No abstract

Sustainability has become one of the most important goals when optimizing traffic signals. This goal is achieved through utilizing various objective functions to reduce sustainability metrics (e.g., fuel consumption and emissions). However, most available objective functions do not distinguish between the reduction mechanism of various types of emissions. Further, such functions do not consider the compound impact of multiple operational conditions (e.g., road gradient) influencing emissions on the optimized signal plans. This study derives a new Environmental Performance Index representing a surrogate measure for emission estimates that can be used as an objective function in signal timings optimization to reduce emissions under various operational conditions. The Environmental Performance Index is a linear combination of delays and stops. The key factor of the Environmental Performance Index is the emissions-based stop penalty, which represents an emission stop equivalency measured in seconds of delay. This study also uses traffic simulation and emission models to investigate the compound impact of several operational conditions on the stop penalty. Results show that the stop penalty varies significantly with all the investigated conditions and that the stop penalty is unique for different types of emissions. These findings may have significant implications on the current practice of sustainable signal timing optimization.

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “K”, which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC.

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

Optimizing traffic signals to improve traffic progression relies on minimizing mobility performance measures (e.g., delays and stops). However, delay and stop minimizations do not necessarily lead to minimal sustainability measures (e.g., fuel consumption and emissions). For that reason, researchers have focused, for decades, on integrating traffic models, signal optimization models, and fuel consumption and emissions models to minimize sustainability metrics while keeping acceptable levels of mobility metrics. Therefore, this paper reviews, classifies, and analyzes studies found in the literature regarding optimizing sustainable traffic signals. This paper provides researchers with a good starting point to further develop solutions which can address sustainable traffic control. To achieve that, this study details the most notable sustainable signal timing optimization studies from six perspectives: traffic models, fuel consumption and emissions models, optimization methods, objective functions, operating conditions, and reported sustainability savings. Outcomes of this research show that the previous studies deployed many combinations of elements from the six-perspective mentioned above, leading to a wide range of fuel consumption and emissions savings. The study also concludes that the available fuel consumption and emissions models are relatively old. Hence, future research is needed to develop new fuel consumption and emissions models based on recently collected data.