Development and Benefits of Computer-Aided Travel Time Data Collection

Benz, Robert; Ogden, M A

doi:10.1177/0361198196155100101

Cited by 9 publications

(4 citation statements)

References 1 publication

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“…The test vehicles used in the study were instrumented with a DMI that allows for the collection of speed information at half-second intervals. This technology was successfully demonstrated in previous studies (3,4,(6)(7)(8). Electronic pulses are read from the vehicle's transmission into the DMI, and the subsequent output from the DMI is collected on an on-board laptop computer.…”

Section: Instrumented Test Vehiclesmentioning

confidence: 81%

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

Eisele

Rilett

2002

Transportation Research Record: Journal of the Transportation R

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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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Section: Instrumented Test Vehiclesmentioning

confidence: 81%

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

Eisele

Rilett

2002

Transportation Research Record: Journal of the Transportation R

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“…The test vehicles used in the study were instrumented with an electronic distance-measuring instrument (DMI). The DMI allows for the collection of speed information at 0.5-s time intervals, and it was demonstrated successfully in previous studies (3)(4)(5). Electronic pulses are read from the vehicle's transmission into the DMI, and the subsequent output from the DMI is collected by an on-board laptop computer.…”

Section: Instrumented Test Vehiclesmentioning

confidence: 94%

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

Eisele

Rilett

Mhoon

et al. 2001

Transportation Research Record: Journal of the Transportation R

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Intelligent transportation systems (ITS) technologies and infrastructure are a potentially rich travel-time data source for travel-time mean and variance estimates. ITS data traditionally have been deployed and used in real time for passenger cars. How ITS data can be used for multimodal analyses and system monitoring is examined. The methodology used is applicable to any detector technology. Automatic vehicle identification (AVI) data were collected along a 3.2-km (2-mi) segment of US-290 in Houston, Texas. Simultaneous instrumented test vehicles collected travel-time data, and commercial-vehicle travel-time data were collected by video. The nonparametric loess statistical procedure was used to estimate the travel-time distribution properties as a function of time of day. The first application presented investigates how well link travel times from AVI replicate travel conditions for commercial vehicles. During congested conditions, average differences in travel-time estimates of 6.4 percent were found, whereas percent differences in coefficient of variation (reliability) were 14.7 percent. The research concludes that it may be reasonable to provide real-time traffic maps specifically for commercial vehicles. The second application investigates the accuracy of the AVI data for system monitoring. Estimated mean differences between AVI data and test vehicles were small (0.8 percent), whereas the ratio of the mean to the standard deviation (coefficient of variation) was relatively high (37.6 percent) during congested conditions. The AVI data source is found to provide a very cost-effective data collection method with which to estimate mean travel time while increasing confidence in the estimate.

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“…With improvement computers Distance Measuring Instrument (DMI) came as the solution of Floating car method for measurement of the speed distance using pulses from a sensor attached to the test vehicle's transmission (Quiroga and Bullock, 1998). Turner et al (1998), Benz and Ogden (1996) found limitation in this method relating to installation of the DMI unit and data storage problems. The development of Information Technology and advancement of Global Positioning System (GPS) has largely overcome the data quality and quantity shortcomings of the manual and DMI methods of collecting travel time data and become one of the alternatives to moving car observer method for field data collection.…”

Section: Self Organizing Map Of Artificial Neural Network For Defininmentioning

confidence: 99%

Self Organizing Map of Artificial Neural Network for Defining Level of Service Criteria of Urban Streets

Mohapatra¹,

Bhuyan²

2012

IJTTE

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Abstract:In India, Level of Service (LOS) is not well defined for urban streets. The analysis procedure followed in India is that developed by HCM 2000. Speed ranges of LOS categories for various urban Street Classes defined by HCM are appropriate for developed countries having homogenous type of traffic flow. India being a developing country its traffic is very much heterogeneous having vehicles of different operational characteristics. Therefore, LOS criteria in Indian context should be defined correctly considering the traffic and geometric characteristics of urban streets. Defining LOS is basically a classification problem and application of cluster analysis is found to be a suitable technique to solve the problem. Self Organizing Map (SOM) a type of Artificial Neural Network (ANN) used to solve this classification problem. For this study, lot of speed data is required for which GPS is found to be the most suitable method of data collection and hence extensively used. Free flow speed (FFS) and average travel speed during peak and off peak hours inventory of road segments are used in this study. FFS ranges for different urban Street Classes and speed ranges of LOS categories found to be lower than that mentioned in HCM-2000.

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Development and Benefits of Computer-Aided Travel Time Data Collection

Cited by 9 publications

References 1 publication

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

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

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

Self Organizing Map of Artificial Neural Network for Defining Level of Service Criteria of Urban Streets

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