Urban Traffic Flow Forecast Based on FastGCRNN

Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, we use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.

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Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3488902 ◽

2022 ◽

Vol 13 (2) ◽

pp. 1-25

Author(s):

Bin Lu ◽

Xiaoying Gan ◽

Haiming Jin ◽

Luoyi Fu ◽

Xinbing Wang ◽

...

Keyword(s):

Traffic Flow ◽

Road Network ◽

Large Scale ◽

Transportation Systems ◽

Urban Traffic ◽

Propagation Mechanism ◽

Urban Road ◽

The Road ◽

Traffic Flow Forecasting ◽

Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

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A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation

Entropy ◽

10.3390/e20100725 ◽

2018 ◽

Vol 20 (10) ◽

pp. 725 ◽

Cited By ~ 2

Author(s):

Fernando Hermosillo-Reynoso ◽

Deni Torres-Roman ◽

Jayro Santiago-Paz ◽

Julio Ramirez-Pacheco

Keyword(s):

Traffic Flow ◽

Intelligent Transportation Systems ◽

Moving Objects ◽

Color Space ◽

Automatic Detection ◽

Weather Conditions ◽

Transportation Systems ◽

Lane Detection ◽

Computational Time ◽

The Road

Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period τ , its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value; (2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.

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Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.238.503 ◽

2012 ◽

Vol 238 ◽

pp. 503-506 ◽

Cited By ~ 1

Author(s):

Zhi Cheng Li

Keyword(s):

Traffic Flow ◽

Network Model ◽

Large Scale ◽

Choice Model ◽

Transportation Systems ◽

Urban Traffic ◽

Guidance System ◽

Traffic Network ◽

Dynamic Traffic ◽

Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

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A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090624 ◽

2021 ◽

Vol 10 (9) ◽

pp. 624

Author(s):

Kaiqi Chen ◽

Min Deng ◽

Yan Shi

Keyword(s):

Traffic Flow ◽

Directed Graph ◽

Intelligent Transportation Systems ◽

Traffic Management ◽

A Priori ◽

Transportation Systems ◽

Superior Performance ◽

Trajectory Data ◽

Temporal Dependence ◽

Traffic Forecasting

Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend to apply deep learning models; these methods are unexplainable and ignore the a priori characteristics of traffic flow. To address these issues, a temporal directed graph convolution network (T-DGCN) is proposed. A directed graph is first constructed to model the movement characteristics of vehicles, and based on this, a directed graph convolution operator is used to capture spatial dependence. For temporal dependence, we couple a keyframe sequence and transformer to learn the tendencies and periodicities of traffic flow. Using a real-world dataset, we confirm the superior performance of the T-DGCN through comparative experiments. Moreover, a detailed discussion is presented to provide the path of reasoning from the data to the model design to the conclusions.

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Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data

Sensors ◽

10.3390/s19225030 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5030 ◽

Cited By ~ 1

Author(s):

Yang ◽

Liu ◽

Jiang ◽

Xu ◽

Sheng ◽

...

Keyword(s):

Intelligent Transportation Systems ◽

Laser Scanning ◽

Large Scale ◽

Auxiliary Information ◽

Intelligent Transportation ◽

Transportation Systems ◽

Road Maintenance ◽

The Road ◽

Large Scale Data ◽

Road Width

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

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Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

Sustainability ◽

10.3390/su13010266 ◽

2020 ◽

Vol 13 (1) ◽

pp. 266

Author(s):

Jiayu Qin ◽

Gang Mei ◽

Lei Xiao

Keyword(s):

Traffic Flow ◽

Intelligent Transportation Systems ◽

Traffic Congestion ◽

Intelligent Transportation ◽

Transportation Systems ◽

Urban Traffic ◽

Traffic Planning ◽

Flow Network ◽

Gps Trajectories ◽

Congestion Problems

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.

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Multiple-Interval Freeway Traffic Flow Forecasting

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196155400117 ◽

1996 ◽

Vol 1554 (1) ◽

pp. 136-141 ◽

Cited By ~ 10

Author(s):

Brian L. Smith ◽

Michael J. Demetsky

Keyword(s):

Traffic Flow ◽

Nonparametric Regression ◽

Intelligent Transportation Systems ◽

Traffic Management ◽

Past Research ◽

Transportation Systems ◽

Forecasting Model ◽

Freeway Traffic ◽

Traffic Forecasting ◽

Traffic Flow Forecasting

Freeway traffic flow forecasting will play an important role in intelligent transportation systems. The TRB Committee on Freeway Operations has included freeway flow forecasting in its 1995 research program. Much of the past research in traffic flow forecasting has addressed short-term, single-interval predictions. Such limited forecasting models will not support the development of the longer-term operational strategies needed for such events as hazardous material incidents. A multiple-interval freeway traffic flow forecasting model has been developed that predicts traffic volumes in 15-min intervals for several hours into the future. The nonparametric regression modeling technique was chosen for the multiple-interval freeway traffic flow forecasting problem. The technique possesses a number of attractive qualities for traffic forecasting. It is intuitive and uses a data base of past conditions to generate forecasts. It can also be implemented as a generic algorithm and is easily calibrated at field locations, suiting it for wide-scale deployment. The model was applied at two sites on the Capital Beltway monitored by the Northern Virginia Traffic Management System. The nonparametric regression forecasting model produced accurate short- and long-term volume estimates at both sites.

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GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

Remote Sensing ◽

10.3390/rs13040544 ◽

2021 ◽

Vol 13 (4) ◽

pp. 544

Author(s):

Guohao Zhang ◽

Bing Xu ◽

Hoi-Fung Ng ◽

Li-Ta Hsu

Keyword(s):

Urban Area ◽

Intelligent Transportation Systems ◽

Urban Areas ◽

Large Scale ◽

Transportation Systems ◽

Accurate Localization ◽

Gnss Receivers ◽

Multipath Signal ◽

Scale Test ◽

Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

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ANALYSIS OF DIFFERENT VISUAL STRATEGIES OF ‘ISOLATED VEHICLE’ AND ‘DISTURBED VEHICLE’

Transport ◽

10.3846/16484142.2017.1343750 ◽

2018 ◽

Vol 33 (3) ◽

pp. 853-860

Author(s):

Nicola BONGIORNO ◽

Gaetano BOSURGI ◽

Orazio PELLEGRINO ◽

Giuseppe SOLLAZZO

Keyword(s):

Intelligent Transportation Systems ◽

Transportation Systems ◽

Disturbed Condition ◽

The Road ◽

Driving Behaviour ◽

Visual Behaviour ◽

Road Geometry ◽

Potential Applications ◽

Road Characteristics

This paper analyses the driver’ visual behaviour in the different conditions of ‘isolated vehicle’ and ‘disturbed vehicle’. If the meaning of the former is clear, the latter condition considers the influence on the driving behaviour of various objects that could be encountered along the road. These can be classified in static (signage, stationary vehicles at the roadside, etc.) and dynamic objects (cars, motorcycles, bicycles). The aim of this paper is to propose a proper analysis regarding the driver’s visual behaviour. In particular, the authors examined the quality of the visually informa-tion acquired from the entire road environment, useful for detecting any critical safety condition. In order to guaran-tee a deep examination of the various possible behaviours, the authors combined the several test outcomes with other variables related to the road geometry and with the dynamic variables involved while driving. The results of this study are very interesting. As expected, they obviously confirmed better performances for the ‘isolated vehicle’ in a rural two-lane road with different traffic flows. Moreover, analysing the various scenarios in the disturbed condition, the proposed indices allow the authors to quantitatively describe the different influence on the visual field and effects on the visual behaviour, favouring critical analysis of the road characteristics. Potential applications of these results may contribute to improve the choice of the best maintenance strategies for a road, to select the optimal signage location, to define forecasting models for the driving behaviour and to develop useful instruments for intelligent transportation systems.

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Potential Using Vehicle to Vehicle Communication Based on Wireless Fidelity (Wi-Fi) for Supporting Intelligent Transportation Systems (ITS)

ARID International Journal for Science and Technology ◽

10.36772/arid.aijst.2021.483 ◽

2021 ◽

pp. 61-76

Author(s):

إسراء عصام بن موسى ◽

عبدالسلام صالح الراشدي

Keyword(s):

Intelligent Transportation Systems ◽

Ad Hoc ◽

Real Life ◽

Intelligent Transportation ◽

Transportation Systems ◽

The Road ◽

Vehicle Communication ◽

Vehicle To Vehicle ◽

Vehicle To Vehicle Communication ◽

Wireless Fidelity

Vehicular Ad-hoc Network (VANET) becomes one of the most popular modern technologies these days, due to its contribution to the development and modernization of Intelligent Transportation Systems (ITS). The primary goal of these networks is to provide safety and comfort for drivers and passengers in roads. There are many types of VANET that are used in ITS, in this paper, we particularly focus on the Vehicle to Vehicle communication (V2V), which each vehicle can exchange information to inform drivers of other vehicles about the current state of the road flow, in the event of any emergency to avoid accidents, and reduce congestion on roads. We proposed V2V using Wi-Fi (wireless fidelity); the reason of its unique characteristics that distinguish it from other types. There are many difficulties and the challenges in implementing most types of V2V, and the reason is due to the lack of devices and equipment needed for real implementation. To prove the possibility of applying this type in real life, we made a prototype contains a modified toy car, a 12-volt power supply, sensors, visual, audible alarm, a visual “LED” devices, and finally a 12-volt DC relay unit. As a conclusion, the proposed implementation in spite of minimal requirements and use simple equipment, we have achieved the most important main objectives of the paper: preventing vehicles from collision, early warning, and avoiding congestion on the roads.

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