No abstract

Traffic signals may generate bottlenecks due to an unfair timing balance. Facing this problem, adaptive traffic signal controllers have been proposed to compute the phase durations according to conditions monitored from on-road sensors. However, high hardware requirements, as well as complex setups, make the majority of these approaches infeasible for most cities. This paper proposes an adaptive traffic signal fuzzy-logic controller which uses the flow rate, retrieved from simple traffic counters, as a unique input requirement. The controller dynamically computes the cycle duration according to the arrival flow rates, executing a fuzzy inference system guided by the reasoning: the higher the traffic flow, the longer the cycle length. The computed cycle is split into different phases proportionally to the arrival flow rates according to Webster’s method for signalization. Consequently, the controller only requires determining minimum/maximum flow rates and cycle lengths to establish if–then mappings, allowing the reduction of technical requirements and computational overhead. The controller was tested through a microsimulation model of a real isolated intersection, which was calibrated with data collected from a six-month traffic study. Results revealed that the proposed controller with fewer input requirements and lower computational costs has a competitive performance compared to the best and most used approaches, being a feasible solution for many cities.

In the current version of the Highway Capacity Manual (HCM-6), equal-capacity passenger car equivalencies (EC-PCEs) are used to account for the effect of trucks for capacity analyses. The EC-PCEs for freeway segments were estimated using a microsimulation-based methodology where the capacities of the mixed-traffic and car-only flow scenarios were modeled. A nonlinear regression (NLR) model was used to develop capacity adjustment factor (CAF) models using the microsimulation data as input. The NLR model has a complex model structure and includes 15 model parameters. It is argued in this paper that simpler regression models could provide comparable results. This would allow CAF and EC-PCE equations to be used directly in the HCM-6 rather than tables. It would also allow for the development of new regression models for exploring new technologies such as connected and automated vehicles (CAVs). The objective of this paper was to develop alternative and simpler regression models of CAFs needed to derive the EC-PCE values in the HCM-6 methodology for freeway and multilane highway segments. It was found that simpler regression models provided similar results as those obtained with the current NLR model. Additionally, it was found that the current NLR model may not be adequate for analyzing CAV traffic conditions. If the HCM-6 EC-PCE methodology is expected to be used to analyze traffic conditions beyond the scope of the HCM-6, it is important to perform a deeper assessment of the form and error of the regression models used in fitting the simulated and estimated data.