Measures of Speeding from a GPS-based Travel Behavior Survey
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Open Access
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ArticleAbstract
Objective: Lacking information about actual driving speed on most roads in the Minneapolis - St. Paul region, we determine car speeds using observations from a GPS-based travel survey. Speed of travel determines the likelihood of, and consequences of, collisions. We identify the ...
See moreObjective: Lacking information about actual driving speed on most roads in the Minneapolis - St. Paul region, we determine car speeds using observations from a GPS-based travel survey. Speed of travel determines the likelihood of, and consequences of, collisions. We identify the road segments where speeding occurs. This paper then analyzes the relationship between road network structure, traveler characteristics, and speed- ing using GPS data collected from 152 individuals over a 7 day period as part of the Minneapolis - St. Paul Travel Behavior Inventory. Methods: To investigate the relationship, we employed an algorithm and process to match the GPS data with GIS databases accurately (1). Comparing actual travel speed from GPS data with posted speed limits, we measure where and when speeding occurs, and by whom. We posit that road network structure and demographics shape the decision to speed. Results: Speeding is widespread in both high speed limit zones (e.g. 60 mph (97 km/h)) and low speed limit zones (less than 25 mph (40 km/h)); in contrast, speeding is less common in the 30 - 35 mph (48-56 km/h) zones. The results suggest driving patterns depend on the road type. We also find that when there are many intersections on the road, the average link speed (and speeding) drops. Long links are conducive to speeding. Younger drivers, and more educated drivers also speed more, and speeding is higher in the evening. Conclusions: Road design and network structure affects the likelihood of speeding. Use of increasingly available GPS data allows more systematic empirical analysis of designs and topologies that are conducive to road safety.
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See moreObjective: Lacking information about actual driving speed on most roads in the Minneapolis - St. Paul region, we determine car speeds using observations from a GPS-based travel survey. Speed of travel determines the likelihood of, and consequences of, collisions. We identify the road segments where speeding occurs. This paper then analyzes the relationship between road network structure, traveler characteristics, and speed- ing using GPS data collected from 152 individuals over a 7 day period as part of the Minneapolis - St. Paul Travel Behavior Inventory. Methods: To investigate the relationship, we employed an algorithm and process to match the GPS data with GIS databases accurately (1). Comparing actual travel speed from GPS data with posted speed limits, we measure where and when speeding occurs, and by whom. We posit that road network structure and demographics shape the decision to speed. Results: Speeding is widespread in both high speed limit zones (e.g. 60 mph (97 km/h)) and low speed limit zones (less than 25 mph (40 km/h)); in contrast, speeding is less common in the 30 - 35 mph (48-56 km/h) zones. The results suggest driving patterns depend on the road type. We also find that when there are many intersections on the road, the average link speed (and speeding) drops. Long links are conducive to speeding. Younger drivers, and more educated drivers also speed more, and speeding is higher in the evening. Conclusions: Road design and network structure affects the likelihood of speeding. Use of increasingly available GPS data allows more systematic empirical analysis of designs and topologies that are conducive to road safety.
See less
Date
2018-09-13Share