Dynamic Right-of-Way for Transit Vehicles: Integrated Modeling Approach for Optimizing Signal Control on Mixed Traffic Arterials

Author(s):  
Peter A. Duerr

Public transit and general traffic on many urban arterials are controlled by the same set of signals and must compete for shared road space. In these situations, transit vehicles typically face considerable delays because their dwell times at transit stops remove them from the coordinated green wave for general traffic flow. Although existing control systems allow for local adjustments of signal timings to provide transit priority, these short-term actions often contradict the network control scheme and may preclude a priority scheme or significantly disrupt traffic flow. A new concept for a corridor control system is introduced—the dynamic right-of-way, which serves the demands of public transit and general traffic using an integrated model for evaluation and optimization. The control system is intended to ( a) reduce critical interferences between both modes of transport by dynamically controlling inflow and outflow for all network links, ( b) provide a green signal whenever a transit vehicle approaches an intersection, and ( c) minimize general traffic disruption by maintaining overall signal coordination. Through linking an event-based simulator with a genetic algorithm-based optimization routine, delay-minimizing multicycle signal control schemes are calculated. In offline experiments, the potential for achieving substantial reductions in delays is demonstrated. Finally, a method is presented by which these control schemes are implemented and adjusted dynamically, based on online measurements and a control modification function derived from a neural network model.

2013 ◽  
Vol 846-847 ◽  
pp. 1608-1611 ◽  
Author(s):  
Hui Jie Ding

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.


Author(s):  
A. K. Kanaev ◽  
◽  
A. N. Gorbach ◽  
E. V. Oparin, ◽  
◽  
...  

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 363
Author(s):  
Chii-Dong Ho ◽  
Yih-Hang Chen ◽  
Chao-Min Chang ◽  
Hsuan Chang

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).


Sign in / Sign up

Export Citation Format

Share Document