Using Intelligent Transportation Systems Travel-Time Data for Multimodal Analyses and System Monitoring

2001 ◽  
Vol 1768 (1) ◽  
pp. 148-156 ◽  
Author(s):  
William L. Eisele ◽  
Laurence R. Rilett ◽  
Kendra Brown Mhoon ◽  
Clifford Spiegelman
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
System Monitoring ◽  
Time Data ◽  
Travel Time Data

Arterial Travel Time Validation and Augmentation with Two Independent Data Sources

2015 ◽  
Vol 2526 (1) ◽  
pp. 79-89 ◽  
Author(s):  
Xuechi Zhang ◽  
Masoud Hamedi ◽  
Ali Haghani
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Multiple Sources ◽  
Time Data ◽  
Travel Time Data ◽  
Time Information ◽  
Fusion Framework ◽  
Validation Scheme

Travel time data are a key input to applications of intelligent transportation systems. Advancement in vehicle tracking and reidentification technologies and proliferation of location-aware and connected devices have made networkwide travel time data available to transportation management agencies. The trend started with data collection on freeways and has been quickly extended to arterials. Although the freeway travel time data have been validated extensively in recent years, the quality of arterial travel time data is not well known. This paper presents a comprehensive validation scheme for arterial travel time data based on GPS probe and Bluetooth data as two independent sources. Since travel time on arterials is subject to a higher degree of variation than that on freeways, mainly because of the presence of signals, a new validation methodology based on the coefficient of variation is introduced. Moreover, a context-dependent travel time fusion framework is developed to improve the reliability of travel time information by fusing data from multiple sources. All 2012 data from a busy arterial corridor in Maryland are used to demonstrate the proposed comparison and augmentation model.

Optimizing Aggregation Level for Intelligent Transportation System Data Based on Wavelet Decomposition

2003 ◽  
Vol 1840 (1) ◽  
pp. 10-20 ◽  
Author(s):  
Fengxiang Qiao ◽  
Xin Wang ◽  
Lei Yu
Keyword(s):  
Intelligent Transportation Systems ◽  
Wavelet Decomposition ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Sets ◽  
Time Data ◽  
Aggregated Data ◽  
A New Technique ◽  
Aggregation Level

Appropriate aggregation levels and sampling frames of real-time data in intelligent transportation systems (ITSs) are indispensable to transportation planners and engineers. Conventional techniques for the retrieval of aggregation levels are normally based on statistical comparison of the original and the aggregated data sets. However, it is not guaranteed that errors and noise will not be transmitted to the aggregated data sets and that the desired information will be reserved. Wavelet decomposition is a new technique that can be applied to the determination of aggregation level. An optimization process that can provide the optimized aggregation level of ITS data for different applications was developed. To illustrate the proposed technique, ITS data archived by the TransGuide Center in San Antonio, Texas, were used for a case study. Aggregation levels for different days of a week and different time periods over the whole year of 2001 were obtained through the proposed approach.

IMPROVING ROAD TRAFFIC ORGANIZATION THROUGH THE APPLICATION OF INTELLIGENT TRANSPORTATION SYSTEMS

2021 ◽  
Vol 74 (3) ◽  
pp. 80-86
Author(s):  
L.E. KUSHCHENKO ◽  
◽  
A.S. KAMBUR ◽  
A.A. PEKHOV ◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Russian Federation ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Environmental Situation ◽  
The Russian Federation ◽  
Traffic Organization ◽  
The City

Examples of the use of ITS in various countries are given, improvements in traffic manage-ment, methods of reducing delays, travel time, as well as improving the environmental situation when using systems are considered. The system «Auto-Intellect», used in the territory of the Russian Federation, is presented. On the example of the city of Belgorod, a method of using ITS is pro-posed, by prohibiting the entry of cars into the city, taking into account certain state license plates.

scholarly journals Ann Modeling For Predicting Car Travel Time using Bus As Probe

2019 ◽  
Vol 8 (12) ◽  
pp. 3260-3265
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Critical Issue ◽  
Transportation Systems ◽  
Travel Times ◽  
Time Data ◽  
Comparison Results ◽  
Greater Cairo ◽  
Artificial Neural Network Ann ◽  
Car Travel

The critical issue of Intelligent Transportation Systems (ITS) applications is obtaining the near real time information of travel times. This paper proposes a dependable model for predicting car travel time on urban roads in Greater Cairo using buses as probes. The GPS receivers, which are installed on test vehicles and buses, used to collect real travel time data along the urban roads. The travel times of bus and car are compared in order to recognize similarities and differences between the trip profiles of test vehicles and buses. According to the comparison results, the model is developed and validated using Artificial Neural Network (ANN) for estimating car travel time using buses’ travel time with acceptable level of accuracy equals 10.53%.

Travel-Time Estimates Obtained from Intelligent Transportation Systems and Instrumented Test Vehicles: Statistical Comparison

2002 ◽  
Vol 1804 (1) ◽  
pp. 8-16 ◽  
Author(s):  
William L. Eisele ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Data Sources ◽  
Travel Times ◽  
System Monitoring ◽  
Test Vehicle ◽  
Cellular Telephones ◽  
Mobile Cellular ◽  
Time Estimates

Accurate estimation of travel time is necessary for monitoring the performance of the transportation system. Often, travel times are estimated indirectly by using instantaneous speeds from inductance loop detectors and making a number of assumptions. Although these travel times may be acceptable estimates for uncongested conditions, they may have significant error during congested periods. Travel times also may be obtained directly from intelligent transportation systems (ITS) data sources such as automatic vehicle identification (AVI). In addition, mobile cellular telephones have been touted as a means for obtaining this information automatically. Data sources that collect travel-time estimates directly provide travel-time data for both real-time and off-line transportation system monitoring. Instrumented test vehicle runs are often performed to obtain travel-time estimates for system monitoring and other transportation applications. Distance measuring instruments (DMIs) are a common method of instrumentation for test vehicles. DMI travel-time estimates are compared with AVI travel-time estimates by using a variety of statistical approaches. The results indicate that the travel-time estimates from test vehicles instrumented with DMI are within 1% of travel-time estimates from AVI along the study corridor. These results reflect that DMI is an accurate instrumented test vehicle technology and, more important, AVI data sources can replace traditional system monitoring data collection methods when there is adequate tag penetration and infrastructure. A method for identifying instrumented test vehicle drivers who may require additional data collection training is provided. The described procedures are applicable to any instrumented vehicle technique (e.g., the Global Positioning System) in comparison to any ITS data source that directly estimates travel time (e.g., mobile cellular telephones).

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