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Traffic density is one of the most important parameters to consider in the traffic operation field. Owing to limited data sources, traditional methods cannot extract traffic density directly. In the vehicular ad hoc network (VANET) environment, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction technologies create better conditions for collecting the whole time-space and refined traffic data, which provides a new approach to solving this problem.

On that basis, a real-time traffic density extraction method has been proposed, including lane density, segment density and network density. Meanwhile, using SUMO and OMNet++ as traffic simulator and network simulator, respectively, the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed.

Based on the simulation platform, a simulated intersection in Shanghai was developed to investigate the adaptability of the model.

Most research studies use separate simulation methods, importing trace data obtained by using from the simulation software to the communication simulation software. In this paper, the tight coupling simulation method is applied. Using real-time data and history data, the research focuses on the establishment and validation of the traffic density extraction model.

This paper aims to discuss traffic patterns generated by swarms of robots while commuting to and from a base station.

The paper adopts a mathematical evaluation and robot swarm simulation. The swarm approach is bottom‐up: designing individual agents the authors are looking for emerging group behaviour patterns. Examples of group behaviour patterns are human‐driven motorized traffic which is rigidly structured in two lanes, while army ants develop a three‐lane pattern in their traffic. The authors copy army ant characteristics onto their robots and investigate whether the three lane traffic pattern may emerge. They follow a three‐step approach. The authors first investigate the mathematics and geometry of cases occurring when applying the artificial potential field method to three “perfect” robots. Any traffic pattern (two, three or more lanes) appears to be possible. Next, they use the mathematical cases to study the impact of limited visibility by defining models of sensor designs. In the final step the authors implement ant inspired sensor models and a trail following mechanism on the robots in the swarm and explore which traffic patterns do emerge in open space as well as in bounded roads.

The study finds that traffic lanes emerge in the swarm traffic; however the number of lanes is dependent on the initial situation and environmental conditions. Intrinsically the applied robot models do not determine a specific number of traffic lanes.

The paper presents a method for studying and simulating robot swarms.

This paper aims to use active fine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles (ICVs) in the future.

By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them with field investigation, the paper proposes the active and fine lane management methods for ICVs to optimal driving behavior in a weaving area. The VISSIM simulation of traffic flow vehicle driving behavior in weaving areas of urban expressways was performed using research data. The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area. The active fine lane management methods applied to a weaving area were verified for different scenarios.

The results of the study indicate that ICVs complete their lane changes before they reach a weaving area, their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.

Based on the vehicle group behavior, this paper conducts a simulation study on the active traffic management control-oriented to ICVs. The research results can optimize the management of lanes, improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.

Congestion as a consequence of the rapidly growing air traffic is one of the outstanding problems of the air transportation industry. Traffic impediment rates, having an increasing acceleration while the airport capacities have been kept constant due to several reasons, cause problems such as arrival/departure delays, schedule interruptions, cancellations and customer dissatisfaction. In this paper, the author aims to study transitioning from a single-hub air transportation system to a multi-hub infrastructure via Monte Carlo simulation.

The current hub has reached its capacity limits for long so that the growth potential of the air transportation has been affected adversely. One of the possible remedies suggested by authorities, professionals and academics was to transform the air transportation infrastructure into a multi-hub setting. Current air traffic of the country was modeled by means of simulation. Airport capacities and performances are simulated and analyzed under different scenarios considering a potential alternative hub along with the central one. Possible delays in both hubs are studied in case of moderately increasing traffic congestion.

As a result, decreased delay levels in the central hub are observed, whereas no delays are experienced in the potential one in all the scenarios.

This study, proposing to organize the national and international air traffic of the country while harmonizing the delay rates and increasing the passenger satisfaction, is to contribute significantly to the aviation sector companies, airliners and airport operators by shedding light on the imminent capacity issues air transportation industry is going to face.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The performance of the classical car-following system is easily affected by external disturbances. To enhance the performance of the classical car-following model under sudden external disturbances, a novel car-following model is established to smooth traffic flow.

This paper proposed a Proportion Integration Differentiation (PID) control strategy based on classical control theory and developed a novel car-following model. The linear system theory and Laplace transform are used to derive a closed-loop transfer function. Then, the stability condition is obtained by using the Routh stability criterion and the small gain theorem. Finally, the validity and feasibility of the PID control strategy is proved by numerical simulations.

The analytic results and the numerical simulation results show that both the integration part and the differential part have the positive effect to suppress traffic oscillation efficiently; the collaboration of these two parts has more power to improve the stability of traffic flow. It means that the proposed model integrated with the PID control strategy has the ability of anti-interference and smooth traffic compared with the classical car-following model.

This paper introduces the PID control strategy into the classical car-following system, which enhances the stability of the system and also provides an efficient method for optimizing the traffic flow system.

This paper claims that the parking policy is one of the most obvious tools for reducing traffic congestion, pollutant emissions and conflicts between transportation network users. The purpose of this paper is to propose and implement a strategy, via a simulation tool, for the sharing of parking places between light cars and vans for goods delivery.

Temporal and spatial dynamic booking of on‐street parking places is described by using the multi‐agent paradigm. Main agents concerned by the sharing of parking places, their rules and interactions are implemented. Behavioral models and learning process of cognitive agents based on stated preferences collected beside the network users are designed for capturing multi‐agent interactions.

By coupling a 2D traffic simulation tool and the Copert III methodology, it is possible to simulate the traffic and environmental consequences of several scenarios for different infrastructures, occupancy rate of the places reserved for goods delivery and durations of the delivery process.

Several points are under development: a 3D environment will capture with more realism the behavior of agents in a larger spatial scale and in real time. The behavioral models will be designed by stated preferences obtained from surveys containing questions coupled with pictures of possible scenarios.

Applied in a real context, the sharing of parking places strategy shows benefits for traffic and for the environment. A decision maker can use this strategy for simulating scenarios, in the context of an urban area in particular.

The paper demonstrates how a simulation tool based on strategy of parking place sharing can satisfy constraints of transportation network users.

This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios.

SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model.

Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy.

The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world.

By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%.

The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.

Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The purpose of this paper is to contribute to the continuous search for new alternative solutions to prevent or alleviate these concerns. It particularly deals with the development of decision support system based on a data fusion for the management and control of traffic at signalized intersections. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system is based on multi-detector data fusion, a data processing function that combines imperfect information collected from systems involving several detectors. The developed system is then tested on a virtual junction, and the results obtained are reported and discussed.

This paper presents a new traffic light control based on multi-detectors data fusion theory. The system uses a new multi-detectors data fusion method for traffic data analysis. Moreover, the system integrates a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination.

The paper provides a decision support system for traffic regulation at intersection based on multi-sensors. It suggests the fusion of captured data by many sensors for measuring information. The system use the Belief Functions Theory for information fusion and decision making using combination and decision rules.

The paper proposes a new Adaptive Traffic Control System based on a new data fusion approach that include a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination.

The purpose of this paper is to compare qualitatively two methods for coordinating traffic lights: a static optimization “green wave” method and an adaptive self‐organizing method.

Statistical results were obtained from implementing a recently proposed model of city traffic based on elementary cellular automata in a computer simulation.

The self‐organizing method delivers considerable improvements over the green‐wave method. Seven dynamical regimes and six phase transitions are identified and analyzed for the self‐organizing method.

The paper shows that traffic light coordination can be improved in cities by using self‐organizing methods.

This improvement can have a noticeable effect on the quality of life of citizens.

Understanding how self‐organization obtains adaptive solutions for complex problems can contribute to building more efficient systems.