Chaozhong Wu

Bi‐level programming based contra flow optimization for evacuation events

Purpose – The purpose of this paper is to propose a bi‐level programming optimization model to reduce traffic congestion of transportation network while evacuating people to safe shelters during disasters or special events.Design/methodology/approach – The previous optimization model for contra flow configuration only considered the character of the manager. However, the traffic condition is not only controlled by managers, but also depended on the root choice of travelers. A bi‐level programming optimization model, which considered managers and evacuees character, is proposed to optimize the contra flow of transportation network in evacuation during special events. The upper level model aims to minimize the total evacuation time, while the lower level based on user equilibrium assignment. A solution method based on discrete particle swarm optimization and Frank‐Wolfe algorithm is employed to solve the bi‐level programming problem.Findings – It is found that the bi‐level programming based contra flow opt...

An inexact optimization model for evacuation planning

Purpose – The purpose of this paper is to develop an interval method for vehicle allocation and route planning in case of an evacuation.Design/methodology/approach – First, the evacuation route planning system is described and the notations are defined. An inexact programming model is proposed. The goal of the model is to achieve optimal planning of vehicles allocation with a minimized system time under the condition of inexact information. The constraints of the model include four types: number of vehicles constraint, passengers balance constraints, maximum capacity of links constraints and no negative constraints. The model is solved through the decomposition of the inexact model. A hypothetical case is developed to illustrate the proposed model.Findings – The paper finds that the interval solutions are feasible and stable for evacuation model in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving evacuation managers estimates under different conditions....

Novel Vehicle Motion Model Considering Driver Behavior for Trajectory Prediction and Driving Risk Detection

Accurate prediction of vehicle motion status is critical for developing an advanced driver assistance system (ADAS), which can assess driving safety and detect dangerous scenarios in real time and in the near future. Although previous vehicle motion prediction models developed were mostly built on the basis of kinematic principles, driver behavior was largely ignored. Those models resulted in inaccurate trajectory predictions. To improve forecasting accuracy, the study reported here developed an improved vehicle motion model that includes consideration of both kinematic principles and real-time driver behavior. This improved vehicle motion model incorporates driver behavior into a constant acceleration (CA) model. Data on practical driver behavior, such as perception, identification, volition, and execution under traffic conditions and lane changes were collected. A quantitative approach based on a linear quadratic regulator optimal control method was used to acquire the drivers expected control input. In addition, a Kalman filter was applied to predict short-term vehicle motion, which was then used to analyze driving risks. Finally, CARSIM software was used to simulate driving scenarios. A Monte Carlo method was used to evaluate prediction accuracy and compare the results of the CA model and the improved vehicle motion model. The simulation results showed that the improved model can effectively simulate driver behavior in acceleration control by taking into full consideration the drivers volition and traffic environment. The proposed model yielded better predictions, provided an applicable way to improve the accuracy of vehicle motion prediction, and could be used to enhance the performance of ADAS.

A Study of Chinese Professional Drivers' Electroencephalogram Characteristics under Angry Driving Based on Field Experiments

Although some characteristics of physiological signals under different emotions have been studied previously, the differences in electroencephalograms (EEG) between normal driving and angry driving are generally unknown. Moreover, motorists from different countries may have different EEG characteristics because of their different customs, lifestyles, and cultures. Because of this, it is believed that the traffic environment and driving behaviors in China are very different from those in developed countries. Therefore, a study of a motorists EEG characteristics under angry driving conditions is necessary and beneficial for better understanding the impacts of aggressive driving on traffic safety in China, where road rage is common. This study is different from traditional laboratory simulation experiments because ten professional drivers were recruited to drive along a real and particularly busy route in Wuhan. The EEG data and driver anger level scale (reported by the driver himself) were recorded. Study results show that the amplitude of the EEG signal is significantly bigger under angry driving conditions. Furthermore, the mean value of frequency percentage of β waves is significantly larger under angry driving conditions than the value under normal driving conditions. Finally, the mean value of the frequency percentage of δ waves is much smaller under angry driving conditions than the value under normal driving conditions. The results can provide an effective method to distinguish between angry driving and normal driving, which can then provide theoretical support for designing emotion recognition equipment in the future.

Optimization Approach of Dynamic Parking Guidance Information for Special Events

This paper focuses on dynamic parking guidance information optimization for highway travelers of special events. The original multi-mode traffic network is converted to a novel network by considering parking lots as dummy links; therefore shortest path can be implemented in the new network. A Bi-level programming model based on dynamic route choice and linear programming (LP) is proposed to optimize dynamic PGI. Based on travelers’ reaction to PGI, Stochastic Dynamic User Optimal (SDUO) route choice is employed as lower level model. Upper level model is a LP aiming to minimize network total travel time. The algorithm based on Discrete Particle Swarm Optimization (DPSO) and Method of Successive Average (MSA) is used for solution. The application on Milwaukee Summer fest indicates the optimization model can reduce total parking time.

A Novel Urban Traffic Control Approach Considering Travelers' Intentions

Urban traffic control technology has experienced signal based traffic control traffic flow based on traffic control stage. However, due to information technology constraints, existing traffic control technology in addressing urban congestion and emissions has not achieved the desired results. Considering the future of intelligent transportation technology, this paper proposed a novel traffic approach which considers a traveler’s trip intention. The traffic control system collects travelers’ intentions and preferences through Cooperative Vehicle Infrastructure technology. Dynamic traffic signal control and guidance information are optimized according to a regional traffic coordination control algorithm. This paper discusses the importance, necessity and feasibility of implementing urban Traffic Control considering a travelers’ trip intention. The system framework and key technology of the issue are also discussed.

Speed Control for Automated Highway Vehicles under Road Gradient Conditions

To increase the availability of automated highway vehicles with ACC systems in complex road conditions, such as gradient, curve, and some adverse weather like rain, fog and snow, this paper proposed a new intelligent speed control approach which considers the gradient road when a vehicle cruises. The vehicle longitudinal control system mainly incorporates throttle angle and brake torque as inputs and velocity of the vehicle as output. By regulating the velocity fitting in with the slopes of the road, several unnecessary actions of acceleration and brakes will be reduced, which improves driving safety and comfort. The vehicle longitudinal dynamics model is established, which contains traction force, aerodynamic force, rolling resistance and road slope. According to the desired slope of the road, it is considered the implementation of velocity tracking and reference velocity. The nonlinear properties of vehicle parameter uncertainties are considered together with road slopes. The vehicle control system is designed by using a Backstepping control method. The simulation results in a Matlab indication of the proposed speed control approach considering road gradients have good performance in speed tracking.

Optimization of Dynamic Parking Guidance Information for Special Events

Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

A Recognition Model for Acceleration Intention of Automobile Drivers based on Fuzzy Clustering

Automobile driver’s intention recognition is used to predict what a driver will do the next moment. Generally, driving warning system only considers environment perception, the recognition of safe distance and the warning methods, and it rarely takes driver’s intention into account. Therefore, its effectiveness and accuracy is not satisfied. According to driver’s acceleration characteristic, we analyze the driving operation behavior and vehicle status information, and propose data sequences about driver’s operation data. The data sequences are used to describe the successive driving operation data present and before, the model added by the data sequences can improve the recognition accuracy. Then, based on fuzzy clustering, a model for recognizing driver’s acceleration intention is proposed. The driving simulation experiment aiming at recognizing driver’s acceleration intention is carried out. Experiment results indicate that the proposed recognition model combined fuzzy clustering with the data sequences can effectively recognize driver’s acceleration intention. The model can be used to improve the effectiveness and accuracy of current driving warning system.

Design of Virtual Dynamic Traffic Events for Driver Safety Awareness Training

Driver behavior is an important factor for traffic safety. Enhancing driver safety awareness is an efficient method to reduce traffic accidents. Instead of real vehicles, driving simulator can be employed to simulate dangerous traffic scenes for driver training. In this study, typical traffic scenes with dynamic events were designed. Dynamic events were divided into motion states to describe the behavior of event-participants. Based on vehicle dynamics characteristics，the event occurrence timing and event-participants’ actions were calculated according to vehicle’s real-time speed and position. As an example, the event of Pedestrian Crossing Street (PCS) was designed in the virtual traffic scene. A driver can drive in the virtual traffic scenes and experience dynamic dangerous events. This developed stimulation training can increase driver safety awareness and improve driver skills when facing dangerous traffic situations.