Development and Benefits of Computer-Aided Travel Time Data Collection

Benz, Robert; Ogden, M A

doi:10.3141/1551-01

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With improvement computers, Distance Measuring Instrument (DMI) came as the solution of Floating car method. Benz and Ogden (1996) found limitation in this method relating to installation of the DMI unit and data storage problems. Recent research has demonstrated the feasibility of using GPS and Geographical Information System (GIS) technologies for automated inventory and travel time data collection.…”

Section: Introductionmentioning

confidence: 99%

Affinity Propagation Clustering in Defining Level of Service Criteria of Urban Streets

Bhuyan

Mohapatra

2014

Transport

View full text Add to dashboard Cite

Defining Level Of Service (LOS) criteria is very important as this is the first step of LOS analysis but this is not well defined in Indian context. The analysis followed in India is basically adaptation of Highway Capacity Manual (HCM 2000) methodology which is more suitable for developed countries having homogenous traffic pattern. An attempt has been made in this study to define LOS criteria of urban streets for developing countries having heterogeneous traffic flow condition. Defining LOS is basically a classification problem and to solve it Affinity Propagation (AP), a very recently developed cluster algorithm is used. Inventory details and the required speed data are collected from five major street corridors of Greater Mumbai Region in India through the application of Trimble GeoXT Global Positioning System (GPS) receiver. Six validation parameters are used on Free Flow Speed (FFS) data to find the optimal number of clusters, which is required for the classification of street segments into number of classes. After that speed data collected during both peak and off-peak hours are averaged over street segments and clustered into six groups to get the speed ranges of different LOS categories. Using validation parameters, considering the physical and surrounding environmental characteristics it is found that street segments can be classified into four classes in Indian context as mentioned in Highway Capacity Manual 2000. However, the FFS range for urban street class IV (urban design category) is significantly lower because of varying road geometric characteristics. The speed ranges of LOS categories under urban street classes are proportionately lower to that values mentioned in HCM 2000 because of highly heterogeneous traffic flow on urban Indian roads. The travel speed data collection procedure using GPS is simple and accurate. In addition, AP clustering is highly efficient in terms of time saving and provides a very accurate solution to classification problems. Hence, both GPS and AP techniques can be applied in other countries to define the speed ranges of LOS categories considering the local conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Affinity Propagation Clustering in Defining Level of Service Criteria of Urban Streets

Bhuyan

Mohapatra

2014

Transport

View full text Add to dashboard Cite

show abstract

Traffic Monitoring Using GPS

Quiroga¹

2004

Handbook of Transport Geography and Spatial Systems

View full text Add to dashboard Cite

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

Mohapatra¹,

Bhuyan²

2012

IJTTE

View full text Add to dashboard Cite

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.

show abstract

Development and Benefits of Computer-Aided Travel Time Data Collection

Cited by 3 publications

References 1 publication

Affinity Propagation Clustering in Defining Level of Service Criteria of Urban Streets

Affinity Propagation Clustering in Defining Level of Service Criteria of Urban Streets

Traffic Monitoring Using GPS

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

Contact Info

Product

Resources

About