Vehicle Delay Research Articles

Multi-objective optimization of traffic signals based on vehicle trajectory data at isolated intersections

Existing fixed-time traffic signal optimization methods mainly use traffic volumes collected by infrastructure-based detectors (e.g., loop detectors). These infrastructure-based detectors generally have high maintenance costs and low coverage. With the deployment of probe vehicles, vehicle trajectory data provide more information about traffic states and can be utilized for signal timing. However, most related studies assume high penetration rates of probe vehicles or short sampling intervals. This paper develops a hierarchical multi-objective optimization framework to optimize fixed-time traffic signals based on sampled vehicle trajectories at isolated signalized intersections, which is applicable to low-resolution trajectory data. Cycle length and green splits are optimized under both under- and slightly over-saturated traffic conditions. The number of over-saturated phases and average vehicle delays are adopted as the primary and the secondary objectives, respectively. Note that the queues of over-saturated phases cannot be discharged. The queue length and traffic delay of over-saturated phases will go infinite as time goes. The consideration of the over-saturated phase number helps increase vehicle throughput and reduce queue length and traffic delay. The aggregation of sampled trajectory data during the same period across multiple cycles and Same-ratio Principles (SRPs) are proposed to compensate for the limitations of low penetration rates of probe vehicles. The evolution of sampled trajectories with varying signal timings are formulated explicitly. A sampled-trajectory-density method is proposed to identify over-saturated phases. Then a mixed integer non-linear programming (MINLP) model is formulated and transformed to a series of mixed integer linear programming (MILP) models by linearization and enumerating cycle lengths. Simulation studies validate the advantages of the proposed model over the one in Synchro Studio. Sensitivity analysis shows that: (1) the proposed model is applicable to Poisson vehicle arrivals; (2) the proposed model can handle sampling intervals as long as 15 s when sufficient sampled vehicle trajectories are collected; (3) the number of sampled trajectories has impacts on the performance of the proposed model instead of probe vehicle penetration rates, especially with under-saturated traffic; (4) cycle lengths of initial signal timing plans have no noticeable impacts on the required number of sampled trajectories when a short sampling interval is applied; and (5) the proposed model is insensitive to the quality of initial signal timing plans with under- and slightly over-saturated traffic. The proposed model is also implemented with field data to demonstrate its applicability in the real world.

A data-driven approach to calibrate microsimulation models based on the degree of saturation at signalized intersections

Microscopic traffic simulation is considered as a reliable tool in transportation planning and management. Rational solutions from such simulations are contingent upon how well the simulation software is calibrated and validated to replicate real-world road network scenarios. Most of the existing calibration and validation efforts are normally based on the comparative analysis between the built-in attributes of VISSIM and the real-world scenarios using measures of effectiveness (MOEs). VISSIM attributes such as the volume-to-capacity ratios, vehicle delay, and queue lengths, are normally used during the validation process of signalized intersections. However, validating VISSIM based on a non-inbuilt attribute is yet to be explored. This paper proposes a step-by-step procedure for calibrating signalized intersections in VISSIM based on a measurable variable, which is the degree of saturation. The approach was applied to a case study of four signalized intersections in Miami, Florida. The methodology utilized real-world vehicle trajectory data to determine the optimal values of VISSIM car-following parameters required for calibration. Statistical results revealed that both the saturation headways obtained from VISSIM and the saturation headways observed in the field follow the same distribution. The results signify that VISSIM could be calibrated using a non-inbuilt attribute, and moreover generates accurate data compared to the field measurements.

Space-time routing in dedicated automated vehicle zones

With the fast development of automated vehicle (AV) technologies, scholars have proposed various innovative local traffic control schemes for more effective management of AV traffic, especially at intersections. However, due to computational intractability, the investigation of network-level AV control is still at the initial stage. This study proposes a space-time routing framework applicable in dedicated AV zones. To relieve the computational load, we establish a node-based conflict point network to model realistic road networks, and at each conflict point, we record the space-time occupations of AVs in continuous timelines. Then, based on the conflict point network, we develop two space-time routing algorithms for each AV once it enters the dedicated AV zone to minimize its trip travel time while maintaining the non-collision insurances; these two algorithms can trade-off between solution quality and computational load. Furthermore, to enhance the network throughput for handling heavy traffic, we develop a ”platoon strategy” that forces AVs to pass through conflict points in platoons, and we adopt Deep Q-learning (DQN) to optimize the platoon sizes at different spots dynamically. Numerical tests show that both proposed algorithms perform well in that they can execute the routing tasks with very limited computational time, and the average vehicle delay approaches zero when the traffic is relatively mild. Meanwhile, compared with the FCFS policy and the optimization-based approach, the platoon strategy can greatly reduce the average vehicle delay under congested scenarios and give a better balance between the optimality and real-time performance.

Long-term profit for electric vehicle charging stations: A stochastic optimization approach

Electric vehicles are gradually finding their position as an alternative to fossil fuel vehicles. To accelerate the replacement process, a number of new infrastructural facilities such as charging stations (CS) have to be widely deployed. In order to encourage the private sector to invest in this area, it is necessary to introduce a combination of business and technical model for CS that comprises management tools for guaranteeing their long-term profit. Given that the price of electricity and the number of vehicles drive to the CS are random variables, the proposed model should consider all these uncertainties. In this paper, the desired model is introduced by utilizing stochastic optimization framework. Long-term Durable Profit for Charging Station (DPCS) algorithm is proposed to maximize the long-term profit of CS owner. By means of the DPCS algorithm, CS owner can decide on the electricity sales price, vehicle entrance and number of working pumps at each time interval. Simulation results show that the CS owner would have an increase of 107% in profit at a cost of incurring 80% more delay to vehicles. If a parking barrier is used in the CS, the achievable profit would be increased by 12% and the waiting delay of vehicles would be decreased by 25%.

Sustainable traffic management and control system for arterial with contraflow left-turn lanes

Nowadays, air pollution caused by traffic congestion has progressively drawn more and more attention. This paper proposes a traffic management and control platform from the perspective of sustainable development to reduce both vehicle emission and delay along arterial with contraflow left-turn lane (CLL). Unlike the traditional intersection layout and the control strategy, the existence of the contraflow left-turn lanes at intersections brings difficulties in setting phase sequences due to the complex interactions among upstream and downstream lanes. Furthermore, the queueing conditions are totally different from traditional intersections due to the complex queueing phenomenon between pre-signals and main signals. A macroscopic traffic model is proposed to model the intersection along arterial with CLL and is established on the basis of finite capacity queueing models. The optimization framework derives fixed-time traffic signal control plans, in which the green splits of the main signal and the pre-signal are explicitly considered. The nondominated sorting genetic algorithm II (NSGA-II) is employed to solve problem. A case study based on the field data of four intersections along the West Yan’an Road in the city of Shanghai is carried out to investigate the performance of the proposed framework. The microscopic simulator AIMSUN is used to evaluate and compare the travel delay and vehicle emissions obtained by the newly proposed signal plans and the signal plans derived by using traditional traffic control method. The simulation results indicate that the proposed framework successfully reduce travel delay and vehicle emission at intersections along arterial road with CLL.

BICYCLE LANES DEVELOPMENT PLAN IN MATARAM CITY

Mataram City Government established a bicycle lane at Udayana Street as an alternative for all community to shift their motorized transport by bicycle as a daily transport and to support the NTB Cycling program from NTB Provincial Government. To accommodate changing mode of transportation from motorbikes or cars to bicycles, it is necessary to develop bicycle lanes in Mataram City. Vissim software used to forecast the impact of bicycles lane development at 9 provincial roads as the study area. From the analysis after implementation of bicycles lane development, there is a decrease in level of service based on Volume per Capacity Ratio. In addition, at signal intersection show an increase in queue length and vehicle delays. To minimize the impact of bicycles lane development, mitigation is carried out by rearranging the intersection cycle time. And the result is a reducing of CO, NOx and VOC emissions, fuel consumption, vehicle queue lengths and vehicle delays.

Simulation-Based Evaluation of a New Integrated Intersection Control Scheme for Connected Automated Vehicles and Pedestrians

In a fully connected traffic environment with automated vehicles, new traffic control strategies could replace traditional traffic signals at intersections. In recent years, several studies about reservation-based intersection control strategies have been published, and a significant increase in capacity was shown. In the strategies presented so far, other road users usually play a minor role or are not considered at all. However, many use cases of automated driving occur in urban environments, where pedestrians and bicyclists play a major role. In this paper, a novel strategy for integrating pedestrians into automated intersection management is introduced and compared with a fully actuated traffic (AT) signal control. The presented control consists of a first-come, first-served strategy for vehicles in combination with an on-demand traffic signal for pedestrians. The proposed intersection control is explained, implemented, and tested on a four-leg intersection with several lanes coming from each direction. It dynamically assigns vehicles to lanes, and vehicles follow a protocol that enables cooperative lane-changing on the approach to the intersection. Demand-responsive pedestrian phases are included in such a way that predefined maximum pedestrian waiting times are not exceeded. A set of demand scenarios is simulated using a microsimulation platform. The evaluation shows that the presented control performs significantly better than the AT control when considering low, medium, and high traffic demand. Pedestrian waiting times are slightly improved and at the same time vehicle delays are substantially decreased. However, the control needs to be improved for scenarios with a very high vehicle demand.

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

A time-slot based signal scheme model for fixed-time control at isolated intersections

Fixed-time signal control is widely used in the real world due to its low cost of implementation. Repeated signal cycles with the same phase structure are applied in the existing fixed-time signal control schemes. However, such signal schemes do not always achieve the best performance, especially when traffic demand is unbalanced. The inclusion of sub-cycles in one cycle provides a potential solution in this situation. This study proposes a time-slot based signal scheme model with a generalized cycle structure for fixed-time signal control at isolated intersections. The proposed signal scheme can generate sub-cycles with different phase structures. It optimizes the number of sub-cycles in one cycle as well as the phase sequence, cycle length, and green splits in each sub-cycle. Certain phases may be skipped in partial sub-cycles. Nonlinear programming models are formulated to minimize average vehicle delay and maximize intersection capacity for under- and over-saturated demand. Solution algorithms for the two nonlinear models are developed by solving a series of mixed-integer-quadratic-programming (MIQP) models and mixed-integer-linear-programming (MILP) models, respectively. The numerical studies validate the advantages of proposed signal scheme in terms of intersection capacity and average vehicle delay. The sensitivity analyses show that: 1) a long maximum green time can enable the conventional dual-ring, eight-phase structure to handle unbalanced demand from the perspective of intersection capacity; 2) the time-slot number of 6 or 7 is suggested considering both the solution optimality and the model complexity.

Exploring the effects of passive transit signal priority design on bus rapid transit operation: a microsimulation-based optimization approach

ABSTRACT The Bus Rapid Transit (BRT) system can be a cost-effective alternative to foster public transportation. However, the delay in signalized intersections is critical to the stable progression of BRT operation. Transit Signal Priority (TSP) control has been studied to enhance the BRT operation. Nevertheless, the implementation of TSP may also impact general traffic and induce objection from the public. Upon this challenging aspect, this study focuses on passive TSP control for the BRT system. A microsimulation-based optimization model is developed to coordinate signal offsets along the arterial of BRT operation, seeking to minimize the delay of BRT vehicles without severely affecting the service level for other traffic. We conduct a case study over the Taichung BRT System in Taiwan; it indicates that the passive TSP control can reduce approximately 22% of transit delay while containing moderate impact on other traffic. In addition, the derived control strategies can increase system capacity with satisfactory service reliability.

OPTIMIZATION OF INTERSECTION CONTROL SCHEME CONSIDERING PHASE-MOVEMENT-COMBINATION UNDER AUTOMATED VEHICLES ENVIRONMENT

For signal control intersection, the Phase-Movement-Combination (PMC) styles could directly impact the control performance of the signal scheme. Automated vehicles use mechatronics technology to drive autonomously and safely according to the predetermined lane trajectory, which caused the phase movement combination and Phase Combination (PC) schemes become more and more complicated. Therefore, this paper proposed a method to consider the extensive PMC styles by fractionalizing movement compatibility relationships, and used discrete mathematics to calculate overall Feasible Phase Combination (FPC) schemes according to the requirements of the signal phase. A corresponding optimal timing model was also established for FPC schemes by minimizing the average vehicle delay and maximizing the intersection capacity. Results were compared against the conventional PC schemes for a variety of demand scenarios. It was concluded that the proposed signal control optimization method was effective to optimize the intersection control scheme, depending on different demand scenarios.

Preliminary Investigation of COVID-19 Impact on Transportation System Delay, Energy Consumption and Emission Levels

A dramatic reduction in traffic demand was observed during the pandemic of COVID-19 producing noticeable reductions in traffic delay, energy consumption, and emissions. This unprecedented event provides us with a perfect chance to investigate how limiting the number of vehicles on the transportation network can contribute to a better environment. This paper quantifies the effects of reduced traffic demand on air pollution and greenhouse gas emissions. Microscopic simulation is used to model traffic and their associated emissions. Our results show that the decreased traffic demand contributed significantly to decreasing vehicle delays and emissions, especially in congested urban areas.

Traffic Signal Control Optimization in a Connected Vehicle Environment Considering Pedestrians

This paper proposes a connected vehicle-based traffic signal control scheme that seeks to improve both vehicle and pedestrian operations. Real-time information on vehicle speeds and locations is combined with knowledge of pedestrian arrivals to optimize signal timings that minimize a weighted average of vehicle and pedestrian delays. Such real-time pedestrian information might be available using existing sensors—such as pedestrian push buttons or infrared detectors—as well as in a connected environment. The algorithm implements a rolling-horizon optimization framework that optimizes signal phase sequences over some period but only implements the first phase in the optimized sequence. The results reveal that considering pedestrians in the optimization can improve delays to both pedestrians and vehicles compared with ignoring pedestrians. Within the proposed framework, average vehicle delay increases and average pedestrian delay decreases as more weight is assigned to pedestrian delay in the optimization. In general, the average person delay can be minimized when the relative weight between vehicle and pedestrian delay is consistent with the average occupancy rate of cars. However, a different weight may be chosen to prioritize pedestrian movement, if desired. These results are robust under varying demand levels and demand patterns. The effectiveness of the algorithm decreases as the information level of pedestrian arrivals decreases, and the algorithm becomes ineffective when information from fewer than 60% pedestrians is available. However, the detection of more than 60% of pedestrians can likely be achieved using existing technologies and thus would likely be available in a connected environment.

Vehicle Delay Modeling at Signalized Intersections with Gene-Expression Programming

AbstractThe accurate determination of vehicle delays is crucial for effective intersection management. Because the optimization of signal duration in a signalized intersection is based on the reduc...

Traffic Management and Engineering Analysis of the Manahan Flyover Area by using Traffic Micro-Simulation VISSIM

The location of the Manahan railway level crossing is a meeting point for movement between trains and vehicles which often causes traffic congestion and traffic accidents in the area. In connection with the construction of the double track railway, the intensity of traffic congestion in the Manahan railway level crossing area is increasing. In order to overcome traffic congestion in the area, the Surakarta City Government built Manahan Flyover infrastructure. However, due to traffic management and engineering in the existing conditions is not optimal resulting in a relatively bad traffic congestion in Manahan Flyover area. The aim of this study is to conduct traffic management and engineering analysis to minimize traffic congestion in Manahan Flyover area. The traffic micro-simulation VISSIM model is used to evaluate the performance of road networks at peak traffic conditions with different simulation model scenarios. The existing model is built based on a field survey data and then calibrated it to approximate conditions on the field. Then based on the existing model, modelling of several traffic management and engineering scenarios are carried out to obtain the optimal performance of the road network in the Manahan Flyover area. The analysis shows that the simulation model Scenario 3 produces the best performance of road network in terms of vehicle delay, queue length, travel time and speed

Impact of an automated road debris removal system on traffic flow using VISSIM

This study introduces a state-of-the-art road debris removal system (i.e., automated road debris removal system (ROBOS)) and analyzes its impact on traffic flow using the microscopic simulation model, VISSIM. Robust simulation scenarios were established, and the major parameters of individual scenarios were also included based on extensive interviews with road workers. The performances of road workers and the ROBOS in regard to road debris removal were investigated in terms of four measures of effectiveness. These included queue, density, percentage delay, and number of vehicle stops. To mimic the major arterial roadways in South Korea, two-lane, three-lane, and four-lane roads were considered for implementing the road debris removal process. The simulation results as well as the statistical analysis revealed that the ROBOS outperforms the direct removal by road workers in all aspects. Thus, the ROBOS can remove the debris more safely and efficiently. The findings of this research can improve institutional operational guidelines for the ROBOS by providing an appropriate range for its parameters. This can result in enhancing traffic safety and efficiency. Notwithstanding the simulation results, the more unexpected cases, such as the driver carelessness during removal work, should be reflected in future research to thoroughly prevent the potential risk of accidents as well as maintaining smooth traffic flow.

Multi-state semi-Markov modeling of recurrent events: Estimating driver waiting time at semi-controlled crosswalks

At “semi-controlled” crosswalks, signs and markings are present, but delay to pedestrians and motorists is largely the result of the “negotiation” between the two parties to determine who yields. This paper proposes a novel approach using multi-state semi-Markov models to investigate motorists’ delay and their interactions with pedestrians. Motorist waiting behavior can be divided into a series of gap acceptance decisions as part of a Markov Chain. Each gap acceptance decision is modeled as a specific transition between two states in the Markov Chain.To demonstrate the reliability of the proposed models, multi-state semi-Markov models are estimated for the waiting behavior of more than 1,000 drivers in the presence of pedestrians at semi-controlled crosswalks. The multi-state semi-Markov models are capable of dealing with specific challenges related to (i) the need to account for recurrent events and (ii) a generalized framework for vehicle delay estimation and simulation at semi-controlled crosswalks. The extent to which motorists behave more aggressively and impatiently as their delay increases is demonstrated. Differences in behavior for operators of buses and trucks were also identified. The semi-Markov method is also able to deal effectively with the “pulsing” arrival patterns of pedestrians at crosswalks as university classes begin and end nearby and handle temporal heterogeneity. Finally, to address aggressive driver behavior, several safety implications are discussed.

Signal Timing Optimization Model Based on Bus Priority

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment’s result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections.

Design Traffic Signal Node Based on Edge Computing

With the development of economy, the traffic jams are more and more serious. The traditional timing traffic signal can’t make full use of the road traffic resources. In order to solve this problems, an edge computing intelligent traffic light node is proposed, video target detection, artificial intelligence and other new technologies are used to realize the effective control of intersection signals, so as to optimize pedestrian traffic, vehicle delay, traffic capacity and parking times. The simulation results show that this method can greatly improve the traffic efficiency and reduce the waiting time of the intersection during normal times. It can effectively delay the peak period and shorten the peak period.

Model Microsimulation Roundabout Utilities in Makassar

The vehicle growth is one of the problems affecting traffic congestion. The circulation of entry vehicles and circulating vehicles in confluence made the conflict point. Avoidance the conflict is the need to increase road performance. The roundabout of the road is one of the utilities to increase the performances of traffic and reduce congestion. The prevention step is to maximize the utilities to control and manage the traffic. The roundabout dispersed and controlled the traffic flow. According to Makassar roundabout observation, the queuing and delay of vehicles recently occurs. This study aims to an analysis of the roundabout using the microsimulation Vissim program. The model uses the geometric condition of the road and volume of the vehicle that produce the models based on real condition and validation. This model performed the visual condition and calculated the capacity and shows the emission of the operational vehicle, and the general performances are summarized for operation performances at roundabout confluence sections. The result indicated that the roundabout is still useful to reduce the potential of queuing and delay the time of vehicles, and it needs supervision periodically for preventing the traffic from the potential congestion, especially in developing countries. The research result is useful as a reference for improving the capacity of roundabouts.