相关论文: Utilizing Bluetooth and Adaptive Signal Control Da…

Automated Driving Systems (ADS) development relies on utilizing real-world vehicle data. The volume of data generated by modern vehicles presents transmission, storage, and computational challenges. Focusing on Dynamic Behavior (DB) offers…

Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is a need to complement naturalistic datasets of road user…

Autonomous driving improves traffic efficiency but presents safety challenges in complex port environments. This study investigates how environmental factors, traffic factors, and pedestrian characteristics influence interaction safety…

Audio-based pedestrian detection is a challenging task and has, thus far, only been explored in noise-limited environments. We present a new dataset, results, and a detailed analysis of the state-of-the-art in audio-based pedestrian…

Deployed SAE level 4+ Automated Driving Systems (ADS) without a human driver are currently operational ride-hailing fleets on surface streets in the United States. This current use case and future applications of this technology will…

Insight into individual driving behavior and habits is essential in traffic operation, safety, and energy management. With Connected Vehicle (CV) technology aiming to address all three of these, the identification of driving patterns is a…

Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work…

Drive-by inspection for bridge health monitoring has gained increasing attention over the past decade. This method involves analysing the coupled vehicle-bridge response, recorded by an instrumented inspection vehicle, to assess structural…

To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the proposed models have been remarkably improved in recent years,…

Every year, many people are killed and injured in highway traffic accidents. In order to reduce such casualties, collisions warning systems has been studied extensively. These systems are built by taking the driver reaction times into…

On roads showing significant violations of posted speed limits, one measure of the safety effect of speeding is the difference between the road's actual accident count and the count that would have occurred if the posted speed limit had…

Advanced signal control algorithms are anticipated with the increasing availability of vehicle speed and position data from vehicle-to-infrastructure communication and from sensors. This study examines the impact of the sensing range,…

Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate…

This paper proposes a novel monitoring methodology for car-following control of automated vehicles that uses real-time measurements of spacing and velocity obtained through vehicle sensors. This study focuses on monitoring the time gap, a…

We present a fluid-dynamic model for the simulation of urban traffic networks with road sections of different lengths and capacities. The model allows one to efficiently simulate the transitions between free and congested traffic, taking…

An essential requirement for scenario-based testing the identification of critical scenes and their associated scenarios. However, critical scenes, such as collisions, occur comparatively rarely. Accordingly, large amounts of data must be…

In smart transportation, intelligent systems avoid potential collisions by predicting the intent of traffic agents, especially pedestrians. Pedestrian intent, defined as future action, e.g., start crossing, can be dependent on traffic…

Recently, the problem of traffic accident risk forecasting has been getting the attention of the intelligent transportation systems community due to its significant impact on traffic clearance. This problem is commonly tackled in the…

In recent years, state-of-the-art traffic-control devices have evolved from standalone hardware to networked smart devices. Smart traffic control enables operators to decrease traffic congestion and environmental impact by acquiring…

Arterial traffic interacts with freeway traffic, yet the two are controlled independently. Arterial traffic signals do not take into account freeway traffic and how ramps control ingress traffic and have no control over egress traffic from…