An aggregate model to estimate intersection fuel consumption is developed, and the effects of signal timing on fuel consumption are investigated. A conceptual framework is proposed to identify interrelationships among traffic characteristics, signal control strategies, and roadway geometric conditions. On the basis of the framework, an Analytical Fuel Consumption Model (AFCM) is developed to estimate fuel consumption at signalized intersections. The AFCM, based on considerations of vehicle operating conditions, describes how fuel is consumed on three street segments (inbound approach, intersection itself, and outbound leg) for three signal cycle stages (the effective red time, queue departure time t0, and remaining green time). Numerical experiments are conducted to test the AFCM estimation capability and to investigate the effects of signal timing on fuel consumption. Results of numerical experiments are compared with results from the TEXAS simulation model using two criteria: differences of total fuel consumption and correlation coefficients of elapsed fuel consumption. Results from the TEXAS model are within 10 percent of those from the AFCM, and the elapsed fuel consumption is highly correlated. This indicates that total fuel consumption estimated from the AFCM is very close to the results from the TEXAS model, which suggests that the mathematical representations of the AFCM might be used to replace the simulation-based model. Moreover, the results from these experiments indicate that total fuel consumption with respect to signal cycle time at signalized intersections possesses a convex pattern, and the optimal cycle length for minimization of fuel consumption can be obtained.


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    Title :

    Development of an Aggregate Fuel Consumption Model for Signalized Intersections


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    1998-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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