Modeling and optimization of dedicated bus lanes space allocation in large networks with dynamic congestion

Dedicated bus lanes provide a low cost and easily implementable strategy to improve transit service by minimizing congestion-related delays. Identifying the best spatial distribution of bus-only lanes in order to maximize traffic performance of an urban network while balancing the trade-off between bus priority and regular traffic disturbance is a challenging task. This paper studies the problem of optimal dedicated bus lane allocation and proposes a modeling framework based on a link-level dynamic traffic modeling paradigm, which is compatible with the dynamic characteristics of congestion propagation that can be correlated with bus lane relative positions. The problem is formulated as a non-linear combinatorial optimization problem with binary variables. An algorithmic scheme based on a problem-specific heuristic and Large Neighborhood Search metaheuristic, potentially combined with a network decomposition technique and a performance-based learning process for increased efficiency, is proposed for deriving good quality solutions for large-scale network instances. Numerical application results for a real city center demonstrate the efficiency of the proposed framework in finding effective bus lane network configurations; when compared to the initial network state they exhibit the potential of bus lanes to improve travel time for car and bus users.

Combinatorial optimization of Dedicated Bus Lane layout in urban networks with dynamic congestion

Nikolaos Geroliminis, Anastasios Kouvelas, Dimitrios Tsitsokas

Transit priority based in exclusive right-of-way is a low-cost way of improving transit service by minimizing delays caused by interaction with other vehicles. This effect can increase the share of public transit against private cars in the mode preferences of commuters and consequently alleviate heavy congestion resulting from the dominance of single-occupancy cars. In this work we propose a modeling and optimization framework for the problem of Dedicated Bus Lanes (DBL) location selection in bi-modal urban networks with time-varying traffic congestion. Traffic flow is replicated by dynamic macroscopic traffic model with queueing characteristics instead of steady state models, to capture potential spill-back effects caused by poor DBL planning. A combinatorial optimization problem is formulated aiming at minimizing total passenger delay. Optimization is performed by Local Search (LS) and Neighborhood Search (NS) algorithms, while a network decomposition technique is proposed for improved computational cost. The results show the proposed algorithms effective in significantly improving an initial DBL plan with reasonable computational cost.

2020

An optimization framework for exclusive bus lane allocation in large networks with dynamic congestion

Nikolaos Geroliminis, Anastasios Kouvelas, Dimitrios Tsitsokas

Road space distribution among multiple modes of transport in urban networks has attracted the interest of several researchers and policy planners. Traditionally, these problems are solved by ignoring operational and dynamic characteristics of multi-modal congestion due to the computational burden involved. Revealing the relation between road space share and global performance of the network can lead to more efficient transport system design. The introduction of dedicated bus lanes in some parts of the network has been proposed as a measure to allow high occupancy vehicles to travel through regions with high traffic load without long delays. In this way, the bus transit system operates more efficiently and is competitive to the private car option in terms of travel time. This effect can stimulate a significant mode shift from car to bus and help alleviate congestion by reducing the number of low occupancy vehicles. In this work we address the problem of optimal allocation of exclusive bus lanes in a multi-modal urban network of fixed total road infrastructure and passenger demand, with the aim of minimizing the total passenger hours travelled (PHT) for all travel modes, within a framework which is consistent with the dynamics of congestion. A queueing theory based traffic flow model with proper treatment of spillbacks and traffic signal settings, known as the Store-and-Forward (SaF) model, is extended to simulate the dynamics of congestion inside the network by keeping track of the queues inside all links over time. The SaF simulation model can be used to evaluate the performance of different allocation policies.

2019

Dynamic lane restrictions on congested arterials

Paul Richard Anderson, Nikolaos Geroliminis

Buses operating in mixed traffic experience problems with delay and travel time variability in peak periods. Numerous transit priority measures are found in the literature but most rely on separating buses from other vehicles. Complete separation such as dedicated bus lanes might underutilize the system capacity when the frequency of buses is not high. We propose a "dynamic bus lane policy", where control adjusts the proportion of cars to buses in one lane of a multi-lane arterial instead of enforcing complete separation of vehicle types. This strategy is analyzed with aggregated macroscopic dynamics for different models of lane choice. It is shown to benefit both cars and buses by maintaining higher outflow during the peak period and helping the system to recover more quickly at the end of the peak. Greater reductions in travel time are possible with increased information available to vehicles. Analytical derivations and non-linear optimization are then used to obtain the system optimal allocation of cars, as well as the toll needed to achieve it. The system optimal tolling would be difficult to implement in practice. Another optimization which includes tolls paid in its objective is nearly as good at reducing vehicle hours traveled and produces a much simpler pricing structure amenable to feedback control. A microsimulation with feedback control confirms the results of the macroscopic model and shows additional benefits attributable to driver behavior. Future work will look into varying control parameters to achieve a global minimum travel time and will incorporate user heterogeneity to test pricing strategies.

PERGAMON-ELSEVIER SCIENCE LTD2020