In the face of urbanization and limited space, traffic management grapples with challenges, especially heavy left-turn volumes at signalized intersections. The pre-signal strategy, proposed by Li et al. (2014), emerges as a promising solution. It involves an additional traffic li
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In the face of urbanization and limited space, traffic management grapples with challenges, especially heavy left-turn volumes at signalized intersections. The pre-signal strategy, proposed by Li et al. (2014), emerges as a promising solution. It involves an additional traffic light upstream of the main intersection, creating a "sorted area" where vehicles distribute before reaching the main intersection. Although simulation studies show increased intersection capacity with pre-signals, behavioral assumptions about drivers' spread behavior in the sorted area lack validation from behavioral science.
This study investigates drivers' behavior in pre-signalized intersections, addressing how their lane selection downstream of pre-signals impacts efficacy. The main research question is: How does drivers' lane selection downstream of pre-signals impact the efficacy of pre-signal implementation for regular traffic? A stated-choice experiment involved over 1000 respondents making preferred lane choices in diverse traffic conditions. Analysis using a MultiNominal Logit (MNL) choice model reveals crucial insights.
Results indicate that pre-signals don't alter drivers' perceptions of traffic crowdedness. However, discomfort with lane changes emerges as a critical factor influencing efficiency. Drivers strongly hesitate to execute multiple lane changes, posing challenges for achieving an equal spread in the sorted area.
Two implementation options are discussed: using the entire sorted area for all traffic or dedicating parts to specific traffic directions. Each option has efficiency and safety trade-offs. Designing a pre-signal system achieving both an equal spread and using the entire sorted area for all traffic proves unfeasible.
The study emphasizes integrating behavioral science into pre-signal studies, offering valuable insights for traffic engineers. It lays the groundwork for understanding pre-signals, highlighting the imperative for continued research and a holistic approach to their implementation.