Project Summary
Arterial traffic state estimation becomes a difficult task in traffic simulation as the network size gets bigger. Even with a long warm-up period, it is not guaranteed for the simulation tool to drive the initial traffic states to the correct ones. Therefore, a more direct approach is required to estimate the traffic states from the field data and place the right number of vehicles at intersection approaches before running simulation. For evaluation of intersection performance, metrics of delay and Level of Service (LOS) are often used. However, estimates computed from the state-of-the-practice Highway Capacity Manual (HCM) method are not reliable under heavy traffic conditions. Therefore, instead of using indirect metrics like delay and LOS, it is more important to come up with a method to directly estimate the traffic states so as to evaluate the intersection performance properly. For the proposed research, we will use loop detector data, as well as signal phasing information, from the field to estimate the traffic states at intersection approaches. The developed algorithms will produce estimates of traffic states (e.g., percentage of congestion, lane blockage, and queue spillback) and averaged queues for different traffic movements (e.g., left-turn, through, and right-turn.). We will validate the accuracy of the developed algorithm using the prevailing simulation tool, Aimsun. Furthermore, we will develop the estimation framework to run in the cloud using Amazon Web Services (AWS) and test with actual sensor data from the field. With good detector coverage and data quality, we expect the proposed method will outperform the conventional way of state estimation and provide more reliable and accurate estimates that can facilitate the developments of: (a) more realistic estimation algorithms for travel time and speed; (b) more effective control algorithms to reduce traffic congestion; and (c) more responsive monitoring systems for traffic incidents.