scholarly journals Highway Travel Time Information System based on Cumulative Count Curves and New Tracking Technologies

2016 ◽  
Vol 18 ◽  
pp. 44-50 ◽  
Author(s):  
F. Soriguera ◽  
M. Martínez-Díaz ◽  
I. Pérez
Keyword(s):  
Information System ◽  
Travel Time ◽  
Cumulative Count ◽  
Time Information

scholarly journals Impact on Road Safety and Operation of Rerouting Traffic in Rural Travel Time Information System

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4145
Author(s):  
Mariusz Kiec ◽  
Carmelo D’Agostino ◽  
Sylwia Pazdan
Keyword(s):  
Information System ◽  
Travel Time ◽  
Traffic Control ◽  
Time Management ◽  
Road Safety ◽  
Road Network ◽  
Safety Level ◽  
The Road ◽  
Traffic Operation ◽  
Time Information

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network.

Application of an Intelligent Transportation System in a Travel Time Information System: Safety Assessment and Management

2017 ◽  
Vol 2635 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Salvatore Cafiso ◽  
Carmelo D’Agostino ◽  
Mariusz Kiec ◽  
Sylwia Pogodzinska
Keyword(s):  
Information System ◽  
Travel Time ◽  
Traffic Control ◽  
Road Network ◽  
Empirical Bayes ◽  
Intelligent Transportation System ◽  
Mobile App ◽  
Safety Performance ◽  
On The Road ◽  
Time Information

The research presented here evaluated road safety on the road sections included in the Intelligent Traffic Control System of the Podhale Region (ISSRRP) in Poland. This travel time information system consists of a remote traffic microwave sensor, cameras, as well as automatic plate number recognition on national roads with variable message signs and a mobile app to suggest alternative routes in the regional road network. The study analyzed changes in safety caused by transferring traffic volume from national to regional rural and suburban road networks. The assessment of the safety performance was performed with an empirical Bayes study, with periods of three years before and after the implementation of ISSRRP. No changes were identified in the safety performance of the national road network after to the introduction of ISSRRP. However, when the overall network is considered, a potential increase in the number of crashes may be expected, depending on the volume of traffic transferred from national to regional roads, and rural or suburban areas. Therefore, a new approach for system management was proposed, taking into account not only improvement in traffic flow, but also safety performance.

scholarly journals Highway travel time information system based on cumulative count curves and new tracking technologies

2016 ◽  
Author(s):  
Margarita Martínez-Díaz ◽  
Francesc Soriguera Martí ◽  
Ignacio Pérez Pérez
Keyword(s):  
Travel Time ◽  
Real Time ◽  
New Technologies ◽  
Time Estimation ◽  
Travel Times ◽  
Important Indicator ◽  
Loop Detectors ◽  
Real Time Estimation ◽  
Cumulative Count ◽  
Time Information

Travel time is probably the most important indicator of the level of service of a highway, and it is also the most appreciated information for its users. Administrations and private companies make increasing efforts to improve its real time estimation. The appearance of new technologies makes the precise measurement of travel times easier than never before. However, direct measurements of travel time are, by nature, outdated in real time, and lack of the desired forecasting capabilities. This paper introduces a new methodology to improve the real time estimation of travel times by using the equipment usually present in most highways, i.e., loop detectors, in combination with Automatic Vehicle Identification or Tracking Technologies. One of the most important features of the method is the usage of cumulative counts at detectors as an input, avoiding the drawbacks of common spot-speed methodologies. Cumulative count curves have great potential for freeway travel time information systems, as they provide spatial measurements and thus allow the calculation of instantaneous travel times. In addition, they exhibit predictive capabilities. Nevertheless, they have not been used extensively mainly because of the error introduced by the accumulation of the detector drift. The proposed methodology solves this problem by correcting the deviations using direct travel time measurements. The method results highly beneficial for its accuracy as well as for its low implementation cost.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3209 

Predicting Travel Times for the South Jersey Real-Time Motorist Information System

2003 ◽  
Vol 1855 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Steven I. J. Chien ◽  
Xiaobo Liu ◽  
Kaan Ozbay
Keyword(s):  
Information System ◽  
Prediction Model ◽  
Simulation Model ◽  
Travel Time ◽  
Real Time ◽  
Travel Times ◽  
The South ◽  
Travel Time Prediction ◽  
Real Time Information ◽  
Time Information

A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real–time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.

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