Abstract
This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).
Highlights
Academic Editor: Jing Zhao is paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation
The binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. e performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 generation simulation (NGSIM) data. e results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-meansquare-error (RMSE) of 0.04
A comparison based on RMSE and analysis of variance (ANOVA) test was made to compare the cases of the CTM with or without the LC models in terms of prediction accuracy according to the actual dataset
Summary
CTM is one the most widely used cell-based macroscopic traffic flow models developed by [8, 9]. Is model has been recognized as the most straightforward means, where only a few parameters are needed to explain the evolution of traffic features and dynamics [8] It is known as the numerical estimation model of the LWR model. [36] proposed a model that posits that drivers can make lane change decisions, with its effect decreasing exponentially as the distance from their current location increases Their empirical analysis included some major lane change implications (e.g., capacity loss and flow balancing impact of discretionary lane shift). Limitations still exist when incorporating lane change into CTM, when fixed values were assigned to estimate the influence of lane change on inflow and outflow traffic. Limitations still exist when incorporating lane change into CTM, when fixed values were assigned to estimate the influence of lane change on inflow and outflow traffic. is assumption might not empirically represent actual lane change events as the surrounding traffic environment (i.e., speed and density between lanes) [13, 37,38,39] or some unknown factors (i.e., driving attitude) might affect the occurrence of a lane change
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