Search

This study proposes a new approach for measuring field pedestrian waiting delay and developed a new pedestrian waiting delay model for signalized midblock crosswalks under mixed traffic conditions. Eight signalized midblock crosswalks were selected in the southern part of India, and data were collected using videographic technique. Pedestrian characteristics, cycle time, and phase lengths were extracted using frame-by-frame extraction technique with a least count of 0.03 s. In the current study, field pedestrian waiting delay was measured by plotting queue length against cycle time. The proposed approach was validated using the conventional approach for measuring field waiting delay. A new pedestrian waiting delay model was developed for signalized midblock crosswalks under mixed traffic conditions. Two new coefficients, i.e., the coefficient of nonuniform arrival rate (Δ1) and the coefficient of noncompliance behavior (Δ2), were incorporated into the existing Webster delay model. The former is modeled using regression analysis, and the latter is calculated as the proportion of violators. The proposed pedestrian waiting delay model was validated with the field waiting delay. The proposed new approach for measuring field waiting delay had an error of 5% compared with the conventional approach for measuring waiting delay. Hence the proposed approach for measuring pedestrian waiting can replace the conventional method for measuring field waiting delay because the new method minimizes the data extraction time. The proposed pedestrian delay model was found to be accurate in estimating the pedestrian waiting delay at signalized midblock crosswalks. The mean absolute percentage error (MAPE) value of the new pedestrian waiting delay was found to be less than 12%. The proposed waiting delay model can be used to define the level of service (LOS) at signalized midblock crosswalks, and will aid traffic engineers and urban planners in improving pedestrian facilities and safety [ABSTRACT FROM AUTHOR]

Following major hazards and incidents, it is crucial to mitigate the congestion at intersections of urban traffic systems with disruptions; maximize the evacuation, rescue, and recovery efficiency; and prevent a hazard from turning into a disaster. An optimized traffic signal design strategy can effectively contribute to maintaining an efficient traffic system operation despite various disruptions. Most of the existing studies focused on static and generic congestion scenarios during the recovery stage, rather than realistic time-progressive scenarios covering the whole process following a disruption. An adaptive traffic signal control strategy in response to traffic disruptions at a single intersection is proposed by covering both the incident and recovery stages. Dynamic phase selection (DPS) technology is applied to adjust the traffic signal control plan adaptively during the incident stage, while the queue length dissipation (QLD) algorithm is adopted to carry out optimal green time calculation during the recovery stage. The proposed methodology is demonstrated by considering disruptions caused by several typical vehicle crashes at intersections. The proposed DPS+QLD traffic signal strategy is found to improve the resiliency of a typical intersection against disruptions by clearing queues faster, reducing overall traffic loss time, and maintaining stable mobility with superior performance over conventional fixed and actuated traffic signal plans. DOI: 10.1061/JTEPBS.0000671. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus disease 2019 (COVID-19) disrupted our regular life. Many local authorities enforced a cordon sanitaire to contain the rapid spread of the disease. Travelers can only pass the cordon after the disease screening. This paper aims to propose a method to design a traffic inflow control scheme for onramp metering to maximize urban freeway network throughput with an endurable queuing delay constraint at all offramps around cordon sanitaire. A bilevel programming model is formulated where the lower level is transportation system equilibrium to predict link traffic flows. The upper level is onramp metering optimization that is nonlinear programming. A stochastic queuing model is used to represent the waiting phenomenon at each offramp where the disease testing is conducted. A heuristic algorithm is designed to solve the proposed bilevel programming model where the method of successive averages (MSA) is adopted for the lower-level model, and a genetic algorithm (GA) with elite strategy is adopted for the upper-level model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that these methods can find a good heuristic optimal solution. These methods are useful for freeway operators to determine the optimal onramp control for disease control and prevention. They are also beneficial to set up checkpoints for all kinds of risks such as drunk driving and criminal searching. [ABSTRACT FROM AUTHOR]