Showing papers in "IEEE Transactions on Intelligent Transportation Systems in 2011"

Data-Driven Intelligent Transportation Systems: A Survey

Abstract: For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

Abstract: This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.

A Review of Computer Vision Techniques for the Analysis of Urban Traffic

Abstract: Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.

Driver Inattention Monitoring System for Intelligent Vehicles: A Review

Abstract: In this paper, we review the state-of-the-art technologies for driver inattention monitoring, which can be classified into the following two main categories: 1) distraction and 2) fatigue. Driver inattention is a major factor in most traffic accidents. Research and development has actively been carried out for decades, with the goal of precisely determining the drivers' state of mind. In this paper, we summarize these approaches by dividing them into the following five different types of measures: 1) subjective report measures; 2) driver biological measures; 3) driver physical measures; 4) driving performance measures; and 5) hybrid measures. Among these approaches, subjective report measures and driver biological measures are not suitable under real driving conditions but could serve as some rough ground-truth indicators. The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system. We also discuss some nonlinear modeling techniques commonly used in the literature.

Greenhouse Gas Emission Impacts of Carsharing in North America

Abstract: This paper evaluates the greenhouse gas (GHG) emission impacts that result from individuals participating in carsharing organizations within North America. The authors con ducted an online survey with members of major carsharing organizations and evaluated the change in annual household emissions (e.g., impact) of respondents that joined carsharing. The results show that a majority of households joining carsharing are increasing their emissions by gaining access to automobiles. However, individually, these increases are small. In contrast, the remaining households are decreasing their emissions by shedding vehicles and driving less. The collective emission reductions outweigh the collective emission increases, which implies that carsharing reduces GHG emissions as a whole. The results are reported in the form of an observed impact, which strictly evaluates the changes in emissions that physically occur, and a full impact, which also considers emissions that would have happened but were avoided due to carsharing. The mean observed impact is -0.58 t GHG/year per household, whereas the mean full impact is -0.84 t GHG/year per household. Both means are statistically significant. We present a sensitivity analysis to evaluate the robustness of the results and find that the overall results hold across a variety of assumptions. The average observed vehicle kilometers traveled (VKT) per year was found to decline by 27%. We conclude with an evaluation of the annual aggregate impacts of carsharing based on current knowledge of the industry membership population.

Practical String Stability of Platoon of Adaptive Cruise Control Vehicles

Abstract: In this paper, the practical string stability of both homogeneous and heterogeneous platoons of adaptive cruise control (ACC) vehicles, which apply the constant time headway spacing policy, is investigated by considering the parasitic time delays and lags of the actuators and sensors when building the vehicle longitudinal dynamics model. The proposed control law based on the sliding-mode controller can guarantee both homogeneous and heterogeneous string stability, if the control parameters and system parameters meet certain requirements. The analysis of the negative effect of the parasitic time delays and lags on the string stability indicates that the negative effect of the time delays is larger than that of the time lags. This paper provides a practical means to evaluate the ACC systems applying the sliding-mode controller and provides a reasonable proposal to design the ACC controller from the perspective of the practical string stability.

Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach

Abstract: Recently, improvements in sensing, communicating, and computing technologies have led to the development of driver-assistance systems (DASs). Such systems aim at helping drivers by either providing a warning to reduce crashes or doing some of the control tasks to relieve a driver from repetitive and boring tasks. Thus, for example, adaptive cruise control (ACC) aims at relieving a driver from manually adjusting his/her speed to maintain a constant speed or a safe distance from the vehicle in front of him/her. Currently, ACC can be improved through vehicle-to-vehicle communication, where the current speed and acceleration of a vehicle can be transmitted to the following vehicles by intervehicle communication. This way, vehicle-to-vehicle communication with ACC can be combined in one single system called cooperative adaptive cruise control (CACC). This paper investigates CACC by proposing a novel approach for the design of autonomous vehicle controllers based on modern machine-learning techniques. More specifically, this paper shows how a reinforcement-learning approach can be used to develop controllers for the secure longitudinal following of a front vehicle. This approach uses function approximation techniques along with gradient-descent learning algorithms as a means of directly modifying a control policy to optimize its performance. The experimental results, through simulation, show that this design approach can result in efficient behavior for CACC.

Road Detection Based on Illuminant Invariance

Abstract: By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms.

Ecological Vehicle Control on Roads With Up-Down Slopes

Abstract: This paper presents a novel development of an ecological (eco) driving system for running a vehicle on roads with up-down slopes. Fuel consumed in a vehicle is greatly influenced by road gradients, aside from its velocity and acceleration characteristics. Therefore, optimum control inputs can only be computed through anticipated rigorous reasoning using information concerning road terrain, model of the vehicle dynamics, and fuel consumption characteristics. In this development, a nonlinear model predictive control method with a fast optimization algorithm is implemented to derive the vehicle control inputs based on road gradient conditions obtained from digital road maps. The fuel consumption model of a typical vehicle is formulated using engine efficiency characteristics and used in the objective function to ensure fuel economy driving. The proposed eco-driving system is simulated on a typical road with various shapes of up-down slopes. Simulation results reveal the ability of the eco-driving system in significantly reducing fuel consumption of a vehicle. The fuel saving behavior is graphically illustrated, compared, and analyzed to focus on the significance of this development.

An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

Abstract: An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.

Vehicle Detection and Tracking in Car Video Based on Motion Model

Abstract: This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.

Reinforcement Learning With Function Approximation for Traffic Signal Control

Abstract: We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e.g., the work of Abdulhai , on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai and Cools , as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

Trajectory Clustering and an Application to Airspace Monitoring

Abstract: This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft.

Local Feedback-Based Mainstream Traffic Flow Control on Motorways Using Variable Speed Limits

Abstract: Recent research has proposed mainstream traffic flow control (MTFC), enabled via variable speed limits (VSLs), as a novel motorway traffic management tool and has demonstrated its efficiency based on sophisticated optimal control methods that may face difficulties in practical field implementations. A simple local MTFC feedback controller is designed in this paper, taking into account a number of practical requirements and restrictions. The MTFC controller relies only on readily available real-time measurements (no online model usage and no demand predictions are needed) and is therefore robust and suitable for field implementations. The controller is evaluated in simulation and compared with optimal control results. Despite its simplicity, the new controller's performance is shown to approach the optimal control results while considering several practical and safety restrictions for a number of investigated scenarios.

Automated On-Ramp Merging System for Congested Traffic Situations

Abstract: Traffic merging in urban environments is one of the main causes of traffic congestion. From the driver's point of view, the difficulty arises along the on-ramp where the merging vehicle's driver has to discern whether he should accelerate or decelerate to enter the main road. In parallel, the drivers of the vehicles already on the major road may have to modify their speeds to permit the entrance of the merging vehicle, thus affecting the traffic flow. This paper presents an approach to merging from a minor to a major road in congested traffic situations. An automated merging system that was developed with two principal goals, i.e., to permit the merging vehicle to sufficiently fluidly enter the major road to avoid congestion on the minor road and to modify the speed of the vehicles already on the main road to minimize the effect on that already congested main road, is described. A fuzzy controller is developed to act on the vehicles' longitudinal control - throttle and brake pedals - following the references set by a decision algorithm. Data from other vehicles are acquired using wireless vehicle-to-infrastructure (V2I) communication. A system installed in the infrastructure that is capable of assessing road traffic conditions in real time is responsible for transmitting the data of the vehicles in the surrounding area. Three production vehicles were used in the experimental phase to validate the proposed system at the facilities of the Centro de Automatica y Robotica with encouraging results.

PACP: An Efficient Pseudonymous Authentication-Based Conditional Privacy Protocol for VANETs

Abstract: In this paper, we propose a new privacy preservation scheme, named pseudonymous authentication-based conditional privacy (PACP), which allows vehicles in a vehicular ad hoc network (VANET) to use pseudonyms instead of their true identity to obtain provably good privacy. In our scheme, vehicles interact with roadside units to help them generate pseudonyms for anonymous communication. In our setup, the pseudonyms are only known to the vehicles but have no other entities in the network. In addition, our scheme provides an efficient revocation mechanism that allows vehicles to be identified and revoked from the network if needed. Thus, we provide conditional privacy to the vehicles in the system, that is, the vehicles will be anonymous in the network until they are revoked, at which point, they cease to be anonymous.

A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems

Abstract: Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.

Active Pedestrian Safety by Automatic Braking and Evasive Steering

Abstract: Active safety systems hold great potential for reducing accident frequency and severity by warning the driver and/or exerting automatic vehicle control ahead of crashes. This paper presents a novel active pedestrian safety system that combines sensing, situation analysis, decision making, and vehicle control. The sensing component is based on stereo vision, and it fuses the following two complementary approaches for added robustness: 1) motion-based object detection and 2) pedestrian recognition. The highlight of the system is its ability to decide, within a split second, whether it will perform automatic braking or evasive steering and reliably execute this maneuver at relatively high vehicle speed (up to 50 km/h). We performed extensive precrash experiments with the system on the test track (22 scenarios with real pedestrians and a dummy). We obtained a significant benefit in detection performance and improved lateral velocity estimation by the fusion of motion-based object detection and pedestrian recognition. On a fully reproducible scenario subset, involving the dummy that laterally enters into the vehicle path from behind an occlusion, the system executed, in more than 40 trials, the intended vehicle action, i.e., automatic braking (if a full stop is still possible) or automatic evasive steering.

Fast Model Predictive Control for Urban Road Networks via MILP

Abstract: In this paper, an advanced control strategy, i.e., model predictive control (MPC), is applied to control and coordinate urban traffic networks. However, due to the nonlinearity of the prediction model, the optimization of MPC is a nonlinear nonconvex optimization problem. In this case, the online computational complexity becomes a big challenge for the MPC controller if it is implemented in a real-life traffic network. To overcome this problem, the online optimization problem is reformulated into a mixed-integer linear programming (MILP) optimization problem to increase the real-time feasibility of the MPC control strategy. The new optimization problem can be very efficiently solved by existing MILP solvers, and the global optimum of the problem is guaranteed. Moreover, we propose an approach to reduce the complexity of the MILP optimization problem even further. The simulation results show that the MILP-based MPC controllers can reach the same performance, but the time taken to solve the optimization becomes only a few seconds, which is a significant reduction, compared with the time required by the original MPC controller.

Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid

Abstract: Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can be modeled and tracked as an occupancy grid. This paper presents a novel occupancy grid tracking solution based on particles for tracking the dynamic driving environment. The particles will have a dual nature-they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell, depending on their motion model and motion parameters, but they will be also created and destroyed using a weighting-resampling mechanism that is specific to particle filtering algorithms. The tracking algorithm will be centered on particles, instead of cells. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulting system is a flexible real-time tracking solution for dynamic unstructured driving environments.

On the Effectiveness of an Opportunistic Traffic Management System for Vehicular Networks

Abstract: Road congestion results in a huge waste of time and productivity for millions of people. A possible way to deal with this problem is to have transportation authorities distribute traffic information to drivers, which, in turn, can decide (or be aided by a navigator) to route around congested areas. Such traffic information can be gathered by relying on static sensors placed at specific road locations (e.g., induction loops and video cameras) or by having single vehicles report their location, speed, and travel time. While the former approach has been widely exploited, the latter has come about only more recently; consequently, its potential is less understood. For this reason, in this paper, we study a realistic test case that allows the evaluation of the effectiveness of such a solution. As part of this process, (a) we designed a system that allows vehicles to crowd-source traffic information in an ad hoc manner, allowing them to dynamically reroute based on individually collected traffic information; (b) we implemented a realistic network-mobility simulator that allowed us to evaluate such a model; and (c) we performed a case study that evaluates whether such a decentralized system can help drivers to minimize trip times, which is the main focus of this paper. This study is based on traffic survey data from Portland, OR, and our results indicate that such navigation systems can indeed greatly improve traffic flow. Finally, to test the feasibility of our approach, we implemented our system and ran some real experiments at UCLA's C-Vet test bed.

Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller

Abstract: Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.

Image Segmentation and Shape Analysis for Road-Sign Detection

Abstract: This paper proposes an automatic road-sign recognition method based on image segmentation and joint transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular, triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs); 2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the unknown signs with the known reference road signs stored in the database. Experimental results on real-life images show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to translation, rotation, scale, and partial occlusions.

A Probabilistic Analysis of Link Duration in Vehicular Ad Hoc Networks

Abstract: The past decade has witnessed a phenomenal market penetration of wireless communications and a steady increase in the number of mobile users. Unlike wired networks, where communication links are inherently stable, in wireless networks, the lifetime of a link is a random variable whose probability distribution depends on mobility, transmission range, and various impairments of radio communications. Because of the very dynamic nature of Vehicular Ad hoc NETworks (VANETs) and the short transmission range mandated by the Federal Communications Commission (FCC), individual communication links come into existence and vanish unpredictably, making the task of establishing and maintaining routing paths between fast-moving vehicles very challenging. The main contribution of this work is to investigate the probability distribution of the lifetime of individual links in a VANET under the combined assumptions of a realistic radio transmission model and a realistic probability distribution model of intervehicle headway distance. Our analytical results were validated and confirmed by extensive simulation.

Online Driver Distraction Detection Using Long Short-Term Memory

Abstract: Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus, we propose a novel technique for online detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data. We show that long short-term memory (LSTM) recurrent neural networks enable a reliable subject-independent detection of inattention with an accuracy of up to 96.6%. Thereby, our LSTM framework significantly outperforms conventional approaches such as support vector machines (SVMs).

Prediction Intervals to Account for Uncertainties in Travel Time Prediction

Abstract: The accurate prediction of travel times is desirable but frequently prone to error. This is mainly attributable to both the underlying traffic processes and the data that are used to infer travel time. A more meaningful and pragmatic approach is to view travel time prediction as a probabilistic inference and to construct prediction intervals (PIs), which cover the range of probable travel times travelers may encounter. This paper introduces the delta and Bayesian techniques for the construction of PIs. Quantitative measures are developed and applied for a comprehensive assessment of the constructed PIs. These measures simultaneously address two important aspects of PIs: 1) coverage probability and 2) length. The Bayesian and delta methods are used to construct PIs for the neural network (NN) point forecasts of bus and freeway travel time data sets. The obtained results indicate that the delta technique outperforms the Bayesian technique in terms of narrowness of PIs with satisfactory coverage probability. In contrast, PIs constructed using the Bayesian technique are more robust against the NN structure and exhibit excellent coverage probability.

Computationally Inexpensive Tracking Control of High-Speed Trains With Traction/Braking Saturation

Abstract: The problem of the position and velocity tracking control of high-speed trains becomes interesting yet challenging when simultaneously considering inevitable factors such as the resistive friction and aerodynamic drag forces, the interactive impacts among the vehicles, and the nonlinear traction/braking notches inherent in train systems. In this paper, a multiple point mass with a single-coordinate dynamic model that reflects resistive and transient impacts is derived, and based on this, computationally inexpensive robust adaptive control designs with optimal task distribution for speed and position tracking are proposed under traction/braking nonlinearities and saturation limitations. It is shown that the proposed method is not only robust to external disturbances, aerodynamic resistance, mechanical resistance, and transient impacts but adaptive to unknown system parameters as well. The effectiveness of the proposed approach is also confirmed through numerical simulations.

Variational Inference for Infinite Mixtures of Gaussian Processes With Applications to Traffic Flow Prediction

Abstract: This paper proposes a new variational approximation for infinite mixtures of Gaussian processes. As an extension of the single Gaussian process regression model, mixtures of Gaussian processes can characterize varying covariances or multimodal data and reduce the deficiency of the computationally cubic complexity of the single Gaussian process model. The infinite mixture of Gaussian processes further integrates a Dirichlet process prior to allowing the number of mixture components to automatically be determined from data. We use variational inference and a truncated stick-breaking representation of the Dirichlet process to approximate the posterior of hidden variables involved in the model. To fix the hyperparameters of the model, the variational EM algorithm and a greedy algorithm are employed. In addition to presenting the variational infinite-mixture model, we apply it to the problem of traffic flow prediction. Experiments with comparisons to other approaches show the effectiveness of the proposed model.

Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars

Abstract: The probabilistic prediction of road traffic scenarios is addressed. One result is a probabilistic occupancy of traffic participants, and the other result is the collision risk for autonomous vehicles when executing a planned maneuver. The probabilistic occupancy of surrounding traffic participants helps to plan the maneuver of an autonomous vehicle, whereas the computed collision risk helps to decide if a planned maneuver should be executed. Two methods for the probabilistic prediction are presented and compared: 1) Markov chain abstraction and 2) Monte Carlo simulation. The performance of both methods is evaluated with respect to the prediction of the probabilistic occupancy and the collision risk. For each comparison test, we use the same models that generate the probabilistic behavior of traffic participants, where the generation of these data is not compared with real-world data. However, the results independently show the behavior generation that Markov chains are preferred for the probabilistic occupancy, whereas Monte Carlo simulation is clearly preferred for determining the collision risk.

Collision Avoidance in Air Traffic Management: A Mixed-Integer Linear Optimization Approach

Abstract: This paper tackles the collision-avoidance problem in air traffic management. The problem consists of deciding the best strategy for new aircraft configurations (velocity and altitude changes) such that all conflicts in the airspace, i.e., the loss of the minimum safety distance that has to be kept between two aircraft, are avoided. A mixed 0-1 linear optimization model based on geometric transformations for collision avoidance between an arbitrary number of aircraft in the airspace is developed. Knowing the initial coordinates, angle direction, and level flight, the new configuration for each aircraft is established by minimizing several objective functions, e.g., velocity variation and total number of changes (velocity and altitude), and forcing to return to the original flight configuration when no aircraft are in conflict. Due to the small computational time for the execution, the new configuration approach can be used in real time by using optimization software.