A newly proposed unconventional intersection design, exit lanes for left-turn (EFL) intersections, has been demonstrated to be very promising for capacity improvement. However, the difference in saturation flow rates at EFL intersections compared with conventional intersections and the influencing factors remain unknown. In this paper, a saturation flow rate adjustment model for EFL control is developed and calibrated based on field data. Five influencing factors, namely the median opening blockage, demand starvation, multilane interference, conflict with opposing vehicles, and lane changing, are considered. The accuracy of the model is validated using nonparametric tests. Results show that the reduction in saturation flow rate for the normal approach left-turn lanes, the left-turn lanes in the mixed-use area, and the left-turn lane at the prestop line caused by EFL control are 6%, 31%, and 19%, respectively. The key impact factors for the saturation flow rate at the main-stop line and the prestop line are the demand starvation (16% reduction) and lane changing (16% reduction), respectively. This research provides guidelines for planning, design, and operation of EFL intersections to prevent negative effects while retaining their efficiency.
The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.
10 March 2021 in Transportation Research Record: Journal of the Transportation Research Board
In most right-driving urban signalized intersections, right-turn vehicle signals do not usually control turns. To address the problem of signal control in a pedestrian–vehicle interaction, this paper establishes a right-turn signal optimization (RTSO) model that considers both efficiency and safety. First, the main factors influencing the behavior of vehicle and pedestrian during pedestrian–vehicle interaction are analyzed, and a pedestrian–vehicle interaction model (PVI model) at an urban road crosswalk is established. This model is used to determine the probabilities of four pedestrian–vehicle interaction situations. Then, based on the traffic conflict theory, the next step was to construct an objective function that minimizes the total delay of traffic participants considering pedestrian–vehicle interactions, and another objective function that minimizes the potential conflicts considering pedestrian–vehicle interactions. Then, an RTSO model is obtained by introducing a safety-efficiency coefficient to combine the previously described two constructed functions. Finally, the PVI model and delay model are verified through video observation data and the establishment of a cellular automata simulation platform of pedestrian–vehicle interaction. Using these models, a field signal plan, the delay minimization scheme, the conflict minimization scheme, and the proposed scheme are numerically analyzed under different yielding rates. This proposed scheme is further numerically analyzed under different safety-efficiency coefficients. The results show that this paper’s RTSO model has certain advantages in increasing safety and reducing delay. In addition, using these results, this paper gives a recommended value for the safety-efficiency coefficients in different application scenarios.
The performance of intersections has been considered a key factor in measuring the efficiency of urban road systems. In this paper, a reliability model for a two‐phase signalized intersection is proposed on the basis of presenting a concept of traffic function reliability (TFR). First, classic cumulative curves are created to derive delay formulas. Then, a model for traffic function reliability is proposed based on the quantitative relationship between the random traffic flows, signal timing, and queue lengths. Finally, the delay threshold of the intersection is determined by referring to the level of service. A numerical simulation has been created to clarify the proposed mechanism of TFR. The results show that the saturation and the green time ratio have a dramatical influence on TFR. Under different saturation levels, the sensitivity of TFR to changes in green time ratio gradually weakened. When the green signal ratio increases above a certain value, TFR remains nearly constant. A microscopic simulation verified the applicability of the proposed model. The results show that the accuracy of the model is close to 90% in the case of low saturation. This method provides road authorities useful insights to understand travel time variability.
11 January 2021 in Journal of Ambient Intelligence and Humanized Computing