Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

2012 ◽  
Vol 253-255 ◽  
pp. 1365-1368
Author(s):  
Ge Qi Qi ◽  
Jian Ping Wu ◽  
Yi Man Du

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.


10.29007/rnq9 ◽  
2018 ◽  
Author(s):  
Christina Flitsch ◽  
Karl-Heinz Kastner ◽  
Károly Bósa ◽  
Matthias Neubauer

Traffic information services and traffic simulations represent a crucial element for to- day’s mobility. Traffic data may be gained using different types of sensor technologies and measurement approaches. However, there is no “one fits for all solution” related to the application of sensor technology and providers of traffic information services need to care- fully decide when to apply which kind of sensor technology and measurement approach to provide traffic information.In Upper Austria, ITS Upper Austria represents such a traffic information provider. For the calculation of travel times and delays, real-time traffic sensors and a traffic simulation are currently in use. The latter is required when the amount of current real–time traffic information related to a link is too low for providing reasonable traffic information.ITS Upper Austria implemented its traffic simulation using the SUMO software. The demand model used for the simulation was built years ago, mainly using data from a household survey in Upper Austria in 2012. Based on this demand model, a route file was composed, which serves as input for the mesoscopic simulation. However, to increase the quality of the simulation, the route file needs to be continuously updated with respect to changing traffic behaviors (e.g. route traces, amount of cars). Different types of sensor data might trigger the calibration of traffic simulation models. For example Floating-Car-data, Bluetooth-data, data gained by permanent counting stations or even traffic times gained within test rides. Triggering updates of the traffic simulation model requires a careful analysis of the data basis and an appropriate update algorithm.This paper presents a traffic simulation update algorithm based upon diverse traffic data sources. Furthermore, findings related to the applicability of different sensor technologies for triggering simulation model updates are discussed. The findings stem from developments and empirical tests of ITS Upper Austria. The results could inform traffic information service provides when selecting sensor technology or when designing update mechanisms related to traffic simulation models.


2018 ◽  
Vol 114 ◽  
pp. 4-11 ◽  
Author(s):  
Yina Wu ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee

2021 ◽  
pp. 101-107
Author(s):  
Mohammad Alshehri ◽  

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.


2006 ◽  
Vol 16 (1) ◽  
pp. 3-30
Author(s):  
Dusan Teodorovic ◽  
Jovan Popovic ◽  
Panta Lucic

This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.


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