Intersection Management Research Articles

A capacity maximization scheme for intersection management with automated vehicles

With the advent of connected and automated vehicle technology, in this paper, we propose an innovative intersection operation scheme named as MCross: Maximum Capacity inteRsection Operation Scheme with Signals. This new scheme maximizes intersection capacity by utilizing all lanes of a road simultaneously. Lane assignment and green durations are dynamically optimized by solving a multi-objective mixed-integer non-linear programming problem. The demand conditions under which full capacity can be achieved in MCross is derived analytically. Numerical examples show that MCross can almost double the intersection capacity (increase by as high as 99.51% in comparison to that in conventional signal operation scheme).

Consensus-based local information coordination for the networked control of the autonomous intersection management

Autonomous intersection management (AIM) will be a future method for improving traffic efficiency in the urban area. Instead of using the traffic signal control like nowadays, it uses wireless communication with autonomous vehicles to support the management of road traffic more safely and efficiently. A single AIM shows an exceptional performance in managing traffics at an intersection. However, it could not be represented a traffic in the real world, which is composed of multiple intersections. We show that coordination of traffic information among vehicles and infrastructures is an essential part of macroscopic traffic management. Coordination of traffic information among the network of AIMs is the key to improve the overall traffic flow throughout the network not only has an optimal flow in some intersections and very heavy traffic in others. In this paper, we introduce the distributed control to a graph-based intersection network to control traffic in a macroscopic level. Vehicle to infrastructure and infrastructure to infrastructure communication are used to exchange the traffic information between a single autonomous vehicle to the network of autonomous intersections. We implement a discrete time consensus algorithm to coordinate the traffic density of an intersection with its neighborhoods and determine the control policy to maximize a traffic throughput of each intersection as well as stabilizing the overall traffic in the network. We use the Greenshields traffic model to define the boundary condition of various traffic flows to the corresponded traffic density and velocity. Our proposed method represents the ability to maintain traffic flow rate of each intersection without having a back up traffic. As well, every intersection operates under the uncongested flow condition. The simulation results of the graph-based networked control of a multiple autonomous intersection showed that the overall traffic flow in the network achieves up to 20% higher than using traffic signal system.

Ahab: A cloud‐based distributed big data analytics framework for the Internet of Things

SummarySmart city applications generate large amounts of operational data during their execution, such as information from infrastructure monitoring, performance and health events from used toolsets, and application execution logs. These data streams contain vital information about the execution environment that can be used to fine‐tune or optimize different layers of a smart city application infrastructure. Current approaches do not sufficiently address the efficient collection, processing, and storage of this information in the smart city domain. In this paper, we present Ahab, a generic, scalable, and fault‐tolerant data processing framework based on the cloud that allows operators to perform online and offline analyses on gathered data to better understand and optimize the behavior of the available smart city infrastructure. Ahab is designed for easy integration of new data sources, provides an extensible API to perform custom analysis tasks, and a domain‐specific language to define adaptation rules based on analysis results. We demonstrate the feasibility of the proposed approach using an example application for autonomous intersection management in smart city environments. Our framework is able to autonomously optimize application deployment topologies by distributing processing load over available infrastructure resources when necessary based on both online analysis of the current state of the environment and patterns learned from historical data. Copyright © 2016 John Wiley & Sons, Ltd.

Paradoxes of reservation-based intersection controls in traffic networks

Reservation-based intersection control is a revolutionary idea for using connected autonomous vehicle technologies to improve intersection controls. Vehicles individually request permission to follow precise paths through the intersection at specific times from an intersection manager agent. Previous studies have shown that reservations can reduce delays beyond optimized signals in many demand scenarios. The purpose of this paper is to demonstrate that signals can outperform reservations through theoretical and realistic examples. We present two examples that exploit the reservation protocol to prioritize vehicles on local roads over vehicles on arterials, increasing the total vehicle delay. A third theoretical example demonstrates that reservations can encourage selfish route choice leading to arbitrarily large queues. Next, we present two realistic networks taken from metropolitan planning organization data in which reservations perform worse than signals. We conclude with significantly positive results from comparing reservations and signals on the downtown Austin grid network using dynamic traffic assignment. Overall, these results indicate that network-based analyses are needed to detect adverse route choices before traffic signals can be replaced with reservation controls. In asymmetric intersections (e.g. local road-arterial intersections), reservation controls can cause several potential issues. However, in networks with more symmetric intersections such as a downtown grid, reservations have great potential to improve traffic.

Management of intersections with multi-modal high-resolution data

A high-resolution (HR) data system for an intersection collects the location (lane), speed, and turn movement of every vehicle as it enters an intersection, together with the signal phase. Some systems also provide video monitoring; others measure pedestrian and bicycle movements; and some have vehicle to infrastructure (V2I) communication capability. The data are available in real time and archived. Real time data are used to implement signal control. Archived data are used to evaluate intersection, corridor, and network performance. The system operates 24×7. Uses of a HR data system for assessing intersection performance and improving mobility and safety are discussed. Mobility applications include evaluation of intersection performance, and the design of better signal control. Safety applications include estimates of dilemma zones, red-light violations, and pedestrian–vehicle conflicts.

Implementation of geographical location based routing protocols in vehicular environment

An important special class of mobile ad hoc networks in vehicular environment is VANET where vehicles communicate among each other and road side units. Since road structures are predefined and vehicles move with very high speed, the connectivity among vehicles breaks very often, therefore routing becomes a very challenge issue in VANET. Most of the authors used IEEE802.11b/g based routing protocols. The standard used for VANET is IEEE802.11p. This paper implements the IEEE802.11p based two geographical routing protocols location-aided-routing (LAR) protocol and zone-routing-protocol (ZRP) for city as well as highway vehicular environment. Vehicular movement traces are obtained with VanetMobiSim tool having intelligent driver model (IDM). IDM with intersection management and IDM with lane-changing modules are used. Packet delivery ratio, throughput, delay, normalized routing load and lost packet ratio metrics are used to evaluate LAR and ZRP routing protocols implemented in VANET.

A Model for Capacity Considering the Interference by Pedestrian Traffic at Signal Intersections

The capacity is impacted badly by pedestrians’ violation behavior at signal intersections. In order to quantify the impact, the time-headway and the vehicular speed from start-up to reach the stable saturation flow with and without pedestrian traffic are used as the direct measurements. Using the statistical analysis, the time-headway of saturation flow is mainly affected by the position of pedestrians and the degree of pedestrians’ influence is classified into four levels. Then the speed-time profile for the vehicular acceleration at each level is fitted by Curve Fitting Software. Based on the effect to the time-headway and acceleration time, the model of capacity influenced by pedestrians is established and the influence is quantified, which enriches the fundamental theory of traffic engineering. The result shows that the vehicular capacity can be decreased by 14% at the worst case (level I ). The conclusions obtained in this paper are valuable for better management of the signal intersection.

Cooperative Intersection Management: A Survey

Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.

Exploring Factors Impacting Paths of Left-Turning Vehicles from Minor Road Approach at Unsignalized Intersections

At unsignalized intersections, left-turning vehicles from minor road approach are more likely to be involved in traffic conflicts and traffic crashes and are one of the most leading factors impacting traffic efficiency and capacity. The authors of the paper observed that some drivers behaved illegally and dangerously while performing left turns from minor road approach, resulting in abnormal trajectories at unsignalized intersections. By applying binary logistic analysis, a probability prediction model was developed to explore various factors affecting probability of normal path taken by drivers while turning left from minor road approach. Based upon the model, measures such as lowering running speeds of vehicles on major road or minor road and adding more lanes on minor road can be used to encourage more drivers to take normal vehicle paths, which is helpful for the improvement of traffic safety, efficiency, and capacity. Results of the paper can be used for the guidance of design and management of unsignalized intersections.

Decentralized management of intersections of automated guided vehicles

The emerging topic of cooperative intersection management for vehicles has raised up new solutions for traffic control in order to avoid collisions, deadlocks, and improve traffic efficiency. The solutions developed for road traffic can easily be applied to automated guided vehicles to overcome the common drawbacks, and improve the number of vehicles in a network. In the context of a network of vehicles only regulated at intersections, we propose an algorithm in order to prevent deadlock at intersections in a network of automated guided vehicles.

Intelligent Intersection Management of Autonomous Traffic Using Discrete-Time Occupancies Trajectory

Intelligent Intersection Management of Autonomous Traffic Using Discrete-Time Occupancies Trajectory

Cooperative Intelligence in Roundabout Intersections Using Hierarchical Fuzzy Behavior Calculation of Vehicle Speed Profile

In this paper, a new fuzzy-behavior-based algorithm for roundabout intersection management is presented. The algorithm employs cooperative intelligence and includes intelligent vehicles and infrastructure to calculate speed profiles for different vehicles, in order to achieve more comfortable driving profiles, as well to reduce congestion and CO 2 emissions. The algorithm uses adaptive spatio-temporal reservation technique and was tested in MATLAB/Simulink environment. The algorithm is designed to function in different scenarios with both cooperative and non-cooperative vehicles, as well as optional intersection infrastructure. Results have show that using the proposed algorithm different vehicle communication types can be successfully combined in order to increase traffic flow through roundabout intersections.

Governance in Times of the Big Data Revolution: Sri Lanka’s Options

This article looks at the management of the critical intersection of governance and technology. Both the concepts and frameworks that the two invoke are experiencing turbulence in navigating the recent global shifts. Governance, since its inception as a critical conceptual and policy framework encounters stiff academic and policy resistance in moving towards creating global governance architecture. Technology is a broad concept and in the context of utility it is extremely diverse. The article attempts to explore a recent phenomenon as a consequence of the information revolution; the rise of big-data and significant changes it has brought to social life. Big data is not just a corporate tool for extracting profits, but is a techno-resource making governance a complex task. Yet if it is well-handled it will increase the efficiency and accuracy of service delivery platforms in the public sector. Comprehending the importance and deployment of data analysis emerging from big data would be a critical sphere that the public sector has to invest in and engage with. Even in the United States, Google seems to be outdoing every national data and statistical analysis and at the same time working with the federal government to deliver better services to the American citizens. Big data is a result of intense data creation by anyone connected to the Internet. The amount of data in the world is outgrowing not just the machines invented to process them but the human imagination. Thus it would be pragmatic for the Sri Lankan public sector to engage with big-data management through systems of governance. Sri Lanka Journal of Development Administration, Vol. 5, pp. 1-13, 2015

PERFORMANCE EVALUATION OF DSDV, AODV ROUTING PROTOCOLS IN VANET

Wireless networks between vehicles formed by VANET (Vehicular Ad hoc Networks). VANET incorporates the wireless communication and data sharing capabilities to turn into vehicles as a network topology. VANET is showing great potential in research area. Inter-vehicle communication system in VANET improves traffic safety. VANET consists of high dynamic topology, Intermittent connectivity Patterned mobility and on board sensors etc. A challenging task in vehicular ad hoc network is routing of data due to its high dynamic behavior. An efficient routing protocol by understanding the dynamic behavior of VANET topology will plays an essential role in communication of vehicles. Mobility of nodes, road topologies are some of the internal factors are presented on the performance of routing protocols. Routing protocols are of mainly categorized into: Proactive, Reactive and Hybrid routing protocols. This paper occurs with performance evaluation of AODV (Ad-Hoc on-Demand Distance Vector), DSDV (Destination-Sequenced Distance Vector) routing protocols using mobility model IDM-IM (Intelligent Driver Model with Intersection Management, this mobility model is from VanetMobisim) based on metrics such as packet delivery ratio, throughput and average end to end delay. AODV is reactive routing protocol and DSDV is proactive routing protocol. IDM_IM is the VanetMobisim tool mobility model with a simulation area. In this paper we also present how the VanetMobisim interacts with Network Simulator. VanetMobisim presents results as a text file. The simulation results are presented with graphs. VanetMobisim and Network Simulator tools are open access tools. VanetMobisim a vehicular ad hoc network tool and network simulator-2(NS2) a simulation tool are used for implementation purpose.

Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication

The development of autonomous vehicle or self-driving car integrates with the wireless communication technology which would be a forward step for road transportation in the near future. The autonomous crossing of an intersection with an autonomous vehicle will play a crucial role in the future of intelligent transportation system (ITS). The fundamental objectives of this work are to manage autonomous vehicles crossing an intersection with no collisions, maintaining that a vehicle drives continuously, and to decrease the waiting time at an intersection. In this paper, a discrete model of the one-way single intersection is designed. The vehicle-to-infrastructure (V2I) communication is implemented to exchange information between a vehicle and an intersection manager which is the roadside infrastructure. The safe trajectory of autonomous vehicles for the autonomous intersection management is determined and presented by using discrete mathematics.

Automation Countermeasure System for Intersection Optimization

To satisfy the demand of congestion problem solving in intersections, this paper studies the method of automation countermeasure system for intersection optimization (ACSIO). Taking into account the extensive contents and objectives of intersection optimization, this paper puts forward the functions and architecture of ACSIO based on intersection optimization problem statement. Seeking optimal design of intersection channelization and signal control, the main goal of ACSIO is to achieve dynamic and coordination management of intersection. The problem is formulated as a multiobjective program, with each objective corresponding to a different player in the system. Moreover, it presents system design of ACSIO. A case study based on a real-world intersection is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

Coordinated intersection traffic management

This paper considers the problem of coordinating the passage of vehicles through a traffic intersection with the aim of minimizing total travel time and energy consumption. The intersection manager communicates with vehicles heading towards the intersection, groups them into clusters (termed bubbles) as they appear, and determines an optimal order of passage and average velocity profiles. Vehicles in a bubble receive the corresponding profile and implement local control to avoid collision with other bubbles in the same road and within the bubble itself, and reach the intersection at the prescribed time and with the bubble occupying the intersection for no more than a prescribed duration.

Assessing risk-taking in a driving simulator study: Modeling longitudinal semi-continuous driving data using a two-part regression model with correlated random effects

Signalized intersection management is a common measure of risky driving in simulator studies. In a recent randomized trial, investigators were interested in whether teenage males exposed to a risk-accepting passenger took more intersection risks in a driving simulator compared with those exposed to a risk-averse peer passenger. Analyses in this trial are complicated by the longitudinal or repeated measures that are semi-continuous with clumping at zero. Specifically, the dependent variable in a randomized trial looking at the effect of risk-accepting versus risk-averse peer passengers on teenage simulator driving is comprised of two components. The discrete component measures whether the teen driver stops for a yellow light, and the continuous component measures the time the teen driver, who does not stop, spends in the intersection during a red light. To convey both components of this measure, we apply a two-part regression with correlated random effects model (CREM), consisting of a logistic regression to model whether the driver stops for a yellow light and a linear regression to model the time spent in the intersection during a red light. These two components are related through the correlation of their random effects. Using this novel analysis, we found that those exposed to a risk-averse passenger have a higher proportion of stopping at yellow lights and a longer mean time in the intersection during a red light when they did not stop at the light compared to those exposed to a risk-accepting passenger, consistent with the study hypotheses and previous analyses. Examining the statistical properties of the CREM approach through simulations, we found that in most situations, the CREM achieves greater power than competing approaches. We also examined whether the treatment effect changes across the length of the drive and provided a sample size recommendation for detecting such phenomenon in subsequent trials. Our findings suggest that CREM provides an efficient method for analyzing the complex longitudinal data encountered in driving simulation studies.

Intersection Management via Vehicle Connectivity: The Intersection Cooperative Adaptive Cruise Control System Concept

Since the introduction of the vehicle infrastructure integration (VII) and connected vehicle (CV) initiatives in the United States, numerous in-vehicle technologies based on wireless communications are currently being deployed. One of these technologies is cooperative adaptive cruise control (CACC) systems, which provide better connectivity, safety, and mobility by allowing vehicles to travel in denser platoons through vehicle-to-vehicle (V2V) communication. Accordingly, the research presented in this article develops a simulation/optimization tool that optimizes the movement of CACC-equipped vehicles as a replacement for traditional intersection control. This system, which is named iCACC, assumes that the intersection controller receives vehicle requests to travel through an intersection and advises each vehicle on the optimum course of action ensuring no crashes occur while at the same time minimizing the intersection delay. Four intersection control scenarios are compared, namely: a traffic signal, an all-way stop control (AWSC), a roundabout, and the iCACC controller. The results show that the proposed iCACC system significantly reduces the average intersection delay and fuel consumption level by 90 and 45%, respectively. Additionally, the article investigates the impact of vehicle dynamics, weather conditions, and level of market penetration of equipped vehicles on the future of automated vehicle control.

An MPC-Based Approach to Provable System-Wide Safety and Liveness of Autonomous Ground Traffic

An important area of cyber-physical systems research is the development of smart ground transportation systems due to their potentially significant impact on safety, the economy, and the environment. We propose an approach based on model predictive control (MPC) for the development of provably collision free autonomous ground transportation systems, and present an autonomous intersection management framework. The MPC approach enables a vehicle to generate its own motion locally in time based on an optimization framework, incorporating constraints based on the states of other vehicles in the neighborhood, the speed limit of a road, the maximum values of acceleration and deceleration, etc. Safety and liveness of the traffic are however system-wide properties, not merely neighborhood properties, and the challenge is to augment this distributed optimization with coordination rules that guarantee overall system-wide safety as well as liveness of the traffic. We design two vehicle-to-vehicle (V2V) coordination rules, along with a vehicle-to-infrastructure rule, and establish the system-wide safety and liveness of the autonomous traffic based on each vehicle's MPC motion planner, operating in conjunction with an algorithm that orders vehicles according to their runtime properties. We also conduct a comparative simulation study of the throughput performance at an intersection of the above approach against another popular algorithm, the All-Way STOP.