Optimization Method of the Cycle and Critical Phase on Signal Control Intersection

Currently, in China, the single-objective optimization methods are commonly used for road intersection signal control, which has one single objective such as minimizing the number of stops, average delays, or queuing length. When the intersection traffic varies large with the time of day, using this method for intersection traffic control will always cause poor adaptability and low efficiency. To solve these problems, a multi-objective optimization method for signal control of single intersection is proposed, with the composite optimization objectives to minimize the average delay, the least number of stops, and the shortest vehicle queue length, which can set preferences of various optimization objectives to adapt to different traffic conditions. The method uses the Pareto multi-objective evolutionary algorithm to optimize signal timing parameters to get the best signal control plan. Simulation results show that this method can significantly improve the adaptability and efficiency of the intersection traffic control.

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

The capacity of intersection affects the maximum capacity of road to a certain extent. The traffic capacity of the intersection can be improved to some extent by optimizing and adjusting the parameters of the signal timing model. Taking the cross-type intersection at the intersection of Minzhu South Road and Fengshou Road in Jiaozuo City as the research background, this study optimizes the signal timing design for the current intersection, optimizes the signal timing design by using Webster algorithm, and conducts simulation by using traffic simulation software VISSIM. The results showed that the average delay at the intersection was reduced by nearly seven seconds.

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.

This work aims to minimize average delay for signalized traffic network along Jalan UMS under oversaturated condition using decentralized genetic algorithm (DGA). Relieving traffic network is a key challenge for a nation for improving its socio-economic systems and welfare to society. Control the traffic signal timing becomes a cost effective solution to reduce congestion since the space constraint limits the authority to build more infrastructures in city. Regrettably, most of the current traffic signal control systems are not yet optimized. They are preset based on historical traffic data; thus they are inadequate to cope with the dynamic changes in real traffic scenario. Therefore, this work proposes DGA to optimize the traffic network signal for reducing average delay during the morning peak hour. A comprehensive traffic model based on Public Works Department, Malaysia has been developed as the platform. The average delay experienced by vehicles to traverse the crossed intersection during morning peak hour is used as the performance metric to evaluate performances of the proposed algorithm. Simulation results show developed DGA is able to reduce the average network delay by optimizing the traffic signal.

In this topic, bi-level optimization methods based on non-deterministic approaches are presented with emphasis on solving process synthesis problems, in large-scale problems, where determinist approaches can lead to non-optimal solutions, with huge computational effort. When integer and real variables are present, bi-level approaches can present good solutions in reasonable computational times. Normally, binary variables are used when decisions must be taken, such as whether to use a piece of equipment, whether a process stream must be split, etc. Real variables are the other design variables. Some case studies are used to show the applicability of these methods. The chosen examples are related to process synthesis and heat integration processes, with distinct applications.

Traffic congestion has become an inevitable problem in modern society, and the signal control of intersection plays an important role to ensure the effective operation of urban transport.The paper proposes an improved actuated signal control model to improve the running efficiency of intersection.In order to verify the effectiveness of the improved algorithm，the paper uses VISSIM to simulate the operating condition of different durations under timing control, actuated signal control and improved actuated signal control based on actual traffic flow, signal timing and other data of intersection.The simulation result shows that travel time and delay time are both reduced under the control of actuated signal in different time periods.And the improved actuated signal control is more effective than other signal controls.

For an actuated signalized intersection, pedestrian calls are likely to affect the effective greens serving the vehicle movements, which affect the capacity and delay of the intersection. However, the current procedure in the Highway Capacity Manual 2000 (HCM) for analyzing actuated signalized intersections treats pedestrian crossing and timing statically, with either pedestrian calls at all signal cycles or no pedestrians at all. In reality, pedestrian arrivals are random events with some cycles having more pedestrians than others and other cycles having no pedestrian call at all. This paper demonstrates that the current procedure can lead to erroneous results in capacity and delay estimations. A model is introduced to overcome the shortcomings in the current procedure. The model takes into account the stochastic nature of pedestrian crossings and their effects. The model computes the probability of having pedestrian calls in a cycle and the corresponding capacities and delays for traffic movements. An implementation framework was developed to help practitioners conduct capacity analyses using the model. The model's results on a semiactuated signal-controlled intersection were comparable with the results from the SimTraffic microsimulation model. The effects of pedestrians on intersection capacity and delay were analyzed using the proposed model. Depending on the pedestrian volume and traffic conditions, the current HCM procedure could produce significant error, especially when the pedestrian volume is low, because it does not consider the stochastic nature of pedestrian arrivals.

SUMMARYThis paper presents an integrated model for optimizing lane assignment and signal timing at tandem intersection, which is introduced recently. The pre‐signal is utilized in the tandem intersection to reorganize the traffic flow; hence, the vehicles, regardless of whether left‐turns or through vehicles, can be discharged in all the lanes. However, the previous work does not consider the extra traffic disruption and the associated delay caused by the additional pre‐signal. In the paper, the extra delay aroused by the coordination is incorporated in a lane assignment and signal timing optimization model, and the problem is converted into a mixed‐integer non‐linear programming. A feasible directions method is hence introduced to solve the mixed‐integer non‐linear programming. The result of the optimization shows that the performance of the tandem intersection is improved and the average delay is minimized. The comparison between the tandem and the conventional configuration is presented, and the results verify that the former shows better performance than the latter. In addition, the optimal sequence corresponding to the turning proportion and the optimal lane assignment at the upstream approach of the pre‐signal are presented. Furthermore, if the number of lanes is equal in all arms, the paper proves that the average delay will be reduced if lane assignment is proportional to the turning proportion and the vehicles with low proportion are discharged in advance. Copyright © 2013 John Wiley & Sons, Ltd.

Traditionally, a signalized intersection capacity can't be discussed without putting signal timing into consideration. In general, signal timing includes determination of cycle length, number of phases, green interval, yellow time and all red. A result capacity varies with changing of parameters in signal timing. This is the traditional way of estimating capacity, named timing-based capacity calculation approach. However, at the planning stage, if planners or decision makers want to find out the future conditions of the network they have planned, they need to evaluate the performance in terms of v/c ratio because of undetermined capacity of intersection. A new concept of so-called "equilibrium-right-of-way based capacity" is introduced in this paper to overcome this difficulty. This capacity is purely based on the geometrics of an intersection (number of lane and lane allocation) rather than the signalization. The purpose for introducing this concept is to assume capacity of an intersection to be a sole value when the geometrics of the intersection are set. Uniqueness of capacity for interrupted flow facility should be the key of capacity studies. With this concept, capacity formula based upon the equilibrium-right-of-way is developed to estimate the capacity of signalized intersections. The result from this study is great significance for the development of capacity analysis, particularly for the interrupted flow facilities.

Bi-level optimization design strategy for compressed air energy storage of a combined cooling, heating, and power system

Urban intersections often face the dual challenge of managing high traffic volumes while ensuring pedestrian safety. This study investigates whether road diet strategies—reducing travel lanes while reallocating space for pedestrians—combined with signal timing optimization, can improve both vehicle throughput and safety at complex urban intersections. The North Washington Street corridor in Boston, including City Square and Keany Square, was selected due to its congestion and long pedestrian crossing distances. Data were collected via sensors and manual counts in December 2022. Microscopic simulations were conducted in PTV Vissim, calibrated to field observations within ±5% accuracy. The intervention included lane reductions, a new northbound receiving lane, pedestrian refuge islands, split crossings, and revised signal timing using VisVAP. Results show average vehicle delays decreased from 171.97 to 31.12 veh-s at City Square and from 146.60 to 64.88 veh-s at Keany Square. Queue lengths dropped by over 50%, and intersection capacities rose by 34.59% and 24.98% respectively. Pedestrian exposure times were cut significantly, particularly at long crossings. These findings challenge conventional assumptions about road diets limiting capacity. Instead, the combined approach fostered safer and more efficient intersections, suggesting that balanced urban mobility can be achieved through thoughtful design. The study provides a replicable model for cities pursuing Vision Zero and sustainable mobility goals.

Due to lack of public transportation, economic growth and the rapid increase in the number of populations, the number of vehicles in Erbil city has recently increased dramatically. This increment caused congestion on the roads and posed most of the loads on the traffic intersections, which already have limited capacity. As the intersection volume increases, the degree of saturation (X) and delay increases and, as a consequence, the Level-of Service (LOS) decreases, leading to instability and platoon formation at the intersection approaches. One of the reasons of congestion at intersections is due to the fixed signal timing during the day, while the traffic volume at the intersections varies from one hour to another. This research suggests solutions to vehicle delays at intersections through using a Genetic Algorithm (GA) based optimization model to adjust the signal parameters to fit the real time traffic condition. The suggested model tries to optimize the cycle length besides the green time per phase for different time lags throughout the day. The results showed distinct and remarkable improvements in the intersection capacity with 2 to 13 % along with an average delay of 28- 64 %.

Multi-modal traffic signal control with priority, signal actuation and coordination