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.

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.

New Vehicle Data Bus Architecture and In-Vehicle Information System Evaluation Platform for Intelligent Transportation Systems Modules

1998 ◽  
Vol 1651 (1) ◽  
pp. 17-22
Author(s):  
Philip F. Spelt ◽  
Allan M. Kirson ◽  
Susan Scott
Keyword(s):  
Information System ◽  
Intelligent Transportation Systems ◽  
Local Area Network ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Area Network ◽  
Test Bed ◽  
Data Bus ◽  
Vehicle Information ◽  
Bus Architecture

An increasing number of intelligent transportation systems- (ITS-) after-market systems present a set of in-vehicle installation and use problems relatively unique in the history of automobile use. Many automobile manufacturers would like to offer these new state-of-the-art devices to customers, but are hampered by the current design cycle of new cars. While automobile manufacturers are indeed using multiplex buses [the automotive equivalent of a computer local area network (LAN)], problems remain because manufacturers are not converging on a single bus standard. A new dual-bus architecture to address these problems is presented with an in-vehicle information system (IVIS) research platform on which the principles embodied in the ITS data bus architecture can be evaluated. The dual-bus architecture has been embodied in a proposed Society of Automotive Engineers (SAE) standard, with support from both automobile and consumer electronics manufacturers. The architecture and a reference model for the interfaces and protocols of the new bus are presented and described. The goals of the ITS data bus are to be inexpensive and easy to install, and to provide for safe and secure functioning. These high-level goals are embodied in the proposed standard. The IVIS development platform comprises a number of personal computers (PCs) linked via ethernet LAN, with a high-end PC serving as the IVIS computer. In this LAN, actual devices can be inserted in place of the original PC that emulated them. This platform will serve as the development and test bed for an ITS data bus conformity test, the SAE standard for which is also being developed.

Simplex-Based Calibration of Traffic Microsimulation Models with Intelligent Transportation Systems Data

2003 ◽  
Vol 1855 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Kyu-Ok Kim ◽  
L. R. Rilett
Keyword(s):  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
System Modeling ◽  
Intelligent Transportation ◽  
Simplex Algorithm ◽  
Transportation System ◽  
Transportation Systems ◽  
Test Bed ◽  
Morning Peak ◽  
Microsimulation Models

In recent years, microsimulation has become increasingly important in transportation system modeling. A potential issue is whether these models adequately represent reality and whether enough data exist with which to calibrate these models. There has been rapid deployment of intelligent transportation system (ITS) technologies in most urban areas of North America in the last 10 years. While ITSs are developed primarily for real-time traffic operations, the data are typically archived and available for traffic microsimulation calibration. A methodology, based on the sequential simplex algorithm, that uses ITS data to calibrate microsimulation models is presented. The test bed is a 23-km section of Interstate 10 in Houston, Texas. Two microsimulation models, CORSIM and TRANSIMS, were calibrated for two different demand matrices and three periods (morning peak, evening peak, and off-peak). It was found for the morning peak that the simplex algorithm had better results then either the default values or a simple, manual calibration. As the level of congestion decreased, the effectiveness of the simplex approach also decreased, as compared with standard techniques.

scholarly journals Real-Time Adaptive Traffic Signals Using Rate-Monotonic Scheduling

2019 ◽  
Vol 29 ◽  
pp. 03002 ◽  
Author(s):  
Mãdãlin-Dorin Pop
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Fixed Time ◽  
Transportation Systems ◽  
Real Time Scheduling ◽  
Traffic Lights ◽  
Rate Monotonic ◽  
Rate Monotonic Scheduling

The studies and real situations shown that the traffic congestion is one of nowadays highest problems. This problem wassolved in the past using roundabouts and traffic signals. Taking in account the number of cars that is increasing continuously, we can see that past approaches using traffic lights with fixed-time controller for traffic signals timing is obsolete. The present and the future is the using of Intelligent Transportation Systems. Traffic lights systems should be aware about realtime traffic parameters and should adapt accordingly to them. The purpose of this paper is to present a new approach to control traffic signals using rate-monotonic scheduling. Obtained results will be compared with the results obtained by using others real-time scheduling algorithms.

Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time

2005 ◽  
Vol 1923 (1) ◽  
pp. 208-217 ◽  
Author(s):  
Mei Chen ◽  
Xiaobo Liu ◽  
Jingxin Xia
Keyword(s):  
Arrival Time ◽  
Recent Observation ◽  
Control Factor ◽  
Automatic Vehicle Location ◽  
Time Prediction ◽  
Schedule Recovery ◽  
Vehicle Location ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction ◽  
The Impact

This study develops a dynamic bus arrival time prediction model using the data collected by the automatic vehicle location and automatic passenger counter systems. It is based on the Kalman filter algorithm with a two-dimensional state variable in which the prediction error in the most recent observation is used to optimize the arrival time estimate for each downstream stop. The impact of schedule recovery is considered as a control factor in the model to reflect the driver's schedule recovery behavior. The algorithm performs well when tested with a set of automatic vehicle location–automatic passenger counter data collected from a real-world bus route. The algorithm does not require intensive computation or an excessive data preprocessing effort. It is a promising approach for real-time bus arrival time prediction in practice.

Impact of Travel Time Models on Quality of Real-Time Routing Instructions

2003 ◽  
Vol 1857 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Elise Miller-Hooks ◽  
Baiyu Yang
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Communication Systems ◽  
Service Providers ◽  
A Priori ◽  
Information Service ◽  
Small Neighborhood ◽  
Transportation Systems ◽  
Travel Times ◽  
Real Time Information

Mobile communication systems coupled with intelligent transportation systems technologies can permit information service providers to supply real-time routing instructions to suitably equipped vehicles as real-time travel times are received. Simply considering current conditions in updating routing decisions, however, may lead to suboptimal path choices, because future travel conditions likely will differ from that currently observed. Even with perfect and continuously updated information about current conditions, future travel times can be known a priori with uncertainty at best. Further, in congested transportation systems, conditions vary over time as recurrent congestion may change with a foreseeable pattern during peak driving hours. It is postulated that better, more robust routing instructions can be provided by explicitly accounting for this inherent stochastic and dynamic nature of future travel conditions in generating the routing instructions. It is further hypothesized that nearly equally good routing instructions can be provided by collecting real-time information from only a small neighborhood within the transportation system as from the entire system. Extensive numerical experiments were conducted to assess the validity of these two hypotheses.

Detection of Real-Time Event in Intelligent Traffic System Based on RFID

2014 ◽  
Vol 926-930 ◽  
pp. 1314-1317 ◽  
Author(s):  
Li Yang
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Engine Model ◽  
Intelligent Traffic System ◽  
Complex Events ◽  
Rules Engine

To solve the demand of real-time event detection in the RFID-based Intelligent Transportation Systems , using Complex Event Processing technology to establish a rule model to detect events.The model allows users to customize the Basic Events and Complex Events, using the rule files describe the complex events modes, clearly expressed the timing and gradation relationships between RFID events, meeting the needs of real-time event detection in the Intelligent Transportation System ,achieving the appropriate rules engine,. Finally, test and verify the effectiveness of the rules file and the rules engine model by experiments.

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