Intelligent traffic control and optimization with cooperative systems on the eHighway: Using the electrified highway-infrastructure for heavy good vehicles and the advanced traffic control center with V2X for a more efficient and safer traffic (for all road users)

2014 ◽  
Author(s):  
Thomas Sachse ◽  
Oliver Grabner ◽  
Meike Mockel ◽  
Claus Kaschwich ◽  
Jens Plattner
Keyword(s):  
Traffic Control ◽  
Cooperative Systems ◽  
Control Center ◽  
Highway Infrastructure ◽  
Road Users

scholarly journals Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3658
Author(s):  
Qingfeng Zhu ◽  
Sai Ji ◽  
Jian Shen ◽  
Yongjun Ren
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Location Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Security Analysis ◽  
Geographical Location ◽  
Experimental Simulation ◽  
Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

scholarly journals Road users behaviors on selected elements of transport infrastructure

2018 ◽  
Vol 19 (12) ◽  
pp. 133-138
Author(s):  
Elżbieta Macioszek ◽  
Nicol Piasecka
Keyword(s):  
Traffic Control ◽  
Transport Infrastructure ◽  
Road Users

The problem of road users behaviors on selected elements of transport infrastructure have been presented in this paper. The study of road users behaviors was carried out at selected five cities located in the Masovian Voivodeship. During the survey the information about drivers, pedestrians and cyclists behaviors at intersections like roundabouts have been collected. The collected information concerned among others recognition and adhere to the existing traffic control at roundabouts as well as respects the pedestrians priority at pedestrian crossings located on the roundabouts entries.

scholarly journals Simple and Efficient Prediction of Near Future State of Traffic Using Only Past Speed Information

2018 ◽  
Vol 30 (5) ◽  
pp. 589-599
Author(s):  
Fevzi Yasin Kababulut ◽  
Damla Kuntalp ◽  
Olcay Akay ◽  
Timur Düzenli
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Real Data ◽  
Urban Life ◽  
Prediction Errors ◽  
Control Center ◽  
Road Section ◽  
Efficient Prediction ◽  
Traffic Systems ◽  
Near Future

Intelligent traffic systems attempt to solve the problem of traffic congestion, which is one of the most important environmental and economic issues of urban life. In this study, we approach this problem via prediction of traffic status using past average traveler speed (ATS). Five different algorithms are proposed for predicting the traffic status. They are applied to real data provided by the Traffic Control Center of Istanbul Metropolitan Municipality. Algorithm 1 predicts future ATS on a highway section based on the past speed information obtained from the same road section. The other proposed algorithms, Algorithms 2 through 5, predict the traffic status as fluent, moderately congested, or congested, again using past traffic state information for the same road segment. Here, traffic states are assigned according to predetermined intervals of ATS values. In the proposed algorithms, ATS values belonging to past five consecutive 10-minute time intervals are used as input data. Performances of the proposed algorithms are evaluated in terms of root mean square error (RMSE), sample accuracy, balanced accuracy, and processing time. Although the  proposed algorithms are relatively simple and require only past speed values, they provide fairly reliable results with noticeably low prediction errors.

Vehicle Detection and Classification Using Model-Based and Fuzzy Logic Approaches

2005 ◽  
Vol 1935 (1) ◽  
pp. 154-162
Author(s):  
Heng-Da Cheng ◽  
Haining Du ◽  
Liming Hu ◽  
Chris Glazier
Keyword(s):  
Fuzzy Logic ◽  
Traffic Control ◽  
Vehicle Detection ◽  
Classification Systems ◽  
Vehicle Classification ◽  
Highway Traffic ◽  
Control Center ◽  
Model Based ◽  
Fixed Orientation ◽  
Classification Information

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are ( a) no special orientation of the camera is required, ( b) no additional devices are needed, and ( c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.

TWEET DATA EXTRACTOR FOR CREATING A TWITTER TRAFFIC MAP MASHUP

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Amirul Afif Jasmi ◽  
Mohamad Hafis Izran Ishak ◽  
Nurul Hawani Idris
Keyword(s):  
Social Media ◽  
Big Data ◽  
Traffic Control ◽  
Transportation Planning ◽  
General Public ◽  
New Technologies ◽  
The Road ◽  
Current Information ◽  
Road Users ◽  
Use Of Social Media

Over recent years, there has been a growth of interest in the use of social media including Facebook and Twitter by the authorities to share and updates current information to the general public. The technology has been used for a variety of purposes including traffic control and transportation planning. There is a concern that the use of new technologies, including social media will lead to data abundance that requires effective operational resources to interpret the big data. This paper proposes a tweet data extractor to extract the traffic tweet by the authority and visualise the reports and mash up on top of online map, namely Twitter map. Visualisation of traffic tweet on a map could assist a user to effectively interpret the text based Twitter report by a location based map viewer. Hence, it could ease the process of planning itinerary by the road users. 

Effect of density feedback on the two-route traffic scenario with bottleneck

2016 ◽  
Vol 27 (06) ◽  
pp. 1650058
Author(s):  
Xiao-Yan Sun ◽  
Zhong-Jun Ding ◽  
Guo-Hua Huang
Keyword(s):  
Transition State ◽  
Traffic Control ◽  
Free Flow ◽  
Flow State ◽  
Control Center ◽  
Current State ◽  
Study Results ◽  
Maximum Current ◽  
Traffic Scenario ◽  
Good Advice

In this paper, we investigate the effect of density feedback on the two-route scenario with a bottleneck. The simulation and theory analysis shows that there exist two critical vehicle entry probabilities [Formula: see text] and [Formula: see text]. When vehicle entry probability [Formula: see text], four different states, i.e. free flow state, transition state, maximum current state and congestion state are identified in the system, which correspond to three critical reference densities. However, in the interval [Formula: see text], the free flow and transition state disappear, and there is only congestion state when [Formula: see text]. According to the results, traffic control center can adjust the reference density so that the system is in maximum current state. In this case, the capacity of the traffic system reaches maximum so that drivers can make full use of the roads. We hope that the study results can provide good advice for alleviating traffic jam and be useful to traffic control center for designing advanced traveller information systems.

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