WenJing Luan

Emergency Traffic-Light Control System Design for Intersections Subject to Accidents

Petri nets (PNs) are well utilized as a visual and mathematical formalism to model discrete-event systems. This paper uses deterministic and stochastic PNs to design an emergency traffic-light control system for intersections providing emergency response to deal with accidents. According to blocked crossing sections, as depicted by dynamic PN models, the corresponding emergency traffic-light strategies are designed to ensure the safety of an intersection. The cooperation among traffic lights/facilities at those affected intersections and roads is illustrated. For the upstream neighboring intersections, a traffic-signal-based emergency control policy is designed to help prevent accident-induced large-scale congestion. Deadlock recovery, livelock prevention, and conflict resolution strategies are developed. We adopt a reachability analysis method to verify the constructed model. To our knowledge, this is the first paper that employs PNs to model and design a real-time traffic emergency system for intersections facing accidents. It can be used to improve the state of the art in real-time traffic accident management and traffic safety at intersections.

A Two-level Traffic Light Control Strategy for Preventing Incident-Based Urban Traffic Congestion

This work designs a two-level strategy at signalized intersections for preventing incident-based urban traffic congestion by adopting additional traffic warning lights. The first-level one is a ban signal strategy that is used to stop the traffic flow driving toward some directions, and the second-level one is a warning signal strategy that gives traffic flow a recommendation of not driving to some directions. As a visual and mathematical formalism for modeling discrete-event dynamic systems, timed Petri nets are utilized to describe the cooperation between traffic lights and warning lights, and then verify their correctness. A two-way rectangular grid network is modeled via a cell transmission model. The effectiveness of the proposed two-level strategy is evaluated through simulations in the grid network. The results reveal the influences of some major parameters, such as the route-changing rates of vehicles, operation time interval of the proposed strategy, and traffic density of the traffic network on a congestion dissipation process. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

An emergency traffic light strategy to prevent traffic congestion

This work designs an emergency traffic light strategy at signalized traffic intersections in order to prevent traffic congestion. Some additional warning lights are used. The emergency strategy is realized by the cooperation of traffic lights and warning lights. It contains a ban signal strategy, and a warning signal strategy. A two-way rectangular grid network is modeled via the cell transmission model. The effectiveness of the proposed strategy is evaluated through simulations in the modeled grid network. Simulation results identify the influence of rout-changing behaviors of drivers, the time when to operate the emergency strategy, and the inflow of the traffic network. They can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Modeling and control of urban road intersections with incidents via timed Petri nets

Petri nets (PNs) are well utilized as a visual and mathematical formalism in modeling, analysis and control of urban traffic. This work adopts timed Petri nets to design a traffic-signal-based emergency control policy such that emergency response is provided and the incident-induced large-scale congestion is prevented. A reachability graph method is adopted to demonstrate how the models are used to enforce the phase of traffic signal transitions. It improves the state of the art in traffic incident response and control.

Partition-based collaborative tensor factorization for POI recommendation

The rapid development of location-based social networks U+0028 LBSNs U+0029 provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location. For example, it can help travelers to choose where to go next, or recommend salesmen the most potential places to deliver advertisements or sell products. In this paper, a method for recommending points of interest U+0028 POIs U+0029 is proposed based on a collaborative tensor factorization U+0028 CTF U+0029 technique. Firstly, a generalized objective function is constructed for collaboratively factorizing a tensor with several feature matrices. Secondly, a 3-mode tensor is used to model all users U+02BC check-in behaviors, and three feature matrices are extracted to characterize the time distribution, category distribution and POI correlation, respectively. Thirdly, each user’s preference to a POI at a specific time can be estimated by using CTF. In order to further improve the recommendation accuracy, PCTF U+0028 Partition-based CTF U+0029 is proposed to fill the missing entries of a tensor after clustering its every mode. Experiments on a real checkin database show that the proposed method can provide more accurate location recommendation.

Impact of Driving Behavior on Traffic Delay at a Congested Signalized Intersection

This paper proposes a methodology to categorize drivers’ behaviors at a congested signalized intersection. As a discrete event system model, timed Petri nets (TPNs) are used in this paper to formally define two kinds of behaviors: non-jam-induced driving behavior and jam-induced one. In order to systematically assess the performances of both behaviors, a new urban traffic network model is built: a cell transmission model is used to depict the road link traffic that is consistent with the kinematic property of traffic flow, and TPNs are used to model the behaviors and the conflicting traffic flow at the intersection. Some simulation results are given to evaluate the impact of driving behavior on the traffic delay.