Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

Alshayeb, Suhaib; Stevanovic, Aleksandar; Mitrović, Nikola; Dimitrijević, B

doi:10.3390/s21185997

Cited by 3 publications

(4 citation statements)

References 70 publications

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“…However, the impact of two additional factors (idling FC rate (FC I /T I ) and deceleration duration (T D )) was not examined in the previous studies. On the one hand, higher FC rates (FC I /T I ) result in lower K values, as it can be concluded from Equation (7). On the other hand, a longer deceleration duration causes a higher K value because the excess FC during the deceleration phase depends on the duration of the deceleration process, which depends on several factors, including the driver's behavior and the traffic dynamics of the vehicle(s) in front of the stopping vehicle.…”

Section: Factors Impacting Stop Penaltymentioning

confidence: 83%

“…The stopped delay varies based on the length of the red interval for a given phase. So, for this reason, the FC I is divided by the total idling time (T I ) in seconds, as shown in Equation (7). This step is important to assign the number of seconds of stopped delay equivalent to a stopping event, which is the stop penalty (K-factor).…”

Section: Overview Of the Stop Penalty Derivationmentioning

confidence: 99%

“…The negative impact of vehicular fuel consumption (FC) is not limited to environmental concerns, but it extends to affect human health by increasing the concentration of some harmful pollutants (e.g., particulate matters) [4]. One of the primary sources of excess fossil FC in the transportation sector is the stop-and-go events [5] that occur primarily at intersections because they involve high traffic density and crossing of two or more roads [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

2021

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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.

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Section: Factors Impacting Stop Penaltymentioning

confidence: 83%

Section: Overview Of the Stop Penalty Derivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

2021

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“…The emergence of novel traffic data sets allowed researchers to address various problems in traffic engineering ( 65–67 ). In this study, high-resolution signal and detection data are used to develop three novel TSPMs to report capacity utilization in “online” fashion, with the purpose of overcoming limitations of the existing V/C and GOR measures.…”

Section: Discussionmentioning

confidence: 99%

Development and Evaluation of Performance Measures for Capacity Utilization of Traffic Signals

Dobrota

Stevanovic

Mitrović³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Emerging high-resolution datasets allow for developing traffic signal performance measures (TSPMs) which can provide a better understanding of traffic signal operations. One of the specific objectives of signal operations is related to the measurement of capacity utilization. A fundamental measure for capacity utilization is the “degree of saturation” ( DS), and it has both volume-based and occupancy-based formulations. The former is known as “volume over capacity ratio” ( V/C), whereas the latter is known as “green occupancy ratio” ( GOR). When V/C is used to observe capacity utilization, it is not known whether the capacity is utilized from interrupted flow, free flow, or a combination of two. Similarly, the amount of time in the cycle utilized by volume (characterized with the first and last vehicle arrival time) is not revealed within V/C. On the other hand, GOR“overlooks” cases when a high number of vehicles travel without stopping or fewer vehicles depart the signal with significant startup delays and similar. In addition, because of a lack of scientific attention, GOR has often been misinterpreted as some similar, yet different TSPM. Therefore, if only V/C or GOR are used to monitor signal performance, the understanding of utilized capacity will be incomplete or misleading. This study proposes three TSPMs: “queued volume in volume to capacity ratio” ( QViV/C), “cycle utilization” ( CLU), and “volume-occupancy capacity utilization” ( VOCU) to overcome the limitations of V/C and GOR. Also, by evaluating existing occupancy-based measures, it was found that they can be used to estimate capacity utilization, including oversaturated conditions.

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Comprehensive Data Analysis Approach for Appropriate Scheduling of Signal Timing Plans

Dobrota

Mitrović

Gavric

et al. 2022

Future Transportation

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Improperly scheduled signal timing plans are one of the main reasons for reduced efficiency of traffic signals at coordinated urban arterials. Recently, most urban arterial roads are equipped with intelligent transportation systems devices capable of reporting the collected data on high temporal and spatial resolution, which gives us the opportunity to overcome traditional signal timing planning flaws. Previous studies have proposed methods for scheduling signal timing plans based on small quantities of data combined with various optimization approaches that ultimately require domain expert intervention to fine-tune proposed solutions. Consequently, the signal timing plans scheduling problem is still being addressed without a comprehensive approach. In this study, we propose a novel data-driven procedure based on visual analytics principles to identify the dominant traffic profiles and appropriate scheduling of signal timing plans. The medium-resolution volume data collected over a one-year period on a real-world corridor consisting of 12 signalized intersections were used to validate the proposed methodology. Applied principles from the visual analytics domain allow for a better understanding of traffic characteristics and ultimately alleviate the development of appropriate signal timing schedules. The results show that the proposed method more reliably schedules signal timing plans when compared to current practice.

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Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

Cited by 3 publications

References 70 publications

Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Development and Evaluation of Performance Measures for Capacity Utilization of Traffic Signals

Comprehensive Data Analysis Approach for Appropriate Scheduling of Signal Timing Plans

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