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Human perceptions of vehicle turning intention: overall performance and contributory factors

Human perceptions of vehicle turning intention: overall performance and contributory factors
Human perceptions of vehicle turning intention: overall performance and contributory factors
All pedestrians, drivers and cyclists regularly make predictions on where they think an oncoming vehicle is intending to travel, so that they can successfully and safely navigate road systems. Despite the importance of these predictions, the effectiveness of this process is currently poorly understood with all existing research being focused on predictions from in-vehicle technologies. This paper therefore investigates how well observers are able to predict a vehicle’s turning intention as it approaches an intersection and explores the explanatory variables involved in the success of this process through a logistic regression analysis. An interactive touch screen experiment was developed so that people’s predictions about turning intention could be investigated. The data set has been created with over 100 participants attempting to predict a number of vehicles’ turning intention.
The key findings of this study are that people are very good overall at predicting turning intention with approximately 90% median success rate when vehicles are between 0 and 20 meters (0-21.9 yards) away from the intersection, but with a substantial fall to approximately 70% median success rate when the vehicle is between 30 and 50 meters (32.8-54.7 yards) away. Other key explanatory variables include both vehicle specific factors (e.g. use of indicator lights) and crucially the intersection layout, providing valuable information on the relationship between intersection design and road safety
Hamilton, Andrew
ae7c13b2-0575-4579-8290-94922544f742
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Snell, Ian
d43b7f02-9903-4fac-ba4f-e1b1266d41f1
Hamilton, Andrew
ae7c13b2-0575-4579-8290-94922544f742
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Snell, Ian
d43b7f02-9903-4fac-ba4f-e1b1266d41f1

Hamilton, Andrew, Waterson, Ben and Snell, Ian (2014) Human perceptions of vehicle turning intention: overall performance and contributory factors. 93rd Annual Meeting of the Transportation Research Board, Washington, United States. 12 - 16 Jan 2014.

Record type: Conference or Workshop Item (Paper)

Abstract

All pedestrians, drivers and cyclists regularly make predictions on where they think an oncoming vehicle is intending to travel, so that they can successfully and safely navigate road systems. Despite the importance of these predictions, the effectiveness of this process is currently poorly understood with all existing research being focused on predictions from in-vehicle technologies. This paper therefore investigates how well observers are able to predict a vehicle’s turning intention as it approaches an intersection and explores the explanatory variables involved in the success of this process through a logistic regression analysis. An interactive touch screen experiment was developed so that people’s predictions about turning intention could be investigated. The data set has been created with over 100 participants attempting to predict a number of vehicles’ turning intention.
The key findings of this study are that people are very good overall at predicting turning intention with approximately 90% median success rate when vehicles are between 0 and 20 meters (0-21.9 yards) away from the intersection, but with a substantial fall to approximately 70% median success rate when the vehicle is between 30 and 50 meters (32.8-54.7 yards) away. Other key explanatory variables include both vehicle specific factors (e.g. use of indicator lights) and crucially the intersection layout, providing valuable information on the relationship between intersection design and road safety

Full text not available from this repository.

More information

Published date: January 2014
Venue - Dates: 93rd Annual Meeting of the Transportation Research Board, Washington, United States, 2014-01-12 - 2014-01-16
Organisations: Transportation Group

Identifiers

Local EPrints ID: 361084
URI: http://eprints.soton.ac.uk/id/eprint/361084
PURE UUID: 5b9d5920-68e6-4e70-8d33-e860b1611948
ORCID for Ben Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 13 Jan 2014 13:07
Last modified: 10 Jul 2020 00:26

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