Vehicle Detector Data Research Articles

Predicting traffic breakdown on expressways using linear combination of vehicle detector data

Traffic congestion is closely associated with various social issues that must be solved urgently. With the recent advancement of machine learning technologies, diverse methods for predicting traffic congestion have been developed. Specifically, traffic prediction using deep learning can provide highly accurate performance. Nevertheless, several difficulties remain because of the complexity of deep learning models: particularly, they require large amounts of data and computational power. For this study, we strive to achieve traffic prediction precision using a simple linear model. Instead of improving complex models, we select training data appropriately with a linear model and then verify the feasibility of prediction by exploring “data complexity”. The prediction results imply that the linear model is as precise as deep learning even with fewer number of data and parameters. We use actual data from expressways collected using detectors.

Visual Cause Analytics for Traffic Congestion.

Urban traffic congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban traffic congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing traffic volume by detecting urban traffic congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of traffic congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of traffic congestion. In this paper, we propose a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning.

Fine-Grained Traffic Flow Prediction of Various Vehicle Types via Fusion of Multisource Data and Deep Learning Approaches

Both road users and road administrators are keen to know traffic flow of fine-grained vehicle type. Successful prediction on the traffic flow of heavy, medium and small vehicle could contribute to the improvement of travel safety and efficiency. However, the classification on vehicle type is always not accurate enough using in practice. It could cost a lot to identify from the additional video cameras to cover the full-length of large-scale freeway with high-resolution to capture vehicles clearly. In this paper, empirical data are cleaned, normalized, compensated, filled, decoded and filtered with help of the fusion of vehicle detector data, remote microwave sensors data and toll collection data. The traffic flows of fine-grained heavy, medium and small vehicles are successfully reconstructed. Improved deep belief network (DBN) are then proposed to forecast traffic flow of different types of vehicles in 30-, 60- and 120-minutes time interval. Random-selected road segments on a ring way around a city are trained with data accumulated three months and predict data in the next month. According to prediction error analysis, the proposed method performs better in estimation and forecasting, with respect to the existing methods, especially for longer time prediction and heavy vehicle prediction. It would benefit traffic control to prevent freeway congestion escalation, protect the traffic infrastructure via heavy vehicle control, reduce the road risk, prompt quick emergency response and eventually contributes to more applications for intelligent transportation system (ITS).

A Visual Analytics System for Exploring, Monitoring, and Forecasting Road Traffic Congestion.

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for detecting the causes of congestion and congestion propagation directions. Our visual analytics system is designed to enable users to explore congestion causes, directions, and severity. Congestion conditions of a city are visualized using a Volume-Speed Rivers (VSRivers) visualization that simultaneously presents traffic volumes and speeds. To evaluate our system, we report performance comparison results, wherein our model is more accurate than other forecasting algorithms. We demonstrate the usefulness of our system in the traffic management and congestion broadcasting domains through three case studies and domain expert feedback.

A Simple Method for Automatic Incident Detection Using Vehicle Detector Data

This study is about quick detection of the incidents such as crash, roadwork, debris, stopped vehicles etc. Vehicle detector data is used to identify the incident by utilizing a simple but robust A ...

The Simulation Model of Expressway Based on Modified Cell Transmission Model

In order to realize the reasonable planning, design and management of expressway, the modified cell transmission model is proposed considering the limitation of equal cell length of traditional cell transmission model, improving the mode of traffic transmission on expressway. The capacity and jam density is calculated and the relationship between traffic speed and density is calibrated by using the vehicle detector data. Meanwhile, the traffic speed-density relationship is refined by adopting the weighted least square method, enhancing the practicality of the model. When the modified cell transmission model is applied in the expressway, the mean percentage error of cell traffic flow can be controlled at around 12 percent, which shows that the simulation result is satisfactory.

Effect of Dynamic Blink Control of Light-Emitting Devices Installed along a Road Shoulder on Congestion Relief

As a speed-reduction measure on a motorway, Central Nippon Expressway Co., Ltd. has been conducting field research of light-emitting devices placed at regular intervals on the shoulder of a road, and controlling the light emission in arbitrary light-emission patterns to control vehicle running speeds. In this paper, operational experiments conducted on the Tomei Expressway to alleviate the traffic congestion caused by sags and tunnel bottlenecks are introduced, and the effects of the installed system on the flow rate and vehicle movement for both the no-congestion and the congested situations are discussed. An analysis is carried out using vehicle detector data and video tape recorder (VTR) image analysis. The results show that the speed of vehicles is affected by the devices, and a significant increase in throughput is observed for the congested situation, whereas there is only a small effect on the flow rate for the no-congestion situation.

Segmentation of vehicle detector data for improved k‐nearest neighbours‐based traffic flow prediction

This study presents a data segmentation method, which was intended to improve the performance of the k-nearest neighbours algorithm for making short-term traffic volume predictions. According to the introduced method, selected segments of vehicle detector data are searched for records similar to the current traffic conditions, instead of the entire database. The data segments are determined on the basis of a segmentation procedure, which aims to select input data that are useful for the prediction algorithm. Advantages of the proposed method were demonstrated in experiments on real-world traffic data. Experimental results show that the proposed method not only improves the accuracy of the traffic volume prediction, but also significantly reduces its computational cost.

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.

Revisiting the empirical fundamental relationship

This paper develops a new methodology for deriving an empirical fundamental relationship from vehicle detector data. The new methodology seeks to address several sources of noise present in conventional measures of the traffic state that arise from the data aggregation process, e.g., averaging across all vehicles over a fixed time period. In the new methodology vehicles are no longer taken successively in the order in which they arrived and there is no requirement to seek out stationary traffic conditions; rather, the traffic state is measured over the headway for each individual vehicle passage and the vehicles are grouped by similar lengths and speeds before aggregation. Care is also taken to exclude measurements that might be corrupted by detector errors. The result is a homogeneous set of vehicles and speeds in each bin. While conventional fixed time averages may have fewer than 10 vehicles in a sample, the new binning process ensures a large number of vehicles in each bin before aggregation. We calculate the median flow and median occupancy for each combined length and speed bin. Then we connect these median points across all of the speed bins for a given vehicle length to derive the empirical fundamental relationship for that length. This use of the median is also important; unlike conventional aggregation techniques that find the average, the median is far less sensitive to outliers arising from uncommon driver behavior or occasional detector errors.The work is applied to real data from a dual loop detector station and it yields consistent results across 18 independent days, 4 independent lanes, and 7 independent length bins. These empirical results from non-stationary traffic are also shown to be consistent with hypothetical results generated with homogeneous vehicles under stationary traffic conditions.

A Multistep Corridor Travel-Time Prediction Method Using Presence-Type Vehicle Detector Data

Travel time is an important measure for transportation system performance evaluation. In particular, it is an essential input to the advanced traveler information systems and route guidance applications that requires reliable traffic information in real time. Therefore, numerous studies have been conducted to predict segment and corridor travel times on the basis of data from loop detectors. Focusing on freeway corridor travel-time prediction that incorporates the effect of traffic propagation, this article presents a dynamic prediction model using the traffic measurements from presence-type vehicle detector data. The authors designed a multistep predictor in the form of a seasonal autoregressive integrated moving average model with an embedded adaptive Kalman filter that allows for continued adjustment of prediction results when new data become available in real time. The authors evaluated the corridor travel-time prediction method against 2 alternative methods using the same procedure as proposed, but one does not consider the effects of traffic propagation along the corridor and the other one does not perform the multiple-step traffic prediction. Along with the performance metrics, this study demonstrates the advantages of the model in capturing the dynamics of traffic and reveals the significant effects of traffic propagation on corridor travel-time prediction for producing more accurate prediction results. The authors also developed an online implementation framework.

PASSENGER CAR EQUIVALENTS OF HEAVY VEHICLES ON MOTORWAYS FROM MICROSCOPIC HEADWAY METHOD

Thisp aper discussest he microscopica pproacho f estimating Passenger Car Equivalents (PCE). The averageh eadway of mixed flow condition with percentage of heavy vehicle p is expressed in terms of four typical headway types, assuming that the sequential occurrence of headway types is random. The formulation for estimating PCE is based on equal V/C ratio approach. The vehicle detector data of each vehicle from Tomei expressway have been used. Headways of each pair of headway types are averaged for fifteen minutes at three minutes moving interval. PCE for lane2 was found to increase with increase in percentage of heavy vehicle to some maximum value and then was found to decrease afterwards.