Prediction Model of Bus Arrival Time for Real-Time Applications

2005 ◽  
Vol 1927 (1) ◽  
pp. 195-204 ◽  
Author(s):  
Ranhee Jeong ◽  
Laurence R. Rilett
Keyword(s):  
Prediction Model ◽  
Travel Time ◽  
Real Time ◽  
Arrival Time ◽  
Historical Data ◽  
Transportation Systems ◽  
Test Bed ◽  
Travel Time Prediction ◽  
Ann Models ◽  
Real Time Applications

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.

scholarly journals Travel Time Prediction Model of Freeway Corridor Based on Real-Time Safety Reliability

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Huazhi Yuan ◽  
Zhaoguo Huang ◽  
Hongying Zhang
Keyword(s):  
Prediction Model ◽  
Travel Time ◽  
Real Time ◽  
Traffic Safety ◽  
Management Unit ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Relative Safety ◽  
Driving Risk ◽  
Freeway Corridor

By considering the feature of vehicle driving on the event management unit of the freeway corridor, according to the system target, a method to divide the management unit of the road network was put forward. The relative safety braking deceleration was taken as the evaluation index of single-vehicle driving risk. The reliability graph relationship and structure-function between the management unit and subunit were analyzed. Then, dynamic safety reliability and real-time safety reliability were determined on the basis of driving risk. In addition, the queuing and dissipating characteristics of the management unit under traffic incidents were analyzed based on the wave theory. The incident duration and dissipation time were also calculated. At the same time, the travel time prediction model of the incident management unit was set up when the real-time safety reliability was taken as a road resistance function. Finally, an improved travel time prediction model established in this paper is of great significance to improve traffic safety and efficiency, and the research results will provide an important theoretical foundation in the freeway corridor route decision.

scholarly journals Advanced traveller information system (ATIS) using GPS/GIS

2021 ◽  
Author(s):  
Bassim Ibrahim.
Keyword(s):  
Information System ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Arrival Time ◽  
Tracking System ◽  
Transportation Systems ◽  
Time Interval ◽  
Test Bed ◽  
Automatic Vehicle Location ◽  
Vehicle Location

Vehicle arrival time is one of the most important factors of intelligent transportation systems (ITS). Accurate transit travel information is important because it attracts additional customers and increases the satisfaction of transit users. A passenger waiting for a train or bus, a person waiting for a cab, a customer waiting for a courier to come to his/her home to pickup or deliver a package, a business office waiting for a truck for goods and a home user waiting for his/her shipment for which he/she did online shopping are a few examples of how important vehicle arrival time is in different areas of life. Most companies are investing a lot of money to improve their systems for better, faster and reliable customer service. As the cost of ITS components have decreased, the automatic vehicle location (AVL) system, which is one component of ITS, has become more widely used. Many transit agencies use an AVL system to track their vehicles in real-time. Tracking systems technology was made possible by the integration of three technologies: global positioning system (GPS), global system for mobile communication (GSM) and the geographic information system (GIS). This project shows detailed research in the area of automatic vehicle location and implements a low cost vehicle tracking system using GPS and GPRS. The system reads the current position, speed and direction using GPS, the data is sent via GPRS service from a GSM network to a server using TCP/IP protocol and the server saves this information to the database on a regular time interval. The web-based application then uses this data and calculates the approximate arrival time. The system allows a user to view the present position of the vehicle using Google Maps and calculates the arrival time. Also, bus location can be monitored in real time by route supervisors. This will allow supervisors to make better service adjustment decisions because they will be able to see how the route is operating. The test bed was a bus route running in the downtown of Toronto.

scholarly journals Advanced traveller information system (ATIS) using GPS/GIS

2021 ◽  
Author(s):  
Bassim Ibrahim.
Keyword(s):  
Information System ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Arrival Time ◽  
Tracking System ◽  
Transportation Systems ◽  
Time Interval ◽  
Test Bed ◽  
Automatic Vehicle Location ◽  
Vehicle Location

Vehicle arrival time is one of the most important factors of intelligent transportation systems (ITS). Accurate transit travel information is important because it attracts additional customers and increases the satisfaction of transit users. A passenger waiting for a train or bus, a person waiting for a cab, a customer waiting for a courier to come to his/her home to pickup or deliver a package, a business office waiting for a truck for goods and a home user waiting for his/her shipment for which he/she did online shopping are a few examples of how important vehicle arrival time is in different areas of life. Most companies are investing a lot of money to improve their systems for better, faster and reliable customer service. As the cost of ITS components have decreased, the automatic vehicle location (AVL) system, which is one component of ITS, has become more widely used. Many transit agencies use an AVL system to track their vehicles in real-time. Tracking systems technology was made possible by the integration of three technologies: global positioning system (GPS), global system for mobile communication (GSM) and the geographic information system (GIS). This project shows detailed research in the area of automatic vehicle location and implements a low cost vehicle tracking system using GPS and GPRS. The system reads the current position, speed and direction using GPS, the data is sent via GPRS service from a GSM network to a server using TCP/IP protocol and the server saves this information to the database on a regular time interval. The web-based application then uses this data and calculates the approximate arrival time. The system allows a user to view the present position of the vehicle using Google Maps and calculates the arrival time. Also, bus location can be monitored in real time by route supervisors. This will allow supervisors to make better service adjustment decisions because they will be able to see how the route is operating. The test bed was a bus route running in the downtown of Toronto.

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.

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