Abstract
SUMMARY Precise estimation of the capacity for right-turn traffic (comparable to left-turn traffic in the USA) is of great importance to determine signal phasing schemes at signalized intersections in Japan, where the left-hand driving rule is valid. However, in most signal timing procedures across the world, the lost time of right-turn traffic is simply determined by the duration of intergreen intervals and thus lacks considerations of various signal phasing and driver behavior. Meanwhile, sneakers per cycle are usually applied to account for the number of drivers completing right turns during the effective red portion of the clearance-and-change intervals. As a result, an initial cycle length must be hypothesized in order to assess the total number of sneakers within the analysis period. Consequently, a time-consuming iterative calculation process often becomes necessary. Therefore, the present study aims to develop a new lost time estimation method for right-turn traffic to overcome the aforementioned drawbacks. Lost times of right-turn traffic under three conventional phasing plans are theoretically formulated on the basis of a time-space diagram and shock-wave theory. The new method is validated using field data, with case studies of its application in the signal timing procedure. Results indicated that the proposed method is capable of offering more accurate estimation than conventional approaches, which leads to shorter cycle length and simplifies signal timing process by eliminating an iterative check to determine the number of sneakers.
Original language | English |
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Pages (from-to) | 704-719 |
Number of pages | 16 |
Journal | Journal of Advanced Transportation |
Volume | 47 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2013 Dec |
Keywords
- lost time
- right-turn traffic
- shock-wave theory
- sneakers
- time-space diagram
ASJC Scopus subject areas
- Automotive Engineering
- Economics and Econometrics
- Mechanical Engineering
- Computer Science Applications
- Strategy and Management