Estimation of Diversion Rate during Incidents on Basis of Main-Line Detector Data

Ponnaluri

2018

Self Cite

State agencies have developed warrants and guidelines for the metering of freeway on-ramps. However, these warrants only consider the traffic conditions in the vicinity of each on-ramp without considering the need to meter multiple ramps to mitigate the impacts of bottlenecks downstream. The warrants do not employ detailed analyses of traffic conditions or take advantage of the increasing availability of data from multiple sources. In addition, the existing local warrants only consider recurrent conditions with no consideration of the benefit of metering during non-recurrent events such as incidents and adverse weather. This study aims to develop a methodology for the identification of the ramps to meter that considers system-wide recurrent and non-recurrent traffic conditions based on detailed analysis of traffic data. This methodology incorporates the stochastic nature of the demand and capacity and the impacts of incidents and weather using Monte Carlo simulation and a ramp selection procedure based on a linear programming formulation. The method allows the calculation of the minimum number of ramps that need to be metered to keep flows below capacity on the freeway mainline, while keeping the on-ramp queues from spilling back to the upstream arterial street segments. The methodology can be used in conjunction with the existing local warrants to identify the ramps that need to be metered. In addition, it can be used in benefit–cost analyses of ramp metering deployments and associated decisions such as which ramps to meter and when to activate metering.

Section: Methodsmentioning

confidence: 99%

Methodology to Identify On-Ramps for Metering with Consideration of System-Wide Recurrent and Non-Recurrent Congestion

Fartash

Ponnaluri

2018

Self Cite

“…The logit model estimates 21.3% and 21.4% diversions for short-term and long-term work zones, respectively. Hadi et al ( 18 ) conducted a study on the diversion of traffic in incident conditions based on detector data and found that the diversion rate ranges from about 8% for one out of five lane blockages to about 25% when four out of the five lanes were blocked.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Incident Responsive Signal Control Plans using Multi-Resolution Modeling

Massahi¹,

Shams³

et al. 2019

Self Cite

The implementation of incident responsive signal control strategies can be effective in relieving congestion during traffic incidents. The goal of this paper is to develop and evaluate a methodology to assess the impacts of incident responsive signal control. This study demonstrates the utilization of a multi-resolution and multi-scenario modeling approach to support the evaluation and design of incident management strategies and the associated incident responsive signal control. The approach utilizes the strength of mesoscopic simulation-based dynamic traffic assignment modeling to determine route diversion and microscopic simulation to estimate the traffic impacts of incident responsive signal timing. The methods also utilize detailed traffic and incident data to inform the analysis, modeling, and simulation. The utilized methods support the modeling of different traffic patterns, confirm the diversion estimated by modeling tools based on field detector data, provide estimation of capacity drop during arterial incidents with different locations from upstream and downstream signals, and address the need for accurate turning movement volumes resulting from the modeling. Results of the case study indicate that implementation of incident responsive signal control strategies provided a reduction in total delay of through movement in the incident direction of 18.5% and 24.5% for 30-min and 45-min incidents, respectively. The corresponding reductions in total delay of all movements in the segment were 7.5% and 9.5%, respectively. These values can be used to inform the return-on-investment as part of planning and operation analysis of active transportation management strategies.

“…Route diversion during incidents has been proven to be a useful tactic to mitigate non-recurrent congestion. Studies revealed that the diversion percentages are between 5% and 25% depending on the incident and traffic status ( 1–5 ). However, the anticipated congestion on the alternative routes discourages drivers from diverting, resulting in lower diversion percentages ( 6 ).…”

mentioning

confidence: 99%

Deep Learning Approach for Predictive Analytics to Support Diversion during Freeway Incidents

Saha

Tariq

2020

Self Cite

Route diversion during incidents on freeways has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day (TOD) signal control cannot handle the sudden increase in the traffic on the arterials because of diversion. Thus, there is a need for active transportation management strategies that support agencies in identifying the potential diversion routes for freeway incidents and the need for adjusting the traffic signal timing under different incident and traffic conditions. This paper investigates the use of a data analytic approach based on the long short-term memory (LSTM) deep neural network method to predict the alternative routes dynamically using incident attributes and traffic status on the freeway, and travel time on both the freeway and alternative routes during the incident. Additionally, a methodology is proposed for the development of special signal plans for the critical intersections on the alternative arterials based on the results from the LSTM neural network, combined with simulation modeling, and signal timing optimization. The methodology developed in the paper can be easily implemented by the transportation agencies, as it is based on data that are generally available to the agencies. The results from this paper indicate that the developed methodology can be used as part of a decision support system (DSS) to manage the traffic proactively during the incidents on the freeways.