Showing papers on "Intelligent transportation system published in 2012"

IEEE 802.11p vehicle to infrastructure communications in urban environments

Abstract: The future exploitation of cooperative vehicular services in urban environments, generally characterized by challenging propagation conditions, will require an efficient deployment of roadside units. This article presents the results of an extensive field testing campaign aimed at analyzing the impact of urban characteristics, RSU deployment conditions, and communication settings on the quality of IEEE 802.11p vehicle-to-infrastructure communications. The reported results show that the streets' layout, urban environment, traffic density, presence of heavy vehicles, trees, and terrain elevation have an effect on V2I communications, and should be taken into account to adequately deploy and configure urban RSUs.

Survey on security attacks in Vehicular Ad hoc Networks (VANETs)

Abstract: Vehicular Ad hoc Networks (VANETs) are emerging mobile ad hoc network technologies incorporating mobile routing protocols for inter-vehicle data communications to support intelligent transportation systems. Among others security and privacy are major research concerns in VANETs due to the frequent vehicles movement, time critical response and hybrid architecture of VANETs that make them different than other Ad hoc networks. Thus, designing security mechanisms to authenticate and validate transmitted message among vehicles and remove adversaries from the network are significantly important in VANETs. This paper presents several existing security attacks and approaches to defend against them, and discusses possible future security attacks with critical analysis and future research possibilities.

Computational Intelligence in Urban Traffic Signal Control: A Survey

Abstract: Urban transportation system is a large complex nonlinear system. It consists of surface-way networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and pedestrians. Traffic congestions occur frequently, which affect daily life and pose all kinds of problems and challenges. Alleviation of traffic congestions not only improves travel safety and efficiencies but also reduces environmental pollution. Among all the solutions, traffic signal control (TSC) is commonly thought as the most important and effective method. TSC algorithms have evolved quickly, especially over the past several decades. As a result, several TSC systems have been widely implemented in the world, making TSC a major component of intelligent transportation system (ITS). In TSC and ITS, many new technologies can be adopted. Computational intelligence (CI), which mainly includes artificial neural networks, fuzzy systems, and evolutionary computation algorithms, brings flexibility, autonomy, and robustness to overcome nonlinearity and randomness of traffic systems. This paper surveys some commonly used CI paradigms, analyzes their applications in TSC systems for urban surface-way and freeway networks, and introduces current and potential issues of control and management of recurrent and nonrecurrent congestions in traffic networks, in order to provide valuable references for further research and development.

Online map-matching based on Hidden Markov model for real-time traffic sensing applications

Abstract: In many Intelligent Transportation System (ITS) applications that crowd-source data from probe vehicles, a crucial step is to accurately map the GPS trajectories to the road network in real time. This process, known as map-matching, often needs to account for noise and sparseness of the data because (1) highly precise GPS traces are rarely available, and (2) dense trajectories are costly for live transmission and storage.

Progress and challenges in intelligent vehicle area networks

Abstract: Vehicle area networks form the backbone of future intelligent transportation systems.

Short-Term Traffic and Travel Time Prediction Models

Abstract: Road traffic is the visible result of the complex interplay between traffic demand (the amount and mix of vehicles arriving at a particular place and time) and traffic supply (e.g., capacity, prevailing speeds, and other average traffic characteristics). As a result, short-term prediction of road traffic variables is a complex nonlinear task that has been the subject of many research efforts in the past few decades. The term “short term” usually entails that the variables of interest are predicted for a period up to 1 h ahead, although the exact definition differs largely between approaches. In practical terms, short-term traffic prediction is an important if not critical component for intelligent transportation systems (ITS) and particularly in traffic control and traffic information provision. This paper briefly discusses some general aspects related to short-term traffic prediction. It then provides a taxonomy of the many different approaches reported in the literature for the general problem of short-term traffic prediction; in these cases, artificial intelligence (AI) techniques are discussed either as a complete solution or as part of a hybrid approach to short-term prediction.

Prediction of parking space availability in real time

Abstract: Intelligent parking reservation (IPR) systems allow customers to select a parking facility according to their preferences, rapidly park their vehicle without searching for a free stall, and pay their reservation in advance avoiding queues. Some IPR systems interact with in-vehicle navigation systems and provide users with information in real time such as capacity, parking fee, and current parking utilization. However, few of these systems provide information on the forecast utilization at specific hours - a process that requires the study of the competition between parking alternatives for the market share. This paper proposes a methodology for predicting real-time parking space availability in IPR architectures. This methodology consists of three subroutines to allocate simulated parking requests, estimate future departures, and forecast parking availability. Parking requests are allocated iteratively using an aggregated approach as a function of simulated drivers' preferences, and parking availability. This approach is based on a calibrated discrete choice model for selecting parking alternatives. A numerical comparison between a one-by-one simulation-based forecast and the proposed aggregated approach indicates that no significant discrepancies exists, validating and suggesting the use of the less time consuming proposed aggregated methodology. Results obtained from contrasting predictions with real data yielded small average error availabilities. The forecast improves as the system registers arrivals and departures. Thus, the forecast is adequate for potential distribution in real-time using different media such as Internet, navigation systems, cell phones or GIS.

Intelligent transportation systems for smart cities: a progress review

Abstract: To better meet the challenge of providing effective, low-cost, energy efficient transport services, the concept of intelligent transport systems (ITS) has been proposed and lauded as an innovative and promising solution for next generation transport networks. In this paper, the progress of ITS research around the world is briefly reviewed and current challenges are outlined, thereby offering further insight into ITS development for all researchers in this area.

A market-inspired approach for intersection management in urban road traffic networks

Abstract: Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to be a promising field of application for artificial intelligence techniques. In particular, multiagent systems seem to be the ideal candidates for the design and implementation of ITS. In fact, drivers can be naturally modelled as autonomous agents that interact with the transportation management infrastructure, thereby generating a large-scale, open, agent-based system. To regulate such a system and maintain a smooth and efficient flow of traffic, decentralised mechanisms for the management of the transportation infrastructure are needed. In this article we propose a distributed, market-inspired, mechanism for the management of a future urban road network, where intelligent autonomous vehicles, operated by software agents on behalf of their human owners, interact with the infrastructure in order to travel safely and efficiently through the road network. Building on the reservationbased intersection control model proposed by Dresner and Stone, we consider two different scenarios: one with a single intersection and one with a network of intersections. In the former, we analyse the performance of a novel policy based on combinatorial auctions for the allocation of reservations. In the latter, we analyse the impact that a traffic assignment strategy inspired by competitive markets has on the drivers' route choices. Finally we propose an adaptive management mechanism that integrates the auction-based traffic control policy with the competitive traffic assignment strategy.

Automotive control systems

Abstract: Preface Part I. Introduction and Background: 1. Introduction 2. Automotive control system design process 3. Review of engine modeling 4. Review of vehicle dynamics 5. Human factors and driver modeling Part II. Powertrain Control Systems: 6. Air-to-fuel ratio control 7. Control of spark timing 8. Idle speed control 9. Transmission control 10. Control of hybrid vehicles 11. Modeling and control of fuel cells for vehicles Part III. Vehicle Control Systems: 12. Cruise and headway control 13. Antilock brake systems and traction control 14. Vehicle stability control 15. Four wheel steering 16. Active suspensions Part IV. Intelligent Transportation Systems (ITS): 17. Overview of ITS 18. Preventing collisions 19. Automated highway systems (AHS) and platooning 20. Lateral active safety systems and automated steering Appendix A. Review of control theory fundamentals Appendix B. Two-mass three DOF vehicle lateral/yaw/roll model.

A computing platform for development and deployment of sensor-driven vehicle telemetry applications and services

Abstract: A computing platform for intelligent development, deployment and management of vehicle telemetry applications is disclosed herein. Further, the present invention provides a method and system that enables provision of Intelligent Transportation Service on the Cloud-based Platform that facilitates creation and deployment of vehicle telemetry applications configured for enabling traffic measurements, traffic shaping, vehicle surveillance and other vehicle related services.

On the Impact of Virtual Traffic Lights on Carbon Emissions Mitigation

Abstract: Considering that the transport sector is responsible for an increasingly important share of current environmental problems, we look at Intelligent Transportation Systems (ITS) as a feasible means of helping in solving this issue. In particular, we evaluate the impact in terms of Carbon Dioxide (CO2)emissions of Virtual Traffic Light (VTL), which is a recently proposed infrastructureless traffic control system solely based on Vehicle-to-Vehicle (V2V) communication. Our evaluation uses a real-city scenario in a complex simulation framework, involving microscopic traffic, wireless communication, and emission models. Compared with an approximation of the physical traffic light system deployed in the city, our results show a significant reduction on CO2 emissions when using VTLs, reaching nearly 20% under high-density traffic.

Intelligent automatic overtaking system using vision for vehicle detection

Abstract: Highlights? We have implemented an autonomous overtaking system in a commercial car. ? The system performs the maneuver as humans do, i.e., depending on the leading vehicle. ? Vision system is use to detect obstacles and to determine its length and width. ? Fuzzy logic was used as control technique to design lateral and longitudinal control. ? Real experiments show the ability of the system to manage any overtaking maneuver. There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous man?uvres involving vehicles - overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking man?uvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle's actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroen car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles - a motorbike, car, and truck - with encouraging results.

Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine

Abstract: Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaBoost algorithm and cascading method are adopted to segment pedestrian candidates from image. To confirm whether each candidate is pedestrian or not, a second stage is needed to eliminate some false positives. In this stage, a pedestrian recognizing classifier is trained with support vector machine (SVM). The input features used for SVM training are extracted from both the sample gray images and edge images. Finally, the performance of the proposed pedestrian detection method is tested with real-world data. Results show that the performance is better than conventional single-stage classifier, such as AdaBoost based or SVM based classifier.

Wireless sensor networks for traffic management and road safety

Abstract: Wireless sensor networks (WSN) employ self-powered sensing devices that are mutually interconnected through wireless ad-hoc technologies. This study illustrates the basics of WSN-based traffic monitoring and summarises the possible benefits in Intelligent Transport Systems (ITS) applications for the improvement of quality and safety of mobility. Compared with conventional infrastructure-based monitoring systems, this technology facilitates a denser deployment of sensors along the road, resulting in a higher spatial resolution of traffic parameter sampling. An experimental data analysis reported in this study shows how the high spatial resolution can enhance the reliability of traffic modelling as well as the accuracy of short-term traffic state prediction. The analysis uses the data published by the freeway performance measurement system of the University of California-Berkeley and the California Department of Transportation. A microscopic cellular automata model is used to estimate traffic flow and occupancy over time on a road segment in which a relevant traffic-flow anomaly is detected. The analysis shows that the estimate accuracy improves for increasing number of active sensors, as feasible in the case of WSN-based monitoring systems.

Field operational testing of ECO-approach technology at a fixed-time signalized intersection

Abstract: As part of a number of Intelligent Transportation (ITS) applications aimed at providing an environmental benefit, eco-approach technology is one that is feasible in the near-term. An eco-approach application uses Signal Phase and Timing (SPaT) and intersection map information of signalized intersections to provide drivers with recommendations in order to encourage “green” driving while approaching, passing through, and departing the intersections. Upon receiving SPaT information, invehicle systems calculate and provide speed advice to the driver, allowing the driver to adapt the vehicle's speed to pass through the upcoming signal on green or to decelerate to a stop in the most eco-friendly manner. Eco-approach methods have been proposed and simulated showing promising results. In this study, both simulation experimentation and field operational testing have been carried out to demonstrate the eco-approach application and to quantify its potential fuel and carbon dioxide (CO 2 ) savings. Furthermore, it has been shown that a communication platform based on a 4G/LTE network link and a cloud-based server infrastructure is effective and sufficient for this kind of application. It was found in both the simulation experiment and the field operational testing that on average 14% fuel and CO 2 savings can be achieved.

CrowdITS: Crowdsourcing in intelligent transportation systems

Abstract: Intelligent Transportation Systems (ITS) and their applications are attracting significant attention in research and industry. ITS makes use of various sensing and communication technologies to assist transportation authorities and vehicles drivers in making informative decisions and provide leisure and safe driving experience. Data collection and dispersion are of utmost importance for the proper operation of ITS applications. Numerous standards, architectures and communication protocols have been anticipated for ITS applications. However, existing schemes are based on assumption that vehicles and roadside devices are equipped with sensing and communication capabilities. One of the major gaps of these approaches is their inability to capture events that can easily be logged by drivers using their mobile phones. In this paper, we propose to fill the gap by the use of Crowdsourcing in ITS namely, CrowdITS. In CrowdITS human inputs, along with available sensory data, are collected and communicated to a processing server using mobile phones. The basic idea is to use the Crowd with smart mobile phones to enable certain ITS applications without the need of any special sensors or communication devices, both in-vehicle and on-road. Alternatively, we integrate and aggregate human inputs with multiple information sources, and then selectively disseminate the aggregated information based on the driver's geo-location. Conceptually, the major change is to integrate human inputs, with multiple information sources, aggregate and finally it is localized according to the driver's geo-location. We describe the design of CrowdITS, report on the development of key ITS applications using Android and iPhone mobile phones, and outline the future work in the development of crowdsourced-based applications for intelligent transportation systems.

Leveraging Smartphone Cameras for Collaborative Road Advisories

Abstract: Ubiquitous smartphones are increasingly becoming the dominant platform for collaborative sensing. Smartphones, with their ever richer set of sensors, are being used to enable collaborative driver-assistance services like traffic advisory and road condition monitoring. To enable such services, the smartphones' GPS, accelerometer, and gyro sensors have been widely used. On the contrary, smartphone cameras, despite being very powerful sensors, have largely been neglected. In this paper, we introduce a collaborative sensing platform that exploits the cameras of windshield-mounted smartphones. To demonstrate the potential of this platform, we propose several services that it can support, and prototype SignalGuru, a novel service that leverages windshield-mounted smartphones and their cameras to collaboratively detect and predict the schedule of traffic signals, enabling Green Light Optimal Speed Advisory (GLOSA) and other novel applications. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66 s, for pretimed traffic signals and within 2.45 s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3 percent, on average.

Global Versus Local MPC Algorithms in Freeway Traffic Control With Ramp Metering and Variable Speed Limits

Abstract: This paper compares global and local model predictive control (MPC) algorithms in a traffic network controlled by intelligent transportation system (ITS) signals (ramp metering and variable speed limits). It will be shown that local techniques have a suboptimal behavior and that centralized techniques are difficult to implement in real time. To deal with this problem, a local MPC with only one communication cycle at each sampling time is proposed. This controller improves the local controller performance, and although it is suboptimal with regard to the centralized controller behavior, it can be implemented in real time.

Synergistic sensor location for link flow inference without path enumeration: A node-based approach

Abstract: Sensors are becoming increasingly critical elements in contemporary transportation systems, gathering essential (real-time) traffic information for the planning, management and control of these complex systems. In a recent paper, Hu, Peeta and Chu introduced the interesting problem of determining the smallest subset of links in a traffic network for counting sensor installation, in such a way that it becomes possible to infer the flows on all remaining links. The problem is particularly elegant because of its limited number of assumptions. Unfortunately, path enumeration was required, which – as recognized by the authors – is infeasible for large-scale networks without further simplifying assumptions (that would destroy the assumption-free nature of the problem). In this paper, we present a reformulation of this link observability problem, requiring only node enumeration. Using this node-based approach, we prove a conjecture made by Hu, Peeta and Chu by deriving an explicit relationship between the number of nodes and links in a transportation network, and the minimum number of sensors to install in order to be able to infer all link flows. In addition, we demonstrate how the proposed method can be employed for road networks that already have sensors installed on them. Numerical examples are presented throughout.

A fuzzy aid rear-end collision warning/avoidance system

Abstract: Highlights? We have implemented a rear-end collision warning/avoidance system in a real car. ? The system decides how to perform the maneuver without leaving the road. ? A vehicle-to-infrastructure communication system is used to exchange data. ? Fuzzy logic is used both for the warning and for the avoidance systems. ? Experiments with real cars were conducted with propper results. To decrease traffic accidents is a declared target of Intelligent Transportation Systems (ITS). Among them, rear-end collisions are one of the most common and constitute one of the as yet unsolved topics in the automotive sector. This paper presents an approach to the avoidance of rear-end collisions in congested traffic situations. To this end, two fuzzy controllers, a Collision Warning System (CWS) and a Collision Avoidance System (CAS), have been developed. The former is in charge of alerting the driver in case of an impending rear-end collision to prevent or mitigate the crash. The latter is in charge of generating an output control signal for the steering wheel in order to avoid the collision. Both CWS and CAS have been tested with real cars using vehicle-to-infrastructure (V2I) communications to acquire data of vehicles. A system installed in the infrastructure capable of assessing road traffic conditions in real time is responsible for transmitting the data of the vehicles in the surrounding area. The systems have been tested at the Center for Automation and Robotics (CAR)'s facilities with two mass-produced cars.

Visible light communications in intelligent transportation systems

Abstract: Emerging intelligent transportation systems (ITS) are based on several technologies. A new concept of integrating visible light communications (VLC) in ITS is introduced in this paper. VLC in ITS is a cost effective method of implementation. This paper presents a VLC broadcast system considering LED-based traffic lights. It discusses the integration of traffic light road side unit (RSUs) within the existing ITS architecture. A prototype demonstrator of the designed VLC systems is also presented. A robust modulation technique based on direct sequence spread spectrum (DSSS) sequence inverse keying (SIK) has been implemented to minimize the effect of noise sources. Experimental results show data communication range of over 40m with 200mm custom designed traffic light, even during bright sun light.

Vision-based drowsiness detector for real driving conditions

Abstract: This paper presents a non-intrusive approach for drowsiness detection, based on computer vision. It is installed in a car and it is able to work under real operation conditions. An IR camera is placed in front of the driver, in the dashboard, in order to detect his face and obtain drowsiness clues from their eyes closure. It works in a robust and automatic way, without prior calibration. The presented system is composed of 3 stages. The first one is preprocessing, which includes face and eye detection and normalization. The second stage performs pupil position detection and characterization, combining it with an adaptive lighting filtering to make the system capable of dealing with outdoor illumination conditions. The final stage computes PERCLOS from eyes closure information. In order to evaluate this system, an outdoor database was generated, consisting of several experiments carried out during more than 25 driving hours. A study about the performance of this proposal, showing results from this testbench, is presented.

Performance Analysis of PMIPv6-Based NEtwork MObility for Intelligent Transportation Systems

Abstract: While host mobility support for individual mobile hosts (MHs) has been widely investigated and developed over the past years, there has been relatively less attention to NEtwork MObility (NEMO). Since NEMO Basic Support (NEMO-BS) was developed, it has been the central pillar in Intelligent Transport Systems (ITS) communication architectures for maintaining the vehicle's Internet connectivity. As the vehicle moves around, it attaches to a new access network and is required to register a new address obtained from the new access network to a home agent (HA). This location update of NEMO-BS often results in unacceptable long handover latency and increased traffic load to the vehicle. To address these issues, in this paper, we introduce new NEMO support protocols, which rely on mobility service provisioning entities introduced in Proxy Mobile IPv6 (PMIPv6), as possible mobility support protocols for ITS. As a base protocol, we present PMIPv6-based NEMO (P-NEMO) to maintain the vehicle's Internet connectivity while moving and without participating in the location update management. In P-NEMO, the mobility management for the vehicle is supported by mobility service provisioning entities residing in a given PMIPv6 domain. To further improve handover performance, fast P-NEMO (FP-NEMO) has been developed as an extension protocol. FP-NEMO utilizes wireless L2 events to anticipate the vehicle's handovers. The mobility service provisioning entities prepare the vehicle's handover prior to the attachment of the vehicle to the new access network. Detailed handover procedures for P-NEMO and FP-NEMO are provided, and handover timing diagrams are presented to evaluate the performance of the proposed protocols. P-NEMO and FP-NEMO are compared with NEMO-BS in terms of traffic cost and handover latency.

Real-time road condition acquiring, analyzing and back-feeding and intelligent transportation integrated service system

Abstract: The invention discloses a real-time road condition acquiring, analyzing and back-feeding and intelligent transportation integrated service system, which is divided into two parts of an intelligent terminal host and a server-side server farm The intelligent terminal host consists of electronic map application, GPS receiving, p2p data transmission, navigation, voice control, general message checking and a communication module; the server-side server farm consists of vehicle information and public TMC receiving, data integration and filtration, road condition analysis, road condition forecast, electronic map management and maintenance, dynamic information prompt, advertising publishing, data feedback and publishing, a server-side data base, parking space information collection and treatment, road section codes, road condition codes, road planning analog and a communication module The invention has wide road condition information acquisition source, strong data handling capacity, can carry out highly-accurate real-time road condition analysis, road condition information forecast, path planning navigation, information inquiry and the like, and can be widely applied in the fields of road planning, intelligent traffic management, vehicle navigation, car scheduling and the like

Accurate Global Localization Using Visual Odometry and Digital Maps on Urban Environments

Abstract: Over the past few years, advanced driver-assistance systems (ADASs) have become a key element in the research and development of intelligent transportation systems (ITSs) and particularly of intelligent vehicles. Many of these systems require accurate global localization information, which has been traditionally performed by the Global Positioning System (GPS), despite its well-known failings, particularly in urban environments. Different solutions have been attempted to bridge the gaps of GPS positioning errors, but they usually require additional expensive sensors. Vision-based algorithms have proved to be capable of tracking the position of a vehicle over long distances using only a sequence of images as input and with no prior knowledge of the environment. This paper describes a full solution to the estimation of the global position of a vehicle in a digital road map by means of visual information alone. Our solution is based on a stereo platform used to estimate the motion trajectory of the ego vehicle and a map-matching algorithm, which will correct the cumulative errors of the vision-based motion information and estimate the global position of the vehicle in a digital road map. We demonstrate our system in large-scale urban experiments reaching high accuracy in the estimation of the global position and allowing for longer GPS blackouts due to both the high accuracy of our visual odometry estimation and the correction of the cumulative error of the map-matching algorithm. Typically, challenging situations in urban environments such as nonstatic objects or illumination exceeding the dynamic range of the cameras are shown and discussed.

ITS-cloud: Cloud computing for Intelligent transportation system

Abstract: Cloud computing is known as services delivery such as shared resources, platforms, software and data, in the interest of end-users. They are located in distributed datacenters over a network such as the Internet. In this paper a new cloud computing model called ITS-Cloud applied to the Intelligent Transportation Systems (ITS) is proposed to improve transport outcomes such as road safety, transport productivity, travel reliability, informed travel choices, environment protection, and traffic resilience. It consists of two sub-models: the statistic and the dynamic cloud sub-models. In the former, vehicles benefit of the conventional cloud advantages however; the dynamic one which is a temporary cloud is formed by the vehicles themselves which represent the cloud datacenters. To validate our proposal, a simulation study is performed to deal with the load balancing as a NP-Complete problem. The reached results are obtained using Bees Life Algorithm (BLA) applied to ITS-Cloud and compared with those reached by (BLA) applied only to the conventional Cloud.

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

Abstract: As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.

A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic

Abstract: This paper presents a novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads. The loop sensor proposed in this paper detects large (e.g., bus) as well as small (e.g., bicycle) vehicles occupying any available space in the roadway, which is the main requirement for sensing heterogeneous and lane-less traffic. To accomplish the sensing of large as well as small vehicles, a multiple loop system with a new inductive loop sensor structure is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle, motor cycle, scooter, car, and bus but also enables accurate counting of the number of vehicles even in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus, the suitability of the proposed sensor system for any type of traffic has been established.

Intelligent Mechatronic Systems: Modeling, Control and Diagnosis

Abstract: Acting as a support resource for practitioners and professionals looking to advance their understanding of complex mechatronic systems, Intelligent Mechatronic Systems explains their design and recent developments from first principles to practical applications. Detailed descriptions of the mathematical models of complex mechatronic systems, developed from fundamental physical relationships, are built on to develop innovative solutions with particular emphasis on physical model-based control strategies. Following a concurrent engineering approach, supported by industrial case studies, and drawing on the practical experience of the authors, Intelligent Mechatronic Systems covers range of topic and includes: An explanation of a common graphical tool for integrated design and its uses from modeling and simulation to the control synthesis Introductions to key concepts such as different means of achieving fault tolerance, robust overwhelming control and force and impedance control Dedicated chapters for advanced topics such as multibody dynamics and micro-electromechanical systems, vehicle mechatronic systems, robot kinematics and dynamics, space robotics and intelligent transportation systems Detailed discussion of cooperative environments and reconfigurable systems Intelligent Mechatronic Systems provides control, electrical and mechanical engineers and researchers in industrial automation with a means to design practical, functional and safe intelligent systems.