. The approach consists in partitioning flights into subgroups that influence each other only weakly, solving the problem independently in each subgroup, and then aggregating the solutions. The core of the approach is a method to build a network representing the interactions among flights, and several options for the definition of an interaction are tested. The network is then partitioned with existing community detection algorithms. The results show that applying a strategic flight planning optimisation algorithm on each subgroup independently reduces significantly the computational time with respect to its application on the entire European air traffic network, at the cost of few and small violations of sector capacity constraints, much smaller than those actually observed on the day of operations.

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