Citation

Bauer T, Medema MP, Jayanthi SV. Transp. Res. Rec. 1995; 1494: 155-160.

Copyright

(Copyright © 1995, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

unavailable

PMID

unavailable

Abstract

The Chicago Central Area Circulator (CAC) is a light rail transit (LRT) system scheduled to serve downtown Chicago by the year 2000. It will operate in its own travel lane parallel to automobile traffic; however, it will interfere with other surface transportation modes at intersections. The traffic and train signal system controlling the interface will be crucial for the successful performance of all modes. The signal control strategy must balance the needs of LRT, buses, automobiles, and pedestrians. For this reason, three LRT priority control strategies were developed. The approach used to analyze train and automobile traffic performance for each of these strategies is described. The CAC design team simulated LRT operation, automobile traffic flow, and intersection control units (ISCs) as the interface between the two modes for all three control strategies. Two different microscopic modeling tools performed the simulation. TransSim II (registered trademark of James R. Hank dba JRH Transportation Engineering) was selected for the transit and signal controller simulation because it realistically models LRT operation. TransSim II (trademark) can also simulate priority strategies, which include arrival time estimation capability for trains and two-way communication between trains and ISCs. TRAF-NETSIM was selected for the traffic flow simulation because of its ability to reproduce traffic conditions, such as individual vehicles, queuing impacts, and potential spillbacks across adjacent intersections. The interface between the simulation programs is signal phasing and timing. This information calculated by TransSim II (trademark) was read into TRAF-NETSIM. The two simulation processes yielded LRT performance measures of speed, travel time, and delay statistics, and automobile performance measures of delay, queue lengths, and spillbacks. This allowed the design team to choose the most appropriate signal control strategy to provide the best overall system performance.

Language: en

Keywords

Automobiles; Intersections; Speed; Light rail transit; Performance; Computer simulation; Roads and streets; Computer software; Railroad rolling stock; Railroad signal systems