Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

As road traffic networks become more congested and information systems are implemented to manage traffic flows, real-time data gathering becomes increasingly important. Classic detectors are placed in one point of the network and are able to provide information only from that area. As useful as this is, it lacks the big picture of the routes the vehicles usually travel. There are applications developed to help individuals make their way into the road network, but these are no solutions that deal with the cause of traffic; rather, they counteract the effects. It becomes obvious that a proper management system, with knowledge of all the relevant aspects will better serve all travelers. The detection solution proposed in this paper is based on Bluetooth detectors. This system is able to match detected devices in the road network, filter the results, and generate a vehicle count that is proved to follow RADAR detection results.

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

Modeling the Vehicle-Bicycle Collision - The Analysis of the Projection Distance and Acceleration at the Cyclist's Head

The ever-growing demand for transportation and the need to carry both people and goods has led to increased congestions of road traffic networks. Subsequently, the main negative effect is the multiplication of serious road accidents. Of the total number of serious road accidents, a significant increase has been registered among cyclists, with 13.9% in 2014 of total vehicles involved in traffic accidents, compared to 6.6% in 2010. The present paper underpins a close analysis of the kinematic and dynamic parameters in the event of a vehicle - bicycle – cyclist assembly – collision type. To study the vehicle-bicycle-collision type, we carried out a comparative analysis with regard to the distance the cyclist is thrown away following the collision, the speed variation of the vehicle and of the bicycle, and the speed variation in the cyclist’s head area, as well as the variation of the acceleration recorded on the vehicle, the bicycle and the cyclist’s head area. Hence, we modelled and simulated the vehicle – bicycle collision for two distinct instances, i.e. a frontal vehicle – rear bicycle collision and a frontal vehicle - frontal bicycle collision.