scholarly journals Data-Driven Multi-Agent Vehicle Routing in a Congested City

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 447
Author(s):  
Alex Solter ◽  
Fuhua Lin ◽  
Dunwei Wen ◽  
Xiaokang Zhou
Keyword(s):  
Information System ◽  
Data Driven ◽  
Road User ◽  
Traffic Information ◽  
Time Data ◽  
Real Time Traffic ◽  
Travel Time Data ◽  
Agent Simulation ◽  
Multi Agent ◽  
System Data

Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This paper studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. These data are made available to all users, such that they may be able to learn and predict the effects of congestion for building a route adaptively. This method is further enhanced by combining the traffic information system data with previous routing experiences to determine the fastest route with less exploration. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone.

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.  

A New Data Aggregation Model for Intelligent Transportation System

2013 ◽  
Vol 671-674 ◽  
pp. 2855-2859
Author(s):  
Jun Wu ◽  
Luo Zhong
Keyword(s):  
Information System ◽  
Data Aggregation ◽  
Wireless Sensors ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
System Model ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Aggregation Problem

Intelligent Transportation System is a new kind of complicated information system which includes many different wireless sensors. With the development in sensor technologies and their applications, it is important to focus on how to find the useful and real-time traffic information from the Intelligent Transportation System. Using this method of building dynamical data system model for the Intelligent Transportation System is the way to solve the data aggregation problem and minimize the number of the multi-sources data.

scholarly journals Real time traffic information system-A step towards total traffic management at Kamakura

2001 ◽  
Vol 18 ◽  
pp. 887-894
Author(s):  
Shinji KAJITANI ◽  
Kunihiro SAKAMOTO ◽  
Hisashi KUBOTA ◽  
Youji Takahashi
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Information ◽  
Real Time Traffic ◽  
System A

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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