Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia

Abstract

The impact of inclement weather on traffic flow has been extensively studied in the literature. However, little research has unveiled how local weather conditions affect real-time traffic flows both spatially and temporally. By analysing the real-time traffic flow data of Traffic Signal Controllers (TSCs) and weather information in Brisbane, Australia, this paper aims to explore weather’s impact on traffic flow, more specifically, rainfall’s impact on traffic flow. A suite of analytic methods has been applied, including the space-time cube, time-series clustering, and regression models at three different levels (i.e., comprehensive, location-specific, and aggregate). Our results reveal that rainfall would induce a change of the traffic flow temporally (on weekdays, Saturday, and Sunday and at various periods on each day) and spatially (in the transportation network). Particularly, our results consistently show that the traffic flow would increase on wet days, especially on weekdays, and that the urban inner space, such as the central business district (CBD), is more likely to be impacted by inclement weather compared with other suburbs. Such results could be used by traffic operators to better manage traffic in response to rainfall. The findings could also help transport planners and policy analysts to identify the key transport corridors that are most susceptible to traffic shifts in different weather conditions and establish more weather-resilient transport infrastructures accordingly.

A Review of Approaches to the Study of Weather's Effect on Road Traffic Parameters

“…Tsapakis & et al [17] analyzed more than 380 travel links in the Greater London network and found that inclement weather conditions have greater effect on travel time decreases on the longer links within outer London than those in inner London, and even greater decreases than the shortest links in central London. Similar type of conclusion but with different results was obtained by Qi & et al who pointed that the urban inner space, such as the central business district, is more likely to be affected by rainfall compared with other suburbs [10].…”

“…One of the newest research has focused on the effects of rainfall on the spatial distribution and changes in traffic-congestion bottlenecks throughout a mega-city [9]. Another example of recent studies presents how local weather conditions affect real-time traffic flows both spatially and temporally [10]. A completely different idea to investigate the topic next, the authors have been checking the effect of strong wind on dynamic characteristic of traffic flow to improve car-following model and explore fuel consumption [11].…”

Multistage Impacts of the Heavy Rain Process on the Travel Speeds of Urban Roads

“…Moreover, some research found that the effects of rain on traffic efficiency vary with different road categories, locations, times, and congestion levels [5,11,20,[25][26][27][28]. Andersen and Torp [23] examined rain's impact on four highways, two rural roads, and four urban roads and concluded that the decline in travel speeds was more pronounced on urban roads than on highways and rural roads.…”

“…Mitsakis et al [11] divided their research area into several geographical zones and found that the impacts of rainfall varied considerably in each study zone. Qi et al [27] found that the traffic flow on the roads within the urban inner space, such as the central business district (CBD), was more likely to be impacted by inclement weather compared with other suburbs. Yao et al [28] indicated that road speeds decreased by 6.20% on weekdays and by 2.37% on weekends in rainfall weather.…”

Short, Mid and Long Term Traffic Flow Forecasting and Data Visualization Tool

“…Tasarlanan sistem ile, yetkililerin gün özelliği (hafta sonu, hafta içi, resmi tatil vs) ve hava durumu bilgisini de göz önünde bulundurularak trafik planlaması yapmalarına yardımcı olacak kullanıcı dostu bir arayüz sunulmuştur. Ayrıca geliştirilen arayüz ile, sinyalizasyon ve kavşak çalışması gibi yol düzenlemelerinin trafiğe olumlu/olumsuz etkisi gözlemlenebilmektedir.Öte yandan literatürde yağmurun trafik karakteristiğine etkilerine işaret eden pek çok çalışma mevcuttur[29]. Önerilen regresyon ve ortalama tabanlı öğrenme modelleri ilgili gün için k hafta kadar geri gidilerek gözlenen hız verilerini tahmin için kullanmaktadır.…”