Disentangling City-Level Macroscopic Traffic Performance Patterns through a Trigonometric Multiseasonal Filtering Algorithm: Inspiration from Big Data of Ride-Sourcing Trips
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 3
Abstract
This study seeks to design a city-level trip speed performance index (CTSPI) providing an alternative aspect in quantifying the traffic performance of an entire city. Another objective is to disentangle the original CTSPI time series into several featured patterns, including a trend pattern, two seasonality patterns, and a remainder pattern. The big data in the form of observations of ride-sourcing trips in Beijing, China were adopted. This study also introduces a state-space model, the TBATS [trigonometric, Box–Cox transformation, auto-regressive moving average (ARMA) errors, trend, and seasonal components] filtering procedure to decompose the CTSPI time series. This study adopts Beijing as a representative example because the city has very typical and complicated traffic performance patterns. The proposed CTSPI directly reflects the average trip speed, normalized by the best performance supplied by the corresponding infrastructure systems. After filtering out fluctuation, noise, and irregular patterns, it reveals a smooth and clear-cut trend in the evolving process of the city’s traffic condition, which was never previously disclosed and is important in understanding the macroscopic long-term tendencies of the city’s traffic performance. The results indicate that the CTSPI is capable of capturing the traffic performance of the city well and can sense the influence of special dates or major events, such as the Beijing 2022 Olympic Winter Games, advising tremendous application of traffic management. City-level macroscopic traffic performance is usually measured as index quantities and used to assess traffic situations in different cities. Most often, it is utilized to provide a quantified impression of the degree of congestion to the public, or as traffic-congestion criteria for ranking cities. This study illustrates the importance of measuring city-level macroscopic traffic performance, especially on a daily basis as is appropriate for gauging the impacts of many macroscopic factors on city-level traffic situations.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National Key R&D Program of China (No. 2019YFF0301400) and the National Natural Science Foundation of China (No. 71971023).
References
Abu-Rayash, A., and I. Dincer. 2020. “Analysis of mobility trends during the COVID-19 coronavirus pandemic: Exploring the impacts on global aviation and travel in selected cities.” Energy Res. Soc. Sci. 68( Oct): 101693. https://doi.org/10.1016/j.erss.2020.101693.
Assen, S. M., and E. T. Quezon. 2019. “Model-based urban road network performance measurement using travel time reliability: A case study of Addis Ababa City, Ethiopia.” Am. J. Civ. Eng. Archit. 7 (5): 202–207.
Bok, G.-C., S.-J. Lee, Y.-H. Choe, J.-G. Gang, and S.-H. Lee. 2009. “Development of a traffic condition index (TCI) on expressways.” J. East. Asia Soc. Transp. Stud. 27 (5): 85–95.
Bremmer, D., K. C. Cotton, D. Cotey, C. E. Prestrud, and G. Westby. 2004. “Measuring congestion: Learning from operational data.” Transp. Res. Rec. 1895 (1): 188–196. https://doi.org/10.3141/1895-24.
Chen, X., M. Zahiri, and S. Zhang. 2017. “Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach.” Transp. Res. Part C Emerging Technol. 76 (Mar): 51–70. https://doi.org/10.1016/j.trc.2016.12.018.
Cleveland, R. B., W. S. Cleveland, J. E. McRae, and I. Terpenning. 1990. “STL: A seasonal-trend decomposition procedure based on loess.” J. Office Stat. 6 (1): 3–73.
Dharia, A., and H. Adeli. 2003. “Neural network model for rapid forecasting of freeway link travel time.” Eng. Appl. Artif. Intell. 16 (7): 607–613. https://doi.org/10.1016/j.engappai.2003.09.011.
Dong, Z., D. Yang, T. Reindl, and W. M. Walsh. 2013. “Short-term solar irradiance forecasting using exponential smoothing state space model.” Energy 55 (Jun): 1104–1113. https://doi.org/10.1016/j.energy.2013.04.027.
Eisele, W. L., Y. Zhang, E. S. Park, and R. Stensrud. 2010. “Incorporating driveway density into an arterial corridor travel time index estimation model.” In Proc., 89th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Eisele, W. L., Y. Zhang, E. S. Park, Y. Zhang, and R. Stensrud. 2011. “Developing and applying models for estimating arterial corridor travel time index for transportation planning in small to medium-sized communities.” Transp. Res. Rec. 2244 (1): 81–90. https://doi.org/10.3141/2244-11.
Feng, B., Y. Lin, and J. Xu. 2020. “A comprehensive traffic ecological performance index based on individual car and bus travel speed in urban road network.” In Resilience and sustainable transportation systems, 666–672. Reston, VA: ASCE.
Gould, P. G., A. B. Koehler, J. K. Ord, R. D. Snyder, R. J. Hyndman, and F. Vahid-Araghi. 2008. “Forecasting time series with multiple seasonal patterns.” Eur. J. Oper. Res. 191 (1): 207–222. https://doi.org/10.1016/j.ejor.2007.08.024.
Grosso, J. M., C. Ocampo-Martínez, V. Puig, and B. Joseph. 2014. “Chance-constrained model predictive control for drinking water networks.” J. Process Control 24 (5): 504–516. https://doi.org/10.1016/j.jprocont.2014.01.010.
Hastie, T., R. J. Tibshirani, and J. Friedman. 2001. The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
He, F., X. Yan, Y. Liu, and L. Ma. 2016. “A traffic congestion assessment method for urban road networks based on speed performance index.” Procedia Eng. 137 (Jan): 425–433. https://doi.org/10.1016/j.proeng.2016.01.277.
Ishak, S., and H. Al-Deek. 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).
Johari, M., M. Keyvan-Ekbatani, and D. Ngoduy. 2020. “Impacts of bus stop location and berth number on urban network traffic performance.” IET Intel. Transport Syst. 14 (12): 1546–1554. https://doi.org/10.1049/iet-its.2019.0860.
Kim, M. S. 2013. “Modeling special-day effects for forecasting intraday electricity demand.” Eur. J. Oper. Res. 230 (1): 170–180. https://doi.org/10.1016/j.ejor.2013.03.039.
Levinson, H. S., and T. J. Lomax. 1996. “Developing a travel time congestion index.” Transp. Res. Rec. 1564 (1): 1–10. https://doi.org/10.1177/0361198196156400101.
Liu, S., G. Song, J. Sun, X. Zhang, and L. Yu. 2016. “Pattern clustering and characteristics of traffic performance index in megacities—A case in Beijing.” In Proc., 95th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Livera, A. M. D., R. J. Hyndman, and R. D. Snyder. 2011. “Forecasting time series with complex seasonal patterns using exponential smoothing.” J. Am. Stat. Assoc. 106 (496): 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771.
Lotfi, S., K. F. Abdelghany, and H. Hashemi. 2019. “Modeling framework and decomposition scheme for on-demand mobility services with ridesharing and transfer.” Comput.-Aided Civ. Infrastruct. Eng. 34 (1): 21–37. https://doi.org/10.1111/mice.12366.
Lovell, D. J. 2018. “Lossless compression of all vehicle trajectories in a common roadway segment.” Comput.-Aided Civ. Infrastruct. Eng. 33 (6): 481–497. https://doi.org/10.1111/mice.12354.
Lv, M., T. Chen, Y. Li, and Y. Li. 2018. “Urban traffic congestion index estimation with open ubiquitous data.” J. Inf. Sci. Eng. 34 (3): 781–799.
Mallinckrodt, J. 2010. “VCI, a regional volume/capacity index model of urban congestion.” J. Transp. Eng. 136 (2): 110–119. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000080.
Morán, C. A. 2011. ‘Relevance and reliability of area-wide congestion performance measures in road networks.” Ph.D. dissertation. KTH Royal Institute of Technology.
Oliveira, E. L., L. de Portugal, S. da, and W. P. Junior. 2014. “Determining critical links in a road network: Vulnerability and congestion indicators.” Proc. Soc. Behav. Sci. 162 (Dec): 158–167. https://doi.org/10.1016/j.sbspro.2014.12.196.
Öz, B., T. Özkan, and T. J. Lajunen. 2010. “Professional and non-professional drivers’ stress reactions and risky driving.” Transp. Res. Part F Traffic Psychol. Behav. 13 (1): 32–40. https://doi.org/10.1016/j.trf.2009.10.001.
Patel, N., and A. B. Mukherjee. 2015. “Assessment of network traffic congestion through traffic congestability value (TCV): A new index.” Bull. Geogr. Soc. Econ. Ser. 30 (30): 123–134. https://doi.org/10.1515/bog-2015-0039.
Peer, E., and L. Solomon. 2012. “Professionally biased: Misestimations of driving speed, journey time and time-savings among taxi and car drivers.” Judgment Decis. Making 7 (2): 165–172.
Quang, D. T., and S. H. Bae. 2021. “A hybrid deep convolutional neural network approach for predicting the traffic congestion index.” Promet-Traffic Transp. 33 (3): 373–385. https://doi.org/10.7307/ptt.v33i3.3657.
Rao, A. M., and K. R. Rao. 2012. “Measuring urban traffic congestion—A review.” Int. J. Traffic Transp. Eng. 2 (4): 286–305. https://doi.org/10.7708/ijtte.2012.2(4).01.
Samant, A., and H. Adeli. 2000. “Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis.” Comput.-Aided Civ. Infrastruct. Eng. 15 (4): 241–250. https://doi.org/10.1111/0885-9507.00188.
Sampathirao, A. K., J. M. Grosso, P. Sopasakis, C. Ocampo-Martinez, A. Bemporad, and V. Puig. 2014. “Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona case study.” IFAC Proc. 47 (3): 10457–10462. https://doi.org/10.3182/20140824-6-ZA-1003.01343.
Song, X., W. Li, D. Ma, D. Wang, L. Qu, and Y. Wang. 2018. “A match-then-predict method for daily traffic flow forecasting based on group method of data handling.” Comput.-Aided Civ. Infrastruct. Eng. 33 (11): 982–998. https://doi.org/10.1111/mice.12381.
Taylor, J. W. 2003. “Short-term electricity demand forecasting using double seasonal exponential smoothing.” J. Oper. Res. Soc. 54 (8): 799–805. https://doi.org/10.1057/palgrave.jors.2601589.
Taylor, J. W. 2010. “Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles.” Int. J. Forecasting 26 (4): 627–646. https://doi.org/10.1016/j.ijforecast.2010.02.009.
Thurgood, G. S. 1995. Development of a freeway congestion index using an instrumented vehicle. Washington, DC: Transportation Research Record.
Vaziri, M. 2002. “Development of highway congestion index with fuzzy set models.” Transp. Res. Rec. 1802 (1): 16–22. https://doi.org/10.3141/1802-03.
Vieira, R. S., and E. A. Haddad. 2020. “A weighted travel time index based on data from Uber movement.” EPJ Data Sci. 9 (1): 1–15. https://doi.org/10.1140/epjds/s13688-020-00241-y.
Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. 2014. “Short-term traffic forecasting: Where we are and where we’re going.” Transp. Res. Part C Emerging Technol. 43 (Jun): 3–19. https://doi.org/10.1016/j.trc.2014.01.005.
Wang, W.-X., R.-J. Guo, and J. Yu. 2018. “Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example.” Adv. Mech. Eng. 10 (6): 1687814018781482. https://doi.org/10.1177/1687814018781482.
Wang, Z., L. Que, J. Liu, and J. Guan. 2010. “Study on the evaluation system and prediction approach on traffic congestion index of RTMS data.” In Proc., 7th Int. Conf. on Traffic and Transportation Studies, 903–914. Reston, VA: ASCE.
Waylen, A. E., M. S. Horswill, J. L. Alexander, and F. P. McKenna. 2004. “Do expert drivers have a reduced illusion of superiority?” Transp. Res. Part F Traffic Psychol. Behav. 7 (4): 323–331. https://doi.org/10.1016/j.trf.2004.09.009.
Wen, H., Z. Hu, and J. Sun. 2010. “Road traffic performance monitoring in Beijing.” In Proc., 6th Advanced Forum on Transportation of China. Beijing: Institution of Engineering and Technology (IET).
Wen, H., J. Sun, and X. Zhang. 2014. “Study on traffic congestion patterns of large city in China taking Beijing as an example.” Proc. Soc. Behav. Sci. 138 (Jul): 482–491. https://doi.org/10.1016/j.sbspro.2014.07.227.
Wen, Y., S. Zhang, J. Zhang, S. Bao, X. Wu, D. Yang, and Y. Wu. 2020. “Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data.” Appl. Energy 260 (Feb): 114357. https://doi.org/10.1016/j.apenergy.2019.114357.
Wu, C.-Y., M.-B. Hu, R. Jiang, and Q.-Y. Hao. 2021. “Effects of road network structure on the performance of urban traffic systems.” Phys. Stat. Mech. Appl. 563 (Feb): 125361. https://doi.org/10.1016/j.physa.2020.125361.
Yang, S., Y. Ji, D. Zhang, and J. Fu. 2019. “Equilibrium between road traffic congestion and low-carbon economy: A case study from Beijing, China.” Sustainability 11 (1): 219. https://doi.org/10.3390/su11010219.
Yao, Y., D. Wu, J. Yang, X. Shi, L. Du, and Y. Cai. 2019. “Analyzing the effects of rainfall on the urban traffic congestion bottlenecks by using floating car data.” In Proc., IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symp., 943–946. New York: IEEE Geoscience and Remote Sensing Society.
Yohee, H., and Y. Kim. 2019. “Spatiotemporal congestion recognition index to evaluate performance under oversaturated conditions.” KSCE J. Civ. Eng. 23 (8): 3714–3723. https://doi.org/10.1007/s12205-019-2232-2.
Zhang, Y., Y. Zhang, and A. Haghani. 2014. “A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model.” Transp. Res. Part C Emerging Technol. 43 (Jun): 65–78. https://doi.org/10.1016/j.trc.2013.11.011.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 3 • March 2022
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© 2021 American Society of Civil Engineers.
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Received: Jan 31, 2021
Accepted: Oct 1, 2021
Published online: Dec 23, 2021
Published in print: Mar 1, 2022
Discussion open until: May 23, 2022
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