Modeling Reductions in Freeway Capacity due to Incidents in Microscopic Simulation Models

Hadi, Mohammed; Sinha, Prasoon; Wang, Amy

doi:10.3141/1999-07

Cited by 32 publications

(27 citation statements)

References 3 publications

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“…(12) Juxtaposing the approach and the results of this study with other studies are quite challenging, since most incident modeling studies utilizing microscopic traffic simulation directed only on controlled freeway lane closure, for example, work zones [25]. Another study by [26] aimed at simulating incidents-induced capacity reduction but did not investigate the SW speeds and queue length due to the incident.…”

Section: Resultsmentioning

confidence: 99%

Untitled

2018

IJSTT

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This study presents a methodology to calibrate a traffic incident simulation model, particularly in a freeway. The queue length was used as the objective of the simulation model calibration in this study. The simulation model was set up using Traffic Simulation Model PTV. VISSIM. Multiple incident durations were simulated, and the generated queue lengths were compared to the observed queue lengths. The observed queue lengths were estimated using the LWR model and shockwave speeds calculated using the field data. The results confirm that calibrated VISSIM incident model can signify the shockwave propagation speeds and queue length in the event of freeway incident. Such a model can be implemented as an instrument for setting up traffic management strategies to alleviate the incident's impact.

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Section: Resultsmentioning

confidence: 99%

Untitled

2018

IJSTT

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“…The incident-reduced capacity and normal capacity cannot be directly defined in microscopic simulators. By measuring traffic throughput from several traffic counters under congestion condition as capacity, Hadi et al (2007) simulated reduced capacities in several popular microscopic simulators and found that when using default parameter settings, they are lower than the reduction factors reported in the HCM 2000 for a one-lane-blocking incident on a three-lane freeway segment. Therefore, for this study, a simple calibration is done to match the specified capacity in HCM 2000.…”

Section: Capacity Reduction Due To An Incidentmentioning

confidence: 95%

Joint Analysis of Queuing Delays Associated With Secondary Incidents

Zhang

Cetin

Khattak

2015

Journal of Intelligent Transportation Systems

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Incidents cause substantial travel delays on urban freeways. Travel delays due to incidents are typically estimated for each incident independently. However, when multiple incidents occur on the same stretch of a roadway, treating them independently and ignoring their interactions leads to inaccurate results, which ultimately may lead to inefficient incident management strategies. This study examines delays due to primary-secondary incident pairs that occur on the same stretch of a freeway within a short time gap. Depending on their correlation in time and space, they can have varying degrees of impacts on the traffic flow. This article assesses the total delays induced by primary-secondary incident pairs by jointly modeling their occurrences. First, incident data combined with roadway inventory data from Hampton Roads, Virginia, are analyzed to understand the attributes of primary-secondary incident pairs, e.g., durations, lane blockages, and time gaps (between start times of the primary and its secondary incidents). Then using microscopic simulation as a primary evaluation tool, the effects of three critical parameters are investigated: time gap, distance between primary and secondary incidents, and traffic demand level. The historical incident analysis shows that, on average, primary-secondary incident pairs have substantially longer durations than single incidents. The joint analysis of queuing delays induced by primary incident and its secondary incident suggests that time gap and distance between a primary incident and its secondary incident are significantly associated with the total delays after controlling for other factors (e.g., the two associated incidents' durations and lane blockages). Furthermore, based on the scenarios tested (i.e., two incidents both blocking the right lane in a three-lane basic freeway section), it is found that the sum of delays induced by a primary incident and its secondary incident separately will overor underestimate the actual delays. For those secondary incidents that end after their associated primary incidents, total delays increase as time gap increases; increasing the distance between primary and secondary incidents is associated with reduction in delays. Other results and the implications of the findings for traffic operations are presented in the article.

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“…A number of studies utilised microscopic simulation to model traffic incidents and the impact of their characteristics (Dia et al, 2006;Chou and MillerHooks, 2011;Jeihani et al, 2011;Martin et al, 2011a). Hadi et al (2007) compared the capabilities of three microscopic simulation models.…”

Section: Simulation Modelsmentioning

confidence: 99%

Modelling the impact of traffic incidents on travel time reliability

Hojati

Ferreira

Washington

et al. 2016

Transportation Research Part C: Emerging Technologies

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Assessing and prioritising cost-effective strategies to mitigate the impacts of non-recurrent congestion on major roads, due to traffic incidents, represents a significant challenge for road network managers. In addition, traffic incidents impose significant unexpected changes in the travel time of travellers.Travel Time Reliability (TTR) has become one of the most important indicators of transport performance. In this regard, the impact of traffic incidents on TTR is crucial for both operators and travellers. An extensive literature review indicated that there is a lack of relevant research in this area. To address and improve the knowledge in this area, this thesis established a four-stage logical framework to achieve the main objective of this research. The research was aimed at modelling traffic incident impacts and quantifying the effects of traffic incidents on TTR on freeways.Based on historical data from an 'integrated database', a robust methodology to identify recurrent and non-recurrent congestion and to recognise traffic incident related congestion was proposed. Further, a number of attributes relating to traffic incidents and traffic measures for both recurrent and non-recurrent congestion were extracted to quantify the impacts of traffic incidents, including incident duration and buffer time as a measure of TTR. This facilitated insight into the factors that affect these two important impacts of incidents.Extra Buffer Time was defined to calculate the extra travel time caused by traffic incidents.This reliability measure indicates how much time is required by travellers to arrive at their destination on time with 95% certainty, in the case of an incident, over and above the travel time that would have been taken under recurrent conditions. The Extra Buffer Time Index (EBTI) was calculated to show the ratio of changes in TTR in the case of an incident.A hazard-based duration modelling approach was considered to model incident duration.Parametric accelerated failure time survival models were developed to consider heterogeneity across duration in the hazard function and in the explanatory variables. In addition, a new approach was proposed to model EBTI using a Tobit model to understand the factors affecting EBTI. Moreover, this research considered both fixed and random parameters to capture unobserved heterogeneity across data. A validation process was performed to assess the level of accuracy of the results of the prediction models.ii An integrated database was established using historical data from different sources including 12 months of incident data, 18 months of traffic data, and weather data for the same period for an Australian freeway network. The results of the analysis showed that the significant variables affecting incident duration include characteristics of the incidents (severity, type, injury, medical required, etc.), infrastructure characteristics (shoulder availability), location, time of day, and traffic characteristics. Moreover, the findings revealed no significant effect...

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Modeling Reductions in Freeway Capacity due to Incidents in Microscopic Simulation Models

Cited by 32 publications

References 3 publications

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Joint Analysis of Queuing Delays Associated With Secondary Incidents

Modelling the impact of traffic incidents on travel time reliability

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