Optimization of dedicated bus lane location on a transportation network while accounting for traffic dynamics

Bayrak, Murat; Guler, S. Ilgin

doi:10.1007/s12469-021-00269-x

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…In this study, the bi-level methodology used in Bayrak and Guler ( 20 ) is used to evaluate the network. In this optimization framework, the lower level evaluates a set of implementation locations of DBLs by using link transmission model (LTM) to estimate total travel time of network users.…”

Section: Network Evaluation Methodologymentioning

confidence: 99%

“…Transportation planning and management involve countless decisions that can have effects that span the entire network. Because of the importance of these decisions, many researchers have studied methods that can identify the optimum decisions for a given problem on a network such as traffic signal coordination (1,2), traffic signal timing (3,4), toll pricing (5)(6)(7)(8), bus fare (9), network design (10,11), transit network design (12,13), and bus lane placement (14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Specifically, the dedicated bus lane (DBL) implementation location selection problem has been formulated as a combinatorial optimization problem in the literature (14)(15)(16)(17)(18)(19)(20)(21)(22).…”

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confidence: 99%

“…The upper level of these methods determines the optimal combination of bus lane locations to minimize total travel time, while the lower level evaluates the network to determine the travel times. The upper level of these models is solved using Benders' decomposition (21), branch and bound (19), or genetic algorithm (GA) (14)(15)(16)(17)(18)20), whereas the lower level is typically solved with the Akcelik or Bureau of Public Roads cost functions. The use of metaheuristic algorithms at the upper level can often find reasonably good solutions that can be used for planning and decision-making purposes.…”

mentioning

confidence: 99%

“…Because of the importance of these decisions, many researchers have studied methods that can identify the optimum decisions for a given problem on a network such as traffic signal coordination ( 1 , 2 ), traffic signal timing ( 3 , 4 ), toll pricing ( 5 – 8 ), bus fare ( 9 ), network design ( 10 , 11 ), transit network design ( 12 , 13 ), and bus lane placement ( 14 – 23 ). Specifically, the dedicated bus lane (DBL) implementation location selection problem has been formulated as a combinatorial optimization problem in the literature ( 14 – 22 ). Typically, a bi-level methodology is used.…”

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confidence: 99%

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Linkage Problem in Location Optimization of Dedicated Bus Lanes on a Network

Bayrak

Guler

2023

Transportation Research Record: Journal of the Transportation R

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Methods for identifying optimal decisions for dedicated bus lane locations (DBLs) on a network have been extensively studied in the literature. However, the impacts in relation to changes to car and bus delays of deploying a DBL on a given link largely depend on where other DBLs exist on the network. Therefore, for a network-wide location optimization or a bus lane design problem, linkages exist between decision variables. Typically used metaheuristic methods to optimize DBL locations, such as genetic algorithms (GAs), do not perform well for such problems with linkages between decision variables. To this end, this paper has two novel contributions to the literature by (a) demonstrating that the linkage problem exists, and (b) testing different heuristic algorithms that are more suitable than GAs for optimizing the locations of DBL on a network. The linkage problem in the location optimization of DBLs is demonstrated by enumerating all possible bus lane locations in a small grid network. Next, optimization algorithms that do not enumerate all possible bus lane locations that are capable of learning linkages between decision variables, namely Bayesian algorithm and a population-based incremental learning algorithm, are proposed. These algorithms are compared with two types of GAs in relation to consistency and quality of the solutions, and exploration capability. Results show that algorithms that can learn linkages between decision variables perform better than the GAs.

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Section: Network Evaluation Methodologymentioning

confidence: 99%

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confidence: 99%

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Linkage Problem in Location Optimization of Dedicated Bus Lanes on a Network

Bayrak

Guler

2023

Transportation Research Record: Journal of the Transportation R

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“…When the penetration of CAVs is low, dedicated lanes for CAVs cannot be used efficiently and can even harm the overall performance of the transport network. Currently, bus lanes are widely used, but many of these lanes are located in older urban areas, where the number of lanes is limited and demand is high during commuting hours, making these hours "no or few buses in dedicated lanes and large queues in other lanes" [16,17]. The allocation of separate rights of way for CAVs based on existing bus lanes is a reasonable option for the design of transport networks in a connected environment.…”

Section: Introductionmentioning

confidence: 99%

Research on the Deployment of Joint Dedicated Lanes for CAVs and Buses

Luo¹,

Du²,

Jia³

et al. 2022

Sustainability

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CAVs (Connected Autonomous Vehicles) can be effective in improving the efficiency of transportation, but heterogeneous multi-modal traffic flows may hinder this efficiency. This paper addresses the issue of heterogeneous traffic flows affecting the efficiency of transportation when CAVs enter the market, and proposes a joint dedicated lane for CAVs and buses. In the bi-level program model for the joint dedicated lane, the lower-level is aimed at the multi-modal traffic assignment problem, while the upper-level is aimed at system optimality. For the lower-level, the paper examines the characteristics of various traffic flows in a mixed traffic flow, investigates the impact of CAV mixing on the road link’s capacity, calculates the travel time of various traffic modes accordingly, and generates a generalized travel cost function for each mode, which is solved using the diagonalized weighted successive averaging method (MSWA) algorithm. The upper-level issue considers the continuity of dedicated and non-dedicated road segments, and the goal is to reduce the overall cost for all travelers by utilizing the dedicated road deployment scheme as the decision variable, which is addressed using a genetic algorithm. Finally, numerical examples and sensitivity analyses are designed accordingly. The numerical example demonstrates that the joint dedicated lane not only lowers the overall cost of the system, but also enhances the efficiency of CAV and bus travel, optimizing the road network and promoting bus and CAV travel modes. The sensitivity analysis shows that in order to set up a joint dedicated lane, the frequency of bus departures and the penetration of CAVs are conditions that must be considered, and that the benefits of a joint dedicated lane can only be fully realized if the frequency of bus departures and the penetration of CAVs are appropriate.

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