scholarly journals Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Lin Dong ◽  
Akira Rinoshika ◽  
Zhixian Tang
Keyword(s):  
Road Network ◽  
Entropy Method ◽  
Urban Traffic ◽  
Traffic Information ◽  
Gated Community ◽  
Entropy Model ◽  
Evaluation Standard ◽  
Traffic System ◽  
Traffic State ◽  
Selection Framework

The opening of a gated community to expand the micro-road network in an urban traffic system is an importance research topic related to urban congestion. To satisfy the demands of opening an early choosing case, this paper proposes a comprehensive selection framework on qualified communities and their appropriate opening times by describing the traffic state at the boundary road network accurately. The traffic entropy model and fuzzy c-means (FCM) method are used in this paper. In the framework, a new opening evaluation entropy model is built using basic theory of the thermodynamic traffic entropy method. The traffic state entropy values of the boundary road network and entropy production are calculated to determinate the opening time. In addition, a specific fuzzy range evaluation standard at a preset gated community is drawn with an FCM algorithm to verify the opening determination. A case study based on the traffic information in a simulated gated community in Shanghai is evaluated and proves that the findings of opening evaluation are in accordance with the actual situation. It is found that the micro-inter-road network of a gated community should be opened as the entropy value reaches 2.5. As the travel time is less than 20 s, the correlation between the opening entropy value and the journey delay time exhibits a good linear correlation, which indicates smooth traffic flow.

scholarly journals Prediction of Road Network Traffic State Using the NARX Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ziwen Song ◽  
Feng Sun ◽  
Rongji Zhang ◽  
Yingcui Du ◽  
Chenchen Li
Keyword(s):  
Neural Network ◽  
Road Network ◽  
Flow Speed ◽  
Prediction Performance ◽  
Traffic Information ◽  
Index Formula ◽  
Fundamental Diagram ◽  
The Road ◽  
Traffic State

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

scholarly journals The Effect of Travel Information on Travelers’ Choice of Travel Modes and Routes: A Case Study of the Travel between the Campuses

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Linjie Gao ◽  
Zhicai Juan ◽  
Anning Ni ◽  
Peng Jing
Keyword(s):  
Linear Programming ◽  
Quantitative Analysis ◽  
Road Network ◽  
Prior Information ◽  
Influential Factors ◽  
Travel Information ◽  
Traffic System ◽  
Urban Road ◽  
Traffic State

The traffic state of the urban road network is determined by travelers’ choices of travel modes and routes. With the development of science and technology, people tend to have more travel choices and their distinctive temperaments often lead to different choices even in the same situation. Therefore, a study of different factors that may affect people’s travel choices plays a crucial role in the optimization of the traffic system. Focusing on the four major travel modes between Minhang campus and Xuhui campus of Shanghai Jiao Tong University (SJTU) in Shanghai, China, this paper tries to gather the information of the factors that affect travel choices and the extent of such effects both in general cases and when prior information is given by means of questionnaires. Based on data processing, the paper draws pie charts on the travel choices under different circumstances and makes a qualitative analysis of the influential factors. Then, a quantitative analysis is made by using the models of utility function and linear programming. Finally, in contrast with the results, the paper finds out the extent of the effect of travel information on the choice of travel modes and routes of the travelers with different temperaments.

scholarly journals Research on Urban Branch Road System Construction under the Perspective of Eco-friendly Traffic

2015 ◽  
Vol 2 ◽  
pp. 5
Author(s):  
Jiajia Luo ◽  
Linqiang Ji ◽  
Jinhe Wang
Keyword(s):  
Road Network ◽  
Urban Traffic ◽  
Low Density ◽  
System Construction ◽  
Urgent Problem ◽  
Traffic System ◽  
Urban Road ◽  
Supportive Role ◽  
Improvement Measures ◽  
Road System

<p>With constant development of urbanization, urban traffic has become the most urgent problem in the contemporary metropolis. Therefore, it is an inevitable trend to positively improve the human-oriented sustainable urban eco-friendly traffic system. The urban branch road constitutes a part of urban road that will be mostly neglected while it plays a positive supportive role to the eco-friendly traffic. This article makes a contrast and analysis to the coordinated relationship between branch road network and eco-friendly traffic in same areas of Germany and Dalian City and concludes the branch road network’s assurance role to the eco-friendly traffic, points out deficiency of branch road network and low density in China. The development of eco-friendly traffic, raises a series of improvement measures to the urban branch road system.</p><p> </p>

Models and Evaluation for Urban Traffic Carrying Capacity

2013 ◽  
Vol 409-410 ◽  
pp. 1258-1261
Author(s):  
Shan Shan Gao ◽  
Zhen Zhou Yuan
Keyword(s):  
Carrying Capacity ◽  
Road Network ◽  
Quantitative Model ◽  
Urban Traffic ◽  
Evaluation Standard ◽  
Utilization Coefficient ◽  
Time Space ◽  
Reduction Coefficient ◽  
The Status ◽  
Integrated Utilization

This paper studies on the status of urban traffic development by analyzing traffic carrying capacity. The concept, characteristics and influencing factors of urban traffic carrying capacity is researched. This paper establishes one quantitative model based on the time-space consumption model, especially taking the reuse coefficient of road resources, reduction coefficient caused by intersections, and integrated utilization coefficient of road network into consideration. And put forward evaluation index which is the level of traffic carrying capacity. Moreover, the evaluation standard and some strategies for improving traffic carrying capacity are provided.

scholarly journals An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Deepa Devasenapathy ◽  
Kathiravan Kannan
Keyword(s):  
Energy Efficient ◽  
Road Network ◽  
Polynomial Regression ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Traffic Information ◽  
Data Set ◽  
The Road

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

scholarly journals A Data-Driven Control Strategy for Urban Express Ramp

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liyan Zhang ◽  
Min Zhang ◽  
Jian Ma ◽  
Jing Ge
Keyword(s):  
Control Strategy ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Cooperative Control ◽  
Road Network ◽  
Urban Traffic ◽  
Data Driven ◽  
Traffic Information ◽  
Network Environment ◽  
Intelligent Network

Expressway, as the main artery of urban traffic, realizes the smooth operation of the whole urban road network through reasonably balancing the traffic flow. However, due to the lack of reasonable and effective traffic control, the safety and congestion of expressways are becoming more and more serious. The development of intelligent network technology provides a new idea to solve the control problem of expressways. In this paper, a data-driven ramp control model of urban expressway is constructed. The interaction of traffic information is realized through intelligent network connection technology. The cooperative control strategy of VSL and RM is adopted. The mutual feedback of VSL and RM is realized based on the improved METANET model. The simulation experiment based on VISSIM secondary development shows that the collaborative control strategy under the intelligent network environment could make the vehicle travel time reduced by 20.59% and the speed difference between adjacent sections of the expressway mainline by 34.07%, which realized the coordinated control of the mainline and the on-ramp under the intelligent network environment, alleviate the expressway traffic congestion, reduce the traffic pressure, and improve the efficiency of the road network.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

scholarly journals Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1996
Author(s):  
Hoe Kyoung Kim ◽  
Younshik Chung ◽  
Minjeong Kim
Keyword(s):  
State Estimation ◽  
Traffic Engineering ◽  
Flow Density ◽  
Microscopic Simulation ◽  
Traffic Demand ◽  
Traffic System ◽  
Traffic Conditions ◽  
Traffic State ◽  
Vehicle Identification ◽  
Traffic State Estimation

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

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