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Modelling driver experience and its role in influencing diversion behaviour

Modelling driver experience and its role in influencing diversion behaviour
Modelling driver experience and its role in influencing diversion behaviour
Traffic assignment, the process by which vehicle flows are loaded on to paths traversing a road network for the purpose of spatial demand forecasting, has been traditionally approached as a mathematical optimisation problem. However, this assumes typical highway network conditions, yielding ‘average day’ traffic forecasts only. Such approaches fail to account for time-dependent variability caused by infrequent events such as traffic accidents, vehicle breakdowns or road works which result in sub-optimal network performance.

On any day, especially when incidents cause abnormal congestion patterns, drivers can only choose routes and diversion strategies according to the best of their own subjective knowledge and experience which is unique to each traveller. Ensuring that knowledge, both within-day and between days, is represented adequately and with realistic assumptions within models is key to forecasting traffic flows in all situations and their resulting network phenomena accurately.

This thesis explores how drivers react under these irregular conditions, termed ‘states’, with a goal of understanding route choice and consequently advancing demand forecasting techniques. To this end, a simulator based survey is used in order to gain further knowledge of driver learning and diversion behaviour, then an agent based simulation modelling approach is developed using this insight to explore the network and traffic flow effects of drivers reacting to uncertain network conditions by altering their route choices. This work argues that representing driver knowledge and choices from a disaggregate agent based perspective, rather than a traditional aggregate approach, is more appropriate for modelling the impact of variable travel conditions.

Results demonstrate that the possibility of incidents occurring and the potential for diverting can have a significant effect on network characteristics and the decisions of drivers, even on incident-free ‘clear’ days. Importantly, results show that drivers diverting can temporarily alleviate congestion but ultimately cause more delays and suboptimal network performance. These results have significant implications for demand forecasting practitioners and policy makers who try to minimise disruption through traffic management systems or effective network design.
University of Southampton
Snowdon, James
48a26581-eba4-41ed-955d-ca84e5aa6908
Snowdon, James
48a26581-eba4-41ed-955d-ca84e5aa6908
Waterson, Benedict
60a59616-54f7-4c31-920d-975583953286

(2015) Modelling driver experience and its role in influencing diversion behaviour. University of Southampton, Engineering and the Environment, Doctoral Thesis, 168pp.

Record type: Thesis (Doctoral)

Abstract

Traffic assignment, the process by which vehicle flows are loaded on to paths traversing a road network for the purpose of spatial demand forecasting, has been traditionally approached as a mathematical optimisation problem. However, this assumes typical highway network conditions, yielding ‘average day’ traffic forecasts only. Such approaches fail to account for time-dependent variability caused by infrequent events such as traffic accidents, vehicle breakdowns or road works which result in sub-optimal network performance.

On any day, especially when incidents cause abnormal congestion patterns, drivers can only choose routes and diversion strategies according to the best of their own subjective knowledge and experience which is unique to each traveller. Ensuring that knowledge, both within-day and between days, is represented adequately and with realistic assumptions within models is key to forecasting traffic flows in all situations and their resulting network phenomena accurately.

This thesis explores how drivers react under these irregular conditions, termed ‘states’, with a goal of understanding route choice and consequently advancing demand forecasting techniques. To this end, a simulator based survey is used in order to gain further knowledge of driver learning and diversion behaviour, then an agent based simulation modelling approach is developed using this insight to explore the network and traffic flow effects of drivers reacting to uncertain network conditions by altering their route choices. This work argues that representing driver knowledge and choices from a disaggregate agent based perspective, rather than a traditional aggregate approach, is more appropriate for modelling the impact of variable travel conditions.

Results demonstrate that the possibility of incidents occurring and the potential for diverting can have a significant effect on network characteristics and the decisions of drivers, even on incident-free ‘clear’ days. Importantly, results show that drivers diverting can temporarily alleviate congestion but ultimately cause more delays and suboptimal network performance. These results have significant implications for demand forecasting practitioners and policy makers who try to minimise disruption through traffic management systems or effective network design.

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More information

Published date: July 2015
Organisations: University of Southampton, Transportation Group

Identifiers

Local EPrints ID: 388114
URI: http://eprints.soton.ac.uk/id/eprint/388114
PURE UUID: 53fea397-1ae5-48e4-8bc4-68f4700b42d0
ORCID for Benedict Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 22 Feb 2016 12:32
Last modified: 20 Jun 2018 04:01

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