Point and Interval Travel Time Prediction in Urban Arterials Using Wi-Fi MAC Scanning Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
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.
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Data Availability Statement
The data used in the analyses are available from the corresponding author and can be obtained upon reasonable request.
Acknowledgments
The authors would like to thank the staff members of Intelligent Transportation System (ITS) laboratories, IIT Madras, for their support in WMS installation and field data collection. We thank the Ministry of Electronics and Information Technology (MEITY), Government of India, for supporting this research through the project “InTranSE-II—Development of bus priority system at signalized intersections using V2I communication.”
References
Abbott-Jard, M., H. Shah, and A. Bhaskar. 2013. “Empirical evaluation of Bluetooth and Wifi scanning for road transport.” In Proc., 36th Australasian Transport Research Forum (ATRF). Crawley, WA: Planning and Transport Research Centre.
Abedi, N., A. Bhaskar, E. Chung, and M. Miska. 2015. “Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses.” Transp. Res. Part C: Emerging Technol. 60 (Nov): 124–141. https://doi.org/10.1016/j.trc.2015.08.010.
Abraham, B., and J. Ledolter. 2009. Statistical methods for forecasting. New York: Wiley.
Adib, F., H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller. 2015. “Smart homes that monitor breathing and heart rate.” In Proc., 33rd Annual ACM Conf. on Human Factors in Computing Systems, 837–846. New York: Association for Computing Machinery.
airodump-ng [Aircrack-ng]. 2020. “Airodump-ng.” Accessed January 10, 2021. https://www.aircrack-ng.org/doku.php?id=airodump-ng.
Amazon DynamoDB | NoSQL Key-Value Database | Amazon Web Services. 2020. “Amazon DynamoDB.” Accessed January 10, 2021. https://aws.amazon.com/dynamodb/.
Armstrong, J. S. 2001. Principles of forecasting: A handbook for researchers and practitioners New York: Springer.
auto.arima function | R Documentation. 2020. “Auto.arima: Fit best ARIMA model to univariate time series.” Accessed January 10, 2021. https://www.rdocumentation.org/packages/forecast/versions/8.9/topics/auto.arima.
Billings, D., and J. Yang. 2006. “Application of the ARIMA models to urban roadway travel time prediction—A case study.” In Proc., 2006 IEEE Int. Conf. on Systems, Man and Cybernetics, 2529–2534. New York: IEEE.
Chakraborty, G., K. Naik, D. Chakraborty, N. Shiratori, and D. Wei. 2010. “Analysis of the Bluetooth device discovery protocol.” Wireless Networks 16 (2): 421–436. https://doi.org/10.1007/s11276-008-0142-1.
Chen, H., and H. A. Rakha. 2014. “Real-time travel time prediction using particle filtering with a non-explicit state-transition model.” Transp. Res. Part C: Emerging Technol. 43: 112–126. https://doi.org/10.1016/j.trc.2014.02.008.
Covariance Stationary Time Series Stochastic Process: Sequence of Rv’s Ordered by Time. 2020. “Covariance stationary time series.” Accessed January 10, 2021. https://faculty.washington.edu/ezivot/econ584/notes/stationarytimeseriesslides.pdf.
Depatla, S., A. Muralidharan, and Y. Mostofi. 2015. “Occupancy estimation using only WiFi power measurements.” IEEE J. Sel. Areas Commun. 33 (7): 1381–1393. https://doi.org/10.1109/JSAC.2015.2430272.
Dhivyabharathi, B., E. S. Hima, and L. Vanajakshi. 2018. “Stream travel time prediction using particle filtering approach.” Transp. Lett. 10 (2): 75–82. https://doi.org/10.1080/19427867.2016.1192016.
Ding, F., X. Chen, S. He, G. Shou, Z. Zhang, and Y. Zhou. 2019. “Evaluation of a Wi-Fi signal based system for freeway traffic states monitoring: An exploratory field test.” Sensors 19 (2): 409. https://doi.org/10.3390/s19020409.
Dongjoo, P., R. R. Laurence, and H. Gunhee. 1999. “Spectral basis neural networks for real-time travel time forecasting.” J. Transp. Eng. 125 (6): 515–523. https://doi.org/10.1061/(ASCE)0733-947X(1999)125:6(515).
Erkan, İ., and H. Hastemoglu. 2016. “Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey.” J. Mod. Transp. 24 (3): 207–214. https://doi.org/10.1007/s40534-016-0101-y.
Golubeva, T., Y. Zaitsev, S. Konshin, and I. Duisenbek. 2018. “A study on the Wi-Fi radio signal attenuation in various construction materials (obstacles).” In Proc., 10th Int. Conf. on Ubiquitous and Future Networks (ICUFN), 718–723. New York: IEEE. https://doi.org/10.1109/ICUFN.2018.8436785.
Google Maps. 2020. “Google maps.” Accessed January 10, 2021. https://www.google.com/maps/.
Haghani, A., M. Hamedi, K. F. Sadabadi, S. Young, and P. Tarnoff. 2010. “Data collection of freeway travel time ground truth with Bluetooth sensors.” Transp. Res. Rec. 2160 (1): 60–68. https://doi.org/10.3141/2160-07.
Han, B., and A. Srinivasan. 2012. “eDiscovery: Energy efficient device discovery for mobile opportunistic communications.” In Proc., 2012 20th IEEE Int. Conf. on Network Protocols (ICNP), 1–10. New York: IEEE.
Haseman, R. J., J. S. Wasson, and D. M. Bullock. 2010. “Real-time measurement of travel time delay in work zones and evaluation metrics using Bluetooth probe tracking.” Transp. Res. Rec. 2169 (1): 40–53. https://doi.org/10.3141/2169-05.
Hidayat, A., S. Terabe, and H. Yaginuma. 2018. “WiFi scanner technologies for obtaining travel data about circulator bus passengers: Case study in Obuse, Nagano Prefecture, Japan.” Transp. Res. Rec. 2672 (45): 45–54. https://doi.org/10.1177/0361198118776153.
Introduction—Scapy 2.4.4. documentation. 2020. “Scapy.” Accessed January 10, 2021. https://scapy.readthedocs.io/en/latest/introduction.html.
ITS International. 2013. “Bluetooth and Wi-Fi offer new options for travel time measurements.” Accessed January 9, 2021. https://www.itsinternational.com/its4/feature/bluetooth-and-wi-fi-offer-new-options-travel-time-measurements.
Kali Linux Documentation. 2020. “Kali linux operating system.” Accessed January 10, 2021. https://www.kali.org/docs/introduction/what-is-kali-linux/.
Karlaftis, M. G., and E. I. Vlahogianni. 2009. “Memory properties and fractional integration in transportation time-series.” Transp. Res. Part C: Emerging Technol. 17 (4): 444–453. https://doi.org/10.1016/j.trc.2009.03.001.
Khosravi, A., E. Mazloumi, S. Nahavandi, D. Creighton, and J. W. C. van Lint. 2011. “Prediction intervals to account for uncertainties in travel time prediction.” IEEE Trans. Intell. Transp. Syst. 12 (2): 537–547. https://doi.org/10.1109/TITS.2011.2106209.
Kwon, J., B. Coifman, and P. Bickel. 2000. “Day-to-day travel-time trends and travel-time prediction from loop-detector data.” Transp. Res. Rec. 1717 (1): 120–129. https://doi.org/10.3141/1717-15.
Leys, C., C. Ley, O. Klein, P. Bernard, and L. Licata. 2013. “Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median.” J. Exp. Soc. Psychol. 49 (4): 764–766. https://doi.org/10.1016/j.jesp.2013.03.013.
Mathew, J. K., V. L. Devi, D. M. Bullock, and A. Sharma. 2016. “Investigation of the use of Bluetooth sensors for travel time studies under Indian conditions.” Transp. Res. Procedia 17 (Jan): 213–222. https://doi.org/10.1016/j.trpro.2016.11.077.
Mehmood, U., I. Moser, P. P. Jayaraman, and A. Banerjee. 2019. “Occupancy estimation using WiFi: A case study for counting passengers on busses.” In Proc., 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 165–170. New York: IEEE.
Nagy, A. M., and V. Simon. 2018. “Survey on traffic prediction in smart cities.” Pervasive Mob. Comput. 50: 148–163. https://doi.org/10.1016/j.pmcj.2018.07.004.
Namaki Araghi, B., R. Krishnan, and H. Lahrmann. 2016. “Mode-specific travel time estimation using Bluetooth technology.” J. Intell. Transp. Syst. 20 (3): 219–228. https://doi.org/10.1080/15472450.2015.1052906.
Nellore, K., and G. P. Hancke. 2016. “A survey on urban traffic management system using wireless sensor networks.” Sensors 16 (2): 157. https://doi.org/10.3390/s16020157.
NetBeez Pricing. 2020. “NetBeez pricing.” Accessed January 9, 2021. https://netbeez.net/pricing.
NTPD (Network Time Protocol Daemon). 2020. “NetBeez pricing.” Accessed January 10, 2021. https://en.wikipedia.org/wiki/Ntpd.
Oda, T. 1990. “An algorithm for prediction of travel time using vehicle sensor data.” In Proc., 3rd Int. Conf. on Road Traffic Control, 40–44. London: Institution of Engineering and Technology.
Oh, S., Y.-J. Byon, K. Jang, and H. Yeo. 2015. “Short-term travel-time prediction on highway: A review of the data-driven approach.” Transp. Rev. 35 (1): 4–32. https://doi.org/10.1080/01441647.2014.992496.
Park, D., and L. R. Rilett. 1998. “Forecasting multiple-period freeway link travel times using modular neural networks.” Transp. Res. Rec. 1617 (1): 163–170. https://doi.org/10.3141/1617-23.
Park, D., and L. R. Rilett. 1999. “Forecasting freeway link travel times with a multilayer feedforward neural network.” Comput.-Aided Civ. Infrastruct. Eng. 14 (5): 357–367. https://doi.org/10.1111/0885-9507.00154.
Patra, S. S., B. R. Chilukuri, and L. Vanajakshi. 2021. “Analysis of road traffic pattern changes due to activity restrictions during COVID-19 pandemic in Chennai.” Transp. Lett. 13 (5–6): 473–481. https://doi.org/10.1080/19427867.2021.1899580.
Patra, S. S., B. Muthurajan, B. R. Chilukuri, and L. Devi. 2019. “Development and evaluation of a low-cost WiFi media access control scanner as traffic sensor.” In Proc., 11th Int. Conf. on Communication Systems & Networks (COMSNETS), 777–782. New York: IEEE.
Perez, L., and O. Lange. 2020. “Traffic prediction with advanced graph neural networks.” Accessed January 5, 2022. https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks.
Raspberry Pi 3 Model B+. 2020. “Raspberry Pi 3 Model B+.” Accessed January 10, 2021. https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/.
Rice, J., and E. Van Zwet. 2004. “A simple and effective method for predicting travel times on freeways.” IEEE Trans. Intell. Transp. Syst. 5 (3): 200–207. https://doi.org/10.1109/TITS.2004.833765.
Robinson, S., and J. W. Polak. 2005. “Modeling urban link travel time with inductive loop detector data by using the -NN method.” Transp. Res. Rec. 1935 (1): 47–56. https://doi.org/10.1177/0361198105193500106.
rtl819x—Debian Wiki. 2020. “RTL819X.” Accessed January 10, 2021. https://wiki.debian.org/rtl819x.
Saito, M., and T. Watanabe. 1995. Prediction and dissemination system for travel time utilizing vehicle detectors. Hamburg, Germany: ITS World Congress.
Sherif, I., and A.-D. Haitham. 2002. “Performance evaluation of short-term time-series traffic prediction model.” J. Transp. Eng. 128 (6): 490–498. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:6(490).
Shiravi, S., K. Hossain, L. Fu, and A. Ghods. 2016. TRA-963: Evaluation of using WIFI signals to estimate intersection travel time. Montreal, Canada: Canadian Society for Civil Engineering.
Sringiwai, K., N. Indra-Payoong, A. Sumalee, P. Raothanachonkun, and W. Sriborrirux. 2010. “RFID-based travel time estimation: Development case in Bangkok.” In Proc., 15th Int. Conf. of Hong Kong Society for Transportation Studies, Hong Kong. Hong Kong: Hong Kong Polytechnic Univ.
Sun, H., C. Zhang, and B. Ran. 2004. “Interval prediction for traffic time series using local linear predictor.” In Proc., 7th Int. IEEE Conf. on Intelligent Transportation Systems (IEEE Cat. No.04TH8749), 410–415. New York: IEEE.
Suwardo, W., M. Napiah, and I. Kamaruddin. 2010. “ARIMA models for bus travel time prediction.” J. Inst. Eng. Malaysia 71 (Jan): 49–58.
TL-WN823N | 300Mbps Mini Wireless N USB Adapter | TP-Link India. 2020. “TL-WN823N.” Accessed January 10, 2021. https://www.tp-link.com/in/home-networking/adapter/tl-wn823n/.
Vanajakshi, L., S. Subramanian, and R. Sivanandan. 2009. “Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses.” IET Intell. Transport Syst. 3 (1): 1–9. https://doi.org/10.1049/iet-its:20080013.
van Lint, J. W. 2006. “Reliable real-time framework for short-term freeway travel time prediction.” J. Transp. Eng. 132 (12): 921–932. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:12(921).
Vinagre Díaz, J. J., A. B. Rodríguez González, and M. R. Wilby. 2016. “Bluetooth traffic monitoring systems for travel time estimation on freeways.” IEEE Trans. Intell. Transp. Syst. 17 (1): 123–132.
Wu, C.-H., J.-M. Ho, and D. T. Lee. 2004. “Travel-time prediction with support vector regression.” IEEE Trans. Intell. Transp. Syst. 5 (4): 276–281. https://doi.org/10.1109/TITS.2004.837813.
Yang, J.-S. 2005. “Travel time prediction using the GPS test vehicle and Kalman filtering techniques.” In Vol. 3 of Proc., 2005, American Control Conf., 2128–2133. New York: IEEE.
Zhang, X., and J. A. Rice. 2003. “Short-term travel time prediction.” Transp. Res. Part C: Emerging Technol. 11 (3): 187–210. https://doi.org/10.1016/S0968-090X(03)00026-3.
Zhang, Y. 2015. “Uncertainty associated with travel time prediction: Advanced volatility approaches and ensemble methods.” Ph.D. dissertation, Dept. of Civil and Environmental Engineering, Univ. of Maryland.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 4 • April 2022
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© 2022 American Society of Civil Engineers.
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Received: Feb 21, 2021
Accepted: Dec 3, 2021
Published online: Jan 20, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 20, 2022
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