Modeling Effects of Precipitation on Vehicle Speed: Floating Car Data Approach

Abstract

Extreme precipitation as a result of the ongoing change of climatic conditions is attributed to significantly hindering vehicle circulation in urban transportation networks. During 2014, Thessaloniki, Greece, experienced significant anomalies with respect to the expected amount and intensity of rainfalls, which severely influenced traffic flow and safety conditions. According to the National Meteorological Service, a 360% deviation in the total rainfall accumulation (compared with the 1959–1997 mean) was observed in the city for July 2014. Based on the increased intensity and occurrence of such events, this research studied the impacts of heavy rainfalls on road operations, with the road network of Thessaloniki, Greece, serving as a case study. Data for these analyses were obtained from floating car data of a 1,200-taxi fleet that contained GPS coordinates and speed information. Findings reveal that vehicle speed drops increase with precipitation intensity yet they differ depending on the examined road type. This paper highlights the importance of developing a sound quantitative understanding of the effects of such events in urban areas and considers the context of the changing climate and the concomitant increasing frequency of extreme events.

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

“…When evaluating the effect of weather change on vehicles’ speed drop, the popular method used in the literature is regression and trend analysis [ 7 , 15 , 25 , 26 ]. Camacho et al [ 7 ] modeled the traffic speed of 15 major freeways in northern Spain using regression analysis based on several indicators: number of trucks, visual visibility, wind speed, precipitation intensity, and snow thickness.…”

“…They found that different rainfall intensity affects traffic flow, as summarized in Table 1 . The regression and analysis approaches were also used by Mitsakis et al [ 25 ] and Stamos et al [ 26 ].…”

“…Table 1 summarizes the relevant studies in the literature regarding the effect of rainy weather on vehicle speed reduction. As can be seen in Table 1 , most of the literature studied traffic conditions in Western countries, such as the United States [ 13 , 33 ], France [ 12 ], Spain [ 7 ], Greece [ 25 , 26 ], Australia [ 31 ], and Sweden [ 34 ]. A few studies reported on the main metropolises in Asia, such as Hong Kong [ 8 ], Beijing [ 15 ], Shenzhen [ 16 ], and Seoul [ 17 ].…”

“…However, measuring vehicle speed in an urban area might need different technologies, such as wireless sensor networks. Although some researchers also focused on traffic studies on urban roads, all of those works considered the developed countries’ road condition, such as in South Korea [ 17 ], Sweden [ 33 ], the United States [ 35 ], Athens, Greece [ 25 ], and Thessaloniki, Greece [ 26 ]. In summary, each work mentioned above shows various weather impact with different precipitation and characteristics.…”

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

“…Fog may increase drivers’ perceived distance compared with that under prevailing conditions ( 3 ), which would increase vehicle headway, reduce road capacity and speed, and increase speed variation ( 4 – 7 ). Rain reduces visibility and sight distance in addition to decreasing road surface friction ( 8 , 9 ).…”