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Linear Seam Elimination of Tunnel Crack Images Based on Statistical Specific Pixels Ratio and Adaptive Fragmented Segmentation

From November 4th to 7th, 2018, the IEEE Intelligent Transportation Systems Society (ITSS) sponsored the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018), one of the most prestigious academic meetings on Intelligent Transportation Systems. The 2018 annual flagship conference of the IEEE ITS Society was held in Maui, Hawaii, reaching unprecedented numbers in terms

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Is It Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

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Traditional industry is experiencing a worldwide evolution with Industry 4.0. Wireless sensor networks (WSNs) have a main role in this evolution as an essential part of data acquisition and the way in which WSNs are powered is one of the main challenges to face, if the industry wants to achieve digital transformation. Energy harvesting technologies are one of the possible solutions to this challenge

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This paper addresses the problem of operator-based vehicle repositioning in free-floating electric car-sharing systems (FFCSs). New algorithmic techniques are developed which derive cost-effective relocation tours by primarily minimizing relocation drivers’ walking time. Car repositioning takes place overnight and aims at complete rebalancing of the system i.e., at achieving an optimal, based on user

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Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

Cloud Computing, a catchy metaphor for utility computing, implemented through the provisioning of various types of hosted services over the Internet, has seen phenomenal growth and quasi-universal adoption in the past two decades. The underlying business model of cloud computing is the familiar “pay-as-you-go” model of metered services, where a user pays for whatever he/she uses and no more, and where

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This paper investigates the dynamic system optimum (DSO) problem with simultaneous route and departure time assignments for a general traffic network partitioned into multiple regions. Regional traffic congestion is modeled with a well-defined macroscopic fundamental diagram (MFD) mapping the trip completion rate to the vehicular accumulation. To overcome the limitation of inconsistent flow propagation

Collecting effective data is a fundamental step in developing transport networks and related research. Social media have become an emerging source of data for traffic analyses. In this paper, we demonstrate that the function of a city influences the utility of social media data in travel demand models by generating models for eight US cities with different functions. Data from Twitter and Foursquare

Computation offloading for vehicular edge computing (VEC) architecture has gained increasing attention, with the emergence of mobile and vehicular applications with high-computing and low-latency demands, such as Intelligent Transportation Systems and IoT-based applications. However, existing challenges need to be addressed for VEC’s resources to be used in an efficient manner. The fundamental challenges

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Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey

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Recognizing human actions, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Intelligent transport systems in particular face this challenge, as interactions with people are often required. The development and testing of technical perception solutions is done

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A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice

This paper introduces a model to analyze route choice behavior of taxi drivers for finding next passenger in urban road network. Considering the situation of path overlapping between selected routes in the process of customer-searching, a mixed path size logit (MPSL) model is proposed to analyze route choice behaviors through considering spatio-temporal features of route including customer generation

For the $N$ -impulse transfer between two earth orbits, this paper introduces $N - 1$ intermediate orbits to describe the orbit transfer scheme. The eccentricity vector of the intermediate orbits and the true anomalies corresponding to the impulsive points are chosen as the optimization variables. Based on the patched conic theory, candidate solution can be analytically derived, constraints are removed

Battery swapping stations (BSSs) present an alternative way of charging electric vehicles (EVs) that can lead toward a sustainable EV ecosystem. Although research focusing on the BSS strategies has been ongoing, the results are fragmented. Currently, an integrated way of considering stochastic EV station visits through planning and operations has not been fully investigated. To create comprehensive

Global localization in 3D point clouds is a challenging task for mobile vehicles in outdoor scenarios, which requires the vehicle to localize itself correctly in a given map without prior knowledge of its pose. This is a critical component of autonomous vehicles or robots on the road for handling localization failures. In this paper, based on reduced dimension scan representations learned from neural

Automated vehicles can change the society by improved safety, mobility, and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed

Background subtraction is an example of a moving object detection technique that uses machine vision systems. Conventional moving object detection methods need complicated thresholds for background modeling to address changes in illumination. This paper proposes a novel background modeling approach without thresholding based on a bit-planes method, which fully utilizes color characteristics through

The performance of offline learned pedestrian detectors significantly drops when they are applied to video scenes of various camera views, occlusions, and background structures. Learning a detector for each video scene can avoid the performance drop but it requires repetitive human effort on data annotation. In this paper, a self-learning approach is proposed, toward specifying a pedestrian detector

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs

Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance

With the development of modern wireless communication technology, especially the vehicle infrastructure integration (VII) technology, vehicles’ information such as identification, location, and speed can be readily obtained at upstream cross-section. This information can be used to support traffic signal timing optimization in real time. A dynamic predictive traffic signal control framework for isolated

Traffic sign detection is a central part of autonomous vehicle technology. Recent advances in deep learning algorithms have motivated researchers to use neural networks to perform this task. In this paper, we look at traffic sign detection as an image segmentation problem and propose a deep convolutional neural network-based approach to solve it. To this end, we propose a new network, the SegU-Net

Accurate short-term passenger demand prediction contributes to the coordination of traffic supply and demand. This paper proposes an end-to-end multi-task learning temporal convolutional neural network (MTL-TCNN) to predict the short-term passenger demand in a multi-zone level. Along with a feature selector named spatiotemporal dynamic time warping (ST-DTW) algorithm, this proposed MTL-TCNN is quite

How well stations are connected with each other is a critical performance assessment of railway systems. Network analysis is a highly intuitive and interpretable approach to characterizing the physical connectivity of railway systems. However, the physical connectivity is often limited in depicting transportation network dynamics due to the lack of traffic flow information. This paper first comprehensively

Protecting critical infrastructures has been deemed a natural security imperative by the U.S. Government for the past 20 years. The resilience of transportation infrastructures is of particular importance, as they serve a crucial rule in economic development and citizen mobility throughout the world. This paper presents a defender–attacker–defender model to analyze the potential impacts of intelligent

In this paper, we propose a novel semantic segmentation-based stereo reconstruction method that can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the classic stereo pipeline, each step in the stereo workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation

Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with

This paper presents a newly developed microsimulation model for motorway roadwork sections to evaluate the efficiency of different temporary traffic management schemes (TTMSs) such as the use of narrow lanes, offside and inside lane closures. The effect on traffic performance of various parameters (e.g. flow rates, percentage of heavy goods vehicles (HGVs), roadwork zone lengths and speed limits) has

This paper presents the problem of block simplex constrained signal recovery, which has been demonstrated to be a suitable formulation for estimation problems in networks such as route flow estimation in traffic. There are several natural approaches to this problem: compressed sensing, Bayesian inference, and convex optimization. This paper presents new methods within each framework and assesses their

This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately

Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently, convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion, and bad light conditions)

Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicenter of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green

Visual tracking is a vital component of autonomous driving perception system. Siamese networks have achieved great success in both accuracy and speed for visual tracking tasks. These Siamese trackers share a similar framework in which each tracker consists of two network branches for exploring semantic information. However, the performance of Siamese trackers is limited by an insufficient semantic

Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks

The advent of vehicles’ connectivity and automation opens up unprecedented opportunities to make road transport more efficient and sustainable going far beyond what can be achieved by the vehicle’s intelligence alone, even in the presence of imperfect information of the surrounding traffic. Our community, which has been trying for decades to manage one of the most complex human dynamics based on limited

Calibration and validation have long been a significant topic in traffic model development. In fact, when moving to dynamic traffic assignment (DTA) models, the need to dynamically update the demand and supply components creates a considerable burden on the existing calibration algorithms, often rendering them impractical. These calibration approaches are mostly restricted either due to non-linearity

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle

Road vehicles are characterized by increasing levels of automation and it is vital to understand the future impact on transport efficiency. Adaptive Cruise Control (ACC) is one of the first and most common automated functionalities available in privately owned vehicles. The effect of ACC on traffic flow has been widely studied by making assumptions on its operating strategy and on some of its important

The expansion of road infrastructure, in spite of increasing congestion levels, faces severe restrictions from all sorts: economical, environmental, social, or technical. An efficient and, usually, less expensive alternative to improve mobility and the use of available infrastructure is the adoption of traffic management. A particular case of interest occurs when inbound and outbound traffic on a given