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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

This paper presents an adaptive linear quadratic optimal traffic control system. The control strategies are solved via a decentralized approach and complemented with a user-optimal network traffic router. The user-optimal routing algorithm assists drivers respond to prevailing traffic state and control settings and seek the quickest route toward their destinations. The proposed control system is implemented

We study the problem of controlling phase activations for signalized junctions in an urban transportation network using local feedback information consisting of measures of the queue-lengths at the incoming lanes of each junction. Our focus is on the validation and performance evaluation through micro-simulations of the recently proposed Generalized Proportional Allocation (GPA) controller. Previous

Eco-driving, a timely and well-known subject, aims at reducing fuel consumption by appropriately maneuvering a vehicle with a human or automated driver. In this work, the eco-driving problem is cast in an optimal control framework. State equations reflect the simple vehicle kinematics for position and speed, with the acceleration acting as a control input. Initial and final states (position and speed)

The increase in traffic volumes in urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles in intelligent transport systems presents unique opportunities for improving traffic flow and reducing delays in urban areas. This paper proposes a novel traffic signal control algorithm called Multi-mode Adaptive Traffic Signals (MATS) which combines

Queue- and delay-based back-pressure control algorithms are appropriate for homogeneous traffic with strict lane discipline. Every traffic movement does not form a separate queue and vehicular dimensions are highly varied in non-lane-based heterogeneous traffic conditions. In this paper, we propose a novel decentralized, area occupancy-based back-pressure control for a non-lane-based heterogeneous

In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal

We introduce a novel hybrid controller, which aims to maintain regularity on bus operations while reducing the number of stops at signalized intersections. The controller combines a rule-based holding criterion with a cooperative intelligent transportation systems based strategy, namely green light optimal dwell time adaptation (GLODTA), which exploits signal phase and timing information to modify

This paper presents a concept, herein called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC), for organizing directionally unrestricted traffic flows in automated vehicle environment. In the proposed concept, vehicles can use lanes traditionally reserved for the opposite direction of travel. This concept allows left and right turning vehicles to align

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In this paper, a real-world problem of the vehicle-type classification for automatic toll collection (ATC) is considered. This problem is very challenging because any loss of accuracy even of the order of 1% quickly turns into a significant economic loss. To deal with such a problem, many companies currently use optical sensors (OSs) and human observers to correct the classification errors. Herein

Public transport buses have uncertainties associated with its arrival/travel times, due to several factors such as signals, dwell times at bus stops, seasonal variations, fluctuating travel demands, and so on. In the developing world, these uncertainties are further magnified by the presence of excess vehicles, diverse modes of transport, and acute lack of lane discipline. Hence, the problem of bus

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Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between vehicles and pedestrians, the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends

The development of the complexity and connectivity of modern automobiles has caused a massive rise in the security risks of in-vehicle networks (IVNs). Nevertheless, existing IVN designs (e.g., controller area network) lack cybersecurity consideration. Intrusion detection, an effective method for defending against cyberattacks on IVNs while providing functional safety and real-time communication guarantees

In metropolitan areas, drivers share their vehicles with people who commute daily via carpooling. In this paper, we first defined a multiobjective carpool service problem with time windows (MOCSPTW) by considering four optimized objectives; subsequently, we propose a coevolutionary algorithm for two solution sets, population and archive, using objective-wise local search and set-based simulated binary

Pantograph is one of the most important components in electrical railway vehicles. To guarantee steady power supply for the train, the surface of the pantograph slide plate should be smooth enough so that the catenary can move on it from one side to the other side steadily with low friction. In addition, the thickness of the pantograph slide plate cannot be smaller than the lower limit for the sake

The video-based separation of foreground (FG) and background (BG) has been widely studied due to its vital role in many applications, including intelligent transportation and video surveillance. Most of the existing algorithms are based on traditional computer vision techniques that perform pixel-level processing assuming that FG and BG possess distinct visual characteristics. Recently, state-of-the-art

Urban traffic passenger flows prediction is practically important to facilitate many real applications including transportation management and public safety. Recently, deep learning based approaches are proposed to learn the spatio-temporal characteristics of the traffic passenger flows. However, it is still very challenging to handle some complex factors such as hybrid transportation lines, mixed

This paper studies a novel sustainable ship routing problem considering a time window concept and bunker fuel management. Ship routing involves the decisions corresponding to the deployment of vessels to multiple ports and time window concept helps to maintain the service level of the port. Reducing carbon emissions within the maritime transportation domain remains one of the most significant challenges

Autonomous vehicles have been emerging over the past few years because of the sophisticated processing of data from different types of perception sensors, such as LiDAR, radar, and camera. Ground segmentation plays an important role in the sequence of data processing for environment perception tasks, as it can help to reduce the size of data to be processed and further decrease the overall computational

Paid curbside parking can be advantageously modeled as a network of interdependent queues. To this end, we introduce methods for analyzing a special class of networks of finite capacity queues where drivers arrive from an exogenous source, join the queue if there is an available parking space, or continue to search at an adjacent queue for an available space. Furthermore, we apply this model to estimate

Insufficient network capacity and conflicts between pedestrians and vehicles at roadway intersections can be critical obstacles to the operational efficiency of evacuation activities. Reducing pedestrian—vehicle conflict points and expanding network capacities are two possible approaches to improving operational efficiency, especially when network accessibility varies in different evacuation stages

Routing protocols for vehicular ad hoc networks resort to clustering in order to optimize network performance. Concerning the optimized link state routing protocol and the plethora of its derivatives, the multipoint relaying (MPR) technique has proven its efficiency as an accurate clustering scheme over the last two decades. However, it has been emphasized recently that the MPR technique, which was

Determining safe and fast routes for first responders is an important issue in a disaster response. Especially when different types of disasters (e.g., toxic plumes, fires, and floods) occur and affect transportation networks simultaneously, special routing strategies (e.g., detour) would be needed to ensure the safety for responders. On the other hand, after disasters happen, a quick response time

In this paper, an adaptive neuro-fuzzy predictor-based control (ANFPC) approach with integrated automotive radar and vehicle-to-vehicle (V2V) communication for the design of the cooperative adaptive cruise control (CACC) system is presented, which concerns not only the safety and riding comfort of a vehicle but also enhances its fuel efficiency. This paper consists of two main parts: preceding vehicle