Network-Wide Route Guidance with Consideration of Fairness: A Macroscopic Fundamental Diagram Approach
Date
2022-05
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Abstract
This thesis introduces fair route guidance (RG) control schemes in a model predictive control (MPC) framework. The modelling approach used is based on the macroscopic fundamental diagram (MFD), which relates aggregated traffic variables, such as vehicle accumulation and trip completion rate. Earlier MFD-based RG schemes focus on improving network efficiency while overlooking fairness and equity issues. As a result, the controllers force some drivers to take longer paths for their trip to minimize total network travel time; thus, creating inequity issues. This problem motivated this thesis to develop a new control strategy that simultaneously addresses fairness and efficiency in RG control models. This is done by introducing various fairness-centered concepts, such as proportional fairness and anticipatory RG control in an MPC framework for online control. The proportional fairness (PF) concept, which is rooted in economics and was successfully applied in wireless networks can address this issue by balancing the trade-off between network efficiency and fairness. This paper presents a two-level RG framework using MFD for a heterogeneous urban network divided into multiple pockets of congestion. The developed framework comprises an MPC-based RG optimization and an estimated route-choice model. Firstly, the optimized RG ratios are obtained from the optimization model with different objective functions including: proportional fairness of regional speed, path speed, and path travel time. These objective functions are examined and the results are compared. Then, to update the network traffic states, the drivers’ actual route choice is estimated based on the linear combination, including the driver routing responses through a logit route-choice model and the optimized routing ratios, which is determined by using the given compliance rate. Also, this thesis presents an MFD-based anticipative RG control approach by modelling a two-level optimization model in an online optimization framework and directly incorporates road users’ routing behaviour in the control model. The anticipatory RG controller is examined by replacing the basic objective function with the proportional fairness objective function. Based on the anticipatory control (AC) concept, incorporating user behaviour proactively as part of the control framework leads to a more optimum solution and more consistent routing schemes. Intensive sensitivity analysis is conducted under high and low-demand profiles and for different compliance rates and MFD parameters. Compared with the basic control model, the results show that the fairness control models were more successful in reducing the variances of region accumulations and speeds. The results indicate that the proportionally fair RG model based on path time improves fairness in an urban network by increasing homogeneity while also maintaining a high level of efficiency. Having more homogenised traffic by FC models was consistent for all examined routing compliance levels, even when the compliance rates dropped to as low as 30%. However, because of integrating drivers routing decision directly with AC control models, the total travel time (TTT) efficiency of AC models was more than FC models in cases where the compliance rate was less than 70%. In addition, when examining the performance of the routing guidance for the scenarios with heterogeneous MFD parameters, the proportionally fair RG models exhibited a more homogenous traffic network by reducing the variance of speed, compared to other routing models.
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Keywords
transportation, route guidance, fairness
Citation
Hosseinzadeh, F. (2022). Network-wide route guidance with consideration of fairness: a macroscopic fundamental diagram approach (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.