Estimation of the Logit Model for the Online Contraflow Problem

Nassiri, Habibollah; Edrissi, Ali; Alibabai, Hamed

doi:10.3846/transport.2010.53

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Urbina [29] conducted a study on emergency evacuation plans using variable guidance lanes in multiple states in the United States, focusing on the use of variable guidance lane flow in modeling and the role of intelligent transportation systems in emergency evacuation plans. Nassiri [30] studied the real-time adjustment technology of variable guidance lanes. An off-line scheme was used to adjust the variable lane through a logit model, and the lane direction was adjusted in real time.…”

Section: Variable Guidance Lane and Its Developmentmentioning

confidence: 99%

Investigating the Effect of Network Traffic Signal Timing Strategy with Dynamic Variable Guidance Lanes

Zhao

Pan

et al. 2022

Sustainability

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This paper aims to investigate the effect of network signal timing strategy with dynamic variable guidance lanes based on a two-step approach, where the first step is an interactive traffic signal optimization model for each single interaction (e.g., lane allocation plans, cycle length) in the network, and the second refers to network signal control (e.g., split, off-sets). The optimization problem in the first step is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-ΙΙ), and the network signal control problem in the second step is solved through SYNCHRO. To verify the effect of dynamic variable guidance lanes and also the reliability and validity of the proposed approach, a numerical case study is carried out. The results show that the average vehicle delay in the entire road network was reduced by 25.06% after optimization using the proposed model. Moreover, the sensitivity of influencing factors of the proposed model is also analyzed. The results show that when the traffic flow is increased by 60% of the original traffic flow, the optimization effect of the model is more significant. However, when the lane capacity is more than 1300 pcu/h, the vehicle delay will increase slowly. To sum up, this method can improve the regional traffic efficiency of the traffic-stressed lanes and further promote the full utilization of space-time resources of the road network.

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Section: Variable Guidance Lane and Its Developmentmentioning

confidence: 99%

Investigating the Effect of Network Traffic Signal Timing Strategy with Dynamic Variable Guidance Lanes

Zhao

Pan

et al. 2022

Sustainability

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“…Since the severity of a crash is recorded in simple ordinal patterns such as fatal, injury and non-injury crashes, thus the ordered probit or logit model could be used to measure the crash involved severity because these approaches yield efficient and consistent estimates [17].…”

Section: Model Specificationmentioning

confidence: 99%

Examining injury severity in left turning crashes at intersections

Wang

Peng

Xiang³

et al. 2011

Per. Pol. Civil Eng.

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In response to the increasing concerns and challenges for most frequently left-turn crashes at intersections, partial proportional odds models, for which some of the beta coefficients vary across variables, are proposed to examine and understand the influence of contributory factors (i.e. human attributes, traffic flow features, roadway geometrics, and environmental factors, etc.) on injury severity involved in left-turn crashes, using the selected 317 crash data over latest 6 years from Xian city. The results show that partial proportional odds model has better performance than general ordered logit or probit probability approach. Specifically, the aged and younger drivers are more prone to cause left-turn crashes, and the increasing effect of trucks involvement, impact points of both vehicles, environmental factors, safety belt usage, alcohol and/or drugs are also significantly associated with higher injury severities, which was underestimated or underreported in previous researches.

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“…To effectively alleviate traffic congestion, it is vital to establish tidal lanes in a rational manner and create suitable signal control schemes. Nassiri et al (2011) focused on the dynamic adjustment mechanism of variable lanes. A Logit choice model is used to simulate the adjustment of offline solutions, and then is extended to real-time adjustment [18].…”

Section: Introductionmentioning

confidence: 99%

“…Nassiri et al (2011) focused on the dynamic adjustment mechanism of variable lanes. A Logit choice model is used to simulate the adjustment of offline solutions, and then is extended to real-time adjustment [18]. Zhao et al (2015) proposed a Binary-Mixed-Integer-Linear-Program (BMILP) to optimize dynamic usage of the reversible lane and signal timings [19].…”

Section: Introductionmentioning

confidence: 99%

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

Dong,

Xie,

et al. 2024

Sensors

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To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

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Estimation of the Logit Model for the Online Contraflow Problem

Cited by 6 publications

References 23 publications

Investigating the Effect of Network Traffic Signal Timing Strategy with Dynamic Variable Guidance Lanes

Investigating the Effect of Network Traffic Signal Timing Strategy with Dynamic Variable Guidance Lanes

Examining injury severity in left turning crashes at intersections

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

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