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LANE DISTRIBUTION OF UNCONGESTED TRAFFIC ON MULTI-LANE FREEWAYS AND INTELLIGENT TRANSPORTATION SYSTEMS (ITS) APPLICATION

Izumi Okura, Kathirgamalingam Somasundaraswaran

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Abstract

Analyzed results from uncongested traffic flow on uninterrupted segments of two and three-lane unidirectional freeways show that lane distribution of traffic volume widely varies between individual lanes and traffic flow rates in some lanes has never been highest. This paper describes a simplified methodology for explaining this traffic distribution behavior in multi-lane freeway segments together with identified capacity improvement measures and application of Intelligent Transport Systems (ITS). There are two simple models involved in this paper. The first model shows that amount of vehicle changeovers between adjacent lanes highly influences the pattern of traffic distribution, which has a strong relationship with two defined traffic parameters such as ratio of speed and ratio of density between adjacent lanes. The second model was used to identify the individual lane's speed characteristics by calculating the percentage of expected number of single or combination of drivers' constraints such as lane changeovers, overtakings or braking events. Results show that there is a strong relationship between drivers' constraints and the above defined ratio of average speed between adjacent lanes. Further, it shows that the pattern of existing speed distribution is a result of balancing these expected drivers' constraints in a lane. Moreover, from the first model it was identified that, controlling vehicles' lane changeovers from a lower utilized lane to a higher utilized lane can increase the traffic volume in a lower utilized lane, which can be used as a freeway capacity improvement measure. However, it shows that depending on the amount of traffic flow, the direction of controlling lane changeovers has to be changed. Therefore, finally, this paper describes a technical prospective; how a flexible lane marking arrangement can be performed based on real-time data information, by efficient ITS application in the existing freeway segments, which is flexibly adapted for various total traffic flow conditions.

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Analyzed results from uncongested traffic flow on uninterrupted segments of two and three-lane unidirectional freeways show that lane distribution of traffic volume widely varies between individual lanes and traffic flow rates in some lanes has never been highest. This paper describes a simplified methodology for explaining this traffic distribution behavior in multi-lane freeway segments together with identified capacity improvement measures and application of Intelligent Transport Systems (ITS). There are two simple models involved in this paper. The first model shows that amount of vehicle changeovers between adjacent lanes highly influences the pattern of traffic distribution, which has a strong relationship with two defined traffic parameters such as ratio of speed and ratio of density between adjacent lanes. The second model was used to identify the individual lane's speed characteristics by calculating the percentage of expected number of single or combination of drivers' constraints such as lane changeovers, overtakings or braking events. Results show that there is a strong relationship between drivers' constraints and the above defined ratio of average speed between adjacent lanes. Further, it shows that the pattern of existing speed distribution is a result of balancing these expected drivers' constraints in a lane. Moreover, from the first model it was identified that, controlling vehicles' lane changeovers from a lower utilized lane to a higher utilized lane can increase the traffic volume in a lower utilized lane, which can be used as a freeway capacity improvement measure. However, it shows that depending on the amount of traffic flow, the direction of controlling lane changeovers has to be changed. Therefore, finally, this paper describes a technical prospective; how a flexible lane marking arrangement can be performed based on real-time data information, by efficient ITS application in the existing freeway segments, which is flexibly adapted for various total traffic flow conditions.

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Available abstract

Analyzed results from uncongested traffic flow on uninterrupted segments of two and three-lane unidirectional freeways show that lane distribution of traffic volume widely varies between individual lanes and traffic flow rates in some lanes has never been highest. This paper describes a simplified methodology for explaining this traffic distribution behavior in multi-lane freeway segments together with identified capacity improvement measures and application of Intelligent Transport Systems (ITS). There are two simple models involved in this paper. The first model shows that amount of vehicle changeovers between adjacent lanes highly influences the pattern of traffic distribution, which has a strong relationship with two defined traffic parameters such as ratio of speed and ratio of density between adjacent lanes. The second model was used to identify the individual lane's speed characteristics by calculating the percentage of expected number of single or combination of drivers' constraints such as lane changeovers, overtakings or braking events. Results show that there is a strong relationship between drivers' constraints and the above defined ratio of average speed between adjacent lanes. Further, it shows that the pattern of existing speed distribution is a result of balancing these expected drivers' constraints in a lane. Moreover, from the first model it was identified that, controlling vehicles' lane changeovers from a lower utilized lane to a higher utilized lane can increase the traffic volume in a lower utilized lane, which can be used as a freeway capacity improvement measure. However, it shows that depending on the amount of traffic flow, the direction of controlling lane changeovers has to be changed. Therefore, finally, this paper describes a technical prospective; how a flexible lane marking arrangement can be performed based on real-time data information, by efficient ITS application in the existing freeway segments, which is flexibly adapted for various total traffic flow conditions.

Key concepts: Traffic flow (computer networking), Traffic volume, Transport engineering, Computer science, Traffic wave, Distribution (mathematics), Traffic congestion reconstruction with Kerner's three-phase theory, Simulation

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