Xiaozheng He

Vulnerability assessment and mitigation for the Chinese railway system under floods

The economy of China and the travel needs of its citizens depend significantly on the continuous and reliable services provided by its railway system. However, this system is subject to frequent natural hazards, such as floods, earthquakes, and debris flow. A mechanism to assess the railway system vulnerability under these hazards and the design of effective vulnerability mitigation strategies are essential to the reliable functioning of the railway system. This article proposes a comprehensive methodology to quantitatively assess the railway system vulnerability under floods using historical data and GIS technology. The proposed methodology includes a network representation of the railway system, the generation of flood event scenarios, a method to estimate railway link vulnerability, and a quantitative vulnerability value computation approach. The railway system vulnerability is evaluated in terms of its service disruption related to the number of interrupted trains and the durations of interruption. A maintenance strategy to mitigate vulnerability is proposed that simultaneously considers link vulnerability and number of trains using it. Numerical experiments show that the flood-induced vulnerability of the proposed representation of the Chinese railway system reaches its maximum monthly value in July, and the proposed vulnerability mitigation strategy is more effective compared to other strategies.

Energy-Optimal Speed Control for Electric Vehicles on Signalized Arterials

Electrification of passenger vehicles has been viewed by many as a way to significantly reduce carbon emissions, operate vehicles more efficiently, and reduce oil dependence. Due to the potential benefits of electric vehicle (EV), many federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future, EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve the efficiency for the EV transportation system? This research aims to contribute to this field by proposing an analytical model that determines a time-dependent optimal velocity profile for an EV in order to minimize the electricity usage along a chosen route by systematically considering road characteristics and real-time traffic conditions. In particular, the proposed multistage optimal control model uniquely considers the impact of the presence of intersection queues in both temporal and spatial dimensions, which has been ignored in most traditional speed control models even for internal combustion engine vehicles. In addition, to facilitate the real-time operations, an approximation model, which simplifies the optimal speed profile, is further developed to increase the computation efficiency. The testing using the field data collected from a six-intersection signalized arterial corridor shows that the optimal velocity profile can significantly save energy for an EV, and the computational efficiency of the proposed approximation model is suitable for real-time applications.

Solving a class of constrained ‘black-box’ inverse variational inequalities

It is well known that a general network economic equilibrium problem can be formulated as a variational inequality (VI) and solving the VI will result in a description of network equilibrium state. In this paper, however, we discuss a class of normative control problem that requires the network equilibrium state to be in a linearly constrained set. We formulate the problem as an inverse variational inequality (IVI) because the variables and the mappings in the IVI are in the opposite positions of a classical VI. In addition, the mappings in IVI usually do not have any explicit forms and only implicit information on the functional value is available through exogenous evaluation or direct observation. For such class of network equilibrium control problem, we present a linearly constrained implicit IVI formulation and a solution method based on proximal point algorithm (PPA) that only needs functional values for given variables in the solution process.

Self-adaptive projection method for co-coercive variational inequalities

In some real-world problems, the mapping of the variational inequalities does not have any explicit forms and only the function value can be evaluated or observed for given variables. In this case, if the mapping is co-coercive, the basic projection method is applicable. However, in order to determine the step size, the existing basic projection method needs to know the co-coercive modulus in advance. In practice, usually even if the mapping can be characterized co-coercive, it is difficult to evaluate the modulus, and a conservative estimation will lead an extremely slow convergence. In view of this point, this paper presents a self-adaptive projection method without knowing the co-coercive modulus. We also give a real-life example to demonstrate the practicability of the proposed method.

Inverse variational inequalities with projection-based solution methods

An inverse variational inequality is defined as to find a vector , such thatIf an inverse function u = F-1(x) exists, the above inverse variational inequality could be transformed as a regular variational inequality. However, in reality, it is not uncommon that the inverse function of F-1(x) does not have explicit form, although its functional values can be observed. Existing line search algorithms cannot be applied directly to solve such inverse variational inequalities. In this paper, we propose two projection-based methods using the co-coercivity of mapping F. A self-adaptive strategy is developed to determine the step sizes efficiently when the co-coercivity modulus is unknown. The convergence of the proposed methods is proved rigorously.

Vulnerability effects of passengers' intermodal transfer distance preference and subway expansion on complementary urban public transportation systems

Abstract The vulnerability studies on urban public transportation systems have attracted growing attentions in recent years, due to their important role in the economy development of a city and the well-beings of its citizens. This paper proposes a vulnerability model of complementary urban public transportation systems (CUPTSs) composed of bus systems and subway systems, with the consideration of passengers’ intermodal transfer distance preference ( PITDP ) to capture different levels of complementary strength between the two systems. Based on the model, this paper further introduces a CUPTSs-aimed vulnerability analysis method from two specific aspects: (a) vulnerability effects of different PITDP values, which facilitate the design of policies to change PITDP to reduce system vulnerability; (b) vulnerability effects of different subway expansion plans, which facilitate the vulnerability investigation of current expansion plan and the identification of the optimal expansion plan from the system vulnerability perspective. The proposed CUPTSs-aimed vulnerability analysis method is applied to investigate the complementary bus and subway systems in the city of Wuhan, China. The insights from this study are helpful to analyze other CUPTSs for valuable planning suggestions from the vulnerability perspective.

An improved linearization technique for a class of quadratic 0-1 programming problems

The recent research on linearization techniques for solving 0-1 quadratic programming problems focuses on providing concise models and tightening constraint bounds. In this paper, we propose a computational enhancement for a linearization technique to make the linearized model much faster to solve. We investigate the computational performance of the proposed approach, by comparing it with other linearization techniques on a class of 0-1 quadratic programming problems. We can further speed up the proposed technique by heuristically tightening the constraint bounds, as demonstrated by solving the uncapacitated single allocation p-hub median problem using the Civil Aeronautics Board data.

Heuristic Solution Techniques for No-Notice Emergency Evacuation Traffic Management

When responding to unanticipated emergency events, time is of the essence. This paper proposes a heuristic algorithm for staged traffic evacuation, referred to as HASTE, which is shown to approximate a solution to the cell transmission-based many-to-one dynamic system optimum (DSO) traffic assignment problem. The proposed algorithm does not contain traffic holding, is fast enough for online applications, and produces evacuee routing schedules as its output. As an application of HASTE, a mixed 0-1 integer programming extension to the DSO is proposed to identify critical signalized intersection locations in the network for deployment of a limited number of police officers aimed at improving network throughput and further minimizing evacuee exposure time to the hazard. For the combined problem, a genetic algorithms-based solution procedure is proposed that uses HASTE for solution fitness. Efficiency and quality of the heuristic strategies are demonstrated via numerical experiments for moderately sized problems.

An extended microscopic traffic flow model based on the spring-mass system theory

This study proposes a new microscopic traffic flow model based on the spring-mass system theory. In particular, considering the similarity between the acceleration or deceleration behavior in traffic flow and the scaling properties of a spring, a car-following (CF) model is proposed based on the fundamental physical law of the spring-mass system. Stability of the proposed model is analyzed using the perturbation method to obtain the stability condition. Numerical experiments are performed through simulation. The results demonstrate the proposed model can capture the characteristic of propagation backwards of disturbance in traffic flow. In addition, the findings of this study provide insights in modeling traffic flow from the mechanical system theory perspective.

A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels

Though experimental results have shown a strong correlation between contextual features and the driver’s fatigue state, contextual features have been applied only offline to evaluate a driver’s fatigue state. This paper identifies three of the most effective contextual features, i.e., continuous driving time, sleep duration time, and current time, to facilitate the real-time (online) recognition of fatigue state. By applying gray relational analysis, the three contextual features, together with the most effective facial and vehicle behavior features, are introduced in a two-level fusion structure to improve fatigue driving recognition. In the first level of fusion, labeled the feature-level fusion, three separate multiclass support vector machine (MCSVM) classifiers are used for the three feature sources, i.e., contextual features, driver’s facial features, and vehicle behavior features, to fuse information. These three MCSVM classifiers output probabilities as inputs for the three real-time dynamic basic probability assignments (BPAs) at the second level of fusion, labeled decision-level fusion. These BPAs, and the fusion result of the previous time step, are fused in the decision-level fusion based on the Dempster–Shafer evidence theory. This includes modifying the BPAs to accommodate the decision conflict among the different feature sources. Field experiments show that the proposed recognition method can outperform the single-fatigue-feature method and the single-source fusion-based method.