Analysis of a cooperative variable speed limit system using microscopic traffic simulation

Variable speed limit systems where variable message signs are used to show speed limits adjusted to the prevailing road or traffic conditions are installed on motorways in many countries. The objectives of variable speed limit system installations are often to decrease the number of accidents and to increase traffic efficiency. Currently, there is an interest in exploring the potential of cooperative intelligent transport systems including communication between vehicles and/or vehicles and the infrastructure. In this paper, we study the potential benefits of introducing infrastructure to vehicle communication, autonomous vehicle control and individualized speed limits in variable speed limit systems. We do this by proposing a cooperative variable speed limit system as an extension of an existing variable speed limit system. In the proposed system, communication between the infrastructure and the vehicles is used to transmit variable speed limits to upstream vehicles before the variable message signs become visible to the drivers. The system is evaluated by the means of microscopic traffic simulation. Traffic efficiency and environmental effects are considered in the analysis. The results of the study show benefits of the infrastructure to vehicle communication, autonomous vehicle control and individualized speed limits for variable speed limit systems in the form of lower acceleration rates and thereby harmonized traffic flow and reduced exhaust emissions.     

Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56-86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.     

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.     

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.     

Air holding problem solving with reinforcement learning to reduce airspace congestion

The Air Holding Problem Module is proposed as a decision support system to help air traffic controllers in their daily air traffic flow management. This system is developed using an Artificial Intelligence technique known as multiagent systems to organize and optimize the solutions for controllers to handle traffic flow in Brazilian airspace. In this research, the air holding problem is modeled with reinforcement learning, and a solution is proposed and applied in two case studies of the Brazilian airspace. The system can suggest more precise and realistic actions based upon past situations and knowledge of the professionals and forecast the impact of restrictive measures at the local and/or overall level. The first case study shows performance improvements in traffic flows between 8 and 47% at the local level up to 49% at the overall level. In the second case study, performance improvements were between 15 and 57% at the local level and between 41 and 48% at the overall level. Copyright © 2014 John Wiley & Sons, Ltd.     

Coordination of traffic management instruments using agent technology

The objective of this paper is to prove by example the opportunities for cooperation between dynamic traffic management instruments. Agent technology is presented as a useful way to support the deployment of these ideas.In the Netherlands, more and more instruments are installed to promote the flow of traffic. As more and more instruments are deployed, chances are that conflicts will arise when control tools are applied in the same area. The increase in the number of the deployed instruments implies a bigger responsibility for the Dutch Traffic operators, who will have to ascertain which control scenarios are relevant to the situation at hand and implement them.By modeling the separate instruments as intelligent agents, it might be possible to tune the actions of the individual instruments through the agent concept of collaboration. Letting the individual instruments handle the most basic forms of coordination automatically might also relieve the traffic operator. This paper will demonstrate the aforementioned ideas using two simple examples: one in which consecutive ramp metering installations coordinate their actions to promote the flow at a downstream bottleneck and one in which traffic management instruments coordinate their actions to attain a common goal on the network-level.     

A multiclass cell transmission model for shared human and autonomous vehicle roads

Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high.     

Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems

Agent technology is rapidly emerging as a powerful computing paradigm to cope with the complexity in dynamic distributed systems, such as traffic control and management systems. However, while a number of agent-based traffic control and management systems have been proposed and the multi-agent systems have been studied, to the best of our knowledge, the mobile agent technology has not been applied to this field. In this paper, we propose to integrate mobile agent technology with multi-agent systems to enhance the ability of the traffic management systems to deal with the uncertainty in a dynamic environment. In particular, we have developed an IEEE FIPA compliant mobile agent system called Mobile-C and designed an agent-based real-time traffic detection and management system (ABRTTDMS). The system based on Mobile-C takes advantages of both stationary agents and mobile agents. The use of mobile agents allows ABRTTDMS dynamically deploying new control algorithms and operations to respond unforeseen events and conditions. Mobility also reduces incident response time and data transmission over the network. The simulation of using mobile agents for dynamic algorithm and operation deployment demonstrates that mobile agent approach offers great flexibility in managing dynamics in complex systems.     

Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.     

An agent-based multimodal simulation model for capacity planning of a cross-border transit facility

Cross-border transit facilities constitute major public investment, and thus must serve the long-term needs of the communities, such as providing access to schools and businesses, contributing to a shared regional culture and lifestyle, fostering international trade, and supporting jobs for the region’s residents. Numerous studies have been conducted to evaluate the economic implications of vehicular flow delays at border crossings, however none of the studies focused on assessing cross-border flow of bus passengers and pedestrians. Since pedestrians are considered to be autonomous, intelligent, and perceptive, it is a challenging task to predict pedestrian movement and behavior in comparison to vehicular flows which follow a specific set of traffic rules. This paper presents a multiagent based multimodal simulation model to evaluate the capacity and performance of a cross-border transit facility. The significance of this research is the use of dynamic mode choice functionality in the model, which allows an individual person to make instantaneous choices between available modes of transportation. The scope of interest of the paper is limited to simulating access interface, circulation areas, ancillary and processing facilities. The developed model was calibrated to ensure realistic performance, and validated against specific performance criteria such as throughput per processing facility. In order to demonstrate the applicability of the developed simulation model, capacity and operational planning of a pedestrian transit facility was performed. The relative performance of alternative design or configuration was evaluated using the level of service criteria. Lastly, the effectiveness of each proposed capacity or operational improvement strategy was compared to the “do-nothing” scenario.     

Generic model for resource allocation in transportation. Application to urban parking management

In this paper, we define the online localized resource allocation problem, especially relevant for modeling transportation applications. The problem modeling takes into account simultaneously the geographical location of consumers and resources together with their online nondeterministic appearance. We use urban parking management as an illustration of this problem. In fact, urban parking management is an online localized resource allocation problem, where the question is how to find an efficient allocation of parking spots to drivers, while they all have dynamic geographical positions and appear nondeterministically. We define this problem and propose a multiagent system to solve it. The objective of the system is to decrease, for private vehicles drivers, the parking spots search time. The drivers are organized in communities and share information about spots availability. We have defined two cooperative models and compared them: a fully cooperative model, where agents share all the available information, and a “coopetitive” model, where drivers do not share information about the spot that they have chosen. Results show the superiority of the first model.     

An integrated framework for embedding large-scale dynamic traffic assignment models in advanced traveler information systems

In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort.     

Modelling lane changing and merging in microscopic traffic simulation

This paper introduces Simulation of Intelligent TRAnsport Systems (SITRAS), a massive multi-agent simulation system in which driver-vehicle objects are modelled as autonomous agents. The simulation outputs can be used for the evaluation of Intelligent Transport Systems applications such as congestion and incident management, public transport priority and dynamic route guidance. The model concepts and specifications, and the first applications of the model in the area of incident modelling in urban arterial networks were described in previous publications. This paper presents the details of the lane changing and merging algorithms developed for the SITRAS model. These models incorporate procedures for ‘forced’ and ‘co-operative’ lane changing which are essential for lane changing under congested (and incident-affected) traffic conditions. The paper describes the algorithms and presents simulation examples to demonstrate the effects of the implemented models. The results indicate that only the forced and cooperative lane changing models can produce realistic flow-speed relationships during congested conditions.     

An algorithm for combining autonomous vehicles and controlled events in driving simulator experiments

Autonomous vehicles can be used to create realistic simulations of surrounding vehicles in driving simulators. However, the use of autonomous vehicles makes it difficult to ensure reproducibility between subjects. In this paper, an effort is made to solve the problem by combining autonomous vehicles and controlled events. A controlled event can be compared to a theatre play. The aim is to achieve the same initial play conditions for each subject, which can be problematic since the traffic situation around the subject will be dependent upon each subject’s actions while driving in autonomous traffic. This paper presents an algorithm that achieves the transition from autonomous traffic to a predefined start condition for a play. The algorithm has been tested in the Swedish National Road and Transport Research Institute (VTI) driving simulator III with promising results. In most of the cases we examined the algorithm could reconstruct the specified start condition and conduct the transition from autonomous to controlled mode in a inconspicuous way. Some problems were observed regarding moving unwanted vehicles away from the closest area around the simulator vehicle, and this part of the algorithm has to be enhanced. The experiment also showed that the subjects drove faster in the presence of controlled everyday life traffic normally used in the VTI driving simulator than in autonomous traffic.     

Control Concepts for Facilitating Motorway On-ramp Merging Using Intelligent Vehicles

Abstract

Congestion at motorway junctions is a traffic phenomenon that degrades operation of infrastructure and can lead to breakdown of traffic flow and associated reduction in capacity. Advanced communication technologies open new possibilities to prevent or at least delay this phenomenon, and innovative active traffic management systems have been developed in the recent years for better control of motorway traffic. This paper presents a review of control strategies for facilitating motorway on-ramp merging using intelligent vehicles. First, the concepts of the control algorithms are reviewed chronologically divided into three types of intelligent vehicle: completely automated, equipped with cooperative adaptive cruise control and equipped with on-board display. Then, a common structure is identified, and the algorithms are presented based on their characteristics in order to identify similarities, dissimilarities, trends and possible future research directions. Finally, using a similar approach, a review of the methods used to evaluate these control strategies identifies important aspects that should be considered by further research on this topic.     

Definition of a merging assistant strategy using intelligent vehicles

In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, we summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies.     

A State-of-the-Art Review of Car-Following Models with Particular Considerations of Heavy Vehicles

Abstract

Car-following (CF) models are fundamental in the replication of traffic flow and thus they have received considerable attention. This attention needs to be reflected upon at particular points in time. CF models are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. This paper presents a review of existing CF models. It classifies them into classic and artificial intelligence models. It discusses the capability of the models and potential limitations that need to be considered in their improvement. This paper also reviews the studies investigating the impacts of heavy vehicles in traffic stream and on CF behaviour. The findings of the study provide promising directions for future research and suggest revisiting the existing models to accommodate different behaviours of drivers in heterogeneous traffic, in particular, heavy vehicles in traffic.     

Machine learning for multi-jurisdictional optimal traffic corridor control

Urban traffic corridors are often controlled by more than one agency. Typically in North America, a state of provincial transportation department controls freeways while another agency at the municipal or city level controls the nearby arterials. While the different segments of the corridor fall under different jurisdictions, traffic and users know no boundaries and expect seamless service. Common lack of coordination amongst those authorities due to lack of means for information exchange and/or possible bureaucratic ‘institutional grid-lock’ could hinder the full potential of technically-possible integrated control. Such institutional gridlock and related lack of timely coordination amongst the different agencies involved can have a direct impact on traffic gridlock. One potential solution to this problem is through integrated automatic control under intelligent transportation systems (ITS). Advancements in ITS and communication technology have the potential to considerably reduce delay and congestion through an array of network-wide traffic control and management strategies that can seamlessly cross-jurisdictional boundaries. Perhaps two of the most promising such control tools for freeway corridors are traffic-responsive ramp metering and/or dynamic traffic diversion possibly using variable message signs (VMS). Technically, the use of these control methods separately might limit their potential usefulness. Therefore, integrated corridor control using ramp metering and VMS diversion simultaneously might be synergetic and beneficial. Motivated by the above problem and potential solution approach, the aim of the research presented in this paper is to develop a self-learning adaptive integrated freeway-arterial corridor control for both recurring and non-recurring congestion. The paper introduces the use of reinforcement learning, an Artificial Intelligence method for machine learning, to provide optimal control using ramp metering and VMS routing in an integrated agent for a freeway-arterial corridor. Reinforcement learning is an approach whereby the control agent directly learns optimal strategies via feedback reward signals from its environment. A simple but powerful reinforcement learning method known as Q-learning is used. Results from an elaborate simulation study on a key corridor in Toronto are very encouraging and discussed in the paper.     

Logistical restructuring and road freight traffic growth

The forecasting of road freight traffic has relied heavily on the close correlation between GDP and road tonne-kilometers. It has not been rooted in an understanding of the causes of freight traffic growth. The research reported in this paper has investigated this process of traffic growth in two ways: first, by analysing official data on the production, consumption and movement of food and drink products, and second, by conducting a survey of the changing freight transport requirements of 88 large British-based manufacturers.The analysis of secondary data shows how, in the food and drink sector, the relationship between the real value of output and road vehicle-kms hinges on four key parameters: value density, handling factor, average length of haul and consignment size. An attempt is made to explain variations in these parameters.The survey of manufacturers suggests that the growth of lorry traffic is the net result of a complex interaction between factors operating at four levels of logistical management: strategic planning of logistical systems, choice of suppliers and distributors, scheduling of product flow and the management of transport resources. Changes in the frequency and scheduling of freight deliveries in response to tightening customer service requirements and just-in-time management appear to have become a more prevalent cause of freight traffic growth than the physical restructuring of logistical systems. Manufacturers anticipate that their road freight demand will broadly increase in line with sales and be largely unaffected by road transport cost increases at the levels currently proposed. The paper concludes by examining their likely reactions to a much sharper increase in the cost of road freight movement.     

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.