
A properly designed public transport system is expected to improve traffic efficiency. A high-frequency bus service would decrease the waiting time for passengers, but the interaction between buses and cars might result in more serious congestion. On the other hand, a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars. It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users. It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices. However, as far as the authors know, most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function, and do not properly consider the congestion dynamics and their impacts on mode choices. To fill this gap, this paper proposes a bi-level optimization model. A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics. A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm. A surrogate model-based algorithm is used to solve the bi-level programming problem.
surrogate model-based algorithm, Transportation engineering, bi-level programming model, QA76.75-76.765, TA1001-1280, three-dimensional macroscopic fundamental diagram, double projection algorithm, dynamic traffic assignment, Computer software
surrogate model-based algorithm, Transportation engineering, bi-level programming model, QA76.75-76.765, TA1001-1280, three-dimensional macroscopic fundamental diagram, double projection algorithm, dynamic traffic assignment, Computer software
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
