scholarly journals Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions

2018 ◽  
Vol 46 (9) ◽  
pp. 1684-1705 ◽  
Author(s):  
Alireza Ermagun ◽  
David M Levinson
Keyword(s):  
Traffic Flow ◽  
User Equilibrium ◽  
Weight Matrix ◽  
Congested Traffic ◽  
Traffic Forecasting ◽  
Traffic Conditions ◽  
Spatial Weight ◽  
Flow Information ◽  
Network Weight ◽  
Better Than

To capture network dependence between traffic links, we introduce two distinct network weight matrices ([Formula: see text]), which replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we use an unweighted betweenness method and assume all traffic flow is assigned to the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we use the flow information of traffic links and employ user equilibrium assignment and the method of successive averages algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We test and compare the network weight matrices in different traffic conditions using the Nguyen–Dupuis network. The results lead to a conclusion that the network weight matrices operate better than traditional spatial weight matrices. Comparing the unweighted and flow-weighted network weight matrices, we also reveal that the assigned flow network weight matrices perform two times better than a betweenness network weight matrix, particularly in congested traffic conditions.

scholarly journals Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Haji Said Fimbombaya ◽  
Nerey H. Mvungi ◽  
Ndyetabura Y. Hamisi ◽  
Hashimu U. Iddi
Keyword(s):  
Magnetic Field ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Traffic Flow ◽  
Wireless Sensor ◽  
Success Rates ◽  
Earth’S Magnetic Field ◽  
Congested Traffic ◽  
Flow Monitoring ◽  
Flow Information

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

Analysis and Comparison of Microscopic Traffic Flow Models with Real Traffic Microscopic Data

2005 ◽  
Vol 1934 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Vincenzo Punzo ◽  
Fulvio Simonelli
Keyword(s):  
Traffic Flow ◽  
Simulation Models ◽  
Microscopic Traffic Simulation ◽  
Flow Models ◽  
Traffic Conditions ◽  
Measure Model ◽  
Road Systems ◽  
Traffic Flow Models ◽  
Real Traffic ◽  
Better Than

The evermore widespread use of microscopic traffic simulation in the analysis of road systems has refocused attention on submodels, including car-following models. The difficulties of microscopic-level simulation models in the accurate reproduction of real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacies of the submodels themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty with the gathering of accurate field data. In this study, the use of kinematic differential Global Positioning System instruments allowed the trajectories of four vehicles in a platoon to be accurately monitored under real traffic conditions on both urban and extraurban roads. Some of these data were used to analyze the behaviors of four microscopic traffic flow models that differed greatly in both approach and complexity. The effect of the choice of performance measures on the model calibration results was first investigated, and intervehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared with those from previous studies (with peaks in cross validations between urban and extraurban experiments). This confirms the need for real traffic data. On comparison of the models, all models showed similar performances (i.e., similar deviations in validation). Surprisingly, however, the simplest model performed on average better than the others, but the most complex one was the most robust, never reaching particularly high deviations.

scholarly journals Traffic flow cellular automaton model with bi-directional information in an open boundary condition

2020 ◽  
Author(s):  
Liang Zheng ◽  
Zhengpeng He
Keyword(s):  
Traffic Flow ◽  
Traffic Information ◽  
Open Boundary ◽  
Hysteresis Phenomenon ◽  
Connected Vehicle ◽  
Directional Information ◽  
Congested Traffic ◽  
Traffic Conditions ◽  
Ca Model ◽  
Vehicle Technologies

Abstract With Connected Vehicle Technologies being popular, drivers not only perceive downstream traffic information but also get upstream information by routinely checking backward traffic conditions, and the backward-looking frequency or probability is usually affected by prevailing traffic conditions. Meanwhile, the bi-directional perception range of drivers is expected to significantly increase, which results in more informed and coordinated driving behaviours. So, we propose a traffic flow bi-directional CA model with two perception ranges, and perform the numerical simulations with the field data collected from a one-lane highway in Richmond, California, USA as the benchmark data. Numerical results show that the CA model can effectively reproduce the oscillation of relatively congested traffic and the traffic hysteresis phenomenon. When adjusting the backward-looking probability and the perception range, the CA model can well simulate the travel times of all vehicles, and the generation and dissolution of traffic jams under various scenarios.

scholarly journals Towards AI-Based Traffic Counting System with Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Duc-Liem Dinh ◽  
Hong-Nam Nguyen ◽  
Huy-Tan Thai ◽  
Kim-Hung Le
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Control Strategies ◽  
Low Cost ◽  
Counting System ◽  
Traffic Conditions ◽  
Flow Information ◽  
Edge Based

The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management.

A Spatial–Temporal Similar Graph Attention Network for Cyber Physical System Perception via Traffic Forecasting

2021 ◽  
Author(s):  
Kaidi Zhao ◽  
Mingyue Xu ◽  
Zhengzhuang Yang ◽  
Dingding Han
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Temporal Correlation ◽  
Real Data ◽  
Attention Network ◽  
Traffic Forecasting ◽  
Periodic Data ◽  
Flow Information ◽  
Temporal Perturbation ◽  
Real Traffic

Traffic flow forecasting is the basic challenge in intelligent transportation system (ITS). The key problem is to improve the accuracy of model and capture the dynamic temporal and nonlinear spatial dependence. Using real data is one of the ways to improve the spatial–temporal correlation modeling accuracy. However, real traffic flow data are not strictly periodic because of some random factors, which may lead to some deviations. This study focuses on capturing and modeling the temporal perturbation in real periodic data and we propose a spatial–temporal similar graph attention network (STSGAN) to address this problem. In STSGAN, the spatial–temporal graph convolution module is to capture local spatial–temporal relationship in traffic data, and the periodic similar attention module is to treat the nonlinear traffic flow information. Experiments on three datasets demonstrate that our model is best among all methods.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

scholarly journals A Traffic Flow Evolution Process toward Mixed Equilibrium with Multicriteria of Route Choice Behaviour

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiangjun Jiang ◽  
Zhongxiang Huang ◽  
Zhenyu Zhao
Keyword(s):  
Equilibrium State ◽  
Traffic Flow ◽  
Complete Information ◽  
User Equilibrium ◽  
Evolution Model ◽  
Choice Behaviour ◽  
Flow Evolution ◽  
Quantity Adjustment ◽  
Mixed Equilibrium ◽  
Equilibrium Pattern

Based on the price-quantity adjustment behaviour principle of disequilibrium theory, the route choices of travellers are also affected by a quantity signal known as traffic flow, while the route cost is considered as a price signal in economics. Considering the quantity signal’s effect among travellers, a new route comfort choice behaviour criterion and its corresponding equilibrium condition are established. The network travellers are classified into three groups according to their route choice behaviour: travellers in the first group choose the shortest route following the route rapidity behaviour criterion with complete information forming the UE (user equilibrium) pattern, travellers in the second group choose the most comfortable route following the route comfort behaviour criterion with complete information forming the QUE (quantity adjustment user equilibrium) pattern, and travellers in the third group choose a route according to their perceived travel time with incomplete information forming the SUE (stochastic user equilibrium) pattern. The traffic flows of all three groups converge to a new UE-QUE-SUE mixed equilibrium flow pattern after interaction. To depict the traveller-diversified choice behaviour and the traffic flow interaction process, a mixed equilibrium traffic flow evolution model is formulated. After defining the route comfort indicator and the corresponding user equilibrium state, the equilibrium conditions of the three group flows are given under a mixed equilibrium pattern. In addition, an equivalent mathematical programming of the mixed equilibrium traffic flow evolution model is proposed to demonstrate that the developed model converges to the mixed equilibrium state. Finally, numerical examples are examined to evaluate the effect of route comfort proportions on the traffic network flow evolution and analyse the performance of the proposed model.

Design and Development of Logistics and Transport Route Control System

2011 ◽  
Vol 71-78 ◽  
pp. 4261-4264
Author(s):  
Ru Wang ◽  
San Yuan Tang ◽  
Wei Xin Sun
Keyword(s):  
Control System ◽  
Traffic Flow ◽  
Future Development ◽  
Design And Development ◽  
Urban Road ◽  
Traffic Conditions

According that town plan is mainly drawn with software AutoCAD, this article realizes to automatically select a shortest transport route on urban road and dynamically display traffic flow based on VC++ and ObjectARX and lays a foundation for future development taking traffic limit, traffic conditions and other complex conditions into account.

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