Flexible car–following models for mixed traffic and weak lane–discipline conditions
Autor: | Constantinos Antoniou, Vasileia Papathanasopoulou |
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Rok vydání: | 2018 |
Předmět: |
Weak lane discipline
Computer science 0211 other engineering and technologies Transportation 02 engineering and technology Car following Benchmark (surveying) 021105 building & construction 0502 economics and business Virtual lanes Machine-learning 050210 logistics & transportation Mechanical Engineering 05 social sciences lcsh:TA1001-1280 Regression analysis lcsh:HE1-9990 Industrial engineering Identification (information) Automotive Engineering Trajectory Data-driven models Mixed traffic lcsh:Transportation engineering lcsh:Transportation and communications Focus (optics) |
Zdroj: | European Transport Research Review, Vol 10, Iss 2, Pp 1-14 (2018) |
ISSN: | 1866-8887 1867-0717 |
DOI: | 10.1186/s12544-018-0338-0 |
Popis: | Heterogeneous mixture of vehicle types and lack of lane discipline are common characteristics of cities in the developing countries. These conditions lead to driving manoeuvres that combine both longitudinal and lateral movements. Modeling this driving behavior tends to be complex and cumbersome, as various phenomena, such as multiple–leader following, should be addressed. This research attempts to simplify mixed traffic modeling by developing a methodology, which is based on data–driven models. The methodology is applied on mixed traffic, weak lane–discipline trajectory data, which have been collected in India. A well–known car–following model, Gipps’ model, is also applied on the same data and is used as a reference benchmark. Regarding the lateral manoeuvres, the focus is given on identification of significant lateral changes, which could indicate a lane–changing situation. Methods that allow monitoring structural changes in regression models could be used for this purpose. The ability of capturing lane changes is explored. A typical example is illustrated and further discussion is motivated. |
Databáze: | OpenAIRE |
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