A Simplified Real-Time Road Network Model Considering Intersection Delay and its Application on Vehicle Navigation

2011 ◽  
Vol 58-60 ◽  
pp. 1959-1965 ◽  
Author(s):  
Zheng Yu Zhu ◽  
Wei Liu ◽  
Lin Liu ◽  
Ming Cui ◽  
Jin Yan Li
Keyword(s):  
Real Time ◽  
Network Model ◽  
Road Network ◽  
Intelligent Transportation System ◽  
Optimal Path ◽  
Network Models ◽  
Traffic Information ◽  
Vehicle Navigation ◽  
Traffic Data ◽  
New Model

The complexity of a real road network structure of a city and the variability of its real traffic information make a city’s intelligent transportation system (ITS) hard to meet the needs of the city’s vehicle navigation. This paper has proposed a simplified real-time road network model which can take into account the influence of intersection delay on the guidance for vehicles but avoid the calculation of intersection delay and troublesome collection of a city’s traffic data. Based on the new model, a navigation system has been presented, which can plan a dynamic optimal path for a vehicle according to the real-time traffic data received periodically from the city’s traffic center. A simulated experiment has been given. Compared with previous real-time road network models, the new model is much simpler and more effective on the calculation of vehicle navigation.

A Simplified Time-Division Based on Road Network Model Considering Intersection Delay for Vehicle Navigation

2011 ◽  
Vol 52-54 ◽  
pp. 1226-1232 ◽  
Author(s):  
Zheng Yu Zhu ◽  
Jin Yan Li ◽  
Li Na Wang ◽  
Wei Liu ◽  
Ming Cui ◽  
...  
Keyword(s):  
Network Model ◽  
Road Network ◽  
Practical Significance ◽  
Vehicle Navigation ◽  
Traffic Data ◽  
Dijkstra Algorithm ◽  
Road Congestion ◽  
Vehicle Navigation System ◽  
Simulation Results ◽  
Time Division

How to model a dynamic road network has great practical significance in a vehicle navigation system. This paper has proposed a simplified time-division based on road network model which implicitly takes into account the delay time at various intersections, the degree of a road congestion and the different road quality, but avoids a complicated calculation and collection for these traffic data. An improved Dijkstra algorithm based on the new model has also been given. The simulation results show that the model can work well and the algorithm is efficient.

scholarly journals Intelligent Highway Traffic Detection Algorithm based on Deep Learning

2021 ◽  
Author(s):  
ming ji ◽  
Chuanxia Sun ◽  
Yinglei Hu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Traffic Data ◽  
Vehicle Recognition ◽  
Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

Fuel-optimal path finding algorithm using traffic information at urban intersection

2019 ◽  
Author(s):  
Jooin Lee ◽  
Hyeongcheol Lee
Keyword(s):  
Fuel Consumption ◽  
Intelligent Transportation System ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Traffic Model ◽  
Traffic Information ◽  
Traffic Condition ◽  
Decision Algorithm ◽  
The Road

Intelligent Transportation System (ITS) is actively studied as the sensor and communication technology in the vehicle develops. The Intelligent Transportation System collects, processes, and provides information on the location, speed, and acceleration of the vehicles in the intersection. This paper proposes a fuel optimal route decision algorithm. The algorithm estimates traffic condition using information of vehicles acquired from several ITS intersections and determines the route that minimizes fuel consumption by reflecting the estimated traffic condition. Simplified fuel consumption models and road information (speed limit, average speed, etc.) are used to estimate the amount of fuel consumed when passing through the road. Dynamic Programming (DP) is used to determine the route that fuel consumption can be minimized. This algorithm has been verified in an intersection traffic model that reflects the actual traffic environment (Korea Daegu Technopolis) and the corresponding traffic model is modeled using AIMSUN.

scholarly journals Measuring Traffic in Cities Through a Large-Scale Online Platform

2019 ◽  
Vol 1 (2-3) ◽  
pp. 161-173 ◽  
Author(s):  
Vilhelm Verendel ◽  
Sonia Yeh
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Availability ◽  
Traffic Information ◽  
Traffic Data ◽  
Online Platform ◽  
Real Time Traffic ◽  
Data Source ◽  
Road Segments

Abstract Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data every 5 min during 1 year, in total more than 5 million samples covering more than 300 thousand road segments. Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed data for road segments when real-time information was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed but miss instantaneous higher speed measured in some sensors, typically at times when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers, and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for improving our knowledge of traffic in cities.

Fusion of Dynamic Traffic Information and Embedded GIS

2012 ◽  
Vol 433-440 ◽  
pp. 5654-5658
Author(s):  
Shan Shan Fan ◽  
Dian Ge Yang ◽  
Zhao Sheng Zhang ◽  
Ting Li ◽  
Xiao Min Lian
Keyword(s):  
Road Network ◽  
Intelligent Transportation System ◽  
Traffic Information ◽  
Dynamic Traffic ◽  
Dynamic Path Planning ◽  
Road Condition ◽  
Dynamic Navigation ◽  
Embedded Gis ◽  
Integration Algorithms ◽  
Dynamic Path

Dynamic traffic information is an important data for Intelligent Transportation System(ITS). Fusion of dynamic traffic information and embedded GIS is a basis for dynamic path planning and dynamic navigation. The approach proposed in this paper designed a model of dynamic traffic information and a hierarchical sub-model of road condition, and established algorithms for fusion with road network. Experiments with embedded GIS show that the models and integration algorithms are validated and effective. We believe the newly developed approach will be of great potential in ITS and future applications of dynamic traffic information.

RoadRank Algorithm – A Novel Method to Calculate Traffic Index for an Area

2014 ◽  
Vol 1079-1080 ◽  
pp. 769-775
Author(s):  
Fan Wang ◽  
Yu Fang
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Information ◽  
Area Index ◽  
New Model ◽  
Real Time Traffic ◽  
Information Center ◽  
Novel Method ◽  
Definition Of

Traffic index hasbeen used to provide accurate traffic information to users. Many models havebeen developed to calculate the index for a road, but how to define andcalculate the index for an area still needs more investigation. Here we proposea new model for area index, including a definition of area index itself and a methodto calculate it. But this model can’t be widely used, for some innatelimitations. So we put forward another method based on well-known algorithmPageRank to calculate area index. To test the effectiveness, we apply ouralgorithmto conduct several experiments. Our experiments using standard trafficstatistics provided by ShanghaiTraffic Information Center (STIC), show our method have values for real-time traffic information system.

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