Quantitative Assessment on Truck-Related Road Risk for the Safety Control via Truck Flow Estimation of Various Types

Abstract

Traffic conditions of truck flow is one of the critical factors influencing transportation safety and efficiency, which is directly related to traffic accidents, maintenance scheduling, traffic flow interruption, risk control, and management. The estimation of the truck flow of various types could be better to identify the irregular flow variation introduced by various trucks and quantitatively assessed the corresponding road risks. In this paper, the dynamics of truck flow are estimated first. The stochastic and uncertain trucks flow data are obtained in terms of small, medium, heavy, and the oversize truck type and regulated corresponding flow in the time series within five minutes. In order to dig the spatial-temporal correlations behind those data, the deep learning-based method is improved on the basis of the gated recurrent unit (GRU) to estimate the truck flow for various types. To quantitatively assess the truck-related effect for road risk, a multiple logistic regression method is further proposed to classify into safe, risky, and dangerous road risks levels. Different risk level could guide the traffic control and management and traffic information that broadcast drivers to help them to choose travel route. The proposed prediction of the road risk is tested in the randomly selected road segment and shows superior compared to other methods. This could promote road safety in the development of intelligent transport system (ITS).INDEX TERMS Truck flow prediction, road risk assessment, various types of truck, remote traffic microwave sensor (RTMS), gated recurrent unit (GRU).

Distributed Acoustic Sensor Systems for Vehicle Detection and Classification

“…The second case is defined based on common concerns in transportation planning 1 where the dimensions and weight of vehicles on the roads are required in ITS planning. This work does not suggest to provide a complete answer to these questions; instead, we offer an alternative means to detect large vehicles when modelling the traffic flow.…”

“…We finally conclude the article in Section VIII. 1 https://www.hse.gov.uk/workplacetransport/vehicles.htm In this section we first present the general theory of DAS followed by the data acquisition method adopted in the collection of the dataset used for this research.…”

“…ρ(i, j) is set to 0 for all shots j where j < s ω b,s − (d m ˘dω )/2 and j > s ω b,e + (d m ˘dω )/2. The training dataset D t is shown in Eq (1) where ρ(i, j) is a single measurement value (in Radian) for a particular sensor (bin i) at a certain time (shot j) within the data sample x i . In this case, the bin i represents the index of the data sample and j is shot index relative to the start shot of x i .…”

“…The vehicles' characteristics of interest to ITS are multi-fold and include the size, the occupancy, and the engine type, as each would have different impact on traffic planning and environmental pollution. In recent years, numerous traffic detectors have been deployed to monitor traffic flow such as remote traffic microwave sensors [1], magnetic sensors [2], in addition to more traditional sensors such as pneumatic road tubes [3]. However, the data generated by these sensors is mostly limited to counting of vehicles and accordingly, optimising the flow by controlling traffic lights, for instance.…”

Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model

“…Jin et al [36] first improved on the gated recursive unit (GRU) based on a deep learning approach to estimate various types of truck traffic. Then a multiple logistic regression method was proposed to classify the road risk into three classes: safe, risky, and dangerous.…”