scholarly journals The Weight for Travel Time Information in Sightseeing Trip and Its Application to Route Guidance Information System

1997 ◽  
Vol 14 ◽  
pp. 631-641
Author(s):  
Hideki FURUYA ◽  
Kazuo NISHII ◽  
Masanori UENISHI
Keyword(s):  
Information System ◽  
Travel Time ◽  
Route Guidance ◽  
Time Information ◽  
Guidance 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.

Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks

1998 ◽  
Vol 1617 (1) ◽  
pp. 163-170 ◽  
Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Transportation Systems ◽  
Time Profile ◽  
Identification System ◽  
Route Guidance ◽  
Travel Times ◽  
Test Bed ◽  
Link Travel Time ◽  
Time Information

With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.

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.

scholarly journals Modular Approach for Odometry Localization Method for Vehicles with Increased Maneuverability

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 79
Author(s):  
Chenlei Han ◽  
Michael Frey ◽  
Frank Gauterin
Keyword(s):  
Steering System ◽  
Vehicle Control ◽  
Modular Approach ◽  
Route Guidance ◽  
State Variables ◽  
Simulation Environment ◽  
Localization Method ◽  
State Estimators ◽  
Position And Orientation ◽  
Guidance Information

Localization and navigation not only serve to provide positioning and route guidance information for users, but also are important inputs for vehicle control. This paper investigates the possibility of using odometry to estimate the position and orientation of a vehicle with a wheel individual steering system in omnidirectional parking maneuvers. Vehicle models and sensors have been identified for this application. Several odometry versions are designed using a modular approach, which was developed in this paper to help users to design state estimators. Different odometry versions have been implemented and validated both in the simulation environment and in real driving tests. The evaluated results show that the versions using more models and using state variables in models provide both more accurate and more robust estimation.

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