Estimating the Effects of Urban Street Incidents on Capacity

Abstract

The effect of incidents on capacity is the most critical parameter in estimating the influence of incidents on network performance. The Highway Capacity Manual 2010 (HCM 2010) provides estimates of the drop in capacity resulting from incidents as a function of the number of blocked lanes and the total number of lanes in the freeway section. However, there is limited information on the effects of incidents on the capacity of urban streets. This study investigated the effects on capacity of the interaction between the drop in capacity below demand at a midblock urban street segment location and upstream and downstream of signalized intersection operations. A model was developed to estimate the drop in capacity at the incident location as a function of the number of blocked lanes, the distance from the downstream intersection, and the green time–to–cycle length (g:C) ratio of the downstream signal. A second model was developed to estimate the reduction in the upstream intersection capacity resulting from the drop in capacity at the midblock incident location as estimated by the first model. The second model estimated the drop in capacity of the upstream links feeding the incident locations as a function of incident duration time, the volume-to-capacity (V/C) ratio at the incident location, and distance from an upstream signalized intersection. The models were developed on the basis of data generated with the use of a microscopic simulation model calibrated by comparison with parameters suggested in HCM 2010 for incident and no-incident conditions and by comparison with field measurements.

Evaluating Incident Responsive Signal Control Plans using Multi-Resolution Modeling

“…It happens that both incidents occur about 1,000 ft away from the upstream intersection (Northeast 3rd Ave and Broward Boulevard) and 400 ft away from the downstream intersection (US-1 and Broward Boulevard). As stated earlier, the regression models developed by Massahi et al (3,21) (Table 2) were used to confirm the maximum saturation flow rate and capacities of links upstream of the incident. The volumes produced from the DTA models for the two case study incident scenarios were imported to the microscopic simulation tool VISSIM to evaluate the impact of the new signal timing plans on the network performance.…”

“…However, these equations do not address the impacts of incident locations other than those at the stop line. In a previous paper, the authors of this study developed equations that estimate the incident link upstream signal maximum throughputs as a function of incident distances from upstream and downstream signals, incident duration, number of blockage lanes, and volume at incident location ( 3 , 10 ).…”

“…The objective of this paper is to develop and demonstrate methods that use the MRM approach to estimate the impacts of incidents and incident-responsive ATM strategies. In a previous paper ( 3 ), the authors developed macroscopic models to estimate the impacts on capacity because of arterial incidents resulting from the interaction between the drop in capacity below demands at midblock urban street segment locations and upstream and downstream signalized intersection operations. In this research, these models are utilized to improve incident modeling, as part of the mesoscopic simulation-based DTA.…”

Pavement Manager at Risk in Construction of Hot Mix Asphalt

“…Since cracks can allow an influx of water into the pavement, the extent of pavement cracks is a significant indication of pavement condition and can change the life extension of applied treatments [20]. [21]. It can be derived from Table 1 that the most effective maintenance treatment is chip seal, especially for existing pavements with the poor condition because it has the maximum pavement life extension between different maintenance treatments.…”

Exploring Potentials in Mobile Phone GPS Data Collection and Analysis

“…Having accurate data, the different flow can characteristics can be obtained. The three main characteristics that can be obtained with accuracy are travel time, travel speed, and traffic volume (Bar-Gera, 2007;Liu et al, 2008;Caseres et al, 2012;Massahi et al, 2017). For instance, Bar-Gera (2007) aimed to examine the performance of a new operational system to measure travel time and traffic speeds from cellular phone service provider data.…”