A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Point and Interval Travel Time Prediction in Urban Arterials Using Wi-Fi MAC Scanning Data
In recent times, the ubiquity of wireless technology has encouraged researchers to collect travel time data using Wi-Fi media access control scanners (WMS). This notion inspired the current work, which analyzed data from an in-house–developed WMS for potential intelligent transportation system (ITS) applications. First, the WMS was tested against a commercial sensor to validate its performance for travel time data collection. Results showed that the in-house–developed WMS was equivalent to or performed better than the commercial counterpart, with a price reduction of about 80%. Subsequently, the travel time data collected using the developed WMS was used to forecast future travel times and their prediction intervals (PIs). An autoregressive integrated moving average (ARIMA) model was used for the same. The results of the forecasts were evaluated for five selected routes in Chennai, India. In all the analyzed routes, the mean errors in point estimates ranged from 20% to 23%, and for interval predictions, the prediction interval coverage probability (PICP) ranged between 0.78 and 0.84, suggesting good performance.
Point and Interval Travel Time Prediction in Urban Arterials Using Wi-Fi MAC Scanning Data
In recent times, the ubiquity of wireless technology has encouraged researchers to collect travel time data using Wi-Fi media access control scanners (WMS). This notion inspired the current work, which analyzed data from an in-house–developed WMS for potential intelligent transportation system (ITS) applications. First, the WMS was tested against a commercial sensor to validate its performance for travel time data collection. Results showed that the in-house–developed WMS was equivalent to or performed better than the commercial counterpart, with a price reduction of about 80%. Subsequently, the travel time data collected using the developed WMS was used to forecast future travel times and their prediction intervals (PIs). An autoregressive integrated moving average (ARIMA) model was used for the same. The results of the forecasts were evaluated for five selected routes in Chennai, India. In all the analyzed routes, the mean errors in point estimates ranged from 20% to 23%, and for interval predictions, the prediction interval coverage probability (PICP) ranged between 0.78 and 0.84, suggesting good performance.
Point and Interval Travel Time Prediction in Urban Arterials Using Wi-Fi MAC Scanning Data
J. Transp. Eng., Part A: Systems
Patra, Satya S. (author) / Muthurajan, Bharathiraja (author) / Devi Vanajakshi, Lelitha (author)
2022-04-01
Article (Journal)
Electronic Resource
English
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