Abstract
The unprecedented COVID-19 pandemic impacts negatively on the security and development of human society. Comparison and analysis of intercity highway travel patterns before and during the COVID-19 pandemic can bring vital insights for the prevention and control of the pandemic. Empirical studies are conducted using cellular network-based datasets associated with two groups of city pairs in China heavily affected by COVID-19. Spatial matching, full-sample extrapolation, and trajectory feature analysis are adopted to attain travel volumes of intercity highways during four different periods. The reliability of origin-destination (OD) matrices calculated based on the cellular network-based dataset is demonstrated by comparing with the fluctuation trend of traffic count data. The empirical studies show that the OD flows associated with passenger cars on intercity highways in China decreased significantly during COVID-19. With the effective implementation of the pandemic prevention control policy and the orderly promotion of the recovery to work and production, the volumes of intercity highway OD flows returned to the pre-pandemic level in mid-April 2020. Besides, the peak of passenger car trips decreases and the time span for truck trips gets longer owing to implemented control measures in dealing with COVID-19. The results can be applied to the calculation of OD flows between most adjacent cities and analyze the intercity highway traffic travel patterns changes, which provide insightful implications for making intercity travel safety prevention and control policies under epidemic conditions.
Highlights
Introduction e COVID19 pandemic has spread rapidly since December 2019, causing a severe adverse impact on economic development and social security worldwide [1,2,3,4,5]
E call detail records (CDRs) and visitor location registry (VLR) data are usually collected through active and passive events: (i) Active events include making or receiving telephone calls, sending or receiving text messages, mobile phone Internet logs, and switching the phone on and off (ii) Passive events include periodic location updates, movement of users into a new set of cellular station areas, and cellular signal switching between different communication eras e dataset collected for this study covers about 18 million users and contains about 23 billion pieces of information on Internet records daily (Table 1). e dataset contains both the information about coverage areas’ shapes and the geographical location of each base station
Traffic count data are normally collected as part of a continuous count program. e primary objective of the program is to develop hour of day (HOD), day of week (DOW), month of year (MOY), and yearly factors to expand short-duration counts to annual average daily traffic (AADT). e continue count station (CCS) can be used to develop adjustment factors, track traffic volume trends on important highway segments, and provide inputs to traffic management and traveler information systems
Summary
E operator records a set of information throughout the call, including interaction ways of the mobile terminal with the signaling network within the area, other communication devices, or equipment. E operator will record the interaction of the mobile terminal with the regional base station during packet switching, including the timestamp and the necessary fields for user identification. (i) Active events include making or receiving telephone calls, sending or receiving text messages, mobile phone Internet logs, and switching the phone on and off (ii) Passive events include periodic location updates, movement of users into a new set of cellular station areas, and cellular signal switching between different communication eras e dataset collected for this study covers about 18 million users and contains about 23 billion pieces of information on Internet records daily (Table 1). Traffic count data are normally collected as part of a continuous count program. e primary objective of the program is to develop hour of day (HOD), day of week (DOW), month of year (MOY), and yearly factors to expand short-duration counts to annual average daily traffic (AADT). e CCS can be used to develop adjustment factors, track traffic volume trends on important highway segments, and provide inputs to traffic management and traveler information systems
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