Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Laboratory-based methods were used in most previous studies related to road surface friction prediction model

The objective of this study is to model the complex behavior of non-lane based heterogeneous traffic which is predominantly occupied by vehicles with varying physical and dynamical characteristics and their staggered car following behavior. This study proposes a new continuum model by considering the properties of two-sided lateral gap in a non-lane based heterogeneous traffic stream. The model is

Connected automation systems coordinate vehicle communications and automated vehicle control to improve transportation system mobility, safety, and sustainability. Real-world deployment of connected automated vehicle (CAV) applications and related traffic management technologies will require automation of all vehicle and traffic movements, including platooning, lane change, and merge maneuvers, to

In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator

(2020). Special issue on dense surveillance systems for urban traffic. Journal of Intelligent Transportation Systems: Vol. 24, Dense Surveillance Systems for Urban Traffic, pp. 217-220.

Truck weight data is needed for a wide range of applications including but not limited to pavement design, weight enforcement, traffic monitoring, and freight transportation planning. Unfortunately, the low spatial resolution of weight sensors along the transportation network can limit these and other potential applications. The main contribution of this paper is a methodology to estimate gross vehicle

In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic

In the past decades, Vehicular Ad hoc Networks (VANETs) have been increasingly developed. Providing secure and efficient communication is essential in VANETs. One of most important challenges in the secure and efficient communications is proposing an appropriate authentication scheme. In this paper, we suggest an efficient and novel authentication scheme for VANETs. In the proposed scheme, vehicles

This evaluation ascertained the operational impacts of the SUTRAC (Scalable Urban Traffic Control) Adaptive Signal Control Technology (ASCT) in an urban corridor consisting of 23 intersections in Pittsburgh, Pennsylvania. A combination of real-world GPS floating car runs and private sector probe data from INRIX was used to assess the impact of the ASCT. Data were collected with the ASCT active and

The rapid development of sensing, computing, and wireless communication techniques has given rise to an increasing number and increasing availability of high-resolution data that record real-time traffic information. In this study, radio frequency identification (RFID) data collected in Nanjing (a major city in southern China) were used to estimate dynamic travel demands and develop an RFID data-based

Short-term traffic speed forecasting is an important component of Intelligent Transportation Systems (ITS). Multi-step-ahead prediction can provide more information and predict the longer trend of traffic speed than single-step-ahead prediction. This paper presents a multi-step-ahead traffic speed prediction approach by improving the gradient boosting regression tree (GBRT). The traditional multiple

Reliable long-term travel time prediction would be effective support to traffic management, for example, traffic flow control or the pricing of tolls. Gradient boosting (GB) has been suggested as an excellent tool for short-term travel time prediction problems. This paper shows that GB with modifications can also work for long-term prediction. We introduce key variables, regarded as multiple factors

Traffic congestion has become a significant issue in urban road networks. There have been massive works about traffic signal optimization to improve the efficiency of traffic flow operation, and the so-called back-pressure control policy has proven to be excellent for oversaturated conditions. Most of the existing works with back-pressure are based on an adaptive phase sequence, and research with cyclic

Multi-view video surveillance is a highly valuable tool to ensure the safety of the crowd in large public space. By utilizing complementary information captured by multiple cameras, the issue of limited views and occlusion in single views can be addressed to gain better insight into the whole monitored space. However, multi-view surveillance has been widely applied to microscopic crowd analysis, for

We investigates the performance of deep Convolutional Neural Network (CNN) for recognizing highway traffic congestion state in surveillance camera images. Different from the usual images in ImageNet, images generated by highway surveillance cameras usually have much more extensive range of perspective and thus larger area of background. Therefore the objective road and vehicles are not as prominent

Self-driving cars are on the horizon, making it necessary to consider safety assurance and homologation of these autonomously operating vehicles. In this study, we systematically review literature that proposes new methods for these areas. The available methods were categorized into a novel taxonomy, dividing them into the strategies of combinatorial testing, robustness testing and search-based testing

Abstract User-related assessment of a level 3 automated driving system, providing functions such as lane- and distance-keeping, stop & go driving, lane change and overtaking, was carried out on a motorway in Germany with the aim to assess user-related issues of automated driving, i.e., behavior when driving with automation on motorways, user experiences, reactions and acceptance. Twenty-one persons

Classification of vehicles into distinct groups is critical for a number of applications including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The main goal of this paper is to demonstrate how data from Light Detection and Ranging (LiDAR) sensors could be leveraged to distinguish between specific types

Filtering of motorized two-wheelers (MTWs) is a common practice in dense urban heterogeneous traffic environments where they often tend to navigate through the available lateral spaces (commonly termed as ‘pore’ in the literature) described by the vehicles in-front. Considering the increased vulnerability of MTW riders in dense urban systems, proper evaluation and modeling of pore acceptance/rejection

Queue length is one of the most important performance measures for signalized intersections. With recent advancements in connected vehicles and intelligent mobility technologies, utilizing vehicle trajectory data to estimate queue length has received considerable attentions. However, most of the existing methods are based on some assumptions, such as known arrival patterns and/or high penetration rates

Cooperative-adaptive cruise-control (CACC) offers the potential to increase roadway capacity by decreasing the size of gaps between vehicles. However, smaller gaps may reduce drivers’ ability to safely enter CACC strings. Two driving simulator experiments explored merging in CACC strings (also known as CACC platoons). In Experiment 1, drivers attempted to merge into a continuous stream of simulated

Bus bunching resulting from initially small headway irregularities is a widely-known and studied problem. A variety of headway-prediction approaches, as well as corrective strategies, have been developed to identify and correct headway irregularity in real time. Instead of predicting an exact value for future headways, this study explores a probabilistic predictive methodology to forecast whether or

Vehicle-to-vehicle (V2V) communication using Dedicated Short Range Communications (DSRC) technology promises to help drastically reduce vehicle collisions. DSRC allows vehicles in a highly mobile and complex network to send and receive safety messages with more reliability and lower latency compared with other wireless technologies used for automotive communications. However, there are many factors

The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching

Quantifying the effect of Cooperative Adaptive Cruise Control (CACC) on traffic mobility and vehicle fuel consumption has been a challenge because it requires a modeling framework that depicts the interactions among manually driven vehicles and CACC vehicles in the complex multilane traffic stream. This study adopted a state-of-the-art traffic flow modeling framework to explore the impacts of CACC

Lane-changing activities near freeway merging, diverging, and weaving sections are one of the major factors leading to recurrent bottleneck congestion. In recent years, microscopic dynamic merging assistance (DMA) methods are found efficient to improve mobility and safety in merging areas. The article proposes a solution, the connected vehicle (CV) vehicle-to-infrastructure (V2I)-based dynamic merge

The roadside Light Detection and Ranging (LiDAR) sensor can provide the high-resolution micro traffic data (HRMTD) of all road users by collecting real-time point clouds in three-dimensional (3D) space. The HRMTD collected by the roadside LiDAR provides a solution to fill the data gap under the mixed situation (both connected vehicles and unconnected vehicles exist on the roads) for connected vehicle

Currently, accurate traffic flow analysis and modeling are important key steps for intelligent transportation system (ITS). Missing traffic flow data are one of the most critical issues in the application of ITS. In this study, a hybrid method combining fuzzy rough set (FRS) and fuzzy neural network (FNN) is proposed for imputation of missing traffic data. Firstly, FNN is used for data classification

With the emergence of the internet of things, pathfinding problems have recently received a significant amount of attention. Various commercial applications provide automated routing by considering travel time, travel distance, fuel consumption, complexity of the road, etc. However, many of these prospective applications do not consider route safety. Emergence of high-resolution big data generated

Previous studies have shown that cooperative adaptive cruise control (CACC) vehicles have capability to improve traffic flow stability and reduce emissions and fuel consumption. However, previous studies did not investigate the relevance between these two aspects. To address this issue, we calculated stability conditions of traffic flow using car-following models. Simulations are also performed to

With the technological drive for realizing smart cities, work on Autonomous Intersection Management (AIM) protocols opts to replace the traditional traffic light system by using cooperative Vehicle-to-Vehicle (V2V) communication in order to decrease road congestion and increase vehicular throughput. However, these protocols simply ignore pedestrians as road users and do not provision for safe pedestrian

Standstill distances and following time headways are two important microsimulation model parameters associated with driver aggression. This paper investigates the distributions of standstill distances and time headways and incorporates these distributions into car-following models to estimate travel time reliability. By incorporating standstill distance and following headway into car-following models

People spend a significant amount of time behind the wheel of a car. Recent advances in data collection facilitate continuously monitoring this behavior. Previous work demonstrates the importance of this data in driving safety but does not extended beyond the driving domain. One potential extension of this data is to identify driver states related to health conditions such as obstructive sleep apnea

Abstract Given the unstable nature of the bus operations, the regularity of the bus services cannot be maintained throughout the day. Especially in densely populated areas, the inherent variability of the trip travel times affects the regularity of the daily services since the arrival times of several trips at stops might oscillate significantly from their expected values. Because of the high level

This research work is focused on modeling vehicle following behavior under mixed traffic conditions. Vehicle-following behavior can be potentially utilized in building real-time drivers’ assistance systems or identifying collision escaping time thresholds for varying roadway and traffic conditions. For this purpose, initially, vehicular trajectory data was developed over the road sections in India

The growing number of surveillance cameras imposes great demand on high efficiency video coding. Although modern video coding standards have significantly improved the video coding efficiency, they are designed for general video rather than surveillance video. The special characteristics of surveillance video leave a large space for further performance improvement. In this paper, we leverage a deep

In this study, we aim to solve the shared autonomous vehicle (SAV) routing problem under the effects of congestion in the road network. The SAV routing problem is the problem of finding an optimal SAV-traveler assignment as well as the SAV route choice. Since widespread use of SAVs would cause significant congestion of road networks, it is essential to consider the effects of traffic congestion on

In this paper, a restricted path-based ridesharing user equilibrium (RUE) is proposed to build up the rationales between ridesharing activities and traffic congestion. In the traffic assignment problem with ridesharing, travelers simultaneously choose routes from origins to destinations and travel modes (including solo driver, ridesharing driver, and ridesharing passenger). The proposed RUE model with

Based on simulations in the framework of Kerner’s three-phase traffic theory, we present a methodology for the prediction of a moving bottleneck (MB) with the use of a small share of probe vehicles (floating car data – FCD) randomly distributed in traffic flow. In this methodology, a conclusion of the three-phase traffic theory has been used that in the vicinity of any bottleneck there can be observed

Pedestrian traffic is an important subject of surveillance to ensure public safety and traffic management, which may benefit from intelligent and continuous analysis of pedestrian videos. State-of-the-art methods for intelligent pedestrian surveillance have a number of limitations in automating and deriving useful information of high-density pedestrian traffic (HDPT) using closed circuit television

Artificial intelligence (AI) methods for traffic video analysis have been widely identified as potential solutions for solving hard problems in intelligent transport systems (ITS). To exploit the advantages of AI, dense cameras to monitor the traffic are required to be deployed along the road and at the intersections. The captured videos of these cameras should be back-hauled to the control center

Based on on-board diagnostics and Global Position System installed in taxicabs, driver behavior data is collected. Left turn data on six similar curves are extracted, and speed, acceleration, yaw rate, and sideslip angle of drivers in time series are selected as clustering indexes. Initial clustering is implemented by Dynamic Time Warping (DTW) and Hierarchical Clustering, and the clustering results

Based on an empirical study of floating car data (probe vehicle data), we have found the following microscopic features of empirical synchronized flow that are relevant for jam warning methods: (i) In accordance with Kerner’s three-phase traffic theory, the empirical speed decrease (speed drop) denoted by Δv that occurs due to vehicle deceleration at the upstream front of synchronized flow is a complex

Infrared imaging sensors are widely employed in urban traffic systems since they are not affected by lighting conditions. However, due to the limitation of hardware and imaging environment, it is difficult to obtain infrared (IR) images at the desired quality. IR images always lack detailed information, which leads to unsatisfying IR image enhancement results with the conventional method. Compared

Infrared and visible images play an important role in transportation systems since they can monitor traffic conditions around the clock. However, visible images are susceptible to the imaging environments, and infrared images are not rich enough in detail. The infrared and visible images fusion techniques can fuse these two different modal images into a single image with more useful information. In

In this article, we discuss the system-optimum dynamic traffic assignment (SO-DTA) problem in the presence of time-dependent uncertainties on both traffic demands and road link capacities. Building on an earlier formulation of the problem based on the cell transmission model, the SO-DTA problem is robustly solved, in a probabilistic sense, within the framework of random convex programs (RCPs). Different

Wrong way driving (WWD) crashes on high-speed limited access facilities often cause severe injuries and deaths. Conventional “Wrong Way” signs can reduce WWD events but can be insufficient in some cases. Intelligent Transportation Systems (ITS) WWD countermeasures can more effectively notify wrong-way drivers of their mistake. This paper compares the safety performance of two types of ITS WWD countermeasures

Fog is a weather condition that reduces visibility of the driving scene, while slow traffic may be formed due to the bottleneck on freeways. This phenomenon may lead to higher rear-end crash risk when vehicles approach slow traffic, since drivers might not observe the speed reduction ahead of them timely with the reduced visibility and could not have enough time to respond. This study aims to develop

Recently, traffic agencies have implemented Bluetooth detection systems (BDS) on arterials to collect traffic data and purchased data from private sector companies such as HERE, INRIX, and TomTom for real-time traffic monitoring and long-term traffic system management and performance evaluation. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation

Based on a study of probe vehicle data measured on different highways during different days we have revealed some common empirical microscopic characteristic features of sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow occurring before traffic breakdown at a highway bottleneck predicted theoretically by Kerner. Following common empirical

Pedestrian behavior tends to depend on the type of facility. The flow at bottlenecks, for instance, can exceed the maximal rates observed in straight corridors. Consequently, accurate predictions of pedestrians movements in complex buildings including corridors, corners, bottlenecks, or intersections are difficult tasks for minimal models with a single setting of the parameters. Artificial neural networks

The continuous advancement of connected vehicle (CV) technologies and its increasing market penetration (MP) will change the nature of traffic flow fundamentals by upending the traditional traffic composition. The objective of this paper is to create an agent-based modeling framework to evaluate the impact of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) technology on the mobility

Collision prevention is the most important issue in designing adaptive cruise control (ACC) systems. In these systems, passenger's safety and comfort requirements are satisfied by tracking a reference signal which determines the inter-distance policy between vehicles. The time gap between the system initial state and its steady state should be reduced as much as possible so that any collision due to

To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture

Taxi service is one of the most important modes for urban transportation. In recent years, many taxi companies have been routinely collecting data to track the movement of each taxi for improving security, coordination, and service performance. This paper is intended to use the GPS vehicle positioning data to assess the route choice behavior of taxi drivers and explore if the routes selected by taxi

This article is concerned with the development of eco-driving metrics for instrumented vehicles in a longitudinal study environment. Motivations for developing such metrics include an ability to distill driving style effects on fuel use from other confounding factors, to contrast and benchmark driving styles for a cohort of drivers and to ascertain the effects of information and/or incentives on fuel

This paper develops the two-lane traffic surveillance function of a smart road stud (SRS), which has the capabilities of high-resolution traffic data collection and transmission, and a light-based traffic guidance. The SRS addresses the limitations of the current systems with the potential for high performance and usage for a variety of applications. SRS nodes are mounted on the road surface along

Following the rapid development of the Internet of vehicles (IoV), many issues and challenges do come up as the storage of large quantities of vehicle network data and improvement of the retrieval efficiency. A great deal of global positioning system (GPS) log data and vehicle monitoring data is generated on IoV. When many small files in the conventional Hadoop Distributed File System (HDFS) are accessed

Truck platooning is an application of connected and automated vehicle technology that has a near-term potential to revolutionize the trucking industry. In addition to potential operational improvements in capacity and flow, its primary benefits include potential reductions in fuel consumption and emissions. In a real-world environment, there are many factors that can influence the ability to form stable

A new hybrid sensor technology integrating existing weigh-in-motion axle configuration data combined with inductive signature data obtained from advanced inductive loop detectors is gaining interest due to its potential to provide detailed classification of truck body types as well as anonymous tracking of truck movements on freeways. However, selecting optimal deployment locations for the hybrid sensors