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Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China


  • Received: 15 June 2022 Revised: 18 July 2022 Accepted: 26 July 2022 Published: 08 August 2022
  • Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.

    Citation: Peiqun Lin, Xuanyi Liu, Mingyang Pei, Pan Wu. Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China[J]. Electronic Research Archive, 2022, 30(10): 3711-3734. doi: 10.3934/era.2022190

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  • Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.



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