Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis

Wemegah, Tina Dzigbordi; Zhu, Shunying; Atombo, Charles · 2018 · Crossref

DOI: 10.1186/s12544-018-0313-9

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Summary

This study addresses the explicit investigation of how weekday versus weekend travel variability and road type influence peak-hour vehicular movements, a factor previously considered only implicitly in traffic congestion research. Motivated by the need for intelligent transportation management strategies to mitigate urban congestion, the authors aim to determine specific travel patterns for private cars and taxis. The research posits that understanding these temporal and spatial variables can inform transportation authorities in managing road network operations and reducing peak-period congestion. The methodology employs Structural Equation Modeling (SEM) to analyze big data derived from Radio Frequency Identification (RFID) systems in Nanjing, China. The dataset comprises over 111 million records of vehicular movements from May 2014, covering approximately 830,000 vehicles. The study focuses exclusively on private cars and taxis, which constitute over 90% of the city’s vehicle fleet. Vehicle classification was determined by detection frequency, with vehicles detected more than 25 times per day categorized as taxis and those detected fewer times as private cars. The analysis examines three time periods: morning peak (6 am–9 am), evening peak (4 pm–7 pm), and off-peak (9 am–12 noon). Road types were classified into four categories—motorway, primary, secondary, and tertiary—using OpenStreetMap data linked to RFID reader locations. The SEM approach allowed for the simultaneous analysis of multiple variables, including weekdays, weekends, road type choices, and vehicle types, to test four specific hypotheses regarding their influence on peak and off-peak travel. The results indicate that weekday travel patterns exert a significantly stronger influence on peak-hour movements compared to weekend travel. Conversely, off-peak hour travels showed minimal variation between weekdays and weekends. The study found that both road type choice and vehicle type (private car versus taxi) have varying but significant influences on peak-hour travels. Specifically, private cars demonstrated a more significant influence on peak and off-peak period travels than taxis, and their choice of road type was a more significant predictor of travel patterns. The model exhibited high significance ratios, confirming the suitability of the selected variables for investigating peak-hour travel patterns. The significance of this research lies in its validation of SEM as a viable method for analyzing large-scale RFID data to support policy measures aimed at reducing congestion. By explicitly quantifying the impact of days and road types, the study provides transportation engineers with precise insights into vehicular movement patterns. These findings suggest that targeted interventions, such as dynamic traffic management or demand-side policies, can be more effectively designed by accounting for the distinct behaviors of private cars versus taxis and the specific impacts of weekday versus weekend travel on different road infrastructures.

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• traffic density