From Particle Hopping Models to Traffic Flow Theory

1998 ◽  
Vol 1644 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Kai Nagel
Keyword(s):  
Traffic Flow ◽  
Flow Theory ◽  
Traffic Jam ◽  
Traffic Flow Theory ◽  
Hopping Models ◽  
Simple Models

Very simple models of particles hopping on a grid appear too simple to have much similarity to traffic. Yet, some of these models can be proved to generate, in the so-called fluid-dynamical limit, variations of the Lighthill-Whitham theory. For more realistic particle hopping models, the fluid-dynamical limit is not known, but insight can be obtained by observing traffic jam dynamics.

scholarly journals Particle hopping models and traffic flow theory

1996 ◽  
Vol 53 (5) ◽  
pp. 4655-4672 ◽  
Author(s):  
Kai Nagel
Keyword(s):  
Traffic Flow ◽  
Flow Theory ◽  
Traffic Flow Theory ◽  
Hopping Models

A Traffic Capacity Model of Lane Occupation

2014 ◽  
Vol 599-601 ◽  
pp. 2083-2087
Author(s):  
Yi Xuan He
Keyword(s):  
Traffic Flow ◽  
Queue Length ◽  
Production Condition ◽  
Modern Society ◽  
Traffic Jam ◽  
Traffic Flow Theory ◽  
Related Data ◽  
Traffic Capacity ◽  
Capacity Model ◽  
Big City

In modern society, traffic jam has already become a major problem which curbs the development of big city. And lane occupation is an important reason why traffic jam happens. After studying on the production condition, time and queue length of traffic jam after lane occupation happens, we propose a model based on famous traffic flow theory and we use related data to verify the rightness of our model. Result shows that our model can predict the development of traffic jam caused by lane occupation

Traffic Flow Theory

1983 ◽  
pp. 207-217
Author(s):  
Donald A. Drew
Keyword(s):  
Traffic Flow ◽  
Flow Theory ◽  
Traffic Flow Theory

Linking Incidents to Customers (LINC): An Algorithm for Linking Incidents to Rail Customer Delays Inspired by Traffic Flow Theory

2021 ◽  
pp. 036119812110548
Author(s):  
Michael Eichler
Keyword(s):  
Traffic Flow ◽  
Business Processes ◽  
Flow Theory ◽  
Incident Response ◽  
Traffic Flow Theory ◽  
Root Cause ◽  
Rail Systems ◽  
Transit Agencies ◽  
Customer Delay ◽  
Impact Zones

Rail transit agencies have greatly advanced the ability to measure delays to rail system customers and have developed key performance indicators for rail systems based on customer travel time. The ability for operators to link these customer delay metrics to root causes would provide great benefit to agencies, from incident response improvement to capital program prioritization. This paper describes a method for linking late train arrivals to both late customers and incident tickets. Inspired by traffic flow theory, the method identifies impact zones in time and space that can then be linked to a potential root cause by way of incident tickets. This algorithm is currently under development by the Washington Metropolitan Area Transit Authority’s Office of Planning, and its outputs are being integrated into a variety of operations- and capital-related business processes.

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